Reviewerly/exHarmony huggingface dataset:
Gaussian Filter-Based Speech Segmentation Algorithm for Gujarati Language

Automatic speech segmentation is a main step in speech signal production and analysis process. Great advancement in speech synthesis has already been made using concatenative algorithms. Syllable is most suitable speech unit for concatenative speech synthesis because it does not require extensive prosodic models and provide better co-articulations than other sound units. To get natural sounding, output speech segmentation plays very important role. Speech segmentation is the process of dividing speech signal in to smaller units of sound. So accurate selection of speech unit and detection of boundaries are very important. In this research work, Gujarati language is used for segmentation and database is created. This paper suggests a method of syllable segmentation to detect boundaries of syllable by means of start point of syllable and end point of syllable. Performance parameters such as accuracy and peak signal to noise ratio (PSNR) are evaluated. Producing natural sounding speech signal in different Indian languages is a very demanding and ongoing problem.
The Efficiency of Interactive Differential Evolution on Creation of ASMR Sounds

Autonomous Sensory Meridian Response (ASMR) is a popular movie content among Internet users. It is a kind of entertainment content, and the users enjoy positive feelings caused by its binaural sounds in terms of reality. By referring to previous proposals creating ASMR sounds based on Interactive Evolutionary Computations (IECs), this study constructed a system of the Interactive Differential Evolution (IDE) creating ASMR sounds suited to the preference of each user. The purpose of this study is to investigate the efficiency of the IDE, which is one of IEC: its subjective evaluation is based on the user’s paired comparison of the ASMR sounds. A listening experiment was conducted to fundamentally investigate the efficiency of the IDE. The ASMR sounds were composed of six natural sounds. Each of the nine subjects participated in the listening experiment, and they repeatedly compared two ASMR sounds afforded by the IDE system. As a result of the search process, a shrink in the search space was observed in accordance with generations. After the repetitive comparisons, the subjects scored two representatives ASMR sounds picked up from the 0th and the 10th generations respectively. With statistical analysis, a marginal increase in the fitness values was observed.
Effect of loss on linear optical quantum logic gates

Linear optical quantum gates have been proposed as a possible implementation for quantum computers. Most experimental linear optical quantum gates are constructed with free-space optical components with negligible loss. In this work, we analyze symmetric and asymmetric partially polarizing lossy beam splitters. Using the generalized beam splitter equations, we study the effects of loss on two linear optical quantum gates: the first is a commonly used CNOT gate, and the second is a W state expansion gate. Envisioning inherent loss in plasmonics and metamaterials as a new degree of freedom and those materials systems as a route for miniaturization, we reconsider the requirements of the lossy CNOT gate and show it is possible to simplify the three-beam-splitter design to a single beam splitter without sacrificing success probability.
Cross-Cultural Feminist Technologies

As our contemporary problems of climate change, pandemics, tech reform, and worldwide wealth inequality demand global solidarities, cooperation, and collective and empathetic imagination, we need approaches that can carve critical pathways for an inclusive technological future. Much as technology is created to transcend borders and cultures, this essay proposes that cross-cultural feminism can do the same. This essay pioneers a framework that enables us to strive for global solidarities while decolonizing the feminist “common sense” that is institutionalized into how technologies are shaped. We advocate for an approach grounded in the materiality (embodiments), mobility (social movements), and modality (codes and modes of design). We believe this three-pronged lens can inform practice and help set the tenor for how to build cross-cultural feminist technologies for an inclusive future.
Transformer-based Banking Products Recommender System

Credit cards, deposits, loans, pension funds, mutual funds which of these products is relevant to a bank's clients, and at what time in their banking journey? We propose a modeling framework for item recommendation using a Transformer encoder [6] and a novel input data representation accounting for the temporal context of item ownership and user metadata. We evaluate the model on a large dataset from Bank Santander. Our system outperforms industry baselines Amazon Personalize [1], and XGBoost [4], a top performing model in the Santander Kaggle competition [2]. We achieve a 56.6% top-3 precision and significantly outperforms Amazon Personalize and the XGBoost model, with 21.5% and 37.9% top-3 precision, respectively. We engineered an original way of representing input data as a sequence and found that this specific representation, with our Transformer-based architecture, improves the model's performance. We hope that our contribution paves the way for the democratization of recommender systems in banking, and the use of the Transformer model for product recommendation in industry.
Visualising personas as goal models to find security tensions

Purpose This paper aims to present a tool-supported approach for visualising personas as social goal models, which can subsequently be used to identify security tensions. Design/methodology/approach The authors devised an approach to partially automate the construction of social goal models from personas. The authors provide two examples of how this approach can identify previously hidden implicit vulnerabilities and validate ethical hazards faced by penetration testers and their safeguards. Findings Visualising personas as goal models makes it easier for stakeholders to see implications of their goals being satisfied or denied and designers to incorporate the creation and analysis of such models into the broader requirements engineering (RE) tool-chain. Originality/value The approach can be used with minimal changes to existing user experience and goal modelling approaches and security RE tools.
BLE-based approach for detecting daily routine changes

This work proposes a system to detect changes in daily routines in a controlled environment, such as a sensorized-home. Variations in routines can be indicative of physical or cognitive decline in elderly adults, which makes it very attractive to support independent living and healthy ageing. Our proposal is based on an indoor symbolic location system based on low-cost and easy-to-install Bluetooth Low Energy (BLE) transmitter beacons, together with a mobile receiver. The user's symbolic location is estimated from the Received Signal Strength Indicator (RSSI) and K-Nearest Neighbour (KNN) model, which is merged with the acceleration provided by the receiving mobile device. The location is used to estimate the time spent in each monitored room, to infer a time-based routine. The symbolic localization has an accuracy higher than 96%. The subsequent daily monitoring allows for the detection of variations with respect to a defined routine that can serve as an alarm for the user, family members or caregivers.
COVID Dodge: An African-Centric game for promoting COVID-19 Safety Measures

Raising awareness for safety measures against communicable diseases helps reduce the spread among human populations. As the lockdown rules for the COVID-19 pandemic are being relaxed, there is a need for active awareness around the safety measures against the spread of the virus to prevent a second pandemic wave. Therefore, this demonstration paper presents the design and implementation of a mobile persuasive game, titled COVID Dodge, aimed at raising the awareness around social distancing and other precautionary measures against the spread of the COVID-19. This persuasive game employs some strategic features to increase the attention of Africans towards social distancing.
Direct Expected Quadratic Utility Maximization for Mean-Variance Controlled Reinforcement Learning

Risk management is critical in decision-making, and mean-variance (MV) trade-off is one of the most common criteria. However, in reinforcement learning (RL) under a dynamic environment, MV control is not as easy as that under a static environment owing to computational difficulties. For MV controlled RL, this paper proposes direct expected quadratic utility maximization (EQUM), where a mean-variance efficient agent is given as its solution. This approach does not only avoid computational difficulties but also improves empirical performances. In experiments, we demonstrate the effectiveness of the proposed EQUM with benchmark settings.
Exploiting abstractions for grammar‐based learning of complex multi‐agent behaviours

This paper presents a grammar-based evolutionary approach that incorporates abstractions to learn complex collective behaviours through their simpler representations. We propose modifications to the grammar syntax design and genome structure to facilitate evolution of abstractions in separate genome partitions. Two abstraction techniques based on behavioural decomposition and environmental scaffolding are presented to derive these simpler representations. Parallel and incremental learning architectures incorporated with grammatical evolution (GE) are investigated with three complex problems to evaluate their potential in generating collective multi-agent behaviours. The results infer that both learning architectures surpass a generic GE model in performance for evolving complex behaviours. Furthermore, using environmental scaffolding reduces the robustness of the model than when only the behavioural decomposition technique is used. However, it has more potential to generate solutions with better fitness than when scaffoldings are not used. The evaluations suggest that, by incorporating abstraction learning architectures with grammar-based evolution can significantly improve the performance of an agent system in complex problem domains.
Experimental Implementation of Discrete Time Quantum Walk with the IBM Qiskit Library

This paper presents an implementation of quantum random walk using the Qiskit library, given the quantum circuits extracted from the literature. We started by implementing an instance of Coined Discrete Quantum Walk on an 8-node cyclic graph. Observing the limitations of this implementation, we experimented a different approach by proposing an implementation for Szegedy Quantum Walk in the IBM Qiskit library [1]. The proposed solution could be used as a building block to construct quantum implementation of other classic algorithms.
Advancement of Cybersecurity and Information Security Awareness to Facilitate Digital Transformation

As the pace of changes in the digital world is increasing exponentially, the appeal to shift from traditional platforms to digital ones is increasing as well. Accomplishing digital transformation objectives is impossible without information security considerations. Business leaders should rethink information security challenges associated with digital transformation and consider solutions to seize existing opportunities. When it comes to information security, human beings play a critical role. Raising users' awareness is a meaningful approach to avoid or neutralize the likelihood of unwanted security consequences that may occur during transforming a system digitally. This chapter will discuss cybersecurity and information security awareness and examine how digital transformation will be affected by implementing information security awareness. This chapter will discuss the digital transformation advantages and serious challenges associated with cybersecurity, how to enhance cybersecurity, and the role of information security awareness to mitigate cybersecurity risks.
Sub-millisecond Video Synchronization of Multiple Android Smartphones

This paper addresses the problem of building an affordable easy-to-setup synchronized multi-view camera system, which is in demand for many Computer Vision and Robotics applications in high-dynamic environments. In our work, we propose a solution for this problem — a publicly-available Android application for synchronized video recording on multiple smartphones with sub-millisecond accuracy. We present a generalized mathematical model of timestamping for Android smartphones and prove its applicability on 47 different physical devices. Also, we estimate the time drift parameter for those smartphones, which is less than 1.2 ms per minute for most of the considered devices, that makes smartphones’ camera system a worthy analog for professional multi-view systems. Finally, we demonstrate Android-app performance on the camera system built from Android smartphones quantitatively on setup with lights and qualitatively — on panorama stitching task.
Eye Tracking—An Innovative Tool in Medical Parasitology

The innovative Eye Movement Modelling Examples (EMMEs) method can be used in medicine as an educational training tool for the assessment and verification of students and professionals. Our work was intended to analyse the possibility of using eye tracking tools to verify the skills and training of people engaged in laboratory medicine on the example of parasitological diagnostics. Professionally active laboratory diagnosticians working in a multi-profile laboratory (non-parasitological) (n = 16), laboratory diagnosticians no longer working in this profession (n = 10), and medical analyst students (n = 56), participated in the study. The studied group analysed microscopic images of parasitological preparations made with the cellSens Dimension Software (Olympus) system. Eye activity parameters were obtained using a stationary, video-based eye tracker Tobii TX300 which has a 3-ms temporal resolution. Eye movement activity parameters were analysed along with time parameters. The results of our studies have shown that the eye tracking method is a valuable tool for the analysis of parasitological preparations. Detailed quantitative and qualitative analysis confirmed that the EMMEs method may facilitate learning of the correct microscopic image scanning path. The analysis of the results of our studies allows us to conclude that the EMMEs method may be a valuable tool in the preparation of teaching materials in virtual microscopy. These teaching materials generated with the use of eye tracking, prepared by experienced professionals in the field of laboratory medicine, can be used during various training, simulations and courses in medical parasitology and contribute to the verification of education results, professional skills, and elimination of errors in parasitological diagnostics.
Non-Binary Cryptographic Functions for Symmetric Ciphers

This article explores the methods of mathematical description of regular nonlinearity replacement nodes, computing techniques for their design and evaluation of cryptographic indicators. Non-binary functions are also studied with the help of which we form regular nonlinearity replacement nodes. The results of a computational search for S-boxes using the proposed approach are presented. In terms of nonlinearity and autocorrelation, the formed replacement nodes are shown to have improved properties.
Exploring the Effectiveness of Question for Neural Short Answer Scoring System

Automatic short answer scoring is a significant branch in the field of Natural Language Processing. It can release teachers from the complicated correcting work. It is an effective method for students to recognize their knowledge weakness as well. With the development of neural network and deep learning, researchers have built many neural models to resolve the problem. To the best of our knowledge, no one uses question as a feature in neural models. Starting from human experience and common sense, we explore the effectiveness of incorporating question into neural models for short answer scoring. The experimental results show that, on average, our model gains 1.1% improvement compared to the state-of-art model.
BID-HCP: Blockchain Identifier Based Health Certificate Passport System

The COVID-19 outbreak severely affected daily life. The World Health Organiza-tion stated that quarantine was the most effective way to control the spread of the epidemic before the vaccine was developed, but this also led to economic recession. At present, there is an urgent need for solutions to ensure the normal schedule of life while reducing the spread of the epidemic. Several contact tracking solutions have been proposed in the market, but they all have certain limitations, such as health certificates do not recognize each other, and user information is leaked. This paper proposes a health passport system based on blockchain identifier (BID-HCP), which ensures that uninfected people travel normally through a unified health certificate passport. What's more, the method separates the user's location information from the user's identity effectively, which protects the user's privacy and efficiently analyzes the high-risk areas of the epidemic.
Single Identity Clustering-Based Data Anonymization in Healthcare

The modern technology in today’s world is largely dependent on data. At times, data proves to be more valuable than money. Data constitutes information and this information can help to keep peace or even cause wars. Impact of data has increased with the skyrocketing of artificial intelligence, machine learning, and deep learning techniques where data is the main food. Consequently, privacy of data has also become an important aspect to be taken care of from potential hackers and unscrupulous persons. Healthcare sector is a sensitive area often targeted by hackers. Various anonymization techniques like k- anonymization, l-diversity, and t-closeness contribute in maintaining the data privacy thereby securing it. We propose another anonymization technique namely single identity clustering which is inspired from the t-closeness method. Our proposed method enhances the data privacy that is already provided using the existing anonymization techniques.
An Ant Colony Optimization Based Approach for Binary Search

Search is considered to be an important functionality in a computational system. Search techniques are applied in file retrievals and indexing. Though there exists various search techniques, binary search is widely used in many applications due to its advantage over other search techniques namely linear and hash search. Binary search is easy to implement and is used to search for an element in a large search space. The worst case time complexity of binary search is O (log 2 n) where n is the number of elements (search space) in the array. However, in binary search, searching is performed on the entire search space. The complexity of binary search may be further reduced if the search space is reduced. This paper proposes an Ant Colony Optimization based Binary Search (ACOBS) algorithm to find an optimal search space for binary search. ACOBS algorithm categorizes the search space and the key element is searched only in a specific category where the key element can exist thereby reducing the search space. The time complexity of ACOBS algorithm is O (log 2 c) where c is the number of elements in the reduced search space and c < n. The proposal is best suited for real time applications where searching is performed on a large domain.
A Zero-Watermarking Algorithm for Medical Images Based on Gabor-DCT

The development of digital age accelerated the development of digital medical systems, but it also inevitably brings security problems of the transmission and storage of medical images in the network. The wide use of digital watermarking technology in medical images has effectively improved such problems, but the research on the medical image watermarking algorithm is still less and immature. How to improve the invisibility and robustness of watermark information is a difficult problem in the medical image watermarking algorithm. In this paper, a zero watermarking algorithm for medical images based on Gabor-DCT is proposed. The watermark information first encrypted by a Logistic chaotic map and then combined with the medical image feature vector extracted by the Gabor-DCT algorithm to improve the robustness of the watermark information. At last, the watermark is extracted and embedded by zero watermark technology, which improved the invisibility of the watermark information. Experimental results show the proposed algorithm can effectively resist both common attacks and geometric attacks, and has good invisibility and robustness.
Parameter Estimation of the Lotka–Volterra Model with Fractional Order Based on the Modulation Function and Its Application

The Lotka–Volterra model is widely applied in various fields, and parameter estimation is important in its application. In this study, the Lotka–Volterra model with universal applicability is established by introducing the fractional order. Modulation function is multiplied by both sides of the Lotka–Volterra model, and the model is converted into linear equations with parameters to be estimated by the fractional integration method. The parameters are obtained by solving the equations. The state of the system is estimated by shifted Chebyshev polynomial. Last, the implementation program of the model is compiled. The concrete implementation method of the improved model is proposed by an example in this study.
Harpoon: Mechanizing Metatheory Interactively

Abstract Beluga is a proof checker that provides sophisticated infrastructure for implementing formal systems with the logical framework LF and proving metatheoretic properties as total, recursive functions transforming LF derivations. In this paper, we describe Harpoon , an interactive proof engine built on top of Beluga . It allows users to develop proofs interactively using a small, fixed set of high-level actions that safely transform a subgoal. A sequence of actions elaborates into a (partial) proof script that serves as an intermediate representation describing an assertion-level proof. Last, a proof script translates into a Beluga program which can be type-checked independently. Harpoon is available on GitHub. We have used Harpoon to replay a wide array of examples covering all features supported by Beluga . In particular, we have used it for normalization proofs, including the recently proposed POPLMark reloaded challenge.
Cybersecurity Advocates: Force Multipliers in Security Behavior Change

Cybersecurity advocates motivate individuals and organizations to adopt positive security behaviors. Based on our research, we describe qualities of successful advocates. Our findings have practical implications for expanding the cybersecurity workforce by recruiting and developing professionals who can be effective in advocate or other people-oriented security roles.