Reviewerly/exHarmony huggingface dataset:
USE OF INTERACTIVE TECHNOLOGIES IN TEACHING THE COURSE "TEACHING METHODS OF PROFESSIONAL TRAINING DISCIPLINES"

The article is devoted to the actual problem of training future teachers of professional training on the basis of the competence approach during the study of the course "Methodology of teaching the disciplines of professional training" using interactive technologies. It is noted that the use of interactive technologies activates students' thinking, contributes to the formation of key and professional competencies; ensures the appropriate level of students' mastery of the educational material; activates educational, cognitive and research activities; increases the practical orientation of the educational process to the future profession. It was determined that the following interactive methods are the most effective in organizing the educational process from the course "Methodology of training disciplines of professional training": visualized lectures, trainings, discussions, business and role-playing games, which often have an integrative nature and involve a combination of traditional and modern active learning methods . Reasonably, the characteristic features of interactive learning, which include: mandatory feedback; autonomy and initiative of students; cooperation of all participants in the educational process; problematic and reflective nature of learning. The introduction of interactive technologies into the educational process implies that the teacher must determine the students' reactions and understanding of the purpose of the classes, which is important for the formation of program competencies necessary for their future profession. The introduction of multimedia products into the educational process, namely: electronic manuals and textbooks, encyclopedic guides, educational videos, etc., contributes to the improvement of the effectiveness of the use of interactive teaching methods. Such products combine text, graphics, audio and video information, animation. During the implementation of interactive technologies, students actively take part in educational activities, model problematic professional situations, work on the topic and perform creative and research tasks, conduct discussions with classmates, acquire skills to justify and argue their own point of view, develop strategies for optimal behavior in various professional situations. The effectiveness of the formation of professional competences in students during the course "Methodology of teaching the disciplines of professional training" depends on the pedagogical technologies used by the teacher, which often have an integrative nature and involve a combination of traditional and modern active learning methods.
OSCMS: A Decentralized Open-Source Coordination Management System Using a Novel Triple-Blockchain Architecture

Open-source systems help to manage the rapid development of software, while governing open-source systems properly can effectively promote software and software engineering. However, some significant problems, such as code controls, incentives, interaction and cooperation, automation, transparency and fairness of rights and responsibilities, cannot be properly solved by traditional methodologies. The decentralization, immutability, change in trust mode and smart contract programming of blockchain provide new solutions. In order to solve the problems of traditional centralized open-source governance, this paper proposes a decentralized open-source coordination management system using a novel triple-blockchain architecture. Through the analysis of traditional and blockchain-based research, the business and technical issues that need to be addressed in decentralized open-source governance systems have been emphatically studied. Combined with triple-blockchain architecture, smart contracts, oracles and continuous integration tools, we study the decentralization of open-source businesses and make them more trustworthy, automated and coordinated. An identity authentication mechanism is designed for permission control and inter-community collaboration. A decentralized open-source reputation is proposed for incentive and reference. We also improved the DPoS (Delegated Proof of Stake) consensus under triple-blockchain architecture to reduce repeated elections. By constructing the OSCMS prototype based on the proposed architecture model, many comparative experiments were conducted under different parameters and conditions and showed good feasibility, scalability, reliability and performance. The OSCMS not only solves the shortcomings of previous research but also provides a comprehensive and feasible reference for the decentralized practice of open-source governance.
End-to-End Auto-Encoder System for Deep Residual Shrinkage Network for AWGN Channels

With the rapid development of deep learning methods, the data-driven approach has shown powerful advantages over the model-driven one.In this paper, we propose an end-to-end autoencoder communication system based on Deep Residual Shrinkage Networks (DRSNs), where neural networks (DNNs) are used to implement the coding, decoding, modulation and demodulation functions of the communication system.Our proposed autoencoder communication system can better re-duce the signal noise by adding an "attention mechanism" and "soft thresholding" modules and has better performance at various signal-to-noise ratios (SNR).Also, we have shown through comparative experiments that the system can operate at moderate block lengths and support different throughputs.It has been shown to work efficiently in the AWGN channel.Simulation results show that our model has a higher Bit-Error-Rate (BER) gain and greatly improved decoding performance compared to conventional modulation and classical autoencoder systems at various signal-to-noise ratios.
Smart scan of medical device displays to integrate with a mHealth application

The daily monitoring of the physiological parameters is essential for monitoring health condition and to prevent health problems. This is possible due to the democratization of numerous types of medical devices and promoted by the interconnection between these and smartphones. Nevertheless, medical devices that connect to smartphones are typically limited to manufacturers applications.This paper proposes an intelligent scanning system to simplify the collection of data displayed on different medical devices screens, recognizing the values, and optionally integrating them, through open protocols, with centralized databases.To develop this system, a dataset comprising 1614 images of medical devices was created, obtained from manufacturer catalogs, photographs and other public datasets. Then, three object detector algorithms (yolov3, Single-Shot Detector [SSD] 320 × 320 and SSD 640 × 640) were trained to detect digits and acronyms/units of measurements presented by medical devices. These models were tested under 3 different conditions to detect digits and acronyms/units as a single object (single label), digits and acronyms/units as independent objects (two labels), and digits and acronyms/units individually (fifteen labels). Models trained for single and two labels were completed with a convolutional neural network (CNN) to identify the detected objects. To group the recognized digits, a condition-tree based strategy on density spatial clustering was used.The most promising approach was the use of the SSD 640 × 640 for fifteen labels.Lastly, as future work, it is intended to convert this system to a mobile environment to accelerate and streamline the process of inserting data into mobile health (mhealth) applications.
Scalable Bell inequalities for graph states of arbitrary prime local dimension and self-testing

Bell nonlocality -- the existence of quantum correlations that cannot be explained by classical means -- is certainly one of the most striking features of quantum mechanics. Its range of applications in device-independent protocols is constantly growing. Many relevant quantum features can be inferred from violations of Bell inequalities, including entanglement detection and quantification, and state certification applicable to systems of arbitrary number of particles. A complete characterisation of nonlocal correlations for many-body systems is, however, a computationally intractable problem. Even if one restricts the analysis to specific classes of states, no general method to tailor Bell inequalities to be violated by a given state is known. In this work we provide a general construction of Bell inequalities that are maximally violated by graph states of any prime local dimension. These form a broad class of multipartite quantum states that have many applications in quantum information, including quantum error correction. We analytically determine their maximal quantum violation, a number of high relevance for device-independent applications of Bell inequalities. Finally, we show that these inequalities can be used for self-testing of multi-qutrit graph states such as the well-known four-qutrit absolutely maximally entangled state AME(4,3).
An interactive national digital surveillance system to fight against COVID-19 in Bangladesh

Background COVID-19 has affected many people globally, including in Bangladesh. Due to a lack of preparedness and resources, Bangladesh has experienced a catastrophic health crisis, and the devastation caused by this deadly virus has not yet been halted. Hence, precise and rapid diagnostics and infection tracing are essential for managing the condition and limiting its spread. The conventional screening procedure, such as reverse transcription polymerase chain reaction (RT-PCR), is not available in most rural areas and is time-consuming. Therefore, a data-driven intelligent surveillance system can be advantageous for rapid COVID-19 screening and risk estimation. Objectives This study describes the design, development, implementation, and characteristics of a nationwide web-based surveillance system for educating, screening, and tracking COVID-19 at the community level in Bangladesh. Methods The system consists of a mobile phone application and a cloud server. The data is collected by community health professionals via home visits or telephone calls and analyzed using rule-based artificial intelligence (AI). Depending on the results of the screening procedure, a further decision is made regarding the patient. This digital surveillance system in Bangladesh provides a platform to support government and non-government organizations, including health workers and healthcare facilities, in identifying patients at risk of COVID-19. It refers people to the nearest government healthcare facility, collecting and testing samples, tracking and tracing positive cases, following up with patients, and documenting patient outcomes. Results This study began in April 2020, and the results are provided in this paper till December 2022. The system has successfully completed 1,980,323 screenings. Our rule-based AI model categorized them into five separate risk groups based on the acquired patient information. According to the data, around 51% of the overall screened populations are safe, 35% are low risk, 9% are high risk, 4% are mid risk, and the remaining 1% is very high risk. The dashboard integrates all collected data from around the nation onto a single platform. Conclusion This screening can help the symptomatic patient take immediate action, such as isolation or hospitalization, depending on the severity. This surveillance system can also be utilized for risk mapping, planning, and allocating health resources to more vulnerable areas to reduce the virus's severity.
Multi-feature Transformer for Multiclass Cyberbullying Detection in Bangla

Cyberbullying detection is a global issue that must be addressed to improve the cyberspace for millions of online users, services, and organizations. Online harassment of the general public and celebrities is now commonplace on social media, particularly in Bangladesh. In this paper, we present a novel multi-feature transformer followed by a deep neural network for multiple-dimensional cyberbullying detection. Using online Bangla textual data, we introduce the user’s social profile, the lexical features, the contextual embedding, and the semantic similarities among word associations in Bangla in order to develop an effective and robust cyberbullying detection system. Our proposed method can detect cyberbullying in Bangla with a 98% detection accuracy for threats and a 90% detection accuracy for sarcastic comments. The aggregate accuracy of all six multiclass labels is 86.3%. In addition, the experimental results find that the proposed technique outperforms the state-of-the-art methods for detecting cyberbully in Bangla.
CW-NET for multitype cell detection and classification in bone marrow examination and mitotic figure examination

Bone marrow (BM) examination is one of the most important indicators in diagnosing hematologic disorders and is typically performed under the microscope via oil-immersion objective lens with a total 100× objective magnification. On the other hand, mitotic detection and identification is critical not only for accurate cancer diagnosis and grading but also for predicting therapy success and survival. Fully automated BM examination and mitotic figure examination from whole-slide images is highly demanded but challenging and poorly explored. First, the complexity and poor reproducibility of microscopic image examination are due to the cell type diversity, delicate intralineage discrepancy within the multitype cell maturation process, cells overlapping, lipid interference and stain variation. Second, manual annotation on whole-slide images is tedious, laborious and subject to intraobserver variability, which causes the supervised information restricted to limited, easily identifiable and scattered cells annotated by humans. Third, when the training data are sparsely labeled, many unlabeled objects of interest are wrongly defined as background, which severely confuses AI learners.This article presents an efficient and fully automatic CW-Net approach to address the three issues mentioned above and demonstrates its superior performance on both BM examination and mitotic figure examination. The experimental results demonstrate the robustness and generalizability of the proposed CW-Net on a large BM WSI dataset with 16 456 annotated cells of 19 BM cell types and a large-scale WSI dataset for mitotic figure assessment with 262 481 annotated cells of five cell types.An online web-based system of the proposed method has been created for demonstration (see https://youtu.be/MRMR25Mls1A).
Photonic elementary cellular automata for simulation of complex phenomena

Cellular automata are a class of computational models based on simple rules and algorithms that can simulate a wide range of complex phenomena. However, when using conventional computers, these 'simple' rules are only encapsulated at the level of software. This can be taken one step further by simplifying the underlying physical hardware. Here, we propose and implement a simple photonic hardware platform for simulating complex phenomena based on cellular automata. Using this special-purpose computer, we experimentally demonstrate complex phenomena, including fractals, chaos, and solitons, which are typically associated with much more complex physical systems. The flexibility and programmability of our photonic computer present new opportunities to simulate and harness complexity for efficient, robust, and decentralized information processing using light.
MAYAVI: A Cyber-Deception Hardware for Memory Load-Stores

Rapid evolution of security attacks presents a perpetual challenge to computer system defenders in terms of continuously upgrading their defense capabilities and being aware of adversarial tactics. Emerging technologies like cyber-deception offer the unique advantage of intelligently surveying hostile behavior while actively safeguarding sensitive assets by manipulating the malware execution flow to non-useful states or misrepresenting critical data.
Machine Learning for Intrusion Detection: Stream Classification Guided by Clustering for Sustainable Security in IoT

The Internet of Things (IoT) has brought about unprecedented connectivity and convenience in our daily lives, but with this newfound interconnectedness comes the threat of cyber-attacks. With ever-increasing IoT devices being connected to the internet, securing IoT devices is becoming increasingly urgent. Machine learning (ML) is among the most popular techniques used by intrusion detection systems (IDS) to enhance their detection performance when securing IoT. However, a key obstacle of ML-based IDS for IoT is learning from nonstationary streaming data, also known as concept drift. One of the most challenging learning scenarios under concept drift is extreme verification latency (EVL), which occurs when only unlabeled nonstationary streaming data is available after a small set of initial labeled data. Stream Classification Algorithm Guided by Clustering (SCARGC) is an algorithm that can effectively deal with the nonstationary data streams in EVL scenarios. Applying an EVL implementation provides the capability of adapting to nonstationary environments within the IoT domain. The SCARGC model, as an integrated IoT intrusion detection system, allows for sustainable security as new threats are identified in this non-stationary environment. Hence, in this project, we develop an innovative IoT intrusion detection approach by natively integrating SCARGC and intrusion detection to address the EVL challenges to provide sustainable security as the model adapts to nonstationary environments. We evaluated the proposed approach on real-world IoT cybersecurity datasets. The results demonstrate the feasibility of the proposed approach, which can lead to the development of sophisticated intrusion detection systems for IoT.
hChain: Blockchain Based Healthcare Data Sharing with Enhanced Security and Privacy Location-Based-Authentication

In smart healthcare, blockchain technology addresses existing concerns with security, privacy, and electronic healthcare records. In addition, utilizing edge devices with IoMT devices is very advantageous for addressing security, computing, and storage challenges. Symmetric and asymmetric keys are used to conceal sensitive information from unauthorized parties. Moreover, the hash function SHA256 helps for data alteration detection. The proposed system uses a blockchain-based smart healthcare system using IoMT devices for continuous patient monitoring. The edge device is used to hash and encrypt data and provide additional computational capability. A symmetric key maintains data privacy in the blockchain, allowing patients to safely share data through smart contracts while preventing unauthorized physicians from seeing it. A verification node and blockchain sign and validate patient data in the healthcare provider system using an asymmetric key. Location-based authentication is addressed to ensure the authenticity and data source.
A Logarithmic Depth Quantum Carry-Lookahead Modulo (2n - 1) Adder

Quantum Computing is making significant advancements toward creating machines capable of implementing quantum algorithms in various fields, such as quantum cryptography, quantum image processing, and optimization. The development of quantum arithmetic circuits for modulo addition is vital for implementing these quantum algorithms. While it is ideal to use quantum circuits based on fault-tolerant gates to overcome noise and decoherence errors, the current Noisy Intermediate Scale Quantum (NISQ) era quantum computers cannot handle the additional computational cost associated with fault-tolerant designs. Our research aims to minimize circuit depth, which can reduce noise and facilitate the implementation of quantum modulo addition circuits on NISQ machines. This work presents quantum carry-lookahead modulo (2n - 1) adder (QCLMA), which is designed to receive two n-bit numbers and perform their addition with an O(log n) depth. Compared to existing work of O(n) depth, our proposed QCLMA reduces the depth and helps increase the noise fidelity. In order to increase error resilience, we also focus on creating a tree structure based Carry path, unlike the chain based Carry path of the current work. We run experiments on Quantum Computer IBM Cairo to evaluate the performance of the proposed QCLMA against the existing work and define Quantum State Fidelity Ratio (QSFR) to quantify the closeness of the correct output to the top output. When compared against existing work, the proposed QCLMA achieves a 47.21% increase in QSFR for 4-qubit modulo addition showcasing its superior noise fidelity.
Design Space Exploration for PCM-based Photonic Memory

The integration of silicon photonics (SiPh) and phase change materials (PCMs) has created a unique opportunity to realize adaptable and reconfigurable photonic systems. In particular, the nonvolatile programmability in PCMs has made them a promising candidate for implementing optical memory systems. In this paper, we describe the design of an optical memory cell based on PCMs while exploring the design space of the cell in terms of PCM material choice (e.g., GST, GSST, Sb2Se3), cell bit capacity, latency, and power consumption. Leveraging this design-space exploration for the design of efficient optical memory cells, we present the design and implementation of an optical memory array and explore its scalability and power consumption when using different optical memory cells. We also identify performance bottlenecks that need to be alleviated to further scale optical memory arrays with competitive latency and energy consumption, compared to their electronic counterparts.
Locate: Low-Power Viterbi Decoder Exploration using Approximate Adders

Viterbi decoders are widely used in communication systems, natural language processing (NLP), and other domains. While Viterbi decoders are compute-intensive and power-hungry, we can exploit approximations for early design space exploration (DSE) of trade-offs between accuracy, power, and area. We present Locate, a DSE framework that uses approximate adders in the critically compute and power-intensive Add-Compare-Select Unit (ACSU) of the Viterbi decoder. We demonstrate the utility of Locate for early DSE of accuracy-power-area trade-offs for two applications: communication systems and NLP, showing a range of pareto-optimal design configurations. For instance, in the communication system, using an approximate adder, we observe savings of 21.5% area and 31.02% power with only 0.142% loss in accuracy averaged across three modulation schemes. Similarly, for a Parts-of-Speech Tagger in an NLP setting, out of 15 approximate adders, 7 report 100% accuracy while saving 22.75% area and 28.79% power on average when compared to using a Carry-Lookahead Adder in the ACSU. These results show that Locate can be used synergistically with other optimization techniques to improve the end-to-end efficiency of Viterbi decoders for various application domains.