Reviewerly/exHarmony huggingface dataset:
Maximizing the Eigenvalue-Gap and Promoting Sparsity of Doubly Stochastic Matrices with PSO

The eigenvalue-gap of doubly stochastic matrices with sparsity constraints is maximized using the unified particle swarm optimizer. This is possible through the use of an iterative normalization procedure that maps the search space of the swarm to the set of doubly stochastic matrices with given sparsity pattern. We extend the method to the problem of finding doubly-stochastic matrices of given dimensions that are as sparse as possible, and attain a given eigenvalue-gap target.
Fitness Landscape Ruggedness Impact on PSO in Dealing with Three Variants of the Travelling Salesman Problem

Fitness landscape analysis has gained quite some attention in understanding the behaviour of metaheuristics. Swarm intelligence is a type of metaheuristics that has grown considerably on the algorithmic side over the past decade. Nevertheless, only little attention has been paid to understanding the behaviour of algorithms on different fitness landscapes, especially in combinatorial optimization. Our aim in this paper is to re-motivate the importance of this issue. Moreover, by considering particle swarm optimization (PSO), we present a first investigation on its adaptation to three variants of the travelling salesman problem and how its performance is correlated with the ruggedness of the problem instances. The results show that PSO performance deteriorates with the increase in the number of cities and the ruggedness of the instances.
On generalized analytic wavelets

Gabor proposed the concept of analytic signal, i.e., a complex signal related to a given real signal using the Hilbert transform. In this paper, we generalize the approach of constructing analytic signal by using the fractional Boas transform. We call these analytic signals as the generalized analytic signals. We discuss some important properties of these generalized analytic signals. Also, we define generalized analytic wavelets and give the related convolution and cross-correlation theorems.
Comparing entanglement and identity effects in tunnelling

The probability of simultaneous tunnelling of two particles is modified when the system is in a non-separable state, either entangled or symmetrised. We compare both effects in the rectangular potential barrier by evaluating the transmission rates in superposition states. For large momenta, their simultaneous presence greatly changes the form of the transmission rates. The joint effects are much larger than the superposition ones. Moreover, there are significant differences between bosons and fermions. We present an unified view of the combined effects as a quantum interference phenomenon. The analysis also illustrates a novel aspect of exclusion in entangled systems, the existence of superposition states one of whose terms is forbidden by Pauli's principle.
Examination of QUIC-based Website Fingerprinting

Recently, there has been a considerable amount of interest in extending QUIC for new capabilities. Since QUIC is governing most of the internet traffic, questions arise about its security. In this regard, the paper aims to understand the procedure of website fingerprinting in the case of QUIC websites as compared to TCP websites. It aims to provide a comparison of the effectiveness of the earlier used ML methods on the TCP traffic and QUIC traffic. Through this paper, a thorough comparative study of various machine learning models has been incorporated to identify the website to which a packet belongs to. In this paper, Wireshark has been used to capture packets from 50 different websites that use QUIC using a Perl script. The paper presents the results of training the data on a combination QUIC and TCP packets using the commonly used machine learning models p-FP, Var-CNN, CUMUL, Wang-KNN and k-FP. Results show that Random forest classification gives the best accuracy whereas k-FP gives the least. It can also be noted that the overall efficiencies of all the algorithms are much lesser on the combination dataset as compared to datasets with only TCP traces. This can be attributed to the fact that these models have been designed for TCP data, and are unfamiliar with QUIC traffic specifications.
Behavior-Aware Robot Navigation with Deep Reinforcement Learning

Robot navigation in a crowded area is difficult owing to the complexity of the natural world and the difficulties of simulating human interactions. In order for robots to safely and efficiently navigate near humans, it is imperative to include human behavior in robot navigation models. The prior techniques, on the other hand, isolate human behavior from models of robot navigation and solve robot navigation in a context-aware and partially observable setting. To us, this is the most rudimentary type of self-awareness that mobile robots may exhibit. We propose a risk-aware robot navigation model based on deep reinforcement learning that is capable of making improved navigational decisions. Our model provides safety, risk-awareness, and environmental understanding for mobile robots in both outdoor and indoor settings. We demonstrate our model's performance in both simulation and real-world robot environments.
DNN-based speech watermarking resistant to desynchronization attacks

Desynchronization attacks proved to be the greatest challenge to audio watermarking systems as they introduce misalignment between the signal carrier and the watermark. This paper proposes a DNN-based speech watermarking system with two adversarial networks jointly trained on a set of desynchronization attacks to embed a randomly generated watermark. The detector neural network is expanded with spatial pyramid pooling layers to be able to handle signals affected by these attacks. A detailed training procedure of the aforementioned DNN system with gradual attack introduction is proposed in order to achieve robustness. Experiments performed on a speech dataset show that the system achieves satisfactory results according to all the benchmarks it was tested against. The system preserves signal quality after watermark embedding. Most importantly, the system achieved resistance to all considered desynchronization attacks. The majority of the attacks cause less than [Formula: see text]% of incorrectly detected watermarked bits on average, which outperforms comparative techniques in this regard.
Semantic Adaptation to the Interpretation of Gradable Adjectives via Active Linguistic Interaction

When communicating, people adapt their linguistic representations to those of their interlocutors. Previous studies have shown that this also occurs at the semantic level for vague and context-dependent terms such as quantifiers and uncertainty expressions. However, work to date has mostly focused on passive exposure to a given speaker's interpretation, without considering the possible role of active linguistic interaction. In this study, we focus on gradable adjectives big and small and develop a novel experimental paradigm that allows participants to ask clarification questions to figure out their interlocutor's interpretation. We find that, when in doubt, speakers do resort to this strategy, despite its inherent cognitive cost, and that doing so results in higher semantic alignment measured in terms of communicative success. While not all question-answer pairs are equally informative, we show that speakers become better questioners as the interaction progresses. Yet, the higher semantic alignment observed when speakers are able to ask questions does not increase over time. This suggests that conversational interaction's key advantage may be to boost coordination without committing to long-term semantic updates. Our findings shed new light on the mechanisms used by speakers to achieve semantic alignment and on how language is shaped by communication.
Usability Evaluation of Maritime Websites with Different End User Groups

Maritime-related websites should be properly developed to guarantee a good level of usability, especially when end users comprise a variety of people with varied wants and desires. This study’s primary goal was to examine how various end users with different use purposes—including members of the working population in the maritime sector, maritime students, and seafarers’ relatives—react to maritime websites in terms of usage efficiency, satisfaction, which collectively form websites’ usability. The assessments were obtained through formal usability testing including think-aloud protocol during task performing and questionnaires. The results of the statistical analysis show that different groups of end users achieve different levels of efficiency in the completion of tasks. Also, it has been found that only for students there is a satisfaction difference between evaluated maritime websites. Suggestions to enhance websites’ usability, particularly for inexperienced users, based on the identified weaknesses are also provided.
Implementation analysis and problems of implementing of literacy movement in primary schools

The purpose of this study is to analyze the application and problems of implementing literacy movement in primary schools. This type of research is descriptive qualitative. The data sources in this study were all school residents at SDN Tlogomas 2 Malang City. The research data was conducted using interview techniques and documentation using interview instruments and guidelines. The implementation of literacy movement at SDN Tlogomas 2 Malang City is divided into three stages: habituation, development, and learning. The literacy movement also had some problems that trying to overcome together at SDN Tlogomas 2.
Development of thematic e-modules oriented on the ability to write descriptions of students in class V of SDN Boto, Tuban district

Based on the observation results of descriptive writing skills in class V students and interviews with teachers of SDN Boto, Semanding District, Tuban Regency, students' writing ability was low. Students have difficulty determining the topic of writing so that students do not want to start writing activities. In learning activities, students are only given assignments from LKS so that in writing descriptive essays students are not able to involve their five senses to put their ideas into writing. Preliminary data from analysis of student scores obtained data on the number of students in grade 5 were 25 students, only 5 students were able to complete writing the descriptive essay. So that only 20% of classical percentages have the ability to write descriptions. Based on this background, thematic e-modules were developed to make it easier for students to write descriptions of class V students at Boto 5 Elementary School. This type of research was R&amp;D with four stages. The purpose of this e-module development research is to determine feasibility level of linguists, materials, and media. The results of validation by design experts show that the quality of learning e-module is in stage 1 assessment component with an achievement level of 82.85% with a "very decent" qualification. In stage 2 assessment, still with a "very decent" qualification, the achievement rate was 85.71%. Assessment of learning products in the form of modules by material experts shows the quality of learning modules in stage 1 assessment component with an achievement level of 83.33% with a "very decent" qualification. In stage 2 assessment, still with the "very decent" qualification, the achievement rate was 86.66%. Assessment of learning products in the form of modules by linguists shows the quality of learning modules in the stage 1 assessment component with an achievement level of 86.6% with a "very decent" qualification. In stage 2 assessment, still with the "very decent" qualification, the achievement rate was 91%.
Smart UML - Assignment Management Tool for UML Diagrams

The great majority of manual processes have been automated in the current day. Nonetheless, there is no reliable approach for evaluating UML diagrams for plagiarism and correctness. Unified Modeling Language (UML) provides the basis for UML diagrams. UML Diagrams are typically used to graphically express a system’s stakeholders, activities, objects, and classes to get a deep knowledge of a particular system. UML diagrams are widely used by industry experts as well as beginners. Class diagrams and use case diagrams are the most commonly used diagrams in UML. Though most Computer science-related students get assessed in UML modelling, diagram plagiarism isn’t considered in most exams since it’s a tedious task to perform. The proposed system automates capturing details in UML diagram components and generating a model answer from the given question scenario to compare the diagrams and calculate the plagiarism percentage with the correction percentage.
Automated Quantum Memory Compilation with Improved Dynamic Range

Emerging quantum algorithms that process data require that classical input data be represented as a quantum state. These data-processing algorithms often follow the gate model of quantum computing-which requires qubits to be initialized to a basis state, typically| 0 〉-and thus often employ state generation circuits to transform the initialized basis state to a data-representation state. There are many ways to encode classical data in a qubit, and the oft-applied approach of basis encoding does not allow optimization to the extent that other variants do. In this work, we thus consider automatic synthesis of addressable, quantum read-only memory (QROM) circuits, which act as data-encoding state-generation circuits. We investi-gate three data encoding approaches, one of which we introduce to provide improved dynamic range and precision. We present experimental results that compare these encoding methods for QROM synthesis to better understand the implications of and applications for each.
Memory-Efficient Differentiable Programming for Quantum Optimal Control of Discrete Lattices

Quantum optimal control problems are typically solved by gradient-based algorithms such as GRAPE, which suffer from exponential growth in storage with increasing number of qubits and linear growth in memory requirements with increasing number of time steps. Employing QOC for discrete lattices reveals that these memory requirements are a barrier for simulating large models or long time spans. We employ a non-standard differentiable programming approach that significantly reduces the memory requirements at the cost of a reasonable amount of recomputation. The approach exploits invertibility properties of the unitary matrices to reverse the computation during back-propagation. We utilize QOC software written in the differentiable programming framework JAX that implements this approach, and demonstrate its effectiveness for lattice gauge theory.
Voice Enabled Intelligent Programming Assistant

In modern era where software development is of vital importance, software developers are challenged with conditions like Repetitive Strain Injury (RSI) which hinders their ability to work effectively. Furthermore, people with difficulties with using their hands also find it challenging to program in the traditional manner. As a solution, coding with one’s voice has been experimented with, but current solutions lack interactivity and are harder to use and setup leaving much room for improvement in this domain. In this research work, by using input classifier models with accuracies over 90%, intent classifiers with accuracies over 70%, code parsing and various human computer interaction techniques, we developed a conversationally interactive, programming language agnostic, easy to setup and easy to use Voice Coding Assistant. This will potentially help a global audience of programmers to achieve their goals and improve productivity and lead a healthier life. We have named the system thus developed, “Venic”.
Prop Cone: The Virtual Prop House

In films, props are the moving objects that may be seen. Prop masters and art directors’ assistants are responsible for gathering movie-related props. The selection of props is also essential, because the appeal of a film is dependent on its captivating qualities. It is difficult to select without a solid recommendation and a large gallery. Therefore, the suggested platform is a virtual prop house that works as a prop-specific recommendation system and has additional capabilities such as putting previews and history records. The process necessitates a cross-device platform, as the recommendation system functions as a web page, and when it comes to the improved capabilities of positioning previews, it is transitioning to a mobile application with AR Core support. In this study, the recommendations for the props can be made in one of three efficient ways. They are by reading the script and recommending the props using word recognition, recommending props from the available gallery by scanning an image, which can be a screenshot of a movie, and as associated props which makes the prop master’s job easier by suggesting props by grouping and ranking the most prevalently utilized props in the movies that have been screened so far.
Enhancing Conversational AI Model Performance and Explainability for Sinhala-English Bilingual Speakers

Natural language processing has become essential to modern conversational tools and dialogue engines, including Chatbots. However, applying natural language processing to low-resource languages is challenging due to their lack of digital presence. Sinhala is the native language of approximately nineteen million people in Sri Lanka and is one of many low-resource languages. Moreover, the increase in using code-switching: alternating two or more languages within the same conversation, and code-mixing: the practice of representing words of a language using characters of another language, has become another major issue when processing natural languages. Apart from natural language processing, the explainability of opaque machine learning models utilized in chatbots has become another prominent concern. None of the existing modern chatbot development platforms supports explainability and relies on a performance score such as accuracy or f1-score. This paper proposes a no-code chatbot development platform with a series of built-in novel natural language processing, model evaluation, and explainability tools to tackle the problems of processing Sinhala-English code-switching and code-mixing natural language data and model evaluation in modern chatbot development platforms.
TangiGuru: Tangible E-Learning Solution for Early Childhood Development

Exploration and manipulation of physical objects are essential for early childhood learning. Previous investigations found several TUI uses in other fields. Less research has been done on tangible learning for youngsters; thus, it is unclear if they are more collaborative, playful, or functional. TangiGuru consists of 12 tangible, manipulative objects known as TangiCubes, which are used as a tangible user interface between children and the e-Learning application. It can carry out cognitive learning activities related to colors, languages, shapes and basic math by dynamically varying assigned values by changing the external appearance of TangiCubes. This dynamic nature of the TangiCubes makes it possible to use the same tangibles with endless possibilities compared to traditional tangible learning solutions with static value for each tangible. After the prototyping phase, children were evaluated with the traditional tangible learning solutions compared to TangiGuru. They concluded that the more interactive tangible interfaces could make the children perform activities more engagingly.
SMART DIARY: Autonomous System for Daily Diary Creation and Prioritization of Daily Activities for Improved Well-Being Using Neural Networks and Machine Learning

In the present world, the IT (Information Technology) industry is so advanced that it has opened many opportunities to communities with numerous roles. Even though the industry is growing day by day and providing more opportunities, it has had serious effects on human well-being. If a person fails to control the demands of work or study, such as tasks with higher complexity, an unmanageable workload, pressure, enduring conflicts within the team, and other physical and emotional demands, it could lead that person to exhaustion, anxiety, and stress. Such factors can affect the health of a person in an extremely negative way. The proposed topic “Smart Diary: Auto generation of diary and Prioritization of Daily Activities for Improved Well-Being” is a solution for people with uncontrolled job demands and busy work schedules. This helps to keep track of day-to-day life activities and review them to make better plans for the future. It also helps the user prioritize their daily tasks and provides suggestions for people who are stressed and showcasing negative emotions based on text analysis.
A Clustering-based model for the Generation of Diversified Recommendations

The primary goal of a recommender system is to generate accurate recommendations according to the user’s interests. But the user’s satisfaction increases when they get a chance to view the diverse categories of items. There exist several works on the generation of diverse recommendations but the performance of these methods often gets limited due to the issues such as cold start, filter bubble long tail, and grey sheep. Moreover, these methods do not consider the user’s preference regarding exploration and exploitation while generating the recommendations. To this extent, this work proposes a model known as the iterative clustering-based diversity model, which can generate diverse recommendations and also solve the above-said issues. It groups the items based on the item description using the TF-IDF algorithm. The model generates two recommendations in such a way that one recommendation is similar and the other is different in comparison with the last interaction made by the user. The model has been evaluated on the benchmark dataset and has achieved promising results.
A Survey of Dynamic Bandwidth Allocation to Enhance Quality of Service in Internet of Things

Internet of Things (IoT) is one of the most popular buzzwords in the twenty-first century. The Internet of Things (IoT) is flourishing, facilitating, and improving people’s lives all over the world as a result of the expanding technological breakthroughs. IoT-related ideas like augmented reality, a smarter environment, smarter healthcare, and self-driving automobiles have permeated every aspect of our daily lives. There is a huge market of IoT devices especially in the communication networks, several reports are informing. Some nations independently expand about two thirds of these IoT devices. Numerous IoT issues might be caused by low computing power and broadband availability. A few of them are security, heterogeneity, low power, and other issues. Considering this, it is a challenging issue to distribute the bandwidth to billions of IoT devices using the current infrastructure. In this study, we first introduce the IoT and the various industries in which it is used, then we analyze several methods for bandwidth allocation. Finally, it has been observed that more sophisticated bandwidth control techniques are required to offer Quality of Service to all connected IoT devices.