Reviewerly/exHarmony huggingface dataset:
A Review of Quantum Computing

Quantum computing is a computational framework based on the Quantum Mechanism, which has gotten a lot of attention in the past few decades.In comparison to traditional computers, it has achieved amazing performance on several specialized tasks.Quantum computing is the study of quantum computers that use quantum mechanics phenomena such as entanglement, superposition, annealing, and tunneling to solve problems that humans cannot solve in their lifetime.This article offers a brief outline of what is happening in the field of quantum computing, as well as the current state of the art.It also summarizes the features of quantum computing in terms of major elements such as qubit computation, quantum parallelism, and reverse computing.The study investigates the cause of a quantum computer's great computing capabilities by utilizing quantum entangled states.It also emphasizes that quantum computer research requires a combination of the most sophisticated sciences, such as computer technology, micro-physics, and advanced mathematics.
Data Privacy System Using Steganography and Cryptography

Data privacy is being breached occasionally whether in storage or in transmission.This is due to the spate of attack occasioned by the movement of data and information on an insecure internet.This study aimed to design a system that would be used by both sender and receiver of a secret message.The system used the combination of Steganography (MSB) and Cryptography (RSA) approaches to ensure data privacy protection.The system generates two keys: public and private keys, for the sender and receiver to encrypt and decrypt the message respectively.The steganography method used does not affect the size of cover image.The software was designed using python programming language in PyCharmIDE.The designed system enhanced the security and privacy of data.The results of this study reveal the effectiveness of combination of steganography and cryptography over the use of either cryptography or steganography and other existing systems.
An E-mail Spam Detection using Stacking and Voting Classification Methodologies

Nowadays, we use emails almost in every field; there is not a single day, hour, or minute when emails are not used by people worldwide.Emails can be categorized into two types: ham and spam.Hams are useful emails, while spam is junk or unwanted emails.Spam emails may carry some unwanted, harmful information or viruses with them, which might harm user privacy.Spam mails are used to harm people by wasting their time and energy and stealing valuable information.Due to increasing in spam emails rapidly, spam detection and filtering are the prominent problems that need to be solved.This paper discusses various machine learning models like Naï ve Bayes, Support Vector Machine, Decision Tree, Extra Decision Tree, Linear regression., and surveys about these machine learning techniques for email spam detection in terms of their accuracy and precision.In this paper, a comprehensive comparison of these techniques and stacking of different algorithms is also made based on their speed, accuracy, and precision performance.
Extricate Features Utilizing Mel Frequency Cepstral Coefficient in Automatic Speech Recognition System

As of late, Automatic speech recognition has advanced on account of instruments, for example, natural language processing, and deep learning, among others.It is a framework or put in another way, a gadget that changes a raw signal into computer comprehensible text.The genuine creation of speech is comprised of changes in air pressure that outcomes in pressure wave that our ear and cerebrum comprehend.The vocal tract is utilized to deliver a human speech, which is adjusted by teeth, tongue, and lips.Speech recognition alludes to a machine's ability to perceive human speech and transform it into a computer comprehensible text.Speech recognition is a magnificent illustration of good interaction between humans and computers.In this paper, we introduce the process to extricate the feature from the signal utilizing Mel-frequency cepstral coefficients.Mel-frequency cepstral coefficients are a genuinely far wide and proficient methodology for feature extraction from a sound file.This technique improved the speech recognition process and removes the distortion in the voice.In this manuscript we applied the Mel-frequency filtration process to improve speech and remove the background noise.the Therefore, the proposed methodology gives better performance in the automated speech recognition system.
Benchmarking Quality-Diversity Algorithms on Neuroevolution for Reinforcement Learning

We present a Quality-Diversity benchmark suite for Deep Neuroevolution in Reinforcement Learning domains for robot control. The suite includes the definition of tasks, environments, behavioral descriptors, and fitness. We specify different benchmarks based on the complexity of both the task and the agent controlled by a deep neural network. The benchmark uses standard Quality-Diversity metrics, including coverage, QD-score, maximum fitness, and an archive profile metric to quantify the relation between coverage and fitness. We also present how to quantify the robustness of the solutions with respect to environmental stochasticity by introducing corrected versions of the same metrics. We believe that our benchmark is a valuable tool for the community to compare and improve their findings. The source code is available online: https://github.com/adaptive-intelligent-robotics/QDax
A Signal Temporal Logic Motion Planner for Bird Diverter Installation Tasks with Multi-Robot Aerial Systems

This paper addresses the problem of task assignment and trajectory generation for installing bird diverters using a fleet of multi-rotors. The proposed solution extends our previous motion planner to compute feasible and constrained trajectories, considering payload capacity limitations and recharging constraints. Signal Temporal Logic (STL) specifications are employed to encode the mission objectives and temporal requirements. Additionally, an event-based replanning strategy is introduced to handle unforeseen failures. An energy minimization term is also employed to implicitly save multi-rotor flight time during installation operations. The effectiveness and validity of the approach are demonstrated through simulations in MATLAB and Gazebo, as well as field experiments carried out in a mock-up scenario.
Automatic Generation of Web Advertising Layouts: A Synthetic Dataset and a Deep Learning Baseline Model

Automatic generation of advertising layouts shows high economic interest, but as identified with our industrial partner, there is no public document layout dataset that matches this particular application. In this context, we produced two synthetic datasets that allow both the evaluation and training of any learning model on web advertising layout generation, and a small dataset of real cases to demonstrate the contribution of our work. We compared the results obtained by different learning models on the real cases, with and without prior use of our synthetic datasets, and our results show that these datasets allow to build and decisively improve models for the generation of real-world advertising layouts. Our three datasets, as well as useful data processing tools, are available at: https://github.com/romain-rsr/synth_datasets_for_web_advertising_layout/tree/master
Vertical Federated Knowledge Transfer via Representation Distillation for Healthcare Collaboration Networks

Collaboration between healthcare institutions can significantly lessen the imbalance in medical resources across various geographic areas. However, directly sharing diagnostic information between institutions is typically not permitted due to the protection of patients’ highly sensitive privacy. As a novel privacy-preserving machine learning paradigm, federated learning (FL) makes it possible to maximize the data utility among multiple medical institutions. These feature-enrichment FL techniques are referred to as vertical FL (VFL). Traditional VFL can only benefit multi-parties’ shared samples, which strongly restricts its application scope. In order to improve the information-sharing capability and innovation of various healthcare-related institutions, and then to establish a next-generation open medical collaboration network, we propose a unified framework for vertical federated knowledge transfer mechanism (VFedTrans) based on a novel cross-hospital representation distillation component. Specifically, our framework includes three steps. First, shared samples’ federated representations are extracted by collaboratively modeling multi-parties’ joint features with current efficient vertical federated representation learning methods. Second, for each hospital, we learn a local-representation-distilled module, which can transfer the knowledge from shared samples’ federated representations to enrich local samples’ representations. Finally, each hospital can leverage local samples’ representations enriched by the distillation module to boost arbitrary downstream machine learning tasks. The experiments on real-life medical datasets verify the knowledge transfer effectiveness of our framework.
A Neuromorphic Dataset for Object Segmentation in Indoor Cluttered Environment

Taking advantage of an event-based camera, the issues of motion blur, low dynamic range and low time sampling of standard cameras can all be addressed. However, there is a lack of event-based datasets dedicated to the benchmarking of segmentation algorithms, especially those that provide depth information which is critical for segmentation in occluded scenes. This paper proposes a new Event-based Segmentation Dataset (ESD), a high-quality 3D spatial and temporal dataset for object segmentation in an indoor cluttered environment. Our proposed dataset ESD comprises 145 sequences with 14,166 RGB frames that are manually annotated with instance masks. Overall 21.88 million and 20.80 million events from two event-based cameras in a stereo-graphic configuration are collected, respectively. To the best of our knowledge, this densely annotated and 3D spatial-temporal event-based segmentation benchmark of tabletop objects is the first of its kind. By releasing ESD, we expect to provide the community with a challenging segmentation benchmark with high quality.
A Study on ReLU and Softmax in Transformer

The Transformer architecture consists of self-attention and feed-forward networks (FFNs) which can be viewed as key-value memories according to previous works. However, FFN and traditional memory utilize different activation functions (i.e., ReLU and Softmax respectively), which makes them not equivalent. In this paper, we first rebuild the connections between FFN and key-value memory by conducting extensive studies on ReLU and Softmax, and find they are equivalent when adding an additional layer normalization module on Softmax. In addition, ReLU outperforms Softmax on both FFN and key-value memory when the number of value slots is large. We analyze the reasons and then explore this good property of ReLU on the self-attention network where the original Softmax activation performs poorly on long input sequences. We then propose a full ReLU architecture named ReLUFormer which performs better than the baseline Transformer on long sequence tasks such as document translation. This paper sheds light on the following points: 1) Softmax and ReLU use different normalization methods over elements which lead to different variances of results, and ReLU is good at dealing with a large number of key-value slots; 2) FFN and key-value memory are equivalent, and thus the Transformer can be viewed as a memory network where FFNs and self-attention networks are both key-value memories.
Consumer Preference for Virtual Reality Advertisements with Human–Scene Interaction: An Intermediary Based on Psychological Needs

Consumption is undergoing a digital revolution brought about by virtual technology, and the emergence of virtual spokespersons has changed the pattern of advertising endorsement sales. Are virtual character spokespersons better than real celebrity spokespersons? This article aims to explore the relationships between different types of advertising spokespersons, different types of advertising scenarios, and consumers' purchase intentions. The results of 2 pilot experiments and 4 formal experiments show that compared with spokespersons by real people, virtual character spokespersons have a greater positive impact on consumers' purchase intentions, which is achieved through the mediating role of psychological need satisfaction. In addition, different advertising scenarios provided by virtual technology have a moderating effect on this process. This empirical study investigates the possible positive effects of virtual character spokesperson compared with real character spokesperson and expands the results of spokesperson category based on self-determination theory.
Benchmarking Large Language Models for News Summarization

Large language models (LLMs) have shown promise for automatic summarization but the reasons behind their successes are poorly understood. By conducting a human evaluation on ten LLMs across different pretraining methods, prompts, and model scales, we make two important observations. First, we find instruction tuning, and not model size, is the key to the LLM's zero-shot summarization capability. Second, existing studies have been limited by low-quality references, leading to underestimates of human performance and lower few-shot and finetuning performance. To better evaluate LLMs, we perform human evaluation over high-quality summaries we collect from freelance writers. Despite major stylistic differences such as the amount of paraphrasing, we find that LMM summaries are judged to be on par with human written summaries.
Ensurance of artificial intelligence systems cyber security: analysis of vulnerabilities, attacks and countermeasures

In recent years, many companies have begun to integrate artificial intelligence systems (AIS) into their infrastructures. AIS is used in sensitive areas of society, such as the judicial system, critical infrastructure, video surveillance, and others. This determines the need for a reliable assessment and guaranteed provision of cyber security of AIS. The study analyzed the state of affairs regarding the cyber security of these systems. Possible types of attacks are classified and the main ones are considered in detail. Threats and attacks were analyzed by level of severity and security risks were assessed using the IMECA method. “Adversarial attacks” and “Data poisoning” attacks are found to have the highest risks of danger, but the countermeasures are not at the appropriate level. It was concluded that there is a need for formalization and standardization of the life cycle of the development and use of secure AIS. The directions of further research regarding the need to develop methods for evaluating and ensuring cyber security of the AIS are substantiated, including for systems that provide AI as a service.
Organizational Communication and Employees Differences: An Organizational Ethnography

This qualitative organizational ethnography aimed to determine the common issues or concerns in a state university. Respondents were chosen through convenience sampling. A researcher-made survey form was made to determine the common issues and concerns of the State University. The gathered data were coded and analyzed through content analysis. Results showed that organizational communication and employee individual differences were the most common issues or concerns encountered by the University. The researchers concluded that these issues or concerns affected the University in many forms. Thus, it is very important to look into and address such issues and concerns to avoid serious problems in the future. Researchers ultimately add value through interpersonal interactions. Current organizational communication procedures can benefit greatly from simple yet effective changes. Individual differences are the characteristics that set humans apart from one another. Each individual within a group has a unique manner of acting. Understanding individual variations are crucial because they affect how employees feel, think, and behave.
A Hybrid Approach to Sentiment Analysis of Iranian Stock Market User’s Opinions

With the significant growth of social media, individuals and organizations are increasingly using public opinion in these media to make their own decisions. The purpose of sentiment analysis is to automatically extract people’s sentiments from those social networks. Social networks related to financial markets, including stock markets, have recently attracted the attention of many individuals and organizations. people in these networks share their opinions and ideas about each share in the form of a post or tweet. In fact, sentiment analysis in this field is the assessment of people’s attitude towards each share. There are different approaches in sentiment analysis, in this article, a hybrid approach is proposed for sentiment analysis. In this way the feature vector used in machine learning is obtained from a lexicon that is automatically extracted from user’s tweets. This lexicon is made by using stock price information related to user’s opinion. Also, by using the next day’s price information of each share, amendments were suggested to this lexicon. Therefore, the lexicon generated for the feature vector was constructed in three ways, and all three methods reported about an 8% improvement over the baseline method in terms of F-score. The baseline method that is considered for this work, is the Persian version of SentiStrength lexicon which is designed for general purpose.
Measurement of the radius of curvature of a spherical surface based on the transport-of-intensity equation

The transport-of-intensity equation provides a new non-interferometric and non-iterative access to quantitative information about the phase of a light wave by measuring intensity distributions. This equation can be used to implement a simple and accurate spatial phase measurement for optical testing of spherical surfaces. The method requires only a CMOS camera, which records transverse field intensity distributions in several planes. Processing of experimental measurements with specialized software allows one to reconstruct the value of the radius of curvature of the spherical surface under test with high accuracy. The method is compared with measurements made by an interferometer, showing the difference between the values of the surface radius of curvature to be 0.01 % or less and indicating good agreement of the results.
An eye-tracking investigation of visual search strategies and test performance of L1 and L2 listening test takers

Through the use of eye-tracking technology and a while-listening performance (WLP) test, this study examined the differences in gaze behaviors and measured listening performances on test items (across various stages of the test) and compared them between native English-speaking (E-L1) and non-native English-speaking (E-L2) candidates. One hundred students from a public university in Singapore participated in the study. A series of Mann-Whitney U tests indicated that E-L1 candidates outperformed E-L2 candidates in the test with higher test scores. Using stringent data processing cutoffs (presence≥80% gaze data) and a series of non-parametric multivariate analyses, the study further found that the dynamicity of gaze behaviors on the test items across various stages of the test was similar between E-L1 and E-L2 candidates. However, there were distinctive differences in gaze behaviors between the two groups. For E-L1 candidates, none of the gaze behaviors on the test items across the different stages of the test predicted their overall listening test scores. In contrast, the overall listening test scores for E-L2 candidates was predicted by the average proportion of time that they had dwelled on the test items while simultaneously answering them and listening to the auditory text. The study is the first to show that keyword matching on the test items during the while-listening stage significantly contributes to WLP test performance for E-L2 candidates. These results suggest that the focal construct of the listening test is confounded by group-specific reading behaviors on the test items. In line with previous research, the use of the WLP test format for assessing second language listening comprehension is not recommended.
Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challenges

New technological advancements in wireless networks have enlarged the number of connected devices. The unprecedented surge of data volume in wireless systems empowered by artificial intelligence (AI) opens up new horizons for providing ubiquitous data-driven intelligent services. Traditional cloudcentric machine learning (ML)-based services are implemented by centrally collecting datasets and training models. However, this conventional training technique encompasses two challenges: (i) high communication and energy cost and (ii) threatened data privacy. In this article, we introduce a comprehensive survey of the fundamentals and enabling technologies of federated learning (FL), a newly emerging technique coined to bring ML to the edge of wireless networks. Moreover, an extensive study is presented detailing various applications of FL in wireless networks and highlighting their challenges and limitations. The efficacy of FL is further explored with emerging prospective beyond fifth-generation (B5G) and sixth-generation (6G) communication systems. This survey aims to provide an overview of the state-ofthe-art FL applications in key wireless technologies that will serve as a foundation to establish a firm understanding of the topic. Lastly, we offer a road forward for future research directions.
Reasoning on Feature Models: Compilation-Based vs. Direct Approaches

Analyzing a Feature Model (FM) and reasoning on the corresponding configuration space is a central task in Software Product Line (SPL) engineering. Problems such as deciding the satisfiability of the FM and eliminating inconsistent parts of the FM have been well resolved by translating the FM into a conjunctive normal form (CNF) formula, and then feeding the CNF to a SAT solver. However, this approach has some limits for other important reasoning issues about the FM, such as counting or enumerating configurations. Two mainstream approaches have been investigated in this direction: (i) direct approaches, using tools based on the CNF representation of the FM at hand, or (ii) compilation-based approaches, where the CNF representation of the FM has first been translated into another representation for which the reasoning queries are easier to address. Our contribution is twofold. First, we evaluate how both approaches compare when dealing with common reasoning operations on FM, namely counting configurations, pointing out one or several configurations, sampling configurations, and finding optimal configurations regarding a utility function. Our experimental results show that the compilation-based is efficient enough to possibly compete with the direct approaches and that the cost of translation (i.e., the compilation time) can be balanced when addressing sufficiently many complex reasoning operations on large configuration spaces. Second, we provide a Java-based automated reasoner that supports these operations for both approaches, thus eliminating the burden of selecting the appropriate tool and approach depending on the operation one wants to perform.