Reviewerly/exHarmony huggingface dataset:
A comprehensive survey of cryptography key management systems

Cryptographic methods have been extensively employed in various systems to address security objectives, such as data confidentiality, authentication, and secure communication, to name a few. Keys are the most critical parts of any cryptographic system safeguarding the whole underlying infrastructure. Based on the underlying algorithm design, there might be various stages of key generation, exchange, and storage to fulfill an algorithm requirement. In this research, we studied cryptographic techniques along with requirements and corresponding key management systems. Having scrutinized best practices, a taxonomy has been proposed for the key management systems based on the algorithm's requirements, key stages, and applications. This study is a comprehensive literature review on cryptographic key management systems to provide a complete guideline in key management solutions.
A Practical Survey on Zero-shot Prompt Design for In-context Learning

The remarkable advancements in large language models (LLMs) have brought about significant improvements in Natural Language Processing(NLP) tasks. This paper presents a comprehensive review of in-context learning techniques, focusing on different types of prompts, including discrete, continuous, few-shot, and zero-shot, and their impact on LLM performance. We explore various approaches to prompt design, such as manual design, optimization algorithms, and evaluation methods, to optimize LLM performance across diverse tasks. Our review covers key research studies in prompt engineering, discussing their methodologies and contributions to the field. We also delve into the challenges faced in evaluating prompt performance, given the absence of a single "best" prompt and the importance of considering multiple metrics. In conclusion, the paper highlights the critical role of prompt design in harnessing the full potential of LLMs and provides insights into the combination of manual design, optimization techniques, and rigorous evaluation for more effective and efficient use of LLMs in various NLP tasks.
Sesame: A power spectrum emulator pipeline for beyond-$Λ$CDM models

The mysterious nature of the dark sector of the $\Lambda$CDM model is one of the main motivators behind the study of alternative cosmological models. A central quantity of interest for these models is the matter power spectrum, which quantifies structure formation on various scales and can be cross-validated through theory, simulations, and observations. We present a tool that can be used to create emulators for the non-linear matter power spectrum, and similar global clustering statistics, for models beyond $\Lambda$CDM with very little computation effort and without the need for supercomputers. We use fast approximate N-body simulations to emulate the boost, $B(k,z)=P_{\text{beyond}-\Lambda \rm CDM}(k,z) / P_{\Lambda \rm CDM}(k, z)$, and then rely on existing high-quality emulators made for $\Lambda$CDM to isolate $P_{\text{beyond}-\Lambda \rm CDM}(k,z)$. Since both the $\Lambda$CDM and beyond-$\Lambda$CDM models are simulated in our approach, some of the lack of power on small scales is factored out, allowing us to extend the emulator to $k \sim 3-5\,h\,\mathrm{Mpc}^{-1}$ and still maintain good accuracy. In addition, errors from the simulation and emulation process can be estimated and factored into the covariance when using the emulator on data. As an example of using the pipeline, we create an emulator for the well-studied $f(R)$ model with massive neutrinos, using approximately 3000 CPU hours of computation time, as opposed to millions of CPU hours for many existing $\Lambda$CDM emulators. Provided with the paper is a fully functioning pipeline that generates parameter samples, runs a Boltzmann solver to produce initial conditions, runs the simulations, and then gathers all the data and runs it through a machine learning module to develop the emulator. This tool, named Sesame, can be used by anyone to generate a power spectrum emulator for the cosmological model of their choice.
Review of the NIST Light-Weight Cryptography Finalists

Since 2016, NIST has been assessing light-weight encryption methods, and, in 2022, NIST published the final 10: ASCON, Elephant, GIFT-COFB, Grain128-AEAD, ISAP, Photon-Beetle, Romulus, Sparkle, TinyJambu, and Xoodyak. The time that the article was written, NIST announced ASCON as the chosen method that will be published as NIST's lightweight cryptography standard later in 2023. In this article we provide a comparison between these methods in terms of energy efficiency, time for encryption and time for hashing.
Private Product Computation Using Quantum Entanglement

In this work, we show that a pair of entangled qubits can be used to compute a product privately. More precisely, two participants with a private input from a finite field can perform local operations on a shared, Bell-like quantum state, and when these qubits are later sent to a third participant, the third participant can determine the product of the inputs, but without learning more about the individual inputs. We give a concrete way to realize this product computation for arbitrary finite fields of prime order.
A3GC-IP: Attention-oriented adjacency adaptive recurrent graph convolutions for human pose estimation from sparse inertial measurements

Conventional methods for human pose estimation either require a high degree of instrumentation, by relying on many inertial measurement units (IMUs), or constraint the recording space, by relying on extrinsic cameras. These deficits are tackled through the approach of human pose estimation from sparse IMU data. We define attention-oriented adjacency adaptive graph convolutional long-short term memory networks (A3GC-LSTM), to tackle human pose estimation based on six IMUs, through incorporating the human body graph structure directly into the network. The A3GC-LSTM combines both spatial and temporal dependency in a single network operation, more memory efficiently than previous approaches. The recurrent graph learning on arbitrarily long sequences is made possible by equipping graph convolutions with adjacency adaptivity, which eliminates the problem of information loss in deep or recurrent graph networks, while it also allows for learning unknown dependencies between the human body joints. To further boost accuracy, a spatial attention formalism is incorporated into the recurrent LSTM cell. With our presented approach, we are able to utilize the inherent graph nature of the human body, and thus can outperform the state-of-the-art for human pose estimation from sparse IMU data.
Maximum entanglement of mixed symmetric states under unitary transformations

We study the maximum entanglement that can be produced by a global unitary transformation for systems of two and three qubits constrained to the fully symmetric states. This restriction to the symmetric subspace appears naturally in the context of bosonic or collective spin systems. We also study the symmetric states that remain separable after any global unitary transformation, called symmetric absolutely separable (SAS) states, or absolutely classical for spin states. The results for the two-qubit system are deduced analytically. In particular, we determine the maximal radius of a ball of SAS states around the maximally mixed state in the symmetric sector, and the minimal radius of a ball that contains the set of SAS states. As an application of our results, we also analyse the temperature dependence of the maximum entanglement that can be obtained from the thermal state of a spin-1 system with a spin-squeezing Hamiltonian. For the symmetric three-qubit case, our results are mostly numerical, and we conjecture a 3-parameter family of states that achieves the maximum negativity in the unitary orbit of any mixed state. In addition, we derive upper bounds, apparently tight, on the radii of balls containing only/all SAS states.
Evaluating Cognitive Maps and Planning in Large Language Models with CogEval

Recently an influx of studies claim emergent cognitive abilities in large language models (LLMs). Yet, most rely on anecdotes, overlook contamination of training sets, or lack systematic Evaluation involving multiple tasks, control conditions, multiple iterations, and statistical robustness tests. Here we make two major contributions. First, we propose CogEval, a cognitive science-inspired protocol for the systematic evaluation of cognitive capacities in Large Language Models. The CogEval protocol can be followed for the evaluation of various abilities. Second, here we follow CogEval to systematically evaluate cognitive maps and planning ability across eight LLMs (OpenAI GPT-4, GPT-3.5-turbo-175B, davinci-003-175B, Google Bard, Cohere-xlarge-52.4B, Anthropic Claude-1-52B, LLaMA-13B, and Alpaca-7B). We base our task prompts on human experiments, which offer both established construct validity for evaluating planning, and are absent from LLM training sets. We find that, while LLMs show apparent competence in a few planning tasks with simpler structures, systematic evaluation reveals striking failure modes in planning tasks, including hallucinations of invalid trajectories and getting trapped in loops. These findings do not support the idea of emergent out-of-the-box planning ability in LLMs. This could be because LLMs do not understand the latent relational structures underlying planning problems, known as cognitive maps, and fail at unrolling goal-directed trajectories based on the underlying structure. Implications for application and future directions are discussed.
Object Detection In Low-Light Environment Using YOLOv7

Abstract Object detection in low-light conditions is very essential in many applications such as autonomous driving, surveillance and security systems. Deep learning and neural networks are widely used in object detection applications, many algorithms were modified to improve the detection in low-light environments. You Only Look Once (YOLO) is one of the latest computer vision algorithms that provides accurate object detection and optimized algorithm run-time. In this work, the latest YOLO algorithm (version 7) is trained and validated using Exclusively Dark Image Dataset (ExDark) to detect twelve classes in dark environments. Results are displayed and compared with the latest related research work in this field. The detection precision and recall are 0.7153 and 0.6556 respectively. YOLOv7 showed a major improvement in the mean average precision (mAP) over a previous recent work who used the same dataset.
Enhancing Dyadic Relations with Homogeneous Graphs for Multimodal Recommendation

User-item interaction data in recommender systems is a form of dyadic relation, reflecting user preferences for specific items. To generate accurate recommendations, it is crucial to learn representations for both users and items. Recent multimodal recommendation models achieve higher accuracy by incorporating multimodal features, such as images and text descriptions. However, our experimental findings reveal that current multimodality fusion methods employed in state-of-the-art models may adversely affect recommendation performance without compromising model architectures. Moreover, these models seldom investigate internal relations between item-item and user-user interactions. In light of these findings, we propose a model that enhances the dyadic relations by learning Dual RepresentAtions of both users and items via constructing homogeneous Graphs for multimOdal recommeNdation. We name our model as DRAGON. Specifically, DRAGON constructs user-user graphs based on commonly interacted items and item-item graphs derived from item multimodal features. Graph learning on both the user-item heterogeneous and homogeneous graphs is used to obtain dual representations of users and items. To capture information from each modality, DRAGON employs an effective fusion method, attentive concatenation. Extensive experiments on three public datasets and eight baselines show that DRAGON can outperform the strongest baseline by 21.41% on average. Our code is available at https://github.com/hongyurain/DRAGON.
SpArX: Sparse Argumentative Explanations for Neural Networks

Neural networks (NNs) have various applications in AI, but explaining their decisions remains challenging. Existing approaches often focus on explaining how changing individual inputs affects NNs’ outputs. However, an explanation that is consistent with the input-output behaviour of an NN is not necessarily faithful to the actual mechanics thereof. In this paper, we exploit relationships between multi-layer perceptrons (MLPs) and quantitative argumentation frameworks (QAFs) to create argumentative explanations for the mechanics of MLPs. Our SpArX method first sparsifies the MLP while maintaining as much of the original structure as possible. It then translates the sparse MLP into an equivalent QAF to shed light on the underlying decision process of the MLP, producing global and/or local explanations. We demonstrate experimentally that SpArX can give more faithful explanations than existing approaches, while simultaneously providing deeper insights into the actual reasoning process of MLPs.
Chinese Text De-Colloquialization Technique Based on Back-Translation Strategy and End-to-End Learning

With the development of the Internet, there has been a significant increase in various types of textual information. However, when people engage in the composition of formal texts, they often incorporate their colloquial habits, which can diminish the professionalism and formality of the text. Existing research on Chinese texts primarily focuses on correcting misspelt characters that are visually or phonetically similar, as well as obvious grammatical errors, such as redundancy, omissions, and incorrect word order. However, there is limited research addressing the correction of text that exhibits colloquial expressions without apparent grammatical errors or misspelt characters. This article proposes a novel technique that utilizes deep learning methods to directly transform colloquial textual expressions into formal written expressions. Firstly, a parallel corpus dataset of written and spoken language is constructed using a back-translation strategy. Then, an end-to-end learning mechanism based on neural machine translation is employed, with colloquial text as the source language and written text as the target language. This allows the model to directly transform the colloquial text into text with a formal style. Finally, an evaluation of the proposed approach is conducted using the bilingual evaluation understudy (BLEU) and manual assessment techniques. The experimental results demonstrate that the technology proposed in this paper performs well in the task of de-colloquialization in Chinese texts. The contribution of this paper lies in proposing an automated method for collecting a substitute for manually annotated parallel corpora of spoken and written language, which significantly saves time and reduces the manual cost of constructing the dataset. Furthermore, the application of end-to-end learning techniques from neural machine translation to the task of de-colloquialization allows the trained model to directly generate written language flexibly based on the input of spoken language. This presents a novel solution for the task of the de-colloquialization of Chinese text.
How to overcome the nine most common teamwork barriers

Teamwork can have great benefits, but several predictable challenges can negatively impact team performance. In this paper, we examine these challenges, and summarize nine of the most common barriers to effective teamwork (i.e., competing demands, undervaluing teammates, power differentials, a leader not promoting collaboration, inexperience working together, dynamic demands, interdisciplinary teams, team member overload, and lack of resources). We describe each barrier, explain the path of least resistance that should be avoided, and provide evidence-based advice. In doing so, we provide a practical guide for team members and leaders, increasing their awareness of what can make teamwork more challenging and equipping them with ideas for addressing emergent obstacles they may encounter.
An examination of digitalized self-directed language learning practices of tertiary level EFL learners in Türkiye

Research on self-directed learning practices has ascertained various potential benefits of learner achievement. As mobile technology offers highly practical and portable usage for self-directed learning, studies on language learning practices through technology-based applications have become prevalent among adult learners. This study aimed to examine learners’ digitalized self-directed language learning practices for enhancing their English language skills at the tertiary level. The study employed a mixed method design, and the data were collected from 182 EFL learners at a higher education institution in Türkiye. The data were analysed through descriptive statistics and content analyses. The results demonstrated that the majority of the participants use mobile phones besides computers and tablets. However, their level of frequency in using these digital devices in English signaled a low level. Furthermore, results in association with the self-directed practices of learners for improving language skills evinced distinct language learning practices supported by technology-mediated applications and platforms. It was also concluded that learners need a well-organised self-directed learning practice in digital platforms. The study addresses key attributes of practical implications adhering to digitalized self-directed language learning practices for more efficient outcomes in advancing language skills in EFL instruction.
Blockchain Adoption in the Wine Supply Chain: A Systematic Literature Review

Blockchain offers decentralized, trustworthy and immutable data storage based on distributed ledger technology. Blockchain technology is recognized as an innovation enabler in many areas, with the food supply chain being one of them. This paper provides a systematic literature review of the current state of adoption of blockchain technology in the agri-food sector, specifically with a focus on the wine supply chain. Blockchain has the potential to improve the traceability and authenticity of the data provenance of wine products, increase consumer trust, and reduce fraud and errors. With these goals in mind, this study identifies the main research questions pertinent to the value proposition and competitive advantage of blockchain technology in the wine value chain and key players involved in the authentication and value chain recording. The PRISMA methodology was adopted to identify, screen and select only the relevant studies that were included in the analysis. This study also addresses the limitations and challenges for adoption, such as high implementation cost and lack of competences, and the need for standardized protocols and regulations. Finally, this systematic literature review includes an analysis of reports of blockchain applications in the wine sector and outlines the recommendations for future research to further explore the potential of blockchain-based solutions that could benefit all stakeholders across the wine value chain.
Wearable Augmented Reality: Research Trends and Future Directions from Three Major Venues

Wearable Augmented Reality (AR) has attracted considerable attention in recent years, as evidenced by the growing number of research publications and industry investments. With swift advancements and a multitude of interdisciplinary research areas within wearable AR, a comprehensive review is crucial for integrating the current state of the field. In this paper, we present a review of 389 research papers on wearable AR, published between 2018 and 2022 in three major venues: ISMAR, TVCG, and CHI. Drawing inspiration from previous works by Zhou et al. and Kim et al., which summarized AR research at ISMAR over the past two decades (1998–2017), we categorize the papers into different topics and identify prevailing trends. One notable finding is that wearable AR research is increasingly geared towards enabling broader consumer adoption. From our analysis, we highlight key observations related to potential future research areas essential for capitalizing on this trend and achieving widespread adoption. These include addressing challenges in Display, Tracking, Interaction, and Applications, and exploring emerging frontiers in Ethics, Accessibility, Avatar and Embodiment, and Intelligent Virtual Agents.
Research trends in the control of hate speech on social media for the 2016–2022 time frame

The growth in the number of social media users has resulted in a corresponding rise in the spread of hate speech on these platforms, leading to a growing, but little studied, problem. The bibliometric study aimed to examine the research trend and identify the most productive authors, the most active institutions, the leading countries and the most employed virtual hate speech control mechanisms by analyzing 576 relevant publications from the Scopus database published between 2016-2022. The findings showed an increase in publication and India as a leading country/region in research on virtual hate speech control mechanisms. Deep learning and natural language processing systems were identified as the most commonly used control mechanisms. Based on the results, it is recommended that future researchers focus on multidisciplinary collaboration and valid mechanisms for different languages. This paper provides a general overview of the current state of research in this field and serves as a guide for authors and institutions in their research and collaboration strategies.
MAG: A Novel Approach for Effective Anomaly Detection in Spacecraft Telemetry Data

Anomaly detection is a crucial matter to ensure the spacecraft stability. During the spacecraft operation, sensors and controllers generate a large volume of multidimensional time series telemetry data with long periodicity, and one key point to detect the anomaly inside the spacecraft timely and precisely is to extract essential features from the sheer amount of telemetry data. However, great challenges exist owing to the complex coupling relationships and the temporal characteristics inside the telemetry data. To address this issue, we propose a novel approach called maximum information coefficient attention graph network (MAG). The basic frame is a graph neural network, which utilizes embedding vectors to describe the intrinsic properties of each dimension, correlation analysis to investigate long-term dependencies, an attention mechanism to determine short-term interactions among dimensions, and long short term memory (LSTM) to extract temporal features. The fusion of these modules through a graph neural network results in the construction of the MAG model, allowing for a comprehensive analysis of complex variable relationships and temporal characteristics leading to successful detection of various types of anomalies. Since telemetry data has heterogeneous characteristics, we adapt the loss function and design an unsupervised anomaly scoring method suitable for MAG. To verify the effectiveness of the proposed algorithm, we conducted experiments using two publicly and two new available spacecraft telemetry datasets, and the results demonstrate that our algorithm is more efficient and accurate in detecting spacecraft data anomalies than several other state-of-the-art methods.