Miracle-dz/UniFAR-FTUs huggingface dataset:
DOSAGE: Generating Domain-Specific Accelerators for Resource-Constrained Computing

Integrating low-overhead domain-specific accelerators with low-energy general-purpose processors can improve the processors’ performance efficiency in resource-constrained systems (e.g., embedded systems). However, current function-based approaches for designing domain-specific accelerators require substantial programmer efforts for hardware/software partitioning and program modifications to access the best available hardware accelerators. This paper presents DOSAGE, an LLVM compiler-based methodology to generate domain-specific accelerators for resource-constrained computing systems. Given a set of applications, DOSAGE automatically identifies and ranks the recurrent and similar code blocks that would benefit the most from hardware acceleration, based on the code blocks’ composition. We illustrate the benefits of the proposed approach using a case study that involves generating domain-specific accelerators for a diverse set of healthcare applications and evaluate the accelerators via FPGA-based prototyping. Compared to a base low-resource RISC-V processor, DOSAGE accelerators improved the system’s performance and energy by 24.85% and 8.54%, respectively. Furthermore, compared to a state-of-the-art function-based accelerator generation approach, DOSAGE eliminated the function-level granularity constraint of the generation process and reduced the number of required accelerators—and, in effect, the interfacing overhead—by 33.33%, while achieving equal or better program coverage and performance/energy results.
A Blood Glucose Control Framework Based on Reinforcement Learning With Safety and Interpretability: In Silico Validation

Controlling blood glucose levels in diabetic patients is important for managing their health and quality of life. Several algorithms based on model predictive control and reinforcement learning (RL) have been proposed so far, most of which use prior knowledge of physiological systems, the mathematical structure of blood glucose dynamics, and many episodes including failures for training the policy network in RL. To be smoothly adopted in clinical settings, we propose a fast online learning method underlining safety and interpretability. A random forest regressor and a dual attention network were exploited for glucose prediction and extension of state variables. The soft actor-critic network to determine insulin dosing was guided by proportional-integral-derivative (PID) control in the early phase, and an adaptive safe actor with suspension and additional insulin dosing was incorporated. The performance of the models was validated using an FDA-approved type 1 diabetes simulator. The results showed comparable outcomes with PID control. Using this system, glucose dynamics could be captured despite minimal prior knowledge. The extended state variables were correlated with basic states such as glucose, insulin, and meal intake, their derivatives, and their integrals, which can be fundamental elements of mathematical modeling of physiological responses. Attention scores and attribution scores in the prediction and control models represented the focused features and the internal operation of the models with interpretability. We expect this study to provide some insights on how RL can be practically adopted in clinical environments and how interpretability can provide hints of machines’ thoughts for clinical applications.
Student-Industry Matching for Internship Placement

In this project, a profile matching approach is proposed to assist internship placement tasks. Students often have difficulty finding and applying for internship placements, while companies spend much time sorting applications. Computer-assisted applications help job placement and onboarding tasks by mapping the potential job seekers to the corresponding companies. However, lack of focus put on internship placement, as the student profiles are limited to academic achievement without extensive work experience. A student-industry profile matching approach is proposed to evaluate students’ profiles and map the academic criteria to potential companies offering internship placement. The fuzzy matching technique is used to model the correlation ratio between the student profiles and the industry profiles.
Mathematical Model of Operation of a Cell of a Mobile Communication Network With Adaptive Modulation Schemes and Handover of Mobile Users

A model of a cell of a communication network, divided into zones, with the dependence of the users’ service time on the zone, in which they are located, is considered. The arrival flow of users is defined by a marked Markovian process. The number of users that can receive service in a cell simultaneously is finite. If the number of users reached the limit, then new users are lost, except the users that come to the cell already during the service (handover users). For the short-term storage of such users, there is a buffer of a finite capacity. Cell operation is modeled by a multi-server queueing system. The behavior of this system is described by a multi-dimensional Markov chain. The algorithms for calculating the stationary distribution of this chain and the main characteristics of the quality of service have been developed.
Automated Arabic Essay Evaluation

Although the manual evaluation of essays is a time-consuming process, writing essays has a significant role in assessing learning outcomes. Therefore, automated essay evaluation represents a solution, especially for schools, universities, and testing companies. Moreover, the existence of such systems overcomes some factors that influence manual evaluation such as the evaluator’s mental state, the disparity between evaluators, and others. In this paper, we propose an Arabic essay evaluation system based on a support vector regression (SVR) model along with a wide range of features including morphological, syntactic, semantic, and discourse features. The system evaluates essays according to five criteria: spelling, essay structure, coherence level, style, and punctuation marks, without the need for domain-representative essays (a model essay). A specific model is developed for each criterion; thus, the overall evaluation of the essay is a combination of the previous criteria results. We develop our dataset based on essays written by university students and journalists whose native language is Arabic. The dataset is then evaluated by experts. The experimental results show that 96% of our dataset is correctly evaluated in the overall score and the correlation between the system and the experts’ evaluation is 0.87. Additionally, the system shows variant results in evaluating criteria separately.
Prediction of Inhomogeneous Stress in Metal Structures: A Hybrid Approach Combining Eddy Current Technique and Finite Element Method

Concentrated stresses and residual ones are critical for the metal structures’ health, because they can cause microcracks that require emergency maintenance or can result in potential accidents. Therefore, an accurate approach to the measurement of stresses is key for ensuring the health of metal structures. The eddy current technique is an effective approach to detect the stress according to the piezoresistive effect. However, it is limited to detect the surface stress due to the skin effect. In engineering, the stress distribution is inhomogeneous; therefore, to predict the inhomogeneous stress distribution, this paper proposes a nondestructive approach which combines the eddy current technique and finite element (FE) method. The experimental data achieved through the eddy current technique determines the relationship between the applied force and the magnetic flux density, while numerical simulations through the FE method bridge the relationship between the magnetic flux density and the stress distribution in different directions. Therefore, we can predict the inhomogeneous stress nondestructively. As a case study, the applied stress in a three-point-bending simply supported beam was evaluated, and the relative error is less than 8% in the whole beam. This approach can be expected to predict the residual stress in metal structures, such as rail and vehicle structures, if the stress distribution pattern is known.
Multi-Grained Attention Representation With ALBERT for Aspect-Level Sentiment Classification

Aspect-level sentiment classification aims to solve the problem, which is to judge the sentiment tendency of each aspect in a sentence with multiple aspects. Previous works mainly employed Long Short-Term Memory (LSTM) and Attention mechanisms to fuse information between aspects and sentences, or to improve large language models such as BERT to adapt aspect-level sentiment classification tasks. The former methods either did not integrate the interactive information of related aspects and sentences, or ignored the feature extraction of sentences. This paper proposes a novel multi-grained attention representation with ALBERT (MGAR-ALBERT). It can learn the representation that contains the relevant information of the sentence and the aspect, while integrating it into the process of sentence modeling with multi granularity, and finally get a comprehensive sentence representation. In Masked LM (MLM) task, in order to avoid the influence of aspect words being masked in the initial stage of the pre-training, the noise linear cosine decay is introduced into $n-gram$ . We implemented a series of comparative experiments to verify the effectiveness of the method. The experimental results show that our model can achieve excellent results on Restaurant dataset with numerous number of parameters reduced, and it is not inferior to other models on Laptop dataset.
Rational Design of Carbon-Doped Carbon Nitride/Bi12O17Cl2 Composites: A Promising Candidate Photocatalyst for Boosting Visible-Light-Driven Photocatalytic Degradation of Tetracycline

Many recent advances based on 2D materials have opened new possibilities in photocatalysis. In this paper, a new 2D semiconductor composite consisting of carbon-doped carbon nitride (denoted as CCN) layers and Bi12O17Cl2 layers was designed via an in situ method. A supramolecular chemistry approach was employed to form CCN by using the hydrogen-bonded melamine–cyanuric acid and barbituric acid, and Bi12O17Cl2 layers were obtained by using a moderate solvothermal method. CCN/Bi12O17Cl2 composite had superior photocatalytic performance for degrading antibiotic tetracycline (TC) under visible-light irradiation. The degradation rate constant of 20%CCN/Bi12O17Cl2 is 0.0409 min–1, which is approximately 2.9-, 1.5-, and 32.1-fold those of pristine Bi12O17Cl2, CCN, and BiOCl, respectively. Electrochemical measurements showed that CCN/Bi12O17Cl2 composite has high photogenerated charge carrier separation efficiency. According to the trapping and electron spin resonance results, superoxide radicals and holes were t.
Improved Flux Tracing Method Based on Parametric Curve for Calculating Ion Flow Field of HVDC Transmission Lines

An improved method for tracing electric flux lines in the vicinity of the overhead high voltage direct current (HVDC) transmission lines is developed by using parametric curves. Based on the geometric relation between the electric field vector and the derivative of the parametric curve function, the electric flux lines can be successfully traced without accumulation of the numerical error caused by the differentiation for calculating the electric potential. Considering the structural characteristics of the transmission line, the control points at both ends of the parametric curve can be easily located, and the remaining control points are modified to minimize an error function defined by the electric field vectors at nodes along the curve. Once the electric flux lines are traced, a series of flux tubes is created by bundling adjacent flux lines, and the nodal electric field vectors and space charge densities are iteratively updated. In order to consider the ionized space charge effect, new flux lines of modified electric field are traced, which in turn affects the existing space charge distribution. The process is repeated until the effect of the space charge is fully considered. The performance of the proposed algorithm is verified by comparing the resulting ground profiles of the ion flow field distribution with the result of the analytical model and the long-term measurement of the full-scale transmission line.
Fingerprint Identification by Training a LSTM Network with Fingerprint Segments as Sequence Inputs

With the help of biometric traits, an individual can be recognized exclusively. Biometric is a human characteristic which is in-built with every individual. There are some biometric modalities which are in use like fingerprint, face, iris etc., in that, most used biometric trait is fingerprint. The biometric identification systems have challenges like intra and inter class variation, accuracy rate, false negative, false acceptance, etc. To tackle these types of challenges, instead of getting help from traditional methods, the fingerprint identification uses Neural Network concepts. This learns the internal structures of fingerprint by getting trained the fingerprint images. The main goal of this research work is to provide highest accuracy rate and to reduce false acceptance. So, the proposed model uses sequence input technique of Recurrent Neural Network (RNN) to train the images. The motivation of the work is to train the raw images and to get the highest accuracy without performing any alignment or pre-processing techniques. This work has taken the finger images from public databases of FVC2000, 2002 and 2004 to compare the proposed technique with pre-trained CNN (Convolutional Neural Network) methods. Finally, this work gives good accuracy rate than other image classification models.
Toward automatic feedback of coding style for programming courses

In this research, we developed a methodology for automatic evaluation of coding style and estimate its effectiveness compared to human evaluation. To achieve this, we developed 179 features spanning 8 categories to capture code structure and other stylistic properties. Results based on a set of student assignments and code from a Python textbook validate the features as effective in classification tasks. The features were further used to optimise classifiers to predict the teacher’s evaluation of the student code and obtain good classification accuracy. The proposed feature set and experimental results provide a first step towards providing students with automatic coding style feedback.
Applied Spiking Neural Networks for Radar-based Gesture Recognition

Spiking neural networks offer a promising approach for low power edge applications, especially when run on neuromorphic hardware. However, there are no well established approaches to setup such networks for real world applications. We demonstrate the use of spiking neural networks on the basis of radar data-based gesture recognition, while taking three different angle-encoding schemes into account, considering a two antenna based angle estimation. The surrogate gradient approach is used for direct training, while achieving a reasonable accuracy on all proposed encoding schemes. This work proposes a baseline approach for spiking networks and the corresponding encoding for the use in radar-based gesture classification.
Partially Encrypted Multi-Party Computation for Federated Learning

Multi-party computation (MPC) allows distributed machine learning to be performed in a privacy-preserving manner so that end-hosts are unaware of the true models on the clients. However, the standard MPC algorithm also triggers additional communication and computation costs, due to those expensive cryptography operations and protocols. In this paper, instead of applying heavy MPC over the entire local models for secure model aggregation, we propose to encrypt critical part of model (gradients) parameters to reduce communication cost, while maintaining MPC’s advantages on privacy-preserving without sacrificing accuracy of the learnt joint model. Theoretical analysis and experimental results are provided to verify that our proposed method could prevent deep leakage from gradients attacks from reconstructing original data of individual participants. Experiments using deep learning models over the MNIST and CIFAR-10 datasets empirically demonstrate that our proposed partially encrypted MPC method can reduce the communication and computation cost significantly when compared with conventional MPC, and it achieves as high accuracy as traditional distributed learning which aggregates local models using plain text.
Statistical Assessment of Business Intelligence System Adoption Model for Sustainable Textile and Apparel Industry

The textile and apparel industry is one of the biggest competitive industries in the world. Nowadays, industry 4.0 concepts put pressures on textile and apparel companies to integrate advanced technologies. Consequently, Business Intelligence (BI) systems are diffusing rapidly to process large data sets to harness the true value of smart technologies. Regardless of its potentials, most textile and apparel companies are lagging and hesitating to adopt this credible innovation in the presence of a high failure rate (70%-80%) especially in developing countries. To achieve the successful adoption of BI systems, statistical assessment is required to better understand this complex phenomenon. Therefore, a BI system model based on Technology-Organization-Environment (TOE) is developed to evaluate the role of potential determinants pertaining to the users, technology, organization, and environment. Data were collected using a survey with self-administered questionnaires from decision-makers with authoritative designations in the textile and apparel industry, academia, and software companies. Influential relationships among critical determinants were assessed and validated by using Decision-Making Trial and Evaluation Laboratory (DEMATEL) approach. The results of this study would contribute to the success of costly BI system projects and will motivate the industry experts to potentially assign investments for the BI projects in the developing countries to sustain in the competitive markets.
Learning Early Exit for Deep Neural Network Inference on Mobile Devices through Multi-Armed Bandits

We present a novel learning framework that utilizes the early exit of Deep Neural Network (DNN), a device-only solution that reduces the latency of inference by sacrificing a reasonable degree of accuracy. Choosing the optimal exit point is challenging as the delay and the accuracy of each exit point are random and cannot be known in advance. The problem is further complicated as the overall duration of the processing is also unknown. To this end, we propose Learning Early Exit (LEE), an online learning scheme based on multi-armed bandits analysis. LEE efficiently learns the optimal exit point for mobile-based DNN inference while simultaneously balancing the exploration-exploitation trade-off. LEE differs from the standard bandit analyses in two ways: the reward of choosing each exit point addresses the confidence-latency trade-off, and the time duration between each action is random (i.e., the latency of each action is random). LEE addresses the aforementioned challenges and it achieves asymptotically optimal performance. We implement a real-world system with a real-time testbed that can be deployed in a driving system. DNN models with multiple exit points are trained and deployed in the testbed so that the performance of LEE and benchmark schemes can be tested and compared. The result denotes that LEE substantially outperforms the benchmark schemes.
The Impact of Gender on Online Learning Behavioral Patterns: A Comparative Study Based on Lag Sequential Analysis

Despite several studies highlighted the importance of considering gender in online learning, the current literature about how male and female students would behave is still fragmented. Additionally, little attention has been paid to investigating the impact of gender on online learning behavioral patterns. This study applies lag sequential analysis (LSA) to investigate gender-related difference in the behavioral patterns of 116 students in an online course for six weeks. The obtained results indicated that overall there is no significant difference in the frequency of online learning behaviors between female and male students. However, the LSA showed that males and females demonstrated different transitional patterns in their online learning behaviors. Female behaviors were more coherently linked to each other. In contrast, some of the male behaviors were relatively isolated without significant antecedents and consequences, calling for learning supports. Also, females tended to view their achievement reports before starting the main course activities, showing that female students were more achievement-oriented. The findings provide explanations about how gender affects online learning and implications on how to design personalized online learning interventions based on considering gender-related differences.
DivNet: Efficient Convolutional Neural Network via Multilevel Hierarchical Architecture Design

Designing small and efficient mobile neural networks is difficult because the challenge is to determine the architecture that achieves the best performance under a given limited computational scenario. Previous lightweight neural networks rely on a cell module that is repeated in all stacked layers across the network. These approaches do not permit layer diversity, which is critical for achieving strong performance. This paper presents an experimental study to develop an efficient mobile network using a hierarchical architecture. Our proposed mobile network, called Diversity Network (DivNet), has been shown to perform better than the basic architecture generally employed by the best high-efficiency models–with simply stacked layers–, regarding complexity cost and performance. A set of architectural design decisions are described that reduce the proposed model size while yielding a significant performance improvement. Our experiments on image classification show that compared to, respectively, MobileNetV2, SqueezeNet, and ShuffleNetV2, our proposal DivNet can improve accuracy by 2.09%, 0.76%, and 0.66% on the CIFAR100 dataset, and by 0.05%, 4.96%, and 1.13% on the CIFAR10 dataset. On more complex datasets, e.g., ImageNet, our proposal DivNet achieves 70.65% Top-1 accuracy and 90.23% Top-5 accuracy, still better than other small models like MobilNet, SqueezeNet, ShuffleNet.
Deep Learning Based Brain Tumor Detection and Classification

One of the most crucial tasks of neurologists and radiologists is early brain tumor detection. However, manually detecting and segmenting brain tumors from Magnetic Resonance Imaging (MRI) scans is challenging, and prone to errors. That is why an automated brain tumor detection system is required for early diagnosis of the disease. This paper proposes two deep learning based approaches for brain tumor detection and classification using the cutting-edge object detection framework YOLO (You Only Look Once) and the deep learning library FastAi, respectively. This study was done on a subset of the BRATS 2018 dataset that contained 1,992 Brain MRI scans. The YOLOv5 model achieved an accuracy of 85.95% and the FastAi classification model achieved an accuracy of 95.78%. These two models can be applied in real-time brain tumor detection for early diagnosis of brain cancer.