Reviewerly/exHarmony huggingface dataset:
RF Waveform Synthesis Guided by Deep Reinforcement Learning

In this work, we demonstrate a system that enhances radio frequency (RF) fingerprints of individual transmitters via waveform modification to uniquely identify them amidst an ensemble of identical transmitters. This has the potential to enable secure identification, even in the presence of stolen and retransmitted unique device identifiers that are present in the transmitted waveforms, and ensures robust communications. This approach also lends itself to steganography as the waveform modifications can themselves encode information. Our system uses Bayesian program learning to learn specific characteristics of a set of emitters, and integrates the learned programs into a reinforcement learning architecture to build a policy for actions applied to the digital waveform before transmission. This allows the system to learn how to modify waveforms that leverage and emphasize inherent differences within RF front-ends to enhance their distinct characteristics while maintaining robust communications. In this ongoing research, we demonstrate our system in a small population, and provide a road map to expand it to larger populations that are expected in today's interconnected spaces.
.NET implementation of electronic circuit design

This paper aims to present an approach which automates electronic circuit design. It is based on index matrices with real number elements and their software implementation. Both they are developed by the authors as an example of universal tool on a wide class of electrical devices, and this approach is performed for complex components which have more than two terminals (functioning of PID-regulators is implemented). Designed programming classes are used in a .NET application in order to handle electronic circuits, and simulation on power converters functioning is presented.
Movement, time, and space, two ways

Hee-jeong Choi and Galloway discuss the impact of the ongoing Covid-19 pandemic on people's lives Since the global outbreak, the demand for the mobility of a wide range of things continues to increase, while others are under strict control This includes viral and material matters, as well as digital content--things that help sustain human minds and spirits, not just the human body itself More-than-human-centered design and nurturing, care-full relations have been key emerging themes in recent design research and practice They both believe that radical change must start with the self, and that they are not singular but holobionts, a coalescence of animals and other living things
Strategy-oriented Digital Transformation of Logistics Enterprises: The roles of artificial intelligence and blockchain

Currently, affected by the COVID-19, the global economy has experienced varying degrees of impact. The logistics industry, which is the foundation of social and economic operations, has also been affected to varying degrees. Under this economics background, the logistics industry is driven by factors such as resource integration to attract support from technology companies, new demand for contactless delivery, intelligent operating platforms such as blockchain, and artificial intelligence to replace labor shortages. The challenges for the development of logistics enterprises have become more prominent. This paper starts from a new strategic perspective: the strategy-oriented digital transformation of the logistics enterprises analyzes from the three levels of the logistics value chain, delicate operation development, and digital logistics activities. We will present the applications of the most trending digital technology in logistics enterprises. The purpose is to help enterprises better understand how digital transformation through blockchain and artificial intelligence can enhance their competitiveness as a business strategy.
Exploring the Relationship between Internal Information Security, Response Cost, and Security Intention in Container Shipping

This study empirically investigates the influence of information security marketing and response cost on employees’ information security intention in the container shipping industry. Survey data were collected from 285 respondents in Taiwan. Exploratory factor analysis was employed to identify all the measures to be summarized in a relative set. Confirmatory factor analysis was utilized to ensure every measure’s construct’s convergent and discriminant validity. Structural equation modeling was carried out to the proposed model in this article. The results indicate that organizational information security marketing has a positive impact on information security intention. Furthermore, this study conducted hierarchical regression to examine the moderating effects of information security awareness and information security climate. In particular, information security awareness significantly influenced the relationships between organizational information security marketing, response cost, and information security intention. Moreover, information security climate moderated the relationship between response cost and information security intention. This article concludes by discussing these theoretical and practical findings and implications.
Distributed Energy Transaction Model Based on the Alliance Blockchain in Case of China

Distributed energy, mainly composed of new energy, plays an important role in promoting the development of new energy. At present, the development of distributed energy is greatly hindered by imperfect trading platform and unstable output of new energy. Blockchain is decentralized, autonomous and requires collaborative management. Its own technical characteristics have the inherent advantages of reconstructing the energy system. The alliance chain in the blockchain is more suitable for building a distributed energy trading platform. The paper constructs a distributed energy transaction model based on alliance blockchain, studies the integration mode of blockchain and distributed energy transaction, and explores the application of blockchain in distributed transaction. The paper provides a new idea for optimizing and reconstructing the traditional distributed energy trading platform, and providing decision support for promoting distributed energy trading.
Analysis of some aspects of the implementation of the integrated course “Science” in the educational process of schools in Ukraine

Abstract The integrated course “Science” is being introduced into school curricula in Ukraine for the first time. The article analyzes the experience of implementing an integrated course “Science” in schools of Ukraine. The advantages and disadvantages of various curriculum projects of the integrated course “Science” are discussed. The analysis of the content of the project No 2 of the curriculum of the integrated course “Science” is made. Based on the results of two years of work on the implementation of the experimental integrated course “Science” in the profile school, the analysis of the main problems that arose during the implementation of this course and discusses possible ways to solve them.
Label-Efficient Multi-task Segmentation Using Contrastive Learning

Obtaining annotations for 3D medical images is expensive and time-consuming, despite its importance for automating segmentation tasks. Although multi-task learning is considered an effective method for training segmentation models using small amounts of annotated data, a systematic understanding of various subtasks is still lacking. In this study, we propose a multi-task segmentation model with a contrastive learning based subtask and compare its performance with other multi-task models, varying the number of labeled data for training. We further extend our model so that it can utilize unlabeled data through the regularization branch in a semi-supervised manner. We experimentally show that our proposed method outperforms other multi-task methods including the state-of-the-art fully supervised model when the amount of annotated data is limited.
Template-Based Recognition of Human Locomotion in IMU Sensor Data Using Dynamic Time Warping

Increased levels of light, moderate and vigorous physical activity (PA) are positively associated with health benefits. Therefore, sensor-based human activity recognition can identify different types and levels of PA. In this paper, we propose a two-layer locomotion recognition method using dynamic time warping applied to inertial sensor data. Based on a video-validated dataset (ADAPT), which included inertial sensor data recorded at the lower back (L5 position) during an unsupervised task-based free-living protocol, the recognition algorithm was developed, validated and tested. As a first step, we focused on the identification of locomotion activities walking, ascending and descending stairs. These activities are difficult to differentiate due to a high similarity. The results showed that walking could be recognized with a sensitivity of 88% and a specificity of 89%. Specificity for stair climbing was higher compared to walking, but sensitivity was noticeably decreased. In most cases of misclassification, stair climbing was falsely detected as walking, with only 0.2–5% not assigned to any of the chosen types of locomotion. Our results demonstrate a promising approach to recognize and differentiate human locomotion within a variety of daily activities.
On the <i>p</i> ‐Adic Complexity of the Ding‐Helleseth‐Martinsen Binary Sequences

The purpose of this paper is to determine the p-adic complexity of the Ding-Helleseth-Martinsen (DHM) sequences with period N = 2q, where q = 5 (mod 8) is a prime number. We firstly use the p-adic exponential valuation, cyclotomic numbers of order four, “Gauss periods” and “quadratic Gauss sums” on finite field 𝔽q, and valued in ℤN, to determine the p-adic complexity of the DHM sequences.
Blockchain Simulation: A Web Application for IT Education

Although blockchain technology is gaining popularity, there is no clear guideline or widely available education tool for amateurs to learn this new technology. In this research project, we build a web-based simulation to serve as an educational tool on blockchain technology. As a simulative web application, it is to be used to help IT students (particularly computer science students) understand the basic structure of blockchain and why it can be considered as a decentralized system. We describe theoretical aspects and architecture of the developed web application, and present sample execution to explain how it works.
Throughput Prediction on 60 GHz Mobile Devices for High-Bandwidth, Latency-Sensitive Applications

In the near future, high quality VR and video streaming at 4K/8K resolutions will require Gigabit throughput to maintain a high user quality of experience (QoE). IEEE 802.11ad, which standardizes the 14 GHz of unlicensed spectrum around 60 GHz, is a prime candidate to fulfil these demands wirelessly. To maintain QoE, applications need to adapt to the ever changing network conditions by performing quality adaptation. A key component of quality adaptation is throughput prediction. At 60 GHz, due to the much higher frequency, the throughput can vary sharply due to blockage and mobility. Hence, the problem of predicting throughput becomes quite challenging. In this paper, we perform an extensive measurement study of the predictability of the network throughput of an 802.11ad WLAN in downloading data to an 802.11ad-enabled mobile device under varying mobility patterns and orientations of the mobile device. We show that, with carefully designed neural networks, we can predict the throughput of the 60 GHz link with good accuracy at varying timescales, from 10 ms (suitable for VR) up to 2 s (suitable for ABR streaming). We further identify the most important features that affect the neural network prediction accuracy to be past throughput and MCS.
Distributed Photovoltaic Short-Term Power Prediction Based On Genetic Algorithm Optimized BP Neural Network

Abstract With the rapid development of the world economy, energy demand is increasing.Photovoltaic power generation has the advantages of green, environmental protection and renewable, and the proportion of photovoltaic power generation, a new reusable clean energy in the power generation system, has shown a steady increase;However, the power output of photovoltaic power generation systems is affected by many factors, presenting a high degree of uncertainty and volatility, which brings high difficulties to the large-scale grid-connected operation of photovoltaic power generation.Aiming at the shortcomings of existing photovoltaic power generation forecasting, this paper builds a photovoltaic power station power prediction model based on genetic algorithm and BP neural network algorithm, and uses photovoltaic power station operation examples to verify the validity of the model.By using the global search capability of genetic algorithm to optimize the initial weights and thresholds of BP neural network, the prediction accuracy of photovoltaic power generation is improved, which can provide a reference for photovoltaic power generation prediction engineering practice.
From the Beatles to Billie Eilish: Connecting Provider Representativeness and Exposure in Session-Based Recommender Systems

Session-based recommender systems consider the evolution of user preferences in browsing sessions. Existing studies suggest as next item the one that keeps the user engaged as long as possible. This point of view does not account for the providers’ perspective. In this paper, we highlight side effects over the providers caused by state-of-the-art models. We focus on the music domain and study how artists’ exposure in the recommendation lists is affected by the input data structure, where different session lengths are explored. We consider four session-based systems on three types of datasets, with long, short, and mixed playlist length. We provide measures to characterize disparate treatment between the artists, through a systematic analysis by comparing (i) the exposure received by an artist in the recommendations and (ii) their input representation in the data. Results show that artists for which we can observe a lot of interactions, but offering less items, are mistreated in terms of exposure. Moreover, we show how input data structure may impact the algorithms’ effectiveness, possibly due to preference-shift phenomena
LSFB-CONT and LSFB-ISOL: Two New Datasets for Vision-Based Sign Language Recognition

While significant progress have been made in the field of Natural Language Processing (NLP), leading the commercially available products, Sign Language Recognition (SLR) is still in its infancy. The lack of large-scale sign language datasets makes it hard to leverage new Deep Learning methods. In this paper, we introduce LSFB-CONT, a large scale dataset suited for continuous SLR along with LSFB-ISOL, a subset of LSFB-CONT for isolated SLR. Baseline SLR experiments are conducted on LSFB-ISOL and the reached accuracy measures are compared with those obtained on previous datasets. The results suggest that state-of-the-art models for action recognition still lack sufficient internal representation power to capture the high level of variations of a sign language.
A Comparative Performance Analysis of Computational Intelligence Techniques to Solve the Asymmetric Travelling Salesman Problem

This paper presents a comparative performance analysis of some metaheuristics such as the African Buffalo Optimization algorithm (ABO), Improved Extremal Optimization (IEO), Model-Induced Max-Min Ant Colony Optimization (MIMM-ACO), Max-Min Ant System (MMAS), Cooperative Genetic Ant System (CGAS), and the heuristic, Randomized Insertion Algorithm (RAI) to solve the asymmetric Travelling Salesman Problem (ATSP). Quite unlike the symmetric Travelling Salesman Problem, there is a paucity of research studies on the asymmetric counterpart. This is quite disturbing because most real-life applications are actually asymmetric in nature. These six algorithms were chosen for their performance comparison because they have posted some of the best results in literature and they employ different search schemes in attempting solutions to the ATSP. The comparative algorithms in this study employ different techniques in their search for solutions to ATSP: the African Buffalo Optimization employs the modified Karp–Steele mechanism, Model-Induced Max-Min Ant Colony Optimization (MIMM-ACO) employs the path construction with patching technique, Cooperative Genetic Ant System uses natural selection and ordering; Randomized Insertion Algorithm uses the random insertion approach, and the Improved Extremal Optimization uses the grid search strategy. After a number of experiments on the popular but difficult 15 out of the 19 ATSP instances in TSPLIB, the results show that the African Buffalo Optimization algorithm slightly outperformed the other algorithms in obtaining the optimal results and at a much faster speed.
Job Recommendation Based on Curriculum Vitae Using Text Mining

During the last years, the development in diverse areas related to computer science and internet, allowed to generate new alternatives for decision making in the selection of personnel for state and private companies. In order to optimize this selection process, the recommendation systems are the most suitable for working with explicit information related to the likes and dislikes of employers or end users, since this information allows to generate lists of recommendations based on collaboration or similarity of content. Therefore, this research takes as a basis these characteristics contained in the database of curricula and job offers, which correspond to the Peruvian ambit, which highlights the experience, knowledge and skills of each candidate, which are described in textual terms or words. This research focuses on the problem: how we can take advantage from the growth of unstructured information about job offers and curriculum vitae on different websites for CV recommendation. So, we use the techniques from Text Mining and Natural Language Processing. Then, as a relevant technique for the present study, we emphasize the technique frequency of the Term - Inverse Frequency of the documents (TF-IDF), which allows identifying the most relevant CVs in relation to a job offer of website through the average values (TF-IDF). So, the weighted value can be used as a qualification value of the relevant curriculum vitae for the recommendation.
Ellipsis Resolution as Question Answering: An Evaluation

Most, if not all forms of ellipsis (e.g., 'so does Mary') are similar to reading comprehension questions ('what does Mary do'), in that in order to resolve them, we need to identify an appropriate text span in the preceding discourse.Following this observation, we present an alternative approach for English ellipsis resolution relying on architectures developed for question answering (QA).We present both single-task models, and joint models trained on auxiliary QA and coreference resolution datasets, clearly outperforming the current state of the art for Sluice Ellipsis (from 70.00 to 86.01 F 1 ) and Verb Phrase Ellipsis (from 72.89 to 78.66 F 1 ).
Serial or Parallel? Plug-able Adapter for multilingual machine translation.

Developing a unified multilingual translation model is a key topic in machine translation research. However, existing approaches suffer from performance degradation: multilingual models yield inferior performance compared to the ones trained separately on rich bilingual data. We attribute the performance degradation to two issues: multilingual embedding conflation and multilingual fusion effects. To address the two issues, we propose PAM, a Transformer model augmented with defusion adaptation for multilingual machine translation. Specifically, PAM consists of embedding and layer adapters to shift the word and intermediate representations towards language-specific ones. Extensive experiment results on IWSLT, OPUS-100, and WMT benchmarks show that \method outperforms several strong competitors, including series adapter and multilingual knowledge distillation.
Building and Comparing Lemma Embeddings for Latin. Classical Latin versus Thomas Aquinas

This paper presents a new set of lemma embeddings for the Latin language.Embeddings are trained on a manually annotated corpus of texts belonging to the Classical era: different models, architectures and dimensions are tested and evaluated using a novel benchmark for the synonym selection task.In addition, we release vectors pre-trained on the "Opera Maiora" by Thomas Aquinas, thus providing a resource to analyze Latin in a diachronic perspective.The embeddings built upon the two training corpora are compared to each other to support diachronic lexical studies.The words showing the highest usage change between the two corpora are reported and a selection of them is discussed.
Effects of Pre- and Post-Processing on type-based Embeddings in Lexical Semantic Change Detection

Lexical semantic change detection is a new and innovative research field.The optimal fine-tuning of models including pre-and postprocessing is largely unclear.We optimize existing models by (i) pre-training on large corpora and refining on diachronic target corpora tackling the notorious small data problem, and (ii) applying post-processing transformations that have been shown to improve performance on synchronic tasks.Our results provide a guide for the application and optimization of lexical semantic change detection models across various learning scenarios.
Hybrid Approach for Underwater Image Restoration and Enhancement

Application of image processing algorithms to underwater images results in unrealistic pictures with non-uniform scene lighting. In this paper, we propound an innovative algorithm utilizing image restoration and image enhancement techniques, into a single algorithm for underwater image processing. Our image restoration algorithm is a modified approach based on automatic red channel algorithm for the blue channel, since the red color channel does not always have the least intensity underwater. To refine the contrast of the image, we use a modified Integrated Color Model for image enhancement. Results of the proposed algorithm show realistic, high contrast and clear underwater images as compared to other algorithms for the same set of images. To validate our enhanced results, we have used entropy and histogram analysis as a metric for quantitative analysis.
Research on Computer Intelligent Proofreading System of Improved English Phrase Translation Model

Abstract When carrying out English translation based on phrases and sentence patterns, the contextual coherence of phrases and sentence patterns is relatively poor, which will directly affect the effect of English translation. In the process of designing and improving the English translation computer intelligent proofreading system, we need to study the specific modules of the system design. At the same time, we need to improve the design level of the English translation computer intelligent proofreading system based on the improved phrase translation model. Only in this way can it be ensured that vocabulary can be proofread when translating English, and the translation effect can be improved. This is also the main goal to realize the design of an intelligent proofreading system for English translation. In the process of analyzing and researching the proofreading system, we need to analyze and compare the accuracy of the design system and proofread the incoherent context in the English translation results in time. This can give full play to the advantages of the intelligent angle system with high accuracy and contextual coherence.