Reviewerly/exHarmony huggingface dataset:
Accurate diagnosis of non-Hodgkin lymphoma on whole-slide images using deep learning

Digital Pathology is the technique of digitizing histology slides to create high-resolution images. One of the important applications for digital pathology is tissue level classification, such as the identification of the three most common kinds of non-Hodgkin lymphomas; Mantle Cell Lymphoma, Follicular Lymphoma and Chronic Lymphocytic Leukemia, which pose a significant problem for pathologists due to their inherent complexity. In this research, deep learning ideas are combined with an improvement of the CNN algorithm to propose a Non-Hodgkin Lymphoma model which is able, effectively, to categorize these subtypes of non-Hodgkin lymphomas. The proposed model was trained using NIA-curated dataset images and achieved a classification accuracy of 98.7%, about 0.6% better than current classification strategies for non-Hodgkin’s lymphomas based on deep learning.
Cloud ERP API Ontology

Many small and medium companies use different ERP systems in cloud. Providers of ERP cloud systems offer different application programming interfaces (APIs) to support connectivity with other systems and applications. In this work, we present a cloud ERP API ontology to describe main cloud ERP resources used in providers’ APIs. This is a first attempt to deal with cloud ERP vendor lock-in problem using a unified ontology of cloud ERP APIs. Ontology was created using prominent ontology development method after analysis of three most widely used ERP cloud offers provided by Oracle, Microsoft, and SAP. The proposed ontology can be used for interoperability among publicly available cloud ERP APIs.
Impact of Dimensionality Reduction on Sentiment Analysis of Algerian Dialect

In Algeria, sentiment analysis for Algerian dialect becomes very important for organizations and companies to track customer feedback, to predict their satisfaction, and to assess their opinions over time. However, identification of sentiments is a challenging task; (i) the Algerian dialect is an informal language without rigorous rules of writing and standardization. It is mainly based on Modern Standard Arabic (MSA) vocabulary, where the majority of the original words are modified both phonologically and morphologically. It is also based on a set of foreign words from Turkish, Spanish and French as well Tamazight. This is called code switching. (ii) Another problem which is obviously present in the Algerian dialect is the fact that a word with one form of pronunciation can be written in several forms. Therefore, our objective is to consider these two issues within the process of sentiment analysis of Algerian dialect. To this end, we propose to examine the impact of dimensionality reduction techniques such as lemmatization, stemming, feature selection and in particular our extended Soundex algorithm on the system performance. We used a supervised machine learning approach without going through a translation step into MSA or transliteration into another target language like French. We compare the performance of five classifiers with and without the use of dimensionality techniques. Results show that feature selection combined with multinomial Naive Bayes classifier gives an F1 score of 83.20% and attribute reduction rate of 82.65%.
pCGP: A Parallel Implementation of Cartesian Genetic Program-ming for Combinatorial Circuit Design and Time-Series Prediction

Cartesian Genetic Programming (CGP), a variant of genetic programming, is computationally extensive where its runtime is dominated by fitness evaluation, especially when the genotypes are complex. In order to speed up the execution, multiple offspring can be evaluated in parallel on multiple processing units. This paper presents a parallel implementation of CGP, called pCGP, using a multiple-threading scheme. The performance of pCGP is tested on two different standard benchmark datasets: i) classical digital circuit design problem, and ii) time-series dataset of monthly total sunspot number. The results show a significant reduction in the mean evolution time of parallel multi-threaded implementation over the serial single-threaded version of standard CGP. Hence, pCGP can be used for faster convergence in a complex problem having a large search space.
CleverTrash: An ML-based IoT system for waste sorting with continuous learning cycle

Waste recycling is a major part of the life cycle of our goods. The principle of recycling is to reuse the waste. A major challenge that waste recycling facilities are facing is the disruptive wastes that are not well sorted. Sorting errors represent additional management costs. One example of sorting problem is differentiating types of plastic. The most known recyclable plastic type is Polyethylene Terephthalate (PET). One way to solve the sorting errors is going to the source of waste: people. By educating people we can achieve fewer errors. CleverTrash deals with a recycle bin enhanced with a waste recognition system that educates users to properly recycle their waste. It also provides statistics on the sorting quality in recycling bins thanks to an embedded system (Raspberry Pi and sensors). These statistics are provided to the manager to improve the waste collection strategy. We explore the performance of Convolutional Neural Networks (CNNs) inside the waste recognition system. In machine learning, CNNs are mostly specialized in image recognition. We explore the performance of CNNs inside the waste recognition system. We started the project from scratch. We gathered our own dataset by setting up a trash can incorporating an embedded video and lighting system. The camera takes pictures of the garbage being thrown away. A machine learning model classifies the images. The main challenge was to develop the best machine learning classifier model from the pictures and evaluate its performance on this use case. We compare seven types of CNN algorithms and produce the results in the present paper. Our results show that CNNs have difficulties distinguishing between PET bottles and glass bottles on a classification “PET versus Other (non-PET) It suggests that CNNs are strong to classify different shapes but weak when shapes are similar. It led us to try a classification of “Bottles versus Other (non-Bottle)” which performs better than “PET versus Other The best result of the former classification performed as well as the worst result of the latter classification.
Robust Real-time Automatic Voice Command based on Raspberry pi for assistance disabled people

Walkers are widely used by people with limited mobility. Much research work is underway to design human-machine interfaces (HMIs) using physiological signals to improve the control of mechanical mobility devices, mainly wheelchairs. Generating exact control commands through physiology in a suitable HMI is a real challenge because severely disabled people cannot control classic wheelchairs. In this context, this work develops a new voice-signal control system to meet the needs of this physically disabled population. This system is divided into two parts: part one covers the recognition, processing and classification of speech signals in this part the relative spectral-perceptual linear prediction (RASTA-PLP) and discrete wavelet transform (DWT) are combined to process and extract speech features and the second part includes the mechanical collection module, i. e. the wheelchair control by a motor drive circuit. The microphone servers for real-time voice recording. A Raspberry-Pi with a Linux operating system kernel is used as the processor. In order to make the processor more user-friendly and reliable, Voice-received mode is integrated into the Wheelchair. The model works successfully with an average recognition of 100 in clean environment and between 80 and 100 in noisy environment.
Acurracy of ASR Software in Bulgarian Language and its Application in Journalism

The following research study presents Automatic speech recognition (ASR) software in Bulgarian language and its practical use in media production for decrypting live broadcasts. AI and human typists transcribed several live broadcasts aired by the Bulgarian National Radio (BNR). After that, professional editors compared both texts according to several criteria - time to decrypt, accuracy in grammar and word recognition. A comparison was made about what time it takes a professional Web editor to adapt human and AI generated text. Recommendations are made on how to improve both ASR and journalistic approach.
Learning for Time Series Differential Phase-Shift Keying-based Non-coherent Receivers

In this paper, using the recurrent neural network (RNN) for non-coherent differential phase-shift keying (DPSK) signal detection is proposed. While deep-learning has been utilized for estimating the channel and detecting the signal in various communication systems models, making use of the time series encoding of DPSK modulation along with the time series feature of the recurrent neural networks for Rayleigh fading communication channel has not been studied before. The use of both of deep neural network (DNN) and recurrent neural network to detect the differential encoded time series signal is considered and compared. Both of the DNN-based and RNN-based detectors are compared to conventional non-coherent DPSK detection, coherent phase-shift keying (PSK) detection, and the analytical error rate of coherent and non-coherent detection. Extensive simulation results exhibit that the proposed RNN-based signal detector outperforms both of conventional non-coherent differential detection, and the DNN-based detector in terms of symbol error rate performance. In addition, it does not need any statistical information about the channel or require complex online calculations.
Investigating Indoor Navigational Experiences with a Mobile Augmented Reality Prototype versus the Conventional Method

Navigation is a coordinated process of movement and wayfinding. This concept is commonly tied to outdoor navigational tasks like finding a place or indoor navigational chores such as locating a product within a store. Focusing on the challenges of indoor navigation, revealed that shoppers sometimes encounter difficulty locating items or become disoriented, which may lead to frustration. This study proposed a conceptual model for alleviating challenges with indoor navigation. The model was prototyped as two main components - navigation and recognition, working in conjunction with each other. The navigation component applied the A* search algorithm and was simulated using the paper prototyping methodology. On the other hand, the recognition component was developed as a mobile Augmented Reality (AR) application. Within the context of a supermarket, the prototype was compared against the conventional shopping method to determine efficiency. Furthermore, the prototype was assessed for usability, user reactions and experiences, strengths and weaknesses. Key findings revealed that most shoppers enjoyed interacting with the prototype, due to its novel design and efficiency in finding items. This was an indication that AR is indeed a useful advancement in the area of indoor navigation.
ExploitWP2Docker: a Platform for Automating the Generation of Vulnerable WordPress Environments for Cyber Ranges

A cyber range is a realistic simulation of an organization’s network infrastructure, commonly used for cyber security training purposes. It provides a safe environment to assess competencies in both offensive and defensive techniques. An important step during the realization of a cyber range is the generation of vulnerable machines. This step is challenging and requires a laborious manual configuration. Several works aim to reduce this overhead, but the current state-of-the-art focuses on generating network services without considering the effort required to build vulnerable environments for web applications. A cyber range should represent a real system, and nowadays, almost all the companies develop their company site by using WordPress, a common Content Management System (CMS), which is also one of the most critical attackers’ entry points. The presented work proposes an approach to automatically create and conFigure vulnerable WordPress applications by using the information presented in public exploits. Our platform automatically extracts information from the most well-known publicly available exploit database in order to generate and conFigure vulnerable environments. The container-based virtualization is used to generate lightweight and easily deployable infrastructures. A final evaluation highlights promising results regarding the possibility of automating the generation of vulnerable environments through our approach.
Methodology for creating natural language interfaces to information systems in a specific domain area

The research is a crossroads in the fields of Informatics and Computational Linguistics and illustrates the understanding and interpretation of texts in natural language by computers. The focus of the research is the presented methodology for describing domain area. In it, the computer is trained with the help of grammar rules and storage (classified linguistic corpus) of possible word combinations of language units, presented and characterized as relevant grammar classes. The proposed methodology for describing a domain area (DA) is part of a process of creating natural language interface (NLI), which uses existing tools, instruments and approaches, further developed and modified to extract answers from databases of different information systems in the same DA. The components obtained as a result of the steps in the proposed methodology (a model of concepts for the DA, upgraded with their lexical characteristics and relationships between them) are sufficient for the NLI to function and return an answer to a question to the IS.
Exploring ML for Predictive Maintenance Using Imbalance Correction techniques and SHAP

This paper focuses on an application of machine learning in industries - predicting machine failure from sensor data for maintenance purposes. The primary purpose of this paper is to use machine learning models to predict whether a machine is going to fail or function normally. Various machine learning techniques are implemented and evaluated on a synthetic dataset and then a real-world dataset. This method allows a comparison to be drawn between a theoretical approach and real-world application. This paper first introduces the various machine learning (ML) models and describes the datasets. Then, the best performing models are further developed and discussed. Lastly, the models are evaluated quantitatively (using performance metrics) and qualitatively (by Shapley Additive Explanations, SHAP values).
Human body action recognition with machine learning for bionic applications: a Sensor Data Fusion Approach

Health sensors are a valuable source of data for researchers investigating the body’s varied activities. Human activity recognition systems have a special attachment to health sensors as their construction requires a focus on several steps such as pattern classification, feature extraction and selection, classifier design, and the learning process.Our research is primarily concerned with recognizing the various movements of the body’s extremities in order to contribute to research on prosthetic control employing electromyography signals and the development of smart health systems. As a beginning stage, we decided to focus our research on detecting physical activity by utilizing data from two sensors with the intention of improving analysis and hypotheses associated with bionic applications and artificial intelligence solutions for health systems.We demonstrated this approach with a specific case study on the exploitation of the data generated by the accelerometer and gyroscope. This analysis was performed on seven human activities along with a new set of indicator parameters, statistical features, and five well-known machine-learning classifiers to evaluate the results.
Multi-Modal Online Exam Cheating Detection

Online exams have been the standard approach adopted by universities and institutes because of the COVID-19 pandemic which forces the world to go towards distance learning and online exams. But with this approach come the challenges such as online exam proctoring which is considered one of the most difficult challenges to solve. It is a must to ensure the academic honesty and credibility of the online exam. Existing proctoring techniques require a few proctors to observe a huge number of students to detect cheating students, and due to it is time-consuming and labor-intensive, we implemented multi-modalities to detect the student’s activity during the online exam using a webcam and sent a report to the proctor for the suspected student. Those modalities are head-pose, object detection and eye-gaze estimation. This proposed solution is tested and evaluated on 29 students with a total of four exam sessions to ensure the effectiveness of our proposed solution. The events’ detection accuracy of the multi-modalities experiment was 95.69%.
Development of an Automated Viral Plaque Assay Program and ELISpot Counting Program on 96-Well Plate Platform

Plaque assay is a unique method and specialized measurement by virologists that provides a drag discovery until vaccine development. Also, the ELISpot assay is one of the processes that doctors use to detect CTLs to diagnose the patient. The approach of plaque assays and ELISpot assays is challenging and complicated. Both need experts and time to analyze by physical counting in well-plates, which causes abstract and stagnant to the expert. Hence, to dimmish the load of the specialists to physical counting in well plates, we have developed an automated platform to capture images from 96-well plates and do automate assay for both methods. The platform contains two major parts: Hardware used for acquiring images by a modification of IAI Tabletop with USB microscope and light souses and Software for image viral counting, that is simple to use and easy to setup. The software used for automated assays consists of two parts: the ELISpot assay part utilizes the theory of dynamic thresholding algorithms. The plaque assay uses unsupervised learning algorithms named k-mean clustering. The graphic user interface displays images of a virtual plaque and ELISpot, the virtual plaque assays, and the ELISpot assay results. The ELISpot counting algorithm was evaluated with the golden standard with 80% accuracy. The average correction of Plaque assay measurements by the machine is 94%.
FPGA Implementation and Comparison of Sigmoid and Hyperbolic Tangent Activation Functions in an Artificial Neural Network

An artificial neural network(ANN) is a type of computing system that mimics the functioning of the neural networks in a biological brain. In this paper, an ANN has been simulated on an Field Programmable Gate Array (FPGA) for the application of handwritten digit recognition using the sigmoid and hyperbolic tangent(tanh) activation functions. Both software and hardware simulations have been carried out, using python and Verilog HDL respectively. The two activation functions were implemented on an Artix-7 FPGA. A comparison has been made between the sigmoid and tanh activation functions based on speed, accuracy, and hardware required, and it has been inferred that the tanh function is best for the application of handwritten digit recognition as it has 3% higher accuracy and uses 5 less LUTs than the sigmoid activation function.
Analysis of Usability Game Educational Learning of Wayang Characters Using Usability Scale System

Indonesia is a country that has a lot of diversity in customs, arts, and culture in which each region has different characteristics. One of them is Puppet which is an entertainment that shows shadows, namely a jagged painting performed by a puppeteer on the surface of a screen made of white cloth. But lately, the art of puppet culture is less attractive to some people who think that this puppet culture is old and outdated. For this reason, this cultural art needs to be brought back as an effort to preserve local culture and can be used to introduce puppet figures from the Mahabharata epic such as the Five Pandavas. From these problems, an Educational Game of puppet Characters was created for the Android platform which was made using the Unity game engine and developed using the MDLC (Multimedia Development Life Cycle) method. From the application that has been made, usability testing is carried out using a System Usability Scale (SUS) questionnaire. Based on the test results, this application has a SUS score of 78.125. From this score, it can be interpreted that the application has an acceptance level of "Acceptable" on the acceptability ranges assessment, then has value of letter "C" on the grade scale assessment and has a "Good" rating on the adjective ratings assessment.
Compressed and Uncompressed Video Steganography: Review

It is known, that digital communication and the internet are fast-growing with huge multimedia storage and transmission, as the advanced reasons were a major reason for improving information security levels. Cyber security attacks accrue when an unprotected video is transmitted throw unsecure communication. This paper summarized video steganography techniques in two approaches, compressed and uncompressed in both spatial and frequency domains while compressed based on intraframe prediction, Interframe prediction, motion vector estimation, transform coefficients, and entropy coding, and given the remaining issues. Furthermore, discusses performance analyses of these approaches based on imperceptibility, robustness, embedding capacity, and embedding efficiency. Finally, given the solutions for solving the remaining issues in future work.
Nature-inspired Metaheuristic Effectiveness Used in Phishing Intrusion Detection Systems with Firefly Algorithm Techniques

This paper discusses research-based findings of applying metaheuristic optimization techniques and nature-inspired algorithms to detect and mitigate phishing attacks. The focus will be on the Firefly nature-inspired metaheuristic algorithm optimized with Random Forest and Support Vector Machine (SVM) classification. Existing research recommends the development and use of nature-inspired detection techniques to solve complex real-world problems. Existing research using nature-inspired heuristics appears to be promising in solving NP-hard problems such as the traveling salesperson problem. In the same classification of NP-hard, is that of cyber security existing research indicates that the security threats are complex, and that providing security is an NP-hard problem. This study is expanding the existing research with a hybrid optimization of nature-inspired metaheuristic with existing classifiers (random forest and SVM) for an improvement in results to include increased true positives and decreased false positives. The proposed study will present the importance of nature and natural processes in developing algorithms and systems with high precision and accuracy.