Reviewerly/exHarmony huggingface dataset:
Aya Model: An Instruction Finetuned Open-Access Multilingual Language  Model

Authors:
raw_author_name: Üstün, Ahmet
raw_author_name: D'souza, Daniel
raw_author_name: Onilude, Gbemileke
raw_author_name: Kayid, Amr
raw_author_name: Blunsom, Phil
raw_author_name: Longpre, Shayne
raw_author_name: Muennighoff, Niklas
raw_author_name: Fadaee, Marzieh
raw_author_name: Kreutzer, Julia
raw_author_name: Hooker, Sara

Abstract: Recent breakthroughs in large language models (LLMs) have centered around a handful of data-rich languages. What does it take to broaden access to breakthroughs beyond first-class citizen languages? Our work introduces Aya, a massively multilingual generative language model that follows instructions in 101 languages of which over 50% are considered as lower-resourced. Aya outperforms mT0 and BLOOMZ on the majority of tasks while covering double the number of languages. We introduce extensive new evaluation suites that broaden the state-of-art for multilingual eval across 99 languages -- including discriminative and generative tasks, human evaluation, and simulated win rates that cover both held-out tasks and in-distribution performance. Furthermore, we conduct detailed investigations on the optimal finetuning mixture composition, data pruning, as well as the toxicity, bias, and safety of our models. We open-source our instruction datasets and our model at https://hf.co/CohereForAI/aya-101
Secured Auto Encryption and Authentication Process for Cloud Computing Security

Authors:
raw_author_name: None Saiful Islam
raw_affiliation_string: Dhaka University of Engineering and Technology

Abstract: In the IT sector, cloud computing is a rapidly expanding technology. Users are being offered more and more flexible and appropriate services by cloud service providers (CSP). Cloud computing users are increasing at a steady pace. Users and CSP are looking for appropriate security measures as cloud computing becomes a need in modern life. A large number of researchers are studying cloud computing security issues. This work introduces an appropriate cryptography and secure user authentication system, which includes auto encryption and a mechanism for updating keys on a cloud site that is not initially accessible to users. After a predetermined amount of time, this operation will recur frequently on the cloud site. This protocol can be carried out manually by CSP at any time, or it can run automatically after a predetermined amount of time. This will guarantee additional data security. Additionally, this procedure adds a level to the user's credentials. Even with legitimate credentials, these feathers will ensure that hackers cannot obtain original files or data. The suggested protocol is emulated by CloudSim, and we saw that there is an additional step requiring user credentials, requiring some time to access files or data for the first time. Although this small amount of time is insignificant and can be disregarded, it significantly improves security.
Forecasting high-impact research topics via machine learning on evolving  knowledge graphs

Authors:
raw_author_name: Gu, Xuemei
raw_author_name: Krenn, Mario

Abstract: The exponential growth in scientific publications poses a severe challenge for human researchers. It forces attention to more narrow sub-fields, which makes it challenging to discover new impactful research ideas and collaborations outside one's own field. While there are ways to predict a scientific paper's future citation counts, they need the research to be finished and the paper written, usually assessing impact long after the idea was conceived. Here we show how to predict the impact of onsets of ideas that have never been published by researchers. For that, we developed a large evolving knowledge graph built from more than 21 million scientific papers. It combines a semantic network created from the content of the papers and an impact network created from the historic citations of papers. Using machine learning, we can predict the dynamic of the evolving network into the future with high accuracy, and thereby the impact of new research directions. We envision that the ability to predict the impact of new ideas will be a crucial component of future artificial muses that can inspire new impactful and interesting scientific ideas.
Group Decision-Making among Privacy-Aware Agents

Authors:
raw_author_name: Papachristou, Marios

Abstract: How can individuals exchange information to learn from each other despite their privacy needs and security concerns? For example, consider individuals deliberating a contentious topic and being concerned about divulging their private experiences. Preserving individual privacy and enabling efficient social learning are both important desiderata but seem fundamentally at odds with each other and very hard to reconcile. We do so by controlling information leakage using rigorous statistical guarantees that are based on differential privacy (DP). Our agents use log-linear rules to update their beliefs after communicating with their neighbors. Adding DP randomization noise to beliefs provides communicating agents with plausible deniability with regard to their private information and their network neighborhoods. We consider two learning environments one for distributed maximum-likelihood estimation given a finite number of private signals and another for online learning from an infinite, intermittent signal stream. Noisy information aggregation in the finite case leads to interesting tradeoffs between rejecting low-quality states and making sure all high-quality states are accepted in the algorithm output. Our results flesh out the nature of the trade-offs in both cases between the quality of the group decision outcomes, learning accuracy, communication cost, and the level of privacy protections that the agents are afforded.
Learning How To Ask: Cycle-Consistency Refines Prompts in Multimodal  Foundation Models

Authors:
raw_author_name: Diesendruck, Maurice
raw_author_name: Lin, Jianzhe
raw_author_name: Xu, Mingyang
raw_author_name: Zhao, Jie

Abstract: When LLMs perform zero-shot inference, they typically use a prompt with a task specification, and generate a completion. However, there is no work to explore the possibility of the reverse - going from completion to task specification. In this paper, we employ both directions to perform cycle-supervised learning entirely in-context. Our goal is to create a forward map f : X -> Y (e.g. image -> generated caption), coupled with a backward map g : Y -> X (e.g. caption -> generated image) to construct a cycle-consistency "loss" (formulated as an update to the prompt) to enforce g(f(X)) ~= X. The technique, called CyclePrompt, uses cycle-consistency as a free supervisory signal to iteratively craft the prompt. Importantly, CyclePrompt reinforces model performance without expensive fine-tuning, without training data, and without the complexity of external environments (e.g. compilers, APIs). We demonstrate CyclePrompt in two domains: code generation and image captioning. Our results on the HumanEval coding benchmark put us in first place on the leaderboard among models that do not rely on extra training data or usage of external environments, and third overall. Compared to the GPT4 baseline, we improve accuracy from 80.5% to 87.2%. In the vision-language space, we generate detailed image captions which outperform baseline zero-shot GPT4V captions, when tested against natural (VQAv2) and diagrammatic (FigureQA) visual question-answering benchmarks. To the best of our knowledge, this is the first use of self-supervised learning for prompting.
Combining Insights From Multiple Large Language Models Improves  Diagnostic Accuracy

Authors:
raw_author_name: Barabucci, Gioele
raw_author_name: Chu, Eugene

Abstract: Background: Large language models (LLMs) such as OpenAI's GPT-4 or Google's PaLM 2 are proposed as viable diagnostic support tools or even spoken of as replacements for "curbside consults". However, even LLMs specifically trained on medical topics may lack sufficient diagnostic accuracy for real-life applications. Methods: Using collective intelligence methods and a dataset of 200 clinical vignettes of real-life cases, we assessed and compared the accuracy of differential diagnoses obtained by asking individual commercial LLMs (OpenAI GPT-4, Google PaLM 2, Cohere Command, Meta Llama 2) against the accuracy of differential diagnoses synthesized by aggregating responses from combinations of the same LLMs. Results: We find that aggregating responses from multiple, various LLMs leads to more accurate differential diagnoses (average accuracy for 3 LLMs: $75.3\%\pm 1.6pp$) compared to the differential diagnoses produced by single LLMs (average accuracy for single LLMs: $59.0\%\pm 6.1pp$). Discussion: The use of collective intelligence methods to synthesize differential diagnoses combining the responses of different LLMs achieves two of the necessary steps towards advancing acceptance of LLMs as a diagnostic support tool: (1) demonstrate high diagnostic accuracy and (2) eliminate dependence on a single commercial vendor.
Can Text-to-image Model Assist Multi-modal Learning for Visual  Recognition with Visual Modality Missing?

Authors:
raw_author_name: Feng, Tiantian
raw_author_name: Yang, Daniel
raw_author_name: Bose, Digbalay
raw_author_name: Narayanan, Shrikanth

Abstract: Multi-modal learning has emerged as an increasingly promising avenue in vision recognition, driving innovations across diverse domains ranging from media and education to healthcare and transportation. Despite its success, the robustness of multi-modal learning for visual recognition is often challenged by the unavailability of a subset of modalities, especially the visual modality. Conventional approaches to mitigate missing modalities in multi-modal learning rely heavily on algorithms and modality fusion schemes. In contrast, this paper explores the use of text-to-image models to assist multi-modal learning. Specifically, we propose a simple but effective multi-modal learning framework GTI-MM to enhance the data efficiency and model robustness against missing visual modality by imputing the missing data with generative transformers. Using multiple multi-modal datasets with visual recognition tasks, we present a comprehensive analysis of diverse conditions involving missing visual modality in data, including model training. Our findings reveal that synthetic images benefit training data efficiency with visual data missing in training and improve model robustness with visual data missing involving training and testing. Moreover, we demonstrate GTI-MM is effective with lower generation quantity and simple prompt techniques.
Primitive elements of finite fields $\mathbf{F}_{q^r}$ avoiding affine  hyperplanes for $q=4$ and $q=5$

Authors:
raw_author_name: Winterhof, Arne

Abstract: For a finite field $\mathbf{F}_{q^r}$ with fixed $q$ and $r$ sufficiently large, we prove the existence of a primitive element outside of a set of $r$ many affine hyperplanes for $q=4$ and $q=5$. This complements earlier results by Fernandes and Reis for $q\ge 7$. For $q=3$ the analogous result can be derived from a very recent bound on character sums of Iyer and Shparlinski. For $q=2$ the set consists only of a single element, and such a result is thus not possible.
Cryo Chiplets for Quantum Applications

Authors:
raw_author_name: Adam Quinn
raw_affiliation_string: Fermilab

Abstract: that degrades QC performance is referred to as a defect. Here, we focus on a specific defect known as a frequency collision. When transmon frequencies collide, their difference falls within a range that limits two-qubit gate fidelity. Frequency collisions occur with greater probability on larger QCs, causing collision-free yields to decline as the number of on-chip qubits increases. As a solution, we propose exploiting the higher yields associated with smaller QCs by integrating quantum chiplets within quantum multi-chip modules (MCMs). Yield, gate performance, and application-based analysis show the feasibility of QC scaling through modularity. Our results demonstrate that chiplet architectures, relative to monolithic designs, benefit from average yield improvements ranging from 9.6 – 92.6 × for ≲5 qubit machines. In addition, our simulations explore the design space of chiplet systems and discover configurations that demonstrate average two-qubit gate infidelity reductions that are at best 0.815 × their monolithic counterpart. Lastly, we observe that carefully-selected modular systems achieve fidelity improvements on a range of benchmark circuits.
Factors Influencing Acceptance of ILMU E-Learning Among Lecturers: An Empirical Study Based on UTAUT Model

Authors:
raw_author_name: Eristya Maya Safitri
raw_affiliation_string: Universitas Pembangunan Nasional "Veteran" Jawa Timur
raw_author_name: Siti Mukaromah
raw_affiliation_string: Universitas Pembangunan Nasional "Veteran" Jawa Timur
raw_author_name: Asif Faroqi
raw_affiliation_string: Universitas Pembangunan Nasional "Veteran" Jawa Timur

Abstract: E-learning is a form of innovation in technology used in educational field, including higher education. University of Pembangunan Nasional “Veteran” Jawa Timur is one of many universities that have implemented e-learning called ILMU to support the teaching-learning process. The application of ILMU as e-learning has yet to be utilised by lecturers, due to some challenges in implementation of ILMU regarding accessibility and features of ILMU. Meanwhile, successful implementation of a technology requires acceptance from its users. This research was acquited to define what acceptance factors that influence lecturers while accessing ILMU. This study is measured using UTAUT model. The research was carried on by quantitatively distributing questionnaires to 60 lecturers. Data were analyzed and processed using SEM-PLS technique and SMARTPLS 3.0 application. Factors that influence users to receive ILMU e-learning and significantly are effort expectancy, social influence, facilitating conditions, and behavioral intention. Meanwhile, performance expectancy does not influence users significantly to accept ILMU e-learning. These factors are key indicators to of the implementation and improvement of ILMU e-learning, thus it will develop a better implementation for the lecturers to use and accept it.
An exploration system to effectively analyze the visual metaphor used in sentiment visualization

Authors:
raw_author_name: Hyoji Ha
raw_affiliation_string: Graduate School of Metaverse, Sogang University, Seoul, Republic of Korea; Lifemedia Interdisciplinary Program, Ajou University, Suwon, Republic of Korea
raw_author_name: Kwanghyuk Moon
raw_affiliation_string: Lifemedia Interdisciplinary Program, Ajou University, Suwon, Republic of Korea
raw_author_name: Hyerim Joung
raw_affiliation_string: Lifemedia Interdisciplinary Program, Ajou University, Suwon, Republic of Korea
raw_author_name: Hyegyeong Kim
raw_affiliation_string: Department of Digital Media, Ajou University, Suwon, Republic of Korea
raw_author_name: Kyungwon Lee
raw_affiliation_string: Department of Digital Media, Ajou University, Suwon, Republic of Korea

Abstract: In the field of data visualization, there has been a recent trend of using a complex type of visualization with a multidimensional structure or using several visualizations in parallel when summarizing the results of sentiment analysis. Although this trend may be useful for sophisticated sentiment analysis, such analysis is difficult for the general public and novice researchers. To address this issue, there has recently been a trend of visualizing sentiments using visual metaphors. To facilitate the understanding of related cases, it is necessary to have a systematic means of grasping the sentiment target, the purpose and motivation of research, and the representations used as substitutes for visual metaphors. Therefore, the objective of the present study was to develop an exploration system that can analyze the visual metaphors used in the case of sentiment visualization. For this study, (1) sentiment visualization cases in which visual metaphors are used were collected. (2) After a taxonomy composed of the categories of “target, intermediation, representation, visual variable, and visualization technique” was constructed, it was used to analyze sentences of visual metaphors appearing in sentiment visualization cases. (3) An exploration system capable of grasping the semantic relationships of sub-elements within the five categories of the taxonomy and intuitively interpreting visual metaphors was developed so that appropriate cases can be recommended to sentiment visualization researchers. (4) The approach and usefulness of the exploration system were explained using user scenarios. (5) A case study was conducted to show that the provided system can be analyzed from various perspectives. (6) The usability of the exploration system was demonstrated through a verification targeting experts. The proposed system allows researchers and analysts to intuitively grasp “what types of visual metaphor method and idea should be equipped to visualize sentiment data in an easier way to understand.”
Blockchain Technology in Supply Chain Management

Authors:
raw_author_name: Bhavya Alankar -

Abstract: Blockchain is a promising technology with its features such as immutability and decentralized database. It has applications in various fields such as pharmaceutical, finance, &amp; food industry. At the core of its heart lies its feature, traceability which is the most desired key in supply chains. However, supply chains have always been hit rock bottom by scandals and controversies. In this review paper, we have explored the advancement and research gaps, of blockchain technology (BT) in supply chain management (SCM). We have used the Prisma framework for systematic literature review (SLR) and included a minuscule amount of grey literature to reduce publication bias. We found that supply chain traceability and transparency is the most researched objective in SCM. There was hardly any research in supply chain resilience. Further, we found that 40 % of the papers were application based. Most articles have focused on the advantages of BT, rather than analyzing it critically. This study will help identify gaps and, suitable actions to be followed for an efficient implementation of BT in SCM.
Insights and caveats from mining local and global temporal motifs in  cryptocurrency transaction networks

Authors:
raw_author_name: Ba, Cheick Tidiane
raw_author_name: Mondragon, Raul
raw_author_name: Cuadrado, Felix
raw_author_name: Lambiotte, Renaud
raw_author_name: Clegg, Richard G.

Abstract: Distributed ledger technologies have opened up a wealth of fine-grained transaction data from cryptocurrencies like Bitcoin and Ethereum. This allows research into problems like anomaly detection, anti-money laundering, pattern mining and activity clustering (where data from traditional currencies is rarely available). The formalism of temporal networks offers a natural way of representing this data and offers access to a wealth of metrics and models. However, the large scale of the data presents a challenge using standard graph analysis techniques. We use temporal motifs to analyse two Bitcoin datasets and one NFT dataset, using sequences of three transactions and up to three users. We show that the commonly used technique of simply counting temporal motifs over all users and all time can give misleading conclusions. Here we also study the motifs contributed by each user and discover that the motif distribution is heavy-tailed and that the key players have diverse motif signatures. We study the motifs that occur in different time periods and find events and anomalous activity that cannot be seen just by a count on the whole dataset. Studying motif completion time reveals dynamics driven by human behaviour as well as algorithmic behaviour.
LL-GABR: Energy Efficient Live Video Streaming Using Reinforcement  Learning

Authors:
raw_author_name: Raman, Adithya
raw_author_name: Turkkan, Bekir
raw_author_name: Kosar, Tevfik

Abstract: Over the recent years, research and development in adaptive bitrate (ABR) algorithms for live video streaming have been successful in improving users' quality of experience (QoE) by reducing latency to near real-time levels while delivering higher bitrate videos with minimal rebuffering time. However, the QoE models used by these ABR algorithms do not take into account that a large portion of live video streaming clients use mobile devices where a higher bitrate does not necessarily translate into higher perceived quality. Ignoring perceived quality results in playing videos at higher bitrates without a significant increase in perceptual video quality and becomes a burden for battery-constrained mobile devices due to higher energy consumption. In this paper, we propose LL-GABR, a deep reinforcement learning approach that models the QoE using perceived video quality instead of bitrate and uses energy consumption along with other metrics like latency, rebuffering events, and smoothness. LL-GABR makes no assumptions about the underlying video, environment, or network settings and can operate flexibly on different video titles, each having a different bitrate encoding ladder without additional re-training, unlike existing learning-based ABRs. Trace-driven experimental results show that LL-GABR outperforms the state-of-the-art approaches by up to 44% in terms of perceptual QoE and a 73% increase in energy efficiency as a result of reducing net energy consumption by 11%.
DoRA: Weight-Decomposed Low-Rank Adaptation

Authors:
raw_author_name: Liu, Shih-Yang
raw_author_name: Wang, Chien-Yi
raw_author_name: Yin, Hongxu
raw_author_name: Molchanov, Pavlo
raw_author_name: Wang, Yu-Chiang Frank
raw_author_name: Cheng, Kwang-Ting

Abstract: Among the widely used parameter-efficient finetuning (PEFT) methods, LoRA and its variants have gained considerable popularity because of avoiding additional inference costs. However, there still often exists an accuracy gap between these methods and full fine-tuning (FT). In this work, we first introduce a novel weight decomposition analysis to investigate the inherent differences between FT and LoRA. Aiming to resemble the learning capacity of FT from the findings, we propose Weight-Decomposed LowRank Adaptation (DoRA). DoRA decomposes the pre-trained weight into two components, magnitude and direction, for fine-tuning, specifically employing LoRA for directional updates to efficiently minimize the number of trainable parameters. By employing DoRA, we enhance both the learning capacity and training stability of LoRA while avoiding any additional inference overhead. DoRA consistently outperforms LoRA on fine-tuning LLaMA, LLaVA, and VL-BART on various downstream tasks, such as commonsense reasoning, visual instruction tuning, and image/video-text understanding.
Get More with LESS: Synthesizing Recurrence with KV Cache Compression  for Efficient LLM Inference

Authors:
raw_author_name: Dong, Harry
raw_author_name: Yang, Xinyu
raw_author_name: Zhang, Zhenyu
raw_author_name: Wang, Zhangyang
raw_author_name: Chi, Yuejie
raw_author_name: Chen, Beidi

Abstract: Many computational factors limit broader deployment of large language models. In this paper, we focus on a memory bottleneck imposed by the key-value (KV) cache, a computational shortcut that requires storing previous KV pairs during decoding. While existing KV cache methods approach this problem by pruning or evicting large swaths of relatively less important KV pairs to dramatically reduce the memory footprint of the cache, they can have limited success in tasks that require recollecting a majority of previous tokens. To alleviate this issue, we propose LESS, a simple integration of a (nearly free) constant sized cache with eviction-based cache methods, such that all tokens can be queried at later decoding steps. Its ability to retain information throughout time shows merit on a variety of tasks where we demonstrate LESS can help reduce the performance gap from caching everything, sometimes even matching it, all while being efficient.