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Paper Citation Record · LEDGER

DailyLLM: Context-Aware Activity Log Generation Using Multi-Modal Sensors and LLMs

As of 23 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 2 inbound Pith citation observations for arXiv:2507.13737.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2507.13737 v1

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T16:22:33.080354Z

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-21T06:09:44.172188Z

measured 1 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

44 of 44 outbound references displayed

  • verified exact1
  • verified fuzzy31
  • unresolved12
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 9d85eec3-1a3f-4168-ab7b-70f2e6045e46 · outbound

This paper cites (2025) Global smartphone penetration 2016-.

DailyLLM: Context-Aware Activity Log Generation Using Multi-Modal Sensors and LLMs (2025) Global smartphone penetration 2016-

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:22:39.275040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-06T16:22:32.873487Z digest=sha256:6e23bdf28ffc01511391373313a46fbbd79f9507c30487c006832cb09a8eacfb

Observation 48f730e5-fe31-499a-9e60-33d679618f03 · outbound

This paper cites Life-tags: a smartglasses-based system for recording and abstracting life with tag clouds,.

DailyLLM: Context-Aware Activity Log Generation Using Multi-Modal Sensors and LLMs Life-tags: a smartglasses-based system for recording and abstracting life with tag clouds,

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:22:38.933216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-06T16:22:32.884243Z digest=sha256:cfc1148164c401a618c2138e4326caca641c2e14adddb42d84ba95a33507fc51

Observation 6c57b621-71e8-4d83-94ce-89f3969001a7 · outbound

This paper cites Memento: An emotion-driven lifel- ogging system with wearables,.

DailyLLM: Context-Aware Activity Log Generation Using Multi-Modal Sensors and LLMs Memento: An emotion-driven lifel- ogging system with wearables,

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:22:38.700408Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-06T16:22:32.889321Z digest=sha256:bd0963665a71249c1ecfc1814689a3f67b920fef802a00a6a5376f21978a99da

Observation 9afe49a5-f8f8-46b8-a3f5-ab2e7b2e7873 · outbound

This paper cites Integrating extended reality and neural headsets for enhanced emotional lifelogging: A technical overview,.

DailyLLM: Context-Aware Activity Log Generation Using Multi-Modal Sensors and LLMs Integrating extended reality and neural headsets for enhanced emotional lifelogging: A technical overview,

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:22:38.417637Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-06T16:22:32.894134Z digest=sha256:fbeb5791a217052eb2ff1067095b554780189ea24255ecc74df078227bf6e17f

Observation 3b772d1a-04dc-48ad-aab6-57cacbf64a0c · outbound

This paper cites AutoLife: Automatic Life Journaling with Smartphones and LLMs.

DailyLLM: Context-Aware Activity Log Generation Using Multi-Modal Sensors and LLMs AutoLife: Automatic Life Journaling with Smartphones and LLMs

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-06T16:22:32.899061Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:22:32.899061Z digest=sha256:e7ed41953049e4a8f1ff0d19c2888f837a1d97b7a02e61751b8e3dd086fbb48c

Observation c220563d-e975-4c33-ba7f-91a038a90819 · outbound

This paper cites Contextllm: Meaningful context reasoning from multi-sensor and multi- device data using llms,.

DailyLLM: Context-Aware Activity Log Generation Using Multi-Modal Sensors and LLMs Contextllm: Meaningful context reasoning from multi-sensor and multi- device data using llms,

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:22:38.066931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-06T16:22:32.904812Z digest=sha256:f10571adf24bb5e36d8c0c7eee5d6b3c524147ccdc1bae489a91ae669c17fc1d

Observation 08d942f1-a12a-4e11-b4d8-9d6103443f05 · outbound

This paper cites Lifelog: Timelog & diary,.

DailyLLM: Context-Aware Activity Log Generation Using Multi-Modal Sensors and LLMs Lifelog: Timelog & diary,

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:22:37.790162Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-06T16:22:32.909372Z digest=sha256:e7eee2a2ee10b04e23610a3af2430b4439c8faa7ba3363c6b4c19852e2be2a82

Observation 51d53ba6-f6f3-4c4e-9c7c-a7538bd54b67 · outbound

This paper cites (2025) Day one journal app — your journal for life.

DailyLLM: Context-Aware Activity Log Generation Using Multi-Modal Sensors and LLMs (2025) Day one journal app — your journal for life

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:22:37.561133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-06T16:22:32.913880Z digest=sha256:1e17092b25266322dd9c1e786129c63f0e4e3f414c674670aeeeb5e796d3965e

Observation c04e9839-e46b-4ec9-820b-c3fa69d48813 · outbound

This paper cites Foodai: Food image recognition via deep learning for smart food logging,.

DailyLLM: Context-Aware Activity Log Generation Using Multi-Modal Sensors and LLMs Foodai: Food image recognition via deep learning for smart food logging,

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:22:37.328374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-06T16:22:32.918745Z digest=sha256:60cd19091c11b858852cf7638f60a7e18208f6ad3d87cf23a3d4ad9c870fba89

Observation 0cd3106b-03fe-4a36-ab68-3caf758018ce · outbound

This paper cites Cyberslacking or smart work: Smart- phone usage log-analysis focused on app-switching behavior in work and leisure conditions,.

DailyLLM: Context-Aware Activity Log Generation Using Multi-Modal Sensors and LLMs Cyberslacking or smart work: Smart- phone usage log-analysis focused on app-switching behavior in work and leisure conditions,

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:22:37.163239Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-06T16:22:32.923186Z digest=sha256:d0c640251c478d4dc0fb5d1b0bb290b42d31f32ae59d0d637095c6fd5c39ee74

Observation a4698fa8-e5d6-41e7-87cf-d2406c36520e · outbound

This paper cites Analyzing mobile application usage: generat- ing log files from mobile screen recordings,.

DailyLLM: Context-Aware Activity Log Generation Using Multi-Modal Sensors and LLMs Analyzing mobile application usage: generat- ing log files from mobile screen recordings,

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:22:37.000115Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-06T16:22:32.928086Z digest=sha256:3e1403d4b028b06eac9f5df75a58ca16d60987da4c5569f5ddeda395cdfdad5a

Observation 934a011b-9520-4a50-9036-948438e16753 · outbound

This paper cites A novel voice interactive sleep log: concurrent validity with actigraphy and sleep diaries,.

DailyLLM: Context-Aware Activity Log Generation Using Multi-Modal Sensors and LLMs A novel voice interactive sleep log: concurrent validity with actigraphy and sleep diaries,

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:22:36.775276Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-06T16:22:32.933097Z digest=sha256:56e616b15fb73006d9b77f04293e23bdede97ebcd5f1784efa0f17b4e9f27909

Observation a0a7c044-2b64-4d40-a312-69b8a093c24a · outbound

This paper cites Lora: Low-rank adaptation of large language models.

DailyLLM: Context-Aware Activity Log Generation Using Multi-Modal Sensors and LLMs Lora: Low-rank adaptation of large language models

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-06T16:22:32.937930Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:22:32.937930Z digest=sha256:832bf94b07eb623c9bf90ba1ead583470bba8d88037085eca815eca3313203c7

Observation ec42a46f-f67b-44a0-846b-0d8448ab2ac0 · outbound

This paper cites Llasa: Large multimodal agent for human activity analysis through wearable sensors,.

DailyLLM: Context-Aware Activity Log Generation Using Multi-Modal Sensors and LLMs Llasa: Large multimodal agent for human activity analysis through wearable sensors,

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-06T16:22:32.942882Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:22:32.942882Z digest=sha256:a57609ee8bc611a162ba930e9da9c03aee5a874f78b6f94f093777066cea8cbe

Observation 7aebe72f-4e22-4ea2-a09e-76741b21e607 · outbound

This paper cites When iot meet llms: Applications and challenges,.

DailyLLM: Context-Aware Activity Log Generation Using Multi-Modal Sensors and LLMs When iot meet llms: Applications and challenges,

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:22:36.637182Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-06T16:22:32.947380Z digest=sha256:8268e0f12f2446eb6cf0173503ae52ed0383f0d1a02d05548609666ce658b355

Observation c37a6c74-4b38-42c7-9e07-ed0746516867 · outbound

This paper cites Iot-llm: Enhancing real- world iot task reasoning with large language models,.

DailyLLM: Context-Aware Activity Log Generation Using Multi-Modal Sensors and LLMs Iot-llm: Enhancing real- world iot task reasoning with large language models,

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-06T16:22:32.951979Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:22:32.951979Z digest=sha256:78b9c17f1bd476f20ac73b8ac5f30a260caae72310d46cb0c763185491882015

Observation 4580cc77-1e9b-4be0-accc-81986744eac6 · outbound

This paper cites IoT-LM: Large Multisensory Language Models for the Internet of Things.

DailyLLM: Context-Aware Activity Log Generation Using Multi-Modal Sensors and LLMs IoT-LM: Large Multisensory Language Models for the Internet of Things

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-06T16:22:32.956321Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:22:32.956321Z digest=sha256:8ee5f7fba43cff8cd2b6dac22dc8f3453448ddc962cc4dc3087723dba38b8aa4

Observation 2460415f-be07-4ff4-9955-bef84851a076 · outbound

This paper cites Penetrative ai: Making llms comprehend the physical world,.

DailyLLM: Context-Aware Activity Log Generation Using Multi-Modal Sensors and LLMs Penetrative ai: Making llms comprehend the physical world,

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-06T16:22:32.960865Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:22:32.960865Z digest=sha256:f5c811360bcddaf46a50dea265bfb270a7034c1940fd38565d2dc5dfd6c759f1

Observation 141c2fe3-64b9-4fb4-8f05-11f60197c9c5 · outbound

This paper cites HARGPT: Are LLMs Zero-Shot Human Activity Recognizers?.

DailyLLM: Context-Aware Activity Log Generation Using Multi-Modal Sensors and LLMs HARGPT: Are LLMs Zero-Shot Human Activity Recognizers?

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-06T16:22:32.964742Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:22:32.964742Z digest=sha256:91a156318a7b3a8b99c2caca02574081fe4ceb1dcd2d0ea877849c64ce6f5dd2

Observation 92b98f28-956c-48d4-98a9-d9a3dfdbfb60 · outbound

This paper cites Evaluating large language models as virtual annotators for time-series physical sensing data,.

DailyLLM: Context-Aware Activity Log Generation Using Multi-Modal Sensors and LLMs Evaluating large language models as virtual annotators for time-series physical sensing data,

Reference 20

Resolution
verified exact
doi, observed 2026-08-06T16:22:33.119685Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-06T16:22:32.969360Z digest=sha256:8ff7fdf02c959b4fd21b79c2b7144726ec59717af9e3dedf86a6f0534e8686c1

Observation 9462c8e7-69d9-4c8c-ad8a-a4205e44ea18 · outbound

This paper cites Using large language models to enhance the reusability of sensor data,.

DailyLLM: Context-Aware Activity Log Generation Using Multi-Modal Sensors and LLMs Using large language models to enhance the reusability of sensor data,

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:22:36.426032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-06T16:22:32.973759Z digest=sha256:4e7fc3927bae6304c8435c80f7f3f9e57b7a70816120d3df593a646da3bc3176

Observation 8554e9cc-fbd6-4e0d-bf9f-111031bc778a · outbound

This paper cites Barometric formula — wikipedia, the free encyclopedia,.

DailyLLM: Context-Aware Activity Log Generation Using Multi-Modal Sensors and LLMs Barometric formula — wikipedia, the free encyclopedia,

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:22:36.227973Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-06T16:22:32.978103Z digest=sha256:763181c57ba4178e0eca2a28c4f396b5cc4d72011b5a41bb75334aff40b9bff0

Observation 380c5eda-ac28-432b-a228-0f16b98e7bed · outbound

This paper cites Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences,.

DailyLLM: Context-Aware Activity Log Generation Using Multi-Modal Sensors and LLMs Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences,

Reference 23

Resolution
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no resolver link, observed 2026-08-06T16:22:32.982306Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:22:32.982306Z digest=sha256:3fa56099641db1288eea94089fc9acb133e35348d9e091861cecd7bf678c7cc1

Observation 3f7c81ec-111d-4cf7-8c32-630379899e7f · outbound

This paper cites DeepSeek-V3 Technical Report.

DailyLLM: Context-Aware Activity Log Generation Using Multi-Modal Sensors and LLMs DeepSeek-V3 Technical Report

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-06T16:22:32.986599Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:22:32.986599Z digest=sha256:febb4bdc1c9e3aa925d090937a4fe5902506f43e86e5d33a9cfa737554619818

Observation 36fdc760-3a56-4a46-ace0-a279fb07d0bc · outbound

This paper cites Studentlife: assessing mental health, academic performance and behavioral trends of college students using smartphones,.

DailyLLM: Context-Aware Activity Log Generation Using Multi-Modal Sensors and LLMs Studentlife: assessing mental health, academic performance and behavioral trends of college students using smartphones,

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:22:36.058835Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-06T16:22:32.991024Z digest=sha256:74b15adaf7d9a4f5e28411e0952bf607712ef3ca31849e7615f46228cd9c0a09

Observation ac589c6d-573b-48c1-a51d-1380c00b2232 · outbound

This paper cites Smart devices are different: Assessing and mitigatingmobile sensing heterogeneities for activity recognition,.

DailyLLM: Context-Aware Activity Log Generation Using Multi-Modal Sensors and LLMs Smart devices are different: Assessing and mitigatingmobile sensing heterogeneities for activity recognition,

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:22:35.888872Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-06T16:22:32.995285Z digest=sha256:2fb4f11c9f493fd414164c3015228f3661a43d8eea11333c02b9845e6c5d2211

Observation 98a716ff-1aab-4877-a177-58d72c0f5e03 · outbound

This paper cites Mobile sensor data anonymization,.

DailyLLM: Context-Aware Activity Log Generation Using Multi-Modal Sensors and LLMs Mobile sensor data anonymization,

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:22:35.704514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-06T16:22:32.999793Z digest=sha256:ab0bfd8963893b88c7da1eefc12ed1e63a986328b54092b088b6ad16e14b1a98

Observation 6574acd5-984c-4d5e-9d2a-f71a9312475a · outbound

This paper cites Fusion of smartphone motion sensors for physical activity recognition,.

DailyLLM: Context-Aware Activity Log Generation Using Multi-Modal Sensors and LLMs Fusion of smartphone motion sensors for physical activity recognition,

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:22:35.503109Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-06T16:22:33.004100Z digest=sha256:724e416ed22ada1204bd68cd2ad5c9931d5df47e502c0ca95393dc6374adfe5a

Observation 2e7e654c-f860-418d-bd32-89504c7d7786 · outbound

This paper cites Transition-aware human activity recognition using smartphones,.

DailyLLM: Context-Aware Activity Log Generation Using Multi-Modal Sensors and LLMs Transition-aware human activity recognition using smartphones,

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:22:35.265198Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-06T16:22:33.008827Z digest=sha256:0e43f120fd8eb270193a6fe544b25d5d7eb4f38870f9feb69466f7518ab32467

Observation fb4681f6-9adf-4962-b516-fcf553f82614 · outbound

This paper cites Mesaros, T.

DailyLLM: Context-Aware Activity Log Generation Using Multi-Modal Sensors and LLMs Mesaros, T

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:22:35.078206Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-06T16:22:33.014122Z digest=sha256:86265a5414c351e33855458c96f4f4ad6d4d8772c00466a5baeae4210f6ddee6

Observation 3dcf1588-9582-492c-80d5-8fe6f704b81f · outbound

This paper cites DCASE 2017 challenge setup: Tasks, datasets and baseline system,.

DailyLLM: Context-Aware Activity Log Generation Using Multi-Modal Sensors and LLMs DCASE 2017 challenge setup: Tasks, datasets and baseline system,

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:22:34.882186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-06T16:22:33.018809Z digest=sha256:8ebf50e962448af67d5a2dd2e3d8ce7fc9a59c283719778d169ef78621669b72

Observation 7f2d8f0d-2466-4bbd-8226-7c5905f56acd · outbound

This paper cites Introducing wesad, a multimodal dataset for wearable stress and affect detection,.

DailyLLM: Context-Aware Activity Log Generation Using Multi-Modal Sensors and LLMs Introducing wesad, a multimodal dataset for wearable stress and affect detection,

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:22:34.690139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-06T16:22:33.024035Z digest=sha256:034f2f07902b0a76eeac33bf9f096d35e0aabff84588fb245bdecf805b0a6c39

Observation 3aeeacc7-616c-4c9f-a1db-0222232f7355 · outbound

This paper cites Human activities recognition in android smartphone using support vector machine,.

DailyLLM: Context-Aware Activity Log Generation Using Multi-Modal Sensors and LLMs Human activities recognition in android smartphone using support vector machine,

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:22:34.493713Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-06T16:22:33.030044Z digest=sha256:fefeb4f20a82a1f475d73c68ae5b5d29544d0c89930c34b6368394c882ea8e6f

Observation e05c4ec1-d46d-4042-a329-9c1a82eae95d · outbound

This paper cites Human activity recognition using k-nearest neighbor machine learning algorithm,.

DailyLLM: Context-Aware Activity Log Generation Using Multi-Modal Sensors and LLMs Human activity recognition using k-nearest neighbor machine learning algorithm,

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:22:34.294027Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-06T16:22:33.035348Z digest=sha256:6a7ef96df53fbdbad57786616e10c9e7cd79504d34f8a5280e1445a4d4b71008

Observation 7c976cee-a5e1-4fe6-98ac-9887de909d23 · outbound

This paper cites Cnn-based sensor fusion techniques for multimodal hu- man activity recognition,.

DailyLLM: Context-Aware Activity Log Generation Using Multi-Modal Sensors and LLMs Cnn-based sensor fusion techniques for multimodal hu- man activity recognition,

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:22:34.101947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-06T16:22:33.040012Z digest=sha256:7aefe303a83a3cb76699e37ca6e1a55709a89395b73d3936d05f1dfbdb917480

Observation 50b786c2-8917-4ee4-a3da-6bfcd76162d7 · outbound

This paper cites Lstm networks for mobile human activity recognition,.

DailyLLM: Context-Aware Activity Log Generation Using Multi-Modal Sensors and LLMs Lstm networks for mobile human activity recognition,

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:22:33.918591Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-06T16:22:33.044880Z digest=sha256:8d72e8ede3e8c31979c3951e1d5db927c6044e6604d86defcb0d8fc82712ac94

Observation 30f7d3ed-246d-4c25-8f74-7a4d31f500fd · outbound

This paper cites Supervised nonnegative matrix factorization for acoustic scene classification,.

DailyLLM: Context-Aware Activity Log Generation Using Multi-Modal Sensors and LLMs Supervised nonnegative matrix factorization for acoustic scene classification,

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:22:33.687843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-06T16:22:33.049885Z digest=sha256:76fe783b3e41ea26b134ed9249d10488d90c1114396d9121b75c34fca12511ae

Observation a86e9b21-5411-435c-be06-08f26757f85f · outbound

This paper cites Convolutional neural networks with binaural repre- sentations and background subtraction for acoustic scene classification,.

DailyLLM: Context-Aware Activity Log Generation Using Multi-Modal Sensors and LLMs Convolutional neural networks with binaural repre- sentations and background subtraction for acoustic scene classification,

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:22:33.534963Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-06T16:22:33.055299Z digest=sha256:fb407a0e79c70cb67a301731f74ba4aca44c1222c5f19d25f84ce3dea42675de

Observation a0f895c2-c9eb-4649-b63f-002cd23daeb6 · outbound

This paper cites Raspberry pi 5,.

DailyLLM: Context-Aware Activity Log Generation Using Multi-Modal Sensors and LLMs Raspberry pi 5,

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:22:33.488048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-06T16:22:33.060314Z digest=sha256:e55b5ba3d1e69f5a9557345a2af27a53b7444398f95553d3cb5403cd02f1e590

Observation f40fb31f-53c1-4a2c-8d5f-53501d9c80c3 · outbound

This paper cites BERTScore: Evaluating Text Generation with BERT.

DailyLLM: Context-Aware Activity Log Generation Using Multi-Modal Sensors and LLMs BERTScore: Evaluating Text Generation with BERT

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-06T16:22:33.065454Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:22:33.065454Z digest=sha256:a1e53e93037fe5a549535f1876ad6cda5861a6b6e1142ffb20d6f23ec6172044

Observation ce6b19f2-d0d6-4f67-9e0b-574617630a9f · outbound

This paper cites G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment.

DailyLLM: Context-Aware Activity Log Generation Using Multi-Modal Sensors and LLMs G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-06T16:22:33.070840Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:22:33.070840Z digest=sha256:d28c2f2eb6ac565b5f32e2e2b244807a2ca939145cb69d24dee1d5d971b79b4b

Observation 270d5b5a-7212-4f75-8f6a-88f113c7d4fb · outbound

This paper cites Evaluation of geographical distortions in language models,.

DailyLLM: Context-Aware Activity Log Generation Using Multi-Modal Sensors and LLMs Evaluation of geographical distortions in language models,

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:22:33.441938Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-06T16:22:33.075843Z digest=sha256:e479aa54659b39af7481da0560c69fa9f9f5ad7b9e7e49541e4d3ad974bb3e88

Observation 79eca445-f22b-4295-9ea7-d30b08a2f1a1 · outbound

This paper cites GPT-4 Technical Report.

DailyLLM: Context-Aware Activity Log Generation Using Multi-Modal Sensors and LLMs GPT-4 Technical Report

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-06T16:22:33.080354Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:22:33.080354Z digest=sha256:a178ca1707fd517362b4afcb0c92b4fd599d360a31cecb962f649321cc8f2617

Observation cf06014d-cd64-4627-9e39-c1e8908c3970 · outbound

This paper cites Available: https://www.statista.com/statistics/203734/ global-smartphone-penetration-per-capita-since-2005/.

DailyLLM: Context-Aware Activity Log Generation Using Multi-Modal Sensors and LLMs Available: https://www.statista.com/statistics/203734/ global-smartphone-penetration-per-capita-since-2005/

Reference 2023

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:22:39.091669Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-06T16:22:32.878816Z digest=sha256:2668c1d93f5b5e73aa58df70481c1440365046afd6689adc543e4ac185319a32

Pith citing papers

Observation d0791059-4956-4129-a437-79c976a34702 · inbound

Foundation Models Defining A New Era In Sensor-based Human Activity Recognition: A Survey And Outlook cites this paper.

Foundation Models Defining A New Era In Sensor-based Human Activity Recognition: A Survey And Outlook DailyLLM: Context-Aware Activity Log Generation Using Multi-Modal Sensors and LLMs

Reference 139

Resolution
verified exact
arxiv_id, observed 2026-05-13T18:53:08.505234Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-13T18:48:40.813486Z digest=sha256:d1bd579bb8488072afde87551a0b952fed3421674a1b7cb5cf8e35546143d3f5

Observation 84a39f44-f900-4f7d-8018-1b86f4e6d0a3 · inbound

TimeSRL: Generalizable Time-Series Behavioral Modeling via Semantic RL-Tuned LLMs -- A Case Study in Mental Health cites this paper.

TimeSRL: Generalizable Time-Series Behavioral Modeling via Semantic RL-Tuned LLMs -- A Case Study in Mental Health DailyLLM: Context-Aware Activity Log Generation Using Multi-Modal Sensors and LLMs

Reference 62

Resolution
verified exact
arxiv_id, observed 2026-05-21T06:13:59.496668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-21T06:09:44.172188Z digest=sha256:ca2a604caef56bfb116413a3f2f73e1b47f4bc84b493ef8fe3daadc1cb144965