Reviewerly/exHarmony huggingface dataset:
Image encryption using fission diffusion process and a new hyperchaotic map

This paper devises an effective image encryption algorithm that exploits a novel hyperchaotic map and is applicable to a variety of image encryption scenarios. The newly designed hyperchaotic map exhibits enhanced ergodicity, more intricate behavior, and a substantial chaotic range, as revealed by performance evaluation. The encryption algorithm under consideration utilizes a newly developed hyperchaotic map in conjunction with fission diffusion and permutation operations. Fission diffusion effectively spreads minor changes throughout the entire image, while permutation operations relocate these changes to various positions within the image. The results of the performance analysis demonstrate that the algorithm exhibits strong resilience against various attack patterns, while also exhibiting high levels of encryption efficiency.
Metaverse and Extended Realities in Immersive Journalism: A Systematic Literature Review

Immersive journalism is a new form of media communication that uses extended reality systems to produce its content. Despite the possibilities it offers, its use is still limited in the media due to the lack of systematised and scientific knowledge regarding its application. This is a problem because it is a very powerful technology that changes the way audiences receive information and can be used both for new forms of storytelling that generate greater user engagement and for very sophisticated disinformation, which is why it is really important to study it. This study analyses articles published in the last 5 years that cover the use of extended technologies and the metaverse applied to immersive journalism. A systematic literature review applying PRISMA was carried out to identify literature within Web of Science, Scopus and Google Scholar (n = 61). Quantitative and qualitative analyses were conducted on the data collection techniques, the type of the data and the analysis techniques used. The results show a low level of methodological maturity, with research that is fundamentally descriptive and not very formalised, which limits the scope of its results and, therefore, the transfer of knowledge for its application in the configuration of new immersive journalistic products. The metaverse and extended technologies are considered independently and with distinct applications. It is concluded that research in this area is still in an initial exploratory and generalist stage that offers results that are not yet applicable to the promotion of this type of media format.
Risk Assessment Approach for Software Development using Cause and Effect Analysis

In the industry of software development, the risk is the most effective competitor who tries to flop the project at any stage of development. Risk is a critical factor that is obscure and has the potential to wreak massive loss of the project in terms of money, time, and resources. It is also harmful to the credibility of the organization. Most of the organizations don’t focus on this factor, and as a result, might witness the project failing. Lack of risk assessment is very common in most of organizations. This paper introduces risk assessment factors and analyzes the various situations to tackle them. We investigate the most effective key risk variables cost, strategy, technique, operation, and some unknown/unpredictable factors. Based on these variables, survey and interviews are conducted and examined them. We applied empirical studies on these variables and map them on the cause and effect analysis technique. The proposed technique elaborates the factors behind these risk variables. After that, results and analysis of these variables have been incorporated to scale down the impact of risk.
Circuit-Depth Reduction of Unitary-Coupled-Cluster Ansatz by Energy Sorting

Quantum computation represents a revolutionary approach to solving problems in quantum chemistry. However, due to the limited quantum resources in the current noisy intermediate-scale quantum (NISQ) devices, quantum algorithms for large chemical systems remain a major task. In this work, we demonstrate that the circuit depth of the unitary coupled cluster (UCC) and UCC-based ansatzes in the algorithm of the variational quantum eigensolver can be significantly reduced by an energy-sorting strategy. Specifically, subsets of excitation operators are first prescreened from the operator pool according to its contribution to the total energy. The quantum circuit ansatz is then iteratively constructed until convergence of the final energy to a typical accuracy. For demonstration, this method has been successfully applied to molecular and periodic systems. Particularly, a reduction of 50%–98% in the number of operators is observed while retaining the accuracy of the original UCCSD operator pools. This method can be straightforwardly extended to general parametric variational ansatzes.
FedLP: Layer-Wise Pruning Mechanism for Communication-Computation Efficient Federated Learning

Federated learning (FL) has prevailed as an efficient and privacy-preserved scheme for distributed learning.In this work, we mainly focus on the optimization of computation and communication in FL from a view of pruning.By adopting layer-wise pruning in local training and federated updating, we formulate an explicit FL pruning framework, FedLP (Federated Layer-wise Pruning), which is model-agnostic and universal for different types of deep learning models.Two specific schemes of FedLP are designed for scenarios with homogeneous local models and heterogeneous ones.Both theoretical and experimental evaluations are developed to verify that FedLP relieves the system bottlenecks of communication and computation with marginal performance decay.To the best of our knowledge, FedLP is the first framework that formally introduces the layer-wise pruning into FL.Within the scope of federated learning, more variants and combinations can be further designed based on FedLP.
Emotional intelligence of Large Language Models

Large Language Models (LLMs) have demonstrated remarkable abilities across numerous disciplines, primarily assessed through tasks in language generation, knowledge utilization, and complex reasoning. However, their alignment with human emotions and values, which is critical for real-world applications, has not been systematically evaluated. Here, we assessed LLMs' Emotional Intelligence (EI), encompassing emotion recognition, interpretation, and understanding, which is necessary for effective communication and social interactions. Specifically, we first developed a novel psychometric assessment focusing on Emotion Understanding (EU), a core component of EI. This test is an objective, performance-driven, and text-based evaluation, which requires evaluating complex emotions in realistic scenarios, providing a consistent assessment for both human and LLM capabilities. With a reference frame constructed from over 500 adults, we tested a variety of mainstream LLMs. Most achieved above-average Emotional Quotient (EQ) scores, with GPT-4 exceeding 89% of human participants with an EQ of 117. Interestingly, a multivariate pattern analysis revealed that some LLMs apparently did not rely on the human-like mechanism to achieve human-level performance, as their representational patterns were qualitatively distinct from humans. In addition, we discussed the impact of factors such as model size, training method, and architecture on LLMs' EQ. In summary, our study presents one of the first psychometric evaluations of the human-like characteristics of LLMs, which may shed light on the future development of LLMs aiming for both high intellectual and emotional intelligence. Project website: https://emotional-intelligence.github.io/
Is Your Goal-Oriented Dialog Model Performing Really Well? Empirical Analysis of System-wise Evaluation

There is a growing interest in developing goal-oriented dialog systems which serve users in accomplishing complex tasks through multi-turn conversations. Although many methods are devised to evaluate and improve the performance of individual dialog components, there is a lack of comprehensive empirical study on how different components contribute to the overall performance of a dialog system. In this paper, we perform a system-wise evaluation and present an empirical analysis on different types of dialog systems which are composed of different modules in different settings. Our results show that (1) a pipeline dialog system trained using fine-grained supervision signals at different component levels often obtains better performance than the systems that use joint or end-to-end models trained on coarse-grained labels, (2) component-wise, single-turn evaluation results are not always consistent with the overall performance of a dialog system, and (3) despite the discrepancy between simulators and human users, simulated evaluation is still a valid alternative to the costly human evaluation especially in the early stage of development.
cuQuantum SDK: A High-Performance Library for Accelerating Quantum Science

We present the NVIDIA cuQuantum SDK [1], a state-of-the-art library of composable primitives for GPU-accelerated quantum circuit simulations. As the size of quantum devices continues to increase, making their classical simulation progressively more difficult, the availability of fast and scalable quantum circuit simulators becomes vital for quantum algorithm developers, as well as quantum hardware engineers focused on the validation and optimization of quantum devices. The cuQuantum SDK was created to accelerate and scale up quantum circuit simulators developed by the quantum information science community by enabling them to utilize efficient scalable software building blocks optimized for NVIDIA GPU-based platforms. The functional building blocks provided cover the needs of both state vector- and tensor network- based simulators, including approximate tensor network simulation methods based on matrix product state, projected entangled pair state, and other factorized tensor representations. By leveraging the enormous computing power of the latest NVIDIA GPU architectures, quantum circuit simulators that have adopted the cuQuantum SDK demonstrate significant acceleration, compared to CPU-only execution, for both the state vector and tensor network simulation methods. Furthermore, by utilizing the parallel primitives available in the cuQuantum SDK, one can easily transition to distributed GPU-accelerated platforms, including those furnished by cloud service providers and high-performance computing systems deployed by supercomputing centers, extending the scale of possible quantum circuit simulations. The rich capabilities provided by the cuQuantum SDK are conveniently made available via both Python and C application programming interfaces, where the former is directly targeting a broad Python quantum community and the latter allows tight integration with simulators written in any programming language.
Pemanfaatan Canva Sebagai Media Perancangan Grafis

Canva is an online design program application that provides various tools or editing tools to create various graphic designs easily without having to design them from scratch. This research is aimed at providing a solution for using the Canva application as a graphic design medium for novice designers and online marketers. This approach uses a media understanding theory approach. As well as using a qualitative descriptive approach to interpret existing phenomena. The primary data collection process was carried out through in-depth interviews. The results of this research describe that the use of Canva as a graphic design medium is able to provide ease of use, increase designer creativity and produce graphic design content with new innovations.&#x0D;
An Online Ensemble Learning Model for Detecting Attacks in Wireless Sensor Networks

In today's modern world, the usage of technology is unavoidable, and the rapid advances in the Internet and communication fields have resulted in the expansion of wireless sensor network (WSN) technology. However, WSN has been proven to be vulnerable to security breaches. The harsh and unattended deployment of these networks, combined with their constrained resources and the volume of data generated, introduces a major security concern. WSN applications are extremely critical, it is essential to build reliable solutions that involve fast and continuous mechanisms for online stream analysis, allowing the identification of attacks and intrusions. Our aim is to develop an intelligent and efficient intrusion detection system by applying an important machine learning concept known as ensemble learning in order to improve detection performance. Although ensemble models have been proven to be useful in offline learning, they have received less attention in streaming applications. In this paper, we examine the application of different homogeneous and heterogeneous online ensembles in sensory data analysis on a specialized WSN detection system (WSN-DS) dataset in order to classify four types of attacks: Blackhole attack, Grayhole, Flooding, and Scheduling among normal network traffic. Among the proposed novel online ensembles, both the heterogeneous ensemble consisting of an Adaptive Random Forest (ARF) combined with the Hoeffding Adaptive Tree (HAT) algorithm and the homogeneous ensemble HAT made up of 10 models achieved higher detection rates of 96.84 % and 97.2 %, respectively. The above models are efficient and effective in dealing with concept drift while taking into account WSN resource constraints.
On the Relation between Sensitivity and Accuracy in In-Context Learning

In-context learning (ICL) suffers from oversensitivity to the prompt, making it unreliable in real-world scenarios. We study the sensitivity of ICL with respect to multiple perturbation types. First, we find that label bias obscures the true sensitivity, and therefore prior work may have significantly underestimated ICL sensitivity. Second, we observe a strong negative correlation between ICL sensitivity and accuracy: predictions sensitive to perturbations are less likely to be correct. Motivated by these findings, we propose SenSel, a few-shot selective prediction method that abstains from sensitive predictions. Experiments on ten classification datasets show that SenSel consistently outperforms two commonly used confidence-based and entropy-based baselines on abstention decisions.
Systematic testing of three Language Models reveals low language accuracy, absence of response stability, and a yes-response bias

Humans are universally good in providing stable and accurate judgments about what forms part of their language and what not. Large Language Models (LMs) are claimed to possess human-like language abilities; hence, they are expected to emulate this behavior by providing both stable and accurate answers, when asked whether a string of words complies with or deviates from their next-word predictions. This work tests whether stability and accuracy are showcased by GPT-3/text-davinci-002, GPT-3/text-davinci-003, and ChatGPT, using a series of judgment tasks that tap on 8 linguistic phenomena: plural attraction, anaphora, center embedding, comparatives, intrusive resumption, negative polarity items, order of adjectives, and order of adverbs. For every phenomenon, 10 sentences (5 grammatical and 5 ungrammatical) are tested, each randomly repeated 10 times, totaling 800 elicited judgments per LM (total n = 2,400). Our results reveal variable above-chance accuracy in the grammatical condition, below-chance accuracy in the ungrammatical condition, a significant instability of answers across phenomena, and a yes-response bias for all the tested LMs. Furthermore, we found no evidence that repetition aids the Models to converge on a processing strategy that culminates in stable answers, either accurate or inaccurate. We demonstrate that the LMs’ performance in identifying (un)grammatical word patterns is in stark contrast to what is observed in humans (n = 80, tested on the same tasks) and argue that adopting LMs as theories of human language is not motivated at their current stage of development.
DiffusionRet: Generative Text-Video Retrieval with Diffusion Model

Existing text-video retrieval solutions are, in essence, discriminant models focused on maximizing the conditional likelihood, i.e., p(candidates|query). While straightforward, this de facto paradigm overlooks the underlying data distribution p(query), which makes it challenging to identify out-of-distribution data. To address this limitation, we creatively tackle this task from a generative viewpoint and model the correlation between the text and the video as their joint probability p(candidates,query). This is accomplished through a diffusion-based text-video retrieval framework (Diffusion-Ret), which models the retrieval task as a process of gradually generating joint distribution from noise. During training, DiffusionRet is optimized from both the generation and discrimination perspectives, with the generator being optimized by generation loss and the feature extractor trained with contrastive loss. In this way, DiffusionRet cleverly leverages the strengths of both generative and discriminative methods. Extensive experiments on five commonly used text-video retrieval benchmarks, including MSRVTT, LSMDC, MSVD, ActivityNet Captions, and DiDeMo, with superior performances, justify the efficacy of our method. More encouragingly, without any modification, DiffusionRet even performs well in out-domain retrieval settings. We believe this work brings fundamental insights into the related fields. Code is available at https://github.com/jpthu17/DiffusionRet.
Communication in Immersive Social Virtual Reality: A Systematic Review of 10 Years’ Studies

As virtual reality (VR) technologies have improved in the past decade, more research has investigated how they could support more effective communication in various contexts to improve collaboration and social connectedness. However, there was no literature to summarize the uniqueness VR provided and put forward guidance for designing social VR applications for better communication. To understand how VR has been designed and used to facilitate communication in different contexts, we conducted a systematic review of the studies investigating communication in social VR in the past ten years by following the PRISMA guidelines. We highlight current practices and challenges and identify research opportunities to improve the design of social VR to better support communication and make social VR more accessible.
Applications of Blockchain Technology in Supply Chain Management

In this chapter, the authors delve into the utilization of blockchain technology within the realm of supply chain management. The emergence of blockchain technology has heralded substantial progress across diverse sectors, and the domain of supply chain management is undeniably one of them. The decentralized and immutable characteristics of blockchain present a promising resolution to endure hurdles encountered by stakeholders in supply chain management. These challenges encompass issues of transparency, traceability, and efficiency. The abstract commences by acknowledging the transformative essence of blockchain technology, underscoring its profound influence across various sectors. It subsequently narrows its focus to explore the specific applications within the domain of supply chain management. The chapter undertakes an in-depth examination of the multifaceted challenges faced by supply chain stakeholders and elucidates how blockchain technology adeptly tackles these predicaments.
Study on the impact of artificial intelligence tools in the development of university classes at the school of communication of the Universidad Nacional José Faustino Sánchez Carrión

Technological progress has led to the development of computational elements applicable in various spheres of human activity. Artificial Intelligence (AI) stands out, whose growth is transforming, among other aspects, how students experience the educational process. In this context, this study aims to evaluate the impact of the use of AI tools in university classes of students at the Academic Professional School of Communication Sciences of the Universidad Nacional José Faustino Sánchez Carrión (Lima, Peru). Following a quantitative approach with experimental scope, three working days focused on the use of three AI tools applied to the subjects of Journalistic Writing, Advertising Writing and Audiovisual Communication. Eighty-four students from the fifth, seventh and ninth cycles of studies at the aforementioned school participated. The results indicate that students value the relevance of AI tools in their academic training and professional development. However, difficulties related to internet connectivity for optimal performance of AI platforms and full utilization of their benefits are highlighted. The study concludes by emphasizing the need for more spaces for the dissemination and socialization of experiences on the potential of AI, with a view to expanding and connecting the university academic community.
The Role of Blockchain Technology in Enhancing Financial Security amidst Digital Transformation

Amid the digital revolution, this study explores how blockchain technology can improve financial security. The primary goals are to examine how blockchain technology reduces cyber threats in the financial industry, evaluate regulatory factors that should be considered before adopting blockchain, and investigate the prospects for blockchain going forward. A review process based on secondary data is utilized, referencing academic articles, reports, and regulatory documents. Significant discoveries show that blockchain provides creative ways to reduce cyber threats, improve transparency, and foster financial transaction confidence. Nevertheless, issues, including adoption obstacles, regulatory uncertainties, and technological restrictions, must be resolved to reach their full potential. The policy implications indicate that stakeholders must collaborate, do continuous research, and establish clear regulatory frameworks to promote responsible blockchain adoption in the finance industry. Blockchain technology can transform financial security and encourage creativity in the digital world.
Normative Echoes: Use and Manipulation of Player Generated Content by Communities of NPCs

Normative Echoes is an interactive installation that explores ways to combine player-created content with procedural content. Animated autonomous agents inhabit virtual islands on stationary computers; a tablet PC is used as a virtual raft to transfer agents between the islands. The agents on each island communicate with one another and form scenario-based computational representations of their social interactions. These scenarios represent emergent social patterns and norms within the society. Humans can communicate with the animated autonomous agents through the use of a microphone. Utterances spoken by participants are parsed, repeated, and learned by agents, then used by the agents in communications with their social partners. When transferred between islands, agents bring with themselves the utterances and norms they have learned, thus spreading those norms throughout the various communities in the installation. In this way, agents can meaningfully perform procedural operations on player-created content, allowing for a dynamic and engaging experience.