Reviewerly/exHarmony huggingface dataset:
Adjustable short-term memory of SiO<sub> x </sub>:Ag-based memristor for reservoir computing

Temporal information processing is critical for a wide spectrum of applications, such as finance, biomedicine, and engineering. Reservoir computing (RC) can efficiently process temporal information with low training costs. Various memristors have been explored to demonstrate RC systems leveraging the short-term memory and nonlinear dynamic behaviours. However, the short-term memory is fixed after the device fabrication, limiting the applications to diverse temporal analysis tasks. In this work, we propose the approaches to modulating the short-term memory of Pt/SiOx:Ag/Pt memristor for the performance improvement of the RC systems. By controlling the read voltage, pulse amplitude and pulse width applied to the devices, the obtainable range of the characteristic time reaches three orders of magnitude from microseconds to around milliseconds. Based on the fabricated memristor, the classification of 4-bit pulse streams is demonstrated. Memristor-based RC systems with adjustable short-term memory are constructed for time-series prediction and pattern recognition tasks with different requirements for the characteristic times. The simulation results show that low normalized root mean square error of 0.003 (0.27) in Hénon map (Mackey-Glass time series) and excellent classification accuracy of 99.6% (91.7%) in spoken-digit recognition (MNIST image recognition) are achieved, which outperforms most memristor-based RC systems recently reported. Furthermore, the RC networks with diverse short-term memories are constructed to address more complicated tasks with low prediction errors. This work proves the high controllability of memristor-based RC systems to handle multiple temporal processing tasks.
Ubiquitous Technology for Health

The Smart City concept relies on the use of Internet of Things (IoT) technologies to allow for intelligent services to be integrated throughout metropolitan areas, providing innovative services and improving the quality of life. As technology advances, such services become increasingly accessible to a larger population share, and to elderly and frail users in particular.This paper provides an overview of research carried out at Department of Engineering and Architecture, University of Parma, geared towards improving the lives of elderly people by leveraging IoT technologies, also in the framework of recent collaborative-research projects.
Zero time waste in pre-trained early exit neural networks

The problem of reducing processing time of large deep learning models is a fundamental challenge in many real-world applications. Early exit methods strive towards this goal by attaching additional Internal Classifiers (ICs) to intermediate layers of a neural network. ICs can quickly return predictions for easy examples and, as a result, reduce the average inference time of the whole model. However, if a particular IC does not decide to return an answer early, its predictions are discarded, with its computations effectively being wasted. To solve this issue, we introduce Zero Time Waste (ZTW), a novel approach in which each IC reuses predictions returned by its predecessors by (1) adding direct connections between ICs and (2) combining previous outputs in an ensemble-like manner. We conduct extensive experiments across various multiple modes, datasets, and architectures to demonstrate that ZTW achieves a significantly better accuracy vs. inference time trade-off than other early exit methods. On the ImageNet dataset, it obtains superior results over the best baseline method in 11 out of 16 cases, reaching up to 5 percentage points of improvement on low computational budgets.
Natural Language Understanding of Low-Resource Languages in Voice Assistants: Advancements, Challenges and Mitigation Strategies

This paper presents an exploration of low resource languages and the specific challenges that arise in natural language understanding of these by a voice assistant. While voice assistants have made significant strides when it comes to their understanding of mainstream languages, this paper focuses on extending this understanding to low resource languages in order to maintain diversity of linguistics and also delight the customer. In this paper, the specific nuances of natural language understanding when it comes to these low resource languages has been discussed. The paper also proposes techniques to overcome some of the challenges in voice assistants understanding low resource language models. The proposed methods and future direction presented in this doc are poised to drive advancements in voice technology and promote inclusivity by ensuring that voice assistants are accessible to speakers of underrepresented languages.
ON THE QUESTION OF DISTANCE LEARNING IN MATHEMATICS

Distance learning of mathematics naturally has its own peculiarities when conducting classes. This includes monitoring attendance of classes, conducting independent and control work, checking the completion of homework. But direct communication with students is very important when teaching not only mathematics. Some specific recommendations are offered for distance learning of students. Lecture classes, seminar classes, and the student's personal homework are discussed. These notes substantiate the effectiveness of this method of training and suggest how technically it can be done.
Examining the dark force consequences of AI as a new actor in B2B relationships

Artificial intelligence (AI) in industrial marketing has seen significant research attention through various theoretical lenses with an emerging thread examining the dark side effects of AI. Thirty-four semi-structured interviews were conducted with buyers and suppliers of AI marketing solutions to investigate the consequences of AI ‘dark forces’ on B2B relationships. We posit AI as a new actor that has blurred the lines of the actors-resources-activities model. Findings show AI is now considered a new actor within B2B networks wielding dark force consequences such as algorithmic gatekeeping, which initiates dehumanization effects. In addition, AI is reliant on access to datasets which drives up resource costs. A lack of accountability of AI marketing solutions leads to opportunistic behaviours compromising actor relationships. Our conceptual model maps our understanding of the dark force consequences underpinning theoretical and managerial implications and recommendations for increased awareness and mitigation of dark forces.
MHViTPose: multiscale hybrid vision transformer for human pose estimation

Despite the significant progress achieved by visual Transformers, there are still some limitations that need to be addressed in human pose estimation. Firstly, Transformer lacks CNN’s inductive bias and local feature attention capabilities, which require extensive training data and iterations to achieve satisfactory results. Therefore, we propose a hybrid network that combines convolutional and Transformer. Besides, to address the recognition of human body images at different scales, we established a Transformer pyramid structure, which achieves recognition of human body images at different scales through progressive reduction of the input resolution. Specifically, our algorithm achieves an accuracy of 77.3% with a computational complexity of 19.6 GFLOPs. Compared to traditional direct regression methods, our algorithm considerably enhances detection accuracy while reducing the training complexity and significantly increasing the detection speed compared to traditional Transformer methods.
Enhancing Card Transaction’s Security through Cyber Security

This paper highlights the security issues associated with credit cards and underscores the crucial role of encryption in mitigating the risk of credit or debit card data theft. Credit card encryption encompasses safeguarding the card itself, securing the terminal used for card scanning, and ensuring the protection of card information during transmission between the terminal and a backend computer system. The encryption mechanism is specifically engineered to validate and limit access to card security features. In our project, we developed a web application using VS Code, employing HTML for the frontend and PHP for the backend, and implemented AES encryption as a robust security measure.
The RAAF's Frozen Middle Part Two

The concept of the "frozen middle" has become a common phrase in the business world, and it refers to the group of middle managers who are often resistant to change. These managers are known to be stuck in their ways, resistant to new ideas, and hesitant to take risks. The frozen middle is a significant problem for many organisations such as the Royal Australian Air Force (RAAF), as it hinders innovation, slows down decision-making processes, and stifles progress. To understand how the frozen middle can impact organisational efficiency, it is essential to explore why this phenomenon exists and how it can be addressed. In my previous post I used Maslow’s Hierarchy of Needs as a lens to describe a possible reason why the problem exists (Williamson, 2023). In this post, I will provide an alternative explanation from a cognitive lens.
The Repertoire of S. T. Richter: Touches to the Creative Portrait

The prominent musician-performer of the 20th century, Russian pianist Svyatoslav Teofi lovich Richter reached the tops of piano mastery thanks to virtuoso technique, expressive intonation, impeccable sense of style, depth of interpretation and extensive repertoire. The choice of works was due to the personal preferences of the pianist, the depth of the content of opuses, and the desire to bring aesthetic pleasure to the public with the beauty of music. The purpose of the work was to identify the principles of Richter’s selection of a concert repertoire based on an analysis of his statements about composers, aesthetic preferences, a list of works, concert programmes and sound recordings. Richter seeks to perform works of deep content, in which important ideas for all mankind are embodied, passionate temperament and exciting emotions are conveyed. A warm response in Richter’s performing arts was found by compositions combining the sincerity of utterance and the simplicity of images in the spirit of the revelations of romantic composers.
Exploration of Language in Literary Works as a Means of Artistic Expression

Literary works have an important role in expressing language. The language used in literary works can express the beauty and uniqueness of a culture. This paper discusses the exploration of language in literary works as a means of artistic expression. This research uses qualitative methods and literature study as data collection techniques. The results of the study show that literary works can be a means of expressing language. Language in literary works can give a deep and interesting impression. Literary works can also enrich the culture of a society by exploring language in their works. Keywords: language, literature, art, expression DOI: http://dx.doi.org/10.61436/bsscd/v2i2.pp72-76
Exploring Text Mining and Analytics for Applications in Public Security: an in-depth dive into a systematic literature review

Text mining and related analytics emerge as a technological approach to support human activities in extracting useful knowledge through texts in several formats. From a managerial point of view, it can help organizations in planning and decision-making processes, providing information that was not previously evident through textual materials produced internally or even externally. In this context, within the public/governmental scope, public security agencies are great beneficiaries of the tools associated with text mining, in several aspects, from applications in the criminal area to the collection of people's opinions and sentiments about the actions taken to promote their welfare. This article reports details of a systematic literature review focused on identifying the main areas of text mining application in public security, the most recurrent technological tools, and future research directions. The searches covered four major article bases (Scopus, Web of Science, IEEE Xplore, and ACM Digital Library), selecting 194 materials published between 2014 and the first half of 2021, among journals, conferences, and book chapters. There were several findings concerning the targets of the literature review, as presented in the results of this article.
Improved 3D Reconstruction of PCBA Using FPP: Specular Area Reconstruction &amp; Shadow Area Removal

In modern electronics manufacturing, the inspection and analysis of printed circuit board assemblies (PCBA) is crucial for ensuring product quality and reliability. Three-dimensional (3D) shape reconstruction of PCBA has become a rapidly emerging research area and a critical task in this process. In this paper, we propose a novel method based on fringe projection profilometry for reconstructing the 3D shape of PCBA. In order to address the challenges posed by specular and shadow areas, the proposed approach employs multi-exposure fringe projection fusion to recover the specular areas while minimizing the impact of saturation. And a modified error energy function is proposed to identify and eliminate shadow regions, thereby enhancing the accuracy and robustness of the reconstructed 3D model. Finally, the 3D shapes of the PCBA are reconstructed using the Photometric Stereo based calibration model proposed previously by us. The experimental results demonstrate that the proposed method can effectively reconstruct the 3D shape of PCBA with high accuracy and reliability. The proposed method has significant potential for practical applications in the electronics manufacturing industry.
Automated Phrasal Verb and Key-Phrase Checking with LSTM-Based Attention Mechanism

Text prediction and classification are crucial tasks in modern Natural Language Processing (NLP) techniques. Long short-term memory (LSTM), a type of Recurrent Neural Network (RNN), is well-known for its outstanding performance in text classification. Phrasal verbs, also known as Bagdhara in Bangla, play a vital role in making language more expressive and poetic in any language, including Bangla. These two or three-word phrases help us convey our emotions and thoughts more effectively. However, determining whether a phrase is a phrasal verb and appropriate for a given context can be challenging for writers, poets, and the general public. To address this issue, an automatic system capable of identifying and using phrasal verbs is necessary. In this study, we propose a system that can instantly and accurately predict phrasal verbs using the LSTM algorithm, a part of the RNN, and an attention mechanism. Our system achieved an overall phrasal verb prediction accuracy of 78.63%.
Design and Implementation of GPU-Based Frequency Domain Analysis Function in the Digital Oscilloscope

With the increasing complexity of digital signals, the digital oscilloscope which only has the time domain analysis function has been unable to meet the user's needs. In the traditional digital storage oscilloscopes, the frequency domain analysis module is based on the FFT algorithm, and it runs on the CPU in a serial manner. In this paper, we propose a frequency domain analysis framework based on OpenGL technology, which significantly increases the analysis speed of signals while reducing CPU occupancy and memory consumption. the CPU is responsible for the overall scheduling and data flow control, and each sub-module of frequency analysis function runs in the GPU in parallel, and data transfer between CPU and GPU is minimized. The experimental results show that the time consumption and memory consumption of the new architecture is more advantageous when using data volumes over 16k for analysis, and the optimization improves significantly as the data volume increases; when using 128M data volumes for analysis, the total time consumption is only 1/10 of that under the original CPU framework, and the time actually spent on computation is only 1/33 of the original time consumption.
Action Analyzer for Differently Abled People

This paper presents a simulation system for differently abled people that utilizes the Long Short-Term Memory (LSTM) algorithm and the Mediapipe Holistic Model (MHM). The system aims to enhance the daily lives of differently abled individuals by analyzing their body movements and translating them into meaningful actions. The Mediapipe Holistic model extracts key points of the movements from input video frames, and the LSTM algorithm processes the resulting data to recognize various actions such as scared, understand, Wednesday. The system is trained on a diverse set of images to ensure accuracy and robustness in recognizing a wide range of actions. Results show that the combination of MHM and LSTM provides a high accuracy model even with less dataset in comparison to many models proposed over the years for action analyzer system. The model has potential for use in assistive technologies for differently abled individuals. The model shows 92 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> accuracy on the training dataset and 90% accuracy on the testing dataset. Results show that the proposed system outperforms existing approaches and has potential for use in assistive technologies for differently abled individuals.
Exploring the Capabilities of Professionals and Agile Teams: An Updated Review

Context: Agile Software Development (ASD) faces the challenge of effectively measuring and predicting the capabilities of software engineers and teams to enhance individual performance, team productivity, and project success. Objective: This study aims to investigate and identify gaps and research opportunities in measuring and predicting human capabilities in ASD. Method: To achieve this, we conducted a Systematic Literature Review that built upon a previous review spanning the period from 2001 to 2016, performed by different authors. We expanded the scope of the original study to include primary studies published up to 2022. Results: Our findings revealed new attributes for measuring and predicting professionals’ capabilities in ASD. These attributes not only enhance individual performance but also contribute to improved team productivity and facilitate the accurate composition of teams. Conclusion: Technical measures were more dominant than social and innovative aspects. Furthermore, the study identified predicting agile capabilities as a growing research field that requires further investigation due to the limited number of existing models. Most studies (78%) provided measure descriptions, enabling the evolution of the capabilities catalog and supporting future research in this area. Finally, the results can assist agile practitioners in making team composition decisions based on individuals’ and teams’ measured and predicted capabilities.
A method to measure similarity between semantic graphs and its applications

Semantic search in information retrieval processes plays a crucial role in many application fields, such as product recommendation, personnel search, and those that require information matching. In most semantic search systems, semantic similarity technology is already indispensable. User queries can generate many query graphs. These query graphs can then be used to help users generate search results. Semantic similarity technology focuses on measuring the semantic similarity or semantic distance between two concepts. This paper mainly studies using semantic similarity technology to sort query graphs. To represent the similarity between semantic graphs, we calculate semantic distances. Unlike traditional semantic distance calculation methods, we calculate the distance between graphs. Experiments have confirmed the effectiveness and effectiveness of the method proposed in this paper.
Eight-direction least-square phase unwrapping algorithm for optical three-dimensional measurement

The least-square method is widely used in the fast phase unwrapping of optical three-dimensional measurement due to its high-speed and comprehensive characteristics. In order to solve the problem of measurement error propagation in the phase unwrapping algorithm based on least-square method, an improved eight-direction least-square phase unwrapping algorithm is proposed in this paper. This algorithm not only researches the gradient difference between the target phase and its eight-neighbor phases, improving measurement accuracy and reducing measurement errors. The superiority and robustness of this algorithm have been demonstrated through simulation data and measurement of industrial bearing roller end faces
Research on pedestrian flow detection algorithm based on Ghostnetv2 lightweight network

To solve the problems of a large number of parameters, low detection accuracy, slow detection speed of human flow target detection model, this paper proposes a YOLOv5 human flow target detection model based on GhostNetv2. The convolution in the first layer of CBS is retained to replace the remaining convolutions with Ghost Conv, and the C3Ghostv2 module is constructed to replace the original CSP structure, reducing the number of parameters, reducing the calculation cost and improving the calculation speed. Finally, the Deep-Sort algorithm tracks and realizes real-time statistics of people flow. The experimental results indicate that the accuracy of the improved YOLOv5 model is 2.4 percentage points higher than that of the original algorithm, the parameter quantity is compressed by 28 %, and the detection speed has also increased.
Personalized book recommendation algorithm based on fusion of content and improved collaborative filtering

Big data technology is affecting people's lives, leading to an increasing desire for diversity in books. The traditional recommendation algorithms based on data mining techniques can no longer satisfy users to find their favourite books quickly and precisely. Therefore, considering the shortage of traditional book recommendation algorithms in terms of data usage and implementation effect, this paper proposes a hybrid recommendation algorithm that incorporates content and improved item-based collaborative filtering. Firstly, the algorithm uses an improved item-based collaborative filtering algorithm to make recommendations, and the improved collaborative filtering algorithm introduces a common rating weight function on the basis of the traditional algorithm to solve the problem of data sparsity. When the items are less similar, the content-based recommendation algorithm is used instead to make recommendations. In this paper, a cascading approach is undertaken to fuse content with an improved collaborative filtering algorithm, which improves the problem of cold start and data sparseness to a certain extent. Experiments show that the algorithm proposed in this paper can efficiently enhance the accuracy and recall rate of recommendation results, and has excellent recommendation effect.
Primary signal phase suppression technique based on mutual information improved variational mode decomposition

Specific emitter identification (SEI) identifies targets mainly by unintentional modulation of the signal. However, due to the high energy of the primary signal, once the primary signal changes, the recognition becomes less effective or even impossible using a feature database that is not updated. In this paper, we propose to use a mutual information improved variable mode decomposition (VMD) algorithm to suppress the primary signal phase of the transmitter. Furthermore, we simulate the feature extraction of the unintentional phase modulation of the transmitter signal and use support vector machine (SVM) for individual identification. The simulation results show that the algorithm improves the recognition rate by about 6% (0 dB) compared to the retained primary signal. The results demonstrate that our proposed phase suppression technique improves the adaptability and accuracy of individual identification of transmitters.