Reviewerly/exHarmony huggingface dataset:
Recognizing Activities of Daily Living using Multi-sensor Smart Glasses

Continuous and automatic monitoring of an individual’s physical activity using wearable devices provides valuable insights into their daily habits and patterns. This information can be used to promote healthier lifestyles, prevent chronic diseases, and improve overall well-being. Smart glasses are an emerging technology that can be worn comfortably and continuously. Their wearable nature and hands-free operation make them well suited for long-term monitoring of physical activity and other real-world applications. To this end, we investigated the ability of the multi-sensor OCOsense™ smart glasses to recognize everyday activities. We evaluated three end-to-end deep learning architectures that showed promising results when working with IMU (accelerometer, gyroscope, and magnetometer) data in the past. The data used in the experiments was collected from 18 participants who performed pre-defined activities while wearing the glasses. The best architecture achieved an F1 score of 0.81, demonstrating its ability to effectively recognize activities, with the most problematic categories being standing vs. sitting.
Capacities of Western Balkan Economies (and Their Public Sectors) to Respond to Ransomware Attacks**

Ransomware became a global cybersecurity threat affecting not only individuals and private companies, but governments and other public bodies. The paper will firstly introduce the concept of ransomware and emphasize the implications of the global rise of ransomware. Secondly, the paper will provide an overview of the regulatory framework in relation to Ransomware in Western Balkan (WB) economies (Albania, Bosnia and Herzegovina, North Macedonia, Montenegro, and Serbia), while an ethical and practical guidance will be proposed. Then, the paper will briefly present Western Balkan Case Studies, including recent key ransomware attacks occurred in the region. Lastly, the paper will provide recommendations regarding prevention and recovery from the ransom attack focusing on WB region.
Spectral Analysis, Agglomerative, Mean Shift and Affinity Propagation Algorithms, Use on the Content from Social Media for Low-Resource Languages

Social networks, as part of our daily life, affect our behavior and lifestyle in various ways, making it important for each of us to be aware of their impact. Posts on social media platforms can have a profound effect on our mood, depending on our personal interpretation and opinion of them. Therefore, it is crucial to correctly classify these textual data to gain a better understanding of their impact. However, this task can be challenging, particularly when dealing with unlabeled data such as social media posts. An added challenge is working with low-resource languages. In this research, we investigate four unsupervised text clustering methods by testing them on a low-resource language, such as Albanian. The investigated algorithms are Spectral, Agglomerative, Mean Shift and Affinity Propagation, and by adjusting the working parameters, we tried to find a more appropriate application of them. Methods are applied to pre-processed data, textual posts, by use of different preprocessing techniques, and the results are presented and interpreted. This research aims to assist other researchers in the same field who have a specific focus on working with low-resource languages.
Potential Implementation of Augmented Reality Technology in Education

In today’s age of development of science and technology, which is developing extremely fast, the way in which technology is implemented is also changing. One of the important technologies that is used in many areas of business, as well as education, is certainly AR technology, i.e. augmented reality technology. Numerous studies have shown great benefits of applying AR technology in all segments of education, from preschool education, elementary education, secondary education, higher education, as well as within the field of lifelong education. By using AR technology through augmented reality displays and 3D formats, it is possible to facilitate learning processes and experience of the material learned in all areas and segments of education. Scientific research will be carried out for the purposes of drafting this paper. The objectives of the research are to research the attitude towards augmented reality technology in learning and knowledge acquisition and to research the attitude towards the potential for improving educational processes using AR technology. The paper will present the results of research that will show the influence of previous experience with AR technology on the adoption of more positive attitudes towards the implementation of AR technology in learning and acquiring knowledge and will present the potential for the implementation of AR technology within the Croatian educational curriculum in the coming 5 years.
The Forensic Significance of Indexing Applications on the Windows Operating System

When forensic analysis of the Windows operating system and the search for the existence of suspected files, applications, or artifacts of the operating system, the process of restoring deleted data is very often hard drives (SSD) SATA or NVMe interfaces in personal computers and taking into account properties such as wear leveling and garbage collection solid state hard drives, it is significantly difficult to recover deleted data as well as proving the start and presence of suspected files on the computers of the attacker or victim. This article analyzes the Windows Windows Search feature with a linked Windows.edb file as well as the 3rd Party application for indexing operating system files to find records of suspect files, metadata, applications, and their activities relevant to forensic analysis.
Social Engineering Aspects of Email Phishing: an Overview and Taxonomy

Numerous online resources and reports are pointing to the growing effectiveness of email phishing techniques, with some indicating that 85% of IT breaches involve the human element, and that 96% or social attacks arrive via email. Phishing is a common occurrence, and a significantly successful one. While most of the available research on phishing involves phishing detection, prevention, filtering, anti-phishing tools, techniques and countermeasures, the remaining body of research is tackling phishing and social engineering in (too) generic and broad contexts. This paper will propose a focused effort to identify the specific groups of techniques that attackers are using in email phishing and the principles running “behind the scenes” that make these attacks successful. Thus, the goal of this paper is threefold: (1) to propose a taxonomy of the observed email phishing techniques, (2) to associate the principles and factors of influence with observed techniques and shed light behind their effectiveness, and (3) to raise awareness and lay the groundwork for working on the model of human resilience against these manipulative forms of cyber attacks.
Influence of quality of pixel level annotations on text detection performance in natural images

Text detection in natural images is a task that arises in many computer vision applications. State-of-the-art text detection methods are mainly based on deep neural networks designed for instance segmentation task. However, most of the available datasets for text detection do not have fine annotations at the pixel level which are required during supervised learning of such networks. Usually, a whole or reduced text bounding box is used as a segmentation mask. In this paper, a method that generates a synthetic dataset with precise annotations at the pixel level is proposed. The method is based on the available Synthtext script for generating synthetic datasets with text instances. By creating synthetic datasets with precise and coarse annotations at the pixel level we explore the efficiency of the state-of-the-art text detector TextFuseNet.
Compressing Sentence Representation with Maximum Coding Rate Reduction

In most natural language inference problems, sentence representation is needed for semantic retrieval tasks. In recent years, pre-trained large language models have been quite effective for computing such representations. These models produce high-dimensional sentence embeddings. An evident performance gap between large and small models exists in practice. Hence, due to space and time hardware limitations, there is a need to attain comparable results when using the smaller model, which is usually a distilled version of the large language model. In this paper, we assess the model distillation of the sentence representation model Sentence-BERT by augmenting the pre-trained distilled model with a projection layer additionally learned on the Maximum Coding Rate Reduction (MCR2) objective, a novel approach developed for general purpose manifold clustering. We demonstrate that the new language model with reduced complexity and sentence embedding size can achieve comparable results on semantic retrieval benchmarks.
Internet of Behavior – The Transformation of Customer Relationship Management in Logistics

The Internet of Behavior (IoB) is an evolution of the Internet of Things (IoT), which is already widely used in logistics. In addition to the technology and data aspect, IoB also includes behavioral science to track and analyze behavioral patterns. By using IoB, logistics companies can provide not only more reliable but also more flexible services, enabling better insight into customer behavior patterns and changing market conditions in real-time. The IoB can enable logistics companies to streamline Customer Relationship Management (CRM). In this paper, the authors research the potential of IoB to transform CRM in logistics. Therefore, the authors analyze the main features of IoB and current trends in logistics CRM. The focus of this paper is to analyze how the application of IoB could transform logistics CRM (from the aspect of predictive services, automation, CRM processes, CRM software and security strategies).
Does Daily Use of Digital Technologies Influence the Reading and Information Literacy of 15-year-old Students?

This paper focuses on the impact of out-of-school and in-school use of digital technologies on the reading and information literacy skills of 15-year-old Slovenian students who participated in the PISA 2018 study. For our study, we selected PISA 2018 variables related to the use of digital technologies in school or outside school. The sample (N=2612) consists of 50.8% girls and 49.2% boys. Part of the questionnaire refers to the use of digital technologies outside school: for entertainment and practical information purposes, for school purposes, and time spent with digital technologies. Part of the questionnaire refers to the use of digital technologies in school: for various purposes, for learning or teaching, and the time spent using digital technologies in class. The results show that the constructs of using digital technologies in school or out of school are strongly related but have a very low/insignificant impact on reading literacy and information literacy.
Factoring economic biases out of tokenomics

Blockchain technology has enabled decentralized applications and peer-to-peer networks to be sustained by an incentive system based on tokens. At the same time, reputation is a paramount component for any limited-trust or no-trust system. However, in many systems, e.g., proof-of-stake-based systems, tokens are a proxy for reputation, therefore there might be confusion between wealth and reputation. In this paper, we focus on the social layer of dApps and present a two-token system that allows a clear-cut separation between wealth and reputation and fits the case of DAOs where user contribution is essential to maintain and sustain the platform, such as social apps where users are asked to vote about newcomers’ acceptance or to moderate and filter the content proposed by others. The paper delves into the implementation of such tokenomics, describes how the standard ERC-20 token can be adjusted to become a trustworthy reputation meter, and how the model can be optimized to reduce transaction fees. The value of the proposed work lies in moving away from a wealth-affected reputation in favor of a contribution-based one. Using such a system, dApps can reward users more fairly.
Learning Translation Model to Translate Croatian Dialects to Modern Croatian Language

The task of translating dialects into modern language is a challenging task since it requires enormous parallel data. Such data is hard to find especially when it comes to low-resource languages. Among them is the Croatian language which has very few datasets in the standardized version, let alone enough resources for its dialects. In the Croatian language, there are three main groups depending on the geographical position Shtokavian, Kajkavian, and Chakavian which also include other more specific local dialects. For solving these kinds of problems, unsupervised neural machine translation (UNMT) models are considered a good solution since they can be trained on monolingual data. In this paper, we propose an application of a modified version of the state-of-the-art UNMT model for dialect translation on monolingual data of the standardized Croatian language and its dialects. We experimented with several types of cross-lingual embeddings of the input data to determine the best approach that can leverage the similarities and differences between the language and the dialect. All techniques are evaluated on a small parallel dataset using the BLEU metric. Translating these dialects to the modern Croatian language helps in improving communication and access to information for all speakers.
Enabling Hyperparameter-Tuning of AI Models for Healthcare using the CoE RAISE Unique AI Framework for HPC

The European Center of Excellence in Exascale Computing “Research on AI- and Simulation-Based Engineering at Exascale” (CoE RAISE) is a project funded by the European Commission. One of its central goals is to develop a Unique AI Framework (UAIF) that simplifies the development of AI models on cutting-edge supercomputers. However, those supercomputers’ High-Performance Computing (HPC) environments require the knowledge of many low-level modules that all need to work together in different software versions (e.g., TensorFlow, Python, NCCL, PyTorch) and various concrete supercomputer hardware deployments (e.g., JUWELS, JURECA, DEEP, JUPITER and other EuroHPC Joint Undertaking HPC resources). This paper will describe our analyzed complex challenges for AI researchers using those environments and explain how to overcome them using the UAIF. In addition, it will show the benefits of using the UAIF hypertuning capability to make AI models better (i.e., better parameters) and faster by using HPC. Also, to demonstrate that the UAIF approach is indeed simple, we describe the adoption of selected UAIF building blocks by healthcare applications. The examples include AI models for the Acute Respiratory Distress Syndrome (ARDS). Finally, we highlight other AI models of use cases that co-designed the UAIF.
Imitation Learning for Financial Applications

Algorithms for solving decision-making problems under uncertainty are often employed in complex environments such as financial markets. Reinforcement learning (RL), a mathematical framework for sequential decision-making, is widely used in many financial decision-making problems, such as portfolio optimization, optimal execution, and market making. However, RL methods require a manual design of the reward function, which can be challenging in noisy market environments. Moreover, in such environments, it is often easier to demonstrate the desired behavior rather than manually engineer it. These problems can be solved using imitation learning (IL) algorithms, which extract knowledge from expert demonstrations by directly imitating the desired behavior or learning the expert’s reward function. This paper introduces the foundational methods in IL, out-lines the differences with familiar frameworks, and provides a survey of the current IL applications in finance.
Applying balanced scorecard in software process improvement: a case study of small software organization

Software processes consist of a complex set of activities required to deliver software products within predicted quality, costs, and deadlines. To accomplish such goals, a software organization needs a quality and mature software process as a prerequisite for success. Adopting Software Capability Maturity Model Integration (CMMI) represents a well-known path in the pursuit of mature software processes. However, its implementation is a subject of a permanent effort that implies different approaches and methods, and often leads to unsuccessful or limited success, though. This is especially emphasized in small software companies given the dynamic environment influenced by different factors, including insufficient resources, changes in technology, and staff turnover. In this paper, a case study of a small software company implementing software process improvement is presented. In a tailored approach to process improvement, a specific method using the balanced scorecard as input into the IDEAL model has been designed, enabling a narrow link between business goals and specific improvement goals. The results show that the software process and selected performance indicators were improved, and suggest the potential of the proposed approach in small organizations.
The Power of Pre-trained Transformers for Extractive Text Summarization: An Innovative Approach

In this study, we suggest a unique method for text summarization that combines the TextRank algorithm, Kmeans clustering, and neural network classification. To determine which phrases in a given text are most crucial, the basic model uses TextRank, a graph-based algorithm. In order to group together comparable sentences, these sentences are subsequently clustered using K-means. The best representative statement from each cluster is chosen as the final summary in the last phase of our method using neural network classification. In order to enhance TextRank’s functionality, we also suggest an optimization strategy called cosine similarity with TextRank (Cosim-TextRank). In order to further improve the model’s accuracy, we also suggest using weighted cosine similarity. Overall, our method successfully creates a summary of the text by choosing significant and illustrative phrases while maintaining the context and content of the original text. The experimental findings demonstrate that, in terms of ROUGE scores and human evaluation, our suggested strategy performs better than the current state-of-the-art methods.
Hybrid Facial Expression Analysis Model using Quantum Distance-based Classifier and Classical Support Vector Machine

Rapid advancements in image and video processing technologies are poised to create remarkable impacts on a wide range of industries. A significant challenge in these processing technologies resides in identifying the features fed for image classification algorithms. Though all classification algorithms could identify, extract and classify the features of a given image, their accuracy is directly proportional to the number of sample points taken from the image using a sampling technique. As the accuracy improves with a substantial number of sample points, the time consumed to process them looms large. These challenges beseech enormous computing power. Quantum computers avowed exceptional computing power is expected to bridge the growing demands. To address these challenges effectively, we have chosen a specific problem, Facial Expression Analysis, to explore in-depth and arrive at a purposeful approach to deliver the desired outcome. The purpose of this paper is two-pronged. Perform a comparative study of accuracy and performance of classical and quantum image processing algorithms in classical and quantum computers, respectively. Secondly, devise a novel hybrid model using a quantum distance-based classifier augmented with a classical linear support vector machine to overcome the limitations observed. Sample image features derived from the quantum classifier were used to train the linear classifier. The results were observed to be better relative to results from the classical distance-based classifier. Holistically, the novel hybrid model is observed as a promising solution for all image classification problems. Our future work will focus on sophisticated usage of a linear classification algorithm in quantum computing.
Video Label Enhancing and Standardization through Transcription and WikiId Mapping Techniques

Volume of video content surpass all other content types in internet. As per the reports from different sources, video traffic had acquired 82% of internet usage in 2022. Video is going to be more important in the years to come for user engagement, advertisement & marketing, news, education etc. Video information retrieval becomes an important problem to solve in this context. An accurate and fast video tagging system can aid a good content recommendation to the end users. It helps to audit the content automatically thereby platforms can control the contents which are politically and morally harmful. There are not many faster or cost-effective mechanisms to tag user generated videos at this moment. Manual tagging is a costly and highly time taking task. A delay in indexing the videos like news, sports etc., shall reduce its freshness and relevancy. Deep learning techniques have reached its maturity in the contents like text and images, but it is not the case with videos. Deep learning models need more resources to deal with videos due to its multi-modality nature, and temporal behavior. Apart from that, there are not many large-scale video datasets available at this moment. Youtube-8M is the largest dataset which is publicly available as of now. Much research works happened over Youtube-8M dataset. From our study, all these have a potential limitation. For example, in Youtube-8M, Video labels are only around 3.8K which are not covering all real-world tags. It is not covering the new domains which are created along with the surge in the content traffic. This study aims to handle this problem of tag creation through different methods available thereby enhancing the labels to a much wider set. This work also aims to produce a scalable tagging pipeline which uses multiple retrieval mechanisms, combine their results. The work aims to standardize the retrieved tokens across languages. This work creates a dataset as an outcome from ‘WikiData’, which can be used for any NLP based standardization use cases. An attempt has been made to do disambiguation through WikiId embedding. A new WikiData embedding is created in this work, which can be used for eliminating the tags which are noisy.
VLSI Architecture of Generalized Pooling for Hardware Acceleration of Convolutional Neural Networks

Convolutional Neural Networks (CNNs) handle a massive variety of datasets with great accuracy for various image processing and computer vision applications. However, it comes at the cost of the requirement of large hardware resources, which are computational and energy extensive. There is a need for efficient hardware and algorithmic optimization for real-time CNN inference while deploying the CNN model. This paper proposes a novel VLSI architecture of generalized pooling operation for hardware acceleration of CNN inference in real-time. Generalized pooling operation adaptively downsamples the huge parameter set generated by the convolutional layer by generating weights as per the input features. It is capable of accommodating varying feature maps and preserves significant features, unlike other counterparts maximum and average pooling. In order to efficiently compute the output of the generalised pooling operation, the proposed hardware design makes use of the Newton-Raphson reciprocal approximation for division operations, a low number of comparators, and a high degree of parallelism. The proposed architecture is developed and tested for performance evaluation on Xilinx Vivado 2018.3, and the target device chosen is Zynq UltraScale + MPSoC ZCU104 Evaluation board.