Reviewerly/exHarmony huggingface dataset:
Tracking the Sun: Performance Analysis of Single-Axis vs Dual-Axis Solar PV Systems in UAE Desert Climate

In this study, a comparative analysis was conducted for single-axis and dual-axis tracking photovoltaic (PV) systems installed in the Outdoor Test Facility (OTF) at DEWA R&D Center, Dubai UAE. Same type of crystalline modules were installed in both the tracking systems and performance of the two was evaluated based on the daily power output, energy yield, and solar irradiation received. The analysis was conducted using the field data collected for 2 equinox days (March 21, 2018, and September 23, 2018). The results show that the dual-axis tracking system provided higher energy yield by 10.8% for the selected day in March and 8.57% for the selected day in September, compared to the single-axis tracking system. The findings of this study can provide valuable insights for the selection, design and feasibility of the PV tracking systems in desert climates.
Evaluation of Gender Bias in Masked Face Recognition with Deep Learning Models

We explore gender bias in the presence of facial masks in automated face recognition systems using various deep learning algorithms in this research study. The paper focuses on an experimental study using an imbalanced image database with a smaller percentage of female subjects compared to a larger percentage of male subjects and examines the impact of masked images in evaluating gender bias. The conducted experiments aim to understand how different algorithms perform in mitigating gender bias in the presence of face masks and highlight the significance of gender distribution within datasets in identifying and mitigating bias. We present the methodology used to conduct the experiments and elaborate the results obtained from male only, female only, and mixed-gender datasets. Overall, this research sheds light on the complexities of gender bias in masked versus unmasked face recognition technology and its implications for real-world applications.
Opposition-Based Crossover Operation for Differential Evolution Algorithm

Differential Evolution (DE) is widely recognized as an effective, robust, and gradient-free global optimization algorithm. However, the DE algorithm's search strategy has certain limitations that present opportunities for further improvement. Opposition-based Learning (OBL) as one of the efficient computational concepts provides the optimizer with the capability of exploring the search space in opposite directions. This research paper introduces a novel crossover scheme for the DE algorithm based on OBL concept. Unlike existing approaches in the literature, which primarily focus on utilization of OBL in population level, proposed scheme takes the advantage of OBL in operation level. In proposed scheme, the crossover operator generates two trial vectors in opposite directions, enhancing the exploration capability of the search strategy and taking a cautious approach by regularly examining the opposite directions during crossover. To evaluate the effectiveness of the proposed method, a series of experiments are conducted using the CEC-2017 benchmark functions with two different numbers of dimensions: 30 and 50. The results demonstrate a significant improvement in performance of the DE algorithm through the proposed method.
Finding HTML presentation failures using tools based on image comparison techniques: An experiment report

Webpage layout presentation failures can negatively affect the usability of a web application as well as the end-to-end user experience. The need for automated methods of visual inspection becomes obvious in complex web applications. However, visual inspection still heavily relies on manual inspection because the tools currently available are not yet advanced enough. This paper compares the performance results of three visual testing tools: Galen, AyeSpy, and Percy, and focuses on opportunities for their enhancement.
Detecting Falls Through Convolutional Neural Networks Using Infrared Sensor and Accelerometer

Over 20 million seniors in the US live alone, at risk of falling without being able to notify anyone of their distress. Falls among the elderly pose significant health risks, and early detection is crucial for minimizing their impact. Current methods for fall detection utilize camera-based systems and accelerometers to track body position and movement. However, these methods suffer from high false-positive rates and lack of user privacy. In our work, we remedy these issues by combining infrared sensors and accelerometers with advanced machine-learning algorithms for fall detection. Our work shows that through our unique integration of multiple sensors' data, a convolutional neural network (CNN) is able to accurately detect falls while maintaining high precision and recall rates. This research aims to guide future advancements in the field, assisting researchers, engineers, and healthcare professionals in developing innovative solutions to improve the quality of life for the elderly and reduce healthcare burdens.
Fuzzy Detectors Against Adversarial Attacks

Deep learning-based methods have proved useful for adversarial attack detection. However, conventional detection algorithms exploit crisp set theory for classification boundary. Therefore, representing vague concepts is not available. Mo-tivated by the recent success in fuzzy systems, we propose a fuzzy rule-based neural network to improve adversarial attack detection accuracy. The pretrained ImageNet model is exploited to extract feature maps from clean and attacked images. Subsequently, the fuzzification network is used to obtain feature maps to produce fuzzy sets of difference degrees between clean and attacked images. The fuzzy rules control the intelligence that determines the detection boundaries. In the defuzzification layer, the fuzzy prediction from the in-telligence is mapped back into the crisp model predictions for images. The loss between the prediction and label controls the rules to train the fuzzy detector. We show that the fuzzy rule-based network learns rich feature information than binary outputs and offers to obtain an overall performance gain. Our experiments, conducted over a wide range of images, show that the proposed method consistently performs better than conventional crisp set training in adversarial attack detection with various fuzzy system-based neural networks. The source code of the proposed method is available at https://github.com/Yukino-3/Fuzzy.
Real-Time FPGA Implementation of CNN-Based Distributed Fiber Optic Vibration Event Recognition Method

utilizing optical fibers to detect and pinpoint vibrations, Distributed Optical Fiber Vibration Sensing (DVS) technology provides real-time monitoring and surveillance of wide-reaching areas. This field has been leveraging Convolutional Neural Networks (CNN). Recently, a study has accomplished end-to-end vibration event recognition, enabling utilization of CNN-based DVS algorithms as real-time embedded system for edge computing in practical application situations. Considering the power consumption of central processing unit (CPU) and graphics processing unit (GPU), and the inflexibility of application-specific integrated circuit (ASIC), field-Programmable gate array (FPGA) is the optimal computing platform for the system. This paper proposes to compress pre-trained network and adopt a novel hardware structure, to design a fully on-chip, pipelined inference accelerator for CNN-based DVS algorithm, without fine tuning or re-training. This design allows for real-time processing with low power consumption and system requirement. An examination has been executed on an existing DVS algorithm based on a 40-layer CNN model comprising 2.7 million parameters. It is completely implemented on-chip, pipelined, with no reduction in accuracy.
Time-Based Mapping of Space Using Visual Motion Invariants

This paper focuses on visual motion-based invariants that result in a representation of 3-D points in which the stationary environment remains invariant, ensuring shape constancy. This is achieved even as the images undergo constant change due to camera motion. Nonlinear functions of measurable optical flow, which are related to geometric 3D invariants, are utilized to create a novel representation. We refer to the resulting optical flow-based invariants as ‘Time-Clearance’ and the well-known ‘Time-to-Contact’ (TTC). Since these invariants remain constant over time, it becomes straightforward to detect moving points that do not adhere to the expected constancy. We present simulations of a camera moving relative to a 3D object, snapshots of its projected images captured by a rectilinearly moving camera, and the object as it appears unchanged in the new domain over time. In addition, Unity-based simulations demonstrate color-coded transformations of a projected 3D scene, illustrating how moving objects can be readily identified. This representation is straightforward, relying on simple optical flow functions. It requires only one camera, and there is no need to determine the magnitude of the camera's velocity vector. Furthermore, the representation is pixel-based, making it suitable for parallel processing.
Assessing Local Descriptors for Feature-Based Registration of Whole-Slide Images

Feature-based registration has become increasingly popular in digital pathology for achieving initial global alignment between image pairs. However, the selection of algorithms used in this approach is often not well-justified. Specifically, the choice of local feature descriptor is rarely, if ever, discussed in the context of digital pathology. The majority of feature-based whole-slide image registration methods rely on the SIFT descriptor. In this study, we demonstrate that the choice of descriptor significantly influences the quality of registration results and that the BRIEF descriptor captures more optimal information for histological image registration.
An Analysis of Speech Emotion Recognition Based on Hybrid DNN-HMM Framework

This paper presents the results of using toolkit Kaldi and library Vosk for training and online recognition of isolated words in Serbian language. Kaldi allows the training of acoustic models, while Vosk is a Python library for speech recognition that can efficiently utilize those models. The focus of this research is analysis of robustness of automatic speech recognition (ASR) system trained using neutral speech, and tested against speech under different emotional states. The description of training models in Kaldi from scratch is given, along with parameter fine-tuning and the recognition results. Comparison was made against recognizers based on Hidden Markov models (HMM) and Convolutional Neural Networks (CNN). A noticeable increase in recognition accuracy is obtained for all examined emotional states.
FinSenticNet: A Concept-Level Lexicon for Financial Sentiment Analysis

Sentiment lexicons are important tools for research involving opinion mining and sentiment analysis. They are highly inter-operable, and address critical limitations of learning-based or large language model-based sentiment analysis, providing better reproducibility and explainability. Existing financial sentiment lexicons, manually crafted or automatically constructed, primarily comprise single-word entries despite the fact that jargon, terminologies, and collocations in finance are often multi-word expressions. To address this gap, we present FinSenticNet, a concept-level domain-specific lexicon specifically designed for financial sentiment analysis, where over 65% entries are multi-word expressions. Our construction approach is semi-supervised: the framework consists of a concept parser, a sentiment seeds generation module, and a semantic graph construction module. Each concept (graph node) is subsequently classified in terms of its polarity using the Label Propagation Algorithm and Graph Convolutional Network. Compared to other financial sentiment lexicons, FinSenticNet captures domain-specific language features and has a broader coverage. We demonstrate this with superior evaluation results, i.e., sentiment analysis accuracy and F-scores, on multiple well-received benchmark datasets.
Deep Learning Technology to Recognize American Sign Language Alphabet Using Mulit-Focus Image Fusion Technique

In our study, we implemented an innovative approach that harnessed the proven capabilities of ResNet, which achieved an exceptional accuracy rate of 99.98 % in classifying American Sign Language (ASL) alphabets. This approach involved utilizing ResNet to extract features from multiple input images, subsequently amalgamating these features and feeding them into the Vision Transformer (ViT) model for further processing. The underlying strategy of knowledge transfer exemplifies the principles of transfer learning and multi-image-focused fusion. As a result of our efforts, we observed a significant enhancement in the ViT model's accuracy, which surged from 88.59% to an impressive 97.09 %. This accomplishment underscores the potential of our proposed system in advancing ASL alphabet classification tasks, with particular relevance to individuals with hearing impairments. Our study not only emphasizes the significance of innovative fusion techniques in deep learning but also presents a promising solution for enhancing the accuracy and reliability of ViT in image processing applications. By exploring the intersections of various neural network architectures, our work paves the way for more inclusive and effective technologies, fostering seamless engagement for individuals with diverse abilities.
Finding the best channel for tissue segmentation in whole-slide images

In digital pathology, segmentation between tissue and glass slide is a very common pre-processing step in image processing pipelines. It is often presented as relatively trivial, and solved using ad-hoc heuristics that are not always precisely defined nor justified. Most tissue segmentation pipelines start by reducing the color image to a single-channel representation, grayscale being the most common. We show in this study that representations that focus on the colorfulness or entropy offer better separability between tissue and background, and lead to better results in simple thresholding pipelines.
Error Analysis of Translation: A Case Study of ELT Department Students/Tishk International University

The paper aims to investigate the types of errors committed by students of English Language Teaching department who study translation at the third year of university. Identifying the types of errors in students' translation products is not only significant for the teacher who knows how to address the students' difficulties but also for the students who get an idea of the causes and problems they face in order to tackle them in their translation projects. This also allows the teacher to demonstrate the students' level of mastering the linguistic and translations performances. To conduct this study, nearly 50 students of ELT department who study translation at their third-grade students participated in the study. The data of the research was collected form the students’ weekly assignment of translation from Kurdish to English. Third year students who study translation have weekly assignment of translation. The data of their assignments were randomly collected by the teacher to identify and classify types of errors they have in their assignment. Later, with the help of four translation teachers, the errors were identified and classified againTeachers of translation checked the students’ translation tasks at different times so as to count for the reliability and validity of the study. For the current study, the classification of error analysis operated by Pospescu (2012) is used. He used the taxonomy of error analysis in terms of three categories: Linguistic errors, comprehension errors, and translation errors, as well as sub classification of each type. Linguistic errors involve errors in morphology, syntax and collocations. Errors of comprehension include misinterpretation of syntax or lexis. The results showed that the translation and linguistic errors are the most common types of errors committed by the students, of which grammatical and lexical errors are the most common committed by the students. The sources of many errors may be attributed to context and pragmatic analysis of translated texts. The paper offers some suggestions and recommendations for further researches and investigations for improving students' translation performance. Keywords: Error Analysis, Grammatical, Lexical, Linguistic, Performance, Research, Translation
Symmetric Fine-Tuning for Improving Few-Shot Object Detection

Object detection plays a crucial role in automated image analysis by identifying and localizing objects within an image. One-stage Deep Learning (DL)-based object detectors have achieved impressive results, primarily due to large-scale datasets available for training them. However, these approaches rely heavily on abundant labeled data, posing challenges when only a few samples per class are available. To this end, few-shot object detection approaches have been proposed. Among them, fine-tuning the final detection head while keeping the feature extractor/backbone frozen is a commonly used approach for few-shot object detection. This approach effectively utilizes pre-existing knowledge encoded in the backbone, using a small number of samples to learn new object categories. However, in this paper, we argue that fine-tuning only the last layers may limit accuracy and lead to overfitting if the initial layers of the detection head are not adapted for the new task. The data processing inequality, which states that information lost in early network layers cannot be recovered in subsequent ones, supports this argument. To address this issue, we propose a symmetric fine-tuning method that involves both the first and last layers of the detection head, aiming to maintain a fixed trainable parameter budget while strategically selecting parameters for fine-tuning. Experimental results demonstrate the effectiveness and efficiency of this approach and open up several interesting future research directions.
Modelling of Firefly Algorithm with Densely Connected Networks for Near-Duplicate Image Detection System

Near-duplicate image detection is the way of detecting and flagging images that are highly similar to each other but not identical. It is a crucial task in different fields, including search engines, content management, and copyright enforcement, as it helps in content and organization de duplication. To achieve this, techniques and algorithms are employed that calculate similarity metrics, compare features, or analyze image content to define the degree of resemblance between images. Near-duplicate image detection can include approaches such as feature extraction, machine learning, and perceptual hashing to effectively manage and identify similar images in large datasets, which offer benefits in content retrieval and storage optimization. Therefore, this study presents a new firefly algorithm with deep learning-based near-duplicate image detection (FFADL-NDID) technique. Initially, median filtering (MF) approach is used to preprocess the input images. The proposed FFADL-NDID technique exploits the robust feature extraction abilities of DenseNet, a pre-trained DL model for capturing complex visual patterns from database and query images. In addition, the FFA is applied to carry out the hyperparameter tuning, optimizing the system for superior performance. This synergistic fusion enhances the overall efficiency of near-duplicate image detection by successfully searching the hyperparameter space for optimal configurations. Finally, the FFADL-NDID framework applies Euclidean distance-based similarity matching processes, which detects the near-duplicate images significantly. The simulation analysis of the FFADL-NDID method is tested on multiple datasets and the outcomes show its promising performance over other DL models in terms of different measures.
Feature Analysis for Detection of Unreal Video Editing Effects

Unrealistic video effects are extremely popular in video post-production. In this paper several video sequences are recorded and edited in order to apply the alleged magical effects useful in movies and commercials for the testing purposes. This is followed by feature analysis for detection of such effects. In order to automatically detect the presence of intentional changes, several global features extracted from frames are tested. Video sequences with common effects are produced and categorized, where extracted normalized features based on specific color spaces with normalization are shown to be particularly useful for distinguishing frames with unreal effects.
Emerging Trends In Information Security Policy Regulation And Their Potential Impact On Organizations In Rwanda

This research paper investigates emerging trends in information security policy and regulation and their repercussions on Rwandan organizations. Organizations confront escalating cyber threats and data breach risks in an increasingly technology-reliant landscape. The study evaluates the current status of information security policy and regulation in Rwanda, identifying potential gaps and weaknesses. Additionally, it explores evolving trends in this domain, encompassing international standards and best practices. The research assesses how these trends may impact Rwandan organizations, highlighting both challenges and opportunities. Findings indicate that these policies and regulations constrain the activities of organizations, particularly those with inadequate information security practices. This research holds vital implications for Rwandan organizations and policymakers tasked with crafting and enforcing data protection laws and regulations.
Photonics-Assisted Complex-Valued Discrete Fourier Transform Processor Based on Temporal Computing

In this paper, we propose and experimentally demonstrate a photonics-assisted complex-valued discrete Fourier transform (DFT) processor based on temporal computing. In the proposed DFT processor, the complex-valued input data and the orthogonal basis are encoded in sequence on an optical carrier via two high-speed dual-drive Mach-Zehnder modulators (DD-MZMs). By precisely controlling the synchronization time of the input data and orthogonal basis loading, the complex-valued multiplication operation is performed in the time domain. An optical hybrid is used to implement coherent detection, and two low-speed photodetectors are used to perform the complex-valued accumulation operation. As a demonstration, 32-points complex-valued DFT is performed, and the normalized root mean square error (NRMSE) is as small as 0.0133. In addition, a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$16\times 32$</tex> pixels image is employed to implement 2D DFT, which achieves an NRMSE as small as 0.0065. The proposed DFT processor holds unique advantages including high throughput and low latency, which is potential to be widely used in artificial intelligence computing and radar signal processing systems.
A data-driven framework for knowledge exchange analysis of development issues in medical applications: A case study of COVID-19

With medical technological advances being developed in a rapid pace, the need for effective Scientific Software Development (SSD), that can process, store and visualize medical data is ever growing. Particularly during the COVID-19 pandemic, the medical community came together to produce efficient solutions to tackle this global setback. Programmers and developers have an active role in the procurement of medical software, with many of them exchanging knowledge and opinions in Q&A portals like Stack Overflow (SO) about methodologies, techniques and programming queries. In this study we present a data-driven framework that collects, filters, stores and analyzes issues and questions for medical applications from SO, visualizing them in an intuitive manner. To highlight the functionalities of our framework, we present a case study with COVID-19 SSD related questions, providing insights and valuable information about the status of the domain.