Reviewerly/exHarmony huggingface dataset:
Information System Development by Using Agile Teamwork and Service-Learning

Contribution: This is one of the first studies that evaluated the combination of an Agile framework (i.e., Scrum) along with the Service-Learning methodologies applied to education settlement. Another tool that would help improve educational delivery quality is the Service-Learning methodology that enables students to experience hands-on learning by working with real-life cases and firms. Background: The COVID-19 pandemic has become a significant challenge in many areas, and education has not been an exception. Most educational institutions have moved from traditional brick-and-mortar to online or hybrid modern settings. However, teachers and students were not prepared for that significant movement, uncertain about how to deliver and receive it, respectively. Educational institutions faced issues developing competencies with teachers into students effectively. Research Questions: The research question is whether the application of the Agile and Service-Learning methodologies in a software engineering-related course of the “Ingeniería en Información y Control de Gestión” major at the Universidad Catolica del Norte in Chile would make a positive impact in both students and partners. Methodology: A mixed-methods approach with quantitative and qualitative analysis was used to understand and evaluate the underlying situation and outcomes. Findings: Our findings show that this setup of Service-Learning and agile framework increased teamwork, time management, and overall work satisfaction through a case study conducted in the course. Students positively evaluated the setting of goal expectations with community partners (i.e., an SME) and other critical learning aspects. Also, the community partners were more satisfied with the product delivery.
European Processor Initiative Demonstration of Integrated Semi-Autonomous Driving System

The European Processor Initiative (EPI) is developing a processor for various sectors, including the automotive industry. To benchmark the new processor, EPI uses a test vehicle to demonstrate different use cases, like semi-autonomous driving. In this paper, we focus on object detection and describe the use cases in the perception stage of autonomous driving. Therefore, we introduce four applications that include face recognition, blind spot detection, near-range object detection and far-range spatial perception to cover a wide range of different domains. Each use case runs on a different architecture representing future domains of the EPI processor, allowing for optimal utilization and performance. Our evaluation shows that all use cases can be mapped on the future EPI platform. The performance of the image processing tasks such as blind spot detection meets real-time requirements and runs at over 30 FPS. The face recognition task can process an image with low-power consumption, allowing for battery-powered inference. Finally, we validate our results and findings using a BMW X5 car as a real-world demonstrator.
An Enhanced 1440 Coupled CMOS Oscillator Network to Solve Combinatorial Optimization Problems

Today’s world demands more and more computation power combined with increased energy efficiency. Specialized hardware architectures are developed to enable high-performance computing. NP-hard combinatorial optimization problems such as the maximum cut or traveling salesman are especially costly to solve. The usage of analog coupled oscillators to solve such combinatorial optimization problems is a recently emerging approach. Those so-called Oscillator-based Ising Machines (OIMs) exploit the coupling between oscillators for computation. In this work, we present experimental results of an integrated OIM to demonstrate the capabilities. Together with an analysis of the scalability, the strengths, and weaknesses are discussed. We propose a new improved topology, which is optimized towards a versatile OIM solver. Our 4.6mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> prototype implemented in a 28nm node offers 1440 oscillators with 11724 configurable couplers.
Attacking Identity Semantics in DeepFakes via Deep Feature Fusion

DeepFake is now widely known for forging and fabricating falsified faces. In recent years, forensic researchers have built many algorithms as countermeasures against DeepFake. Albeit the great progress made so far in discerning falsified faces from pristine ones, there are now potential new threats to manipulating the semantics information of facial images. To combat the new type of disinformation, it is necessary to investigate them by developing anti-forensics tools for facial semantics. To this end, we propose the identity-feature-extracted adversarial network (IFE-GAN) to launch attacks on identity semantics. Also, we design the ConvolutionFormer block to refine the visual quality of synthesized anti-forensics images. Through experimental evaluations, we justify the effectiveness of our proposed method in disrupting face identity detectors while preserving satisfying visual quality.
Toward Transparent Sequence Models with Model-Based Tree Markov Model

In this study, we address the interpretability issue in complex, black-box Machine Learning models applied to sequence data. We introduce the Model-Based tree Hidden Semi-Markov Model (MOB-HSMM), an inherently interpretable model aimed at detecting high mortality risk events and discovering hidden patterns associated with the mortality risk in Intensive Care Units (ICU). This model leverages knowledge distilled from Deep Neural Networks (DNN) to enhance predictive performance while offering clear explanations. Our experimental results indicate the improved performance of Model-Based trees (MOB trees) via employing LSTM for learning sequential patterns, which are then transferred to MOB trees. Integrating MOB trees with the Hidden Semi-Markov Model (HSMM) in the MOB-HSMM enables uncovering potential and explainable sequences using available information.
Dual-stream Multi-Modal Graph Neural Network for Few-Shot Learning

Few-shot learning aims to rapidly recognize unseen targets using only a limited number of labeled samples, which is one of the core capabilities for humans. However, existing research primarily transforms the samples into the feature space of a single modality, neglecting the hidden features within other modalities. To address this problem, we develop a Dual-stream Multi-Modal Graph Neural Network (DMMG) that leverages additional information from multi-modality for few-shot learning. We convert the representation of text and images into each other's feature space in parallel streams. Then we compare instances of text and images in different vector spaces at the same time, exploiting the potentials of each modality for few-shot classification through skipping the bottleneck of information. This approach can be extended to a wide range of metric-based few-shot learning methods. The experiments on the miniImageNet dataset demonstrate that DMMG outperforms state-of-the-art few-shot learning methods, highlighting the effectiveness of our proposed approach.
A Runtime Security Monitoring Architecture for Embedded Hypervisors

Technical advances in automation, embedded hardware and virtualization nurtured the rapid development of safety-critical embedded systems.Avionic and Automotive are two of the domains that have seen significant development in recent years.However, the consideration of security of these embedded controllers often lags behind other functional improvements.This paper presents a brief threat landscape of embedded controllers and proposes a runtime security monitoring architecture aiming to protect these systems against threats that emerge during runtime.To facilitate rapid prototyping and encourage correct adaptation of security, similar to software development toolchains that guide development, the proposed architecture aims to provide a means for security monitoring that can be applicable to generic platforms.
RECS: A Scalable Platform for Heterogeneous Computing

RECS is a modular, scalable microserver platform designed for heterogeneous computing. Heterogeneous computing leverages combines different computing architectures to optimize performance and energy efficiency. RECS offers a modular hardware framework that integrates various microserver architectures, enabling users to configure optimized processing platforms for specific application requirements. The paper discusses the architecture, key hardware platforms, management subsystems, and communication infrastructure of RECS. Furthermore, its integration into the EU-founded projects eProcessor and VEDLIoT is discussed. The monitoring, management capabilities and robust communication infrastructure further enhance the system’s efficiency and flexibility. RECS demonstrates its potential for resource-efficient computing solutions, providing flexibility and fostering innovation in heterogeneous computing.
Enhanced Yoga Posture Detection using Deep Learning and Ensemble Modeling

Yoga is one of the best at-home exercises for maintaining our physical health. However, yoga is all about successfully performing the 82 Yoga Asanas throughout the course of six classes. Lamentably, not everyone has the knowledge or can perform yoga accurately. So to do yoga poses correctly we will have to find a yoga instructor, but it can be very hard and expensive to find yoga instructors considering all possible general situations and status. Using Deep Learning(DL) and modifying some pre-trained models to some extent can be a possible solution to detect yoga pose and class separately, which can eventually help general people. This work proposes a detailed experiment using two pre-trained CNN models along with an ensemble model to detect yoga poses accurately. The work was done on a total of 18488 images divided into 6 major yoga classes and 82 different poses. The aftermath of using ensemble modeling was instrumental as it was able to detect yoga poses with a 95% chance of assurance.
A Non-deterministic Training Approach for Memory-Efficient Stochastic Neural Networks

We proposed a non-deterministic training approach for memory-efficient stochastic computing neural networks (SCNN). Conventional SCNN simply convert the trained network parameters into stochastic bit-streams at the inference phase. Although stochastic bit-streams can simplify binary multiplication and addition using the principle of probability, memory cost and computation delay are consumed due to extremely long bit-streams. Different from methods that rely on long bit-streams to convert full-precision NN to SCNN during inference, the proposed approach also introduces the concept of non-deterministic computation during training to alleviate the memory requirement growth caused by long bit-streams. To this end, we probabilize NN parameters in the feed-forward process of the training phase, and convert them into 1/4/8-bit stochastic number representations according to the probability, which greatly reduces the memory requirements in SC. In order to alleviate the training instability problem caused by low-bit encoding, we propose a multiple parallel training strategy (MPTS) during the training process to improve the stability of the results. The proposed MPTS achieves a stable training process through a voting mechanism. We evaluate the performance of the proposed training approach on fully connected NN and the MNIST dataset. Compared with the baseline training method with 97.77% accuracy using 32-bit floating point values, the proposed non-deterministic training approach achieves a reasonable accuracy of 90.34% while using 4-bit stochastic number representations to represent layer weights and biases.
Prime Factorization Based on Multiple Quantum Annealings on Partial Constraints with Analytical Variable Reduction

Factorization of a large semiprime is a hard problem on classical computers, and quantum annealing is one way to solve the prime factorization problem by converting to a combinatorial optimization problem. A conversion method using the block-wise sum of partial products has been proposed but that works well up to numbers with 21-bit. To enhance the input bit width of the prime factorization, the paper introduces three proposals for prime factorization based on quantum annealing. First one is to divide the original problem into several sub-problems and solve them using multiple annealings. Second and third ones are analytical variable reduction of LSB and MSB parts. The proposals have been implemented on Python for Fixstars Amplify Annealing Engine (AE) and can factorize numbers with 46-bit or less. The proposals can also factorize numbers up to 57-bit but with a lot of trials for each number.
EvoSh: Evolutionary Search with Shaving to Enable Power-Latency Tradeoff in Deep Learning Computing on Embedded Systems

Deploying deep-learning applications on resource-constrained embedded systems requires exploration of large design spaces for mapping (to heterogeneous processing units) and hardware configuration selection (voltage/frequency levels) to balance power consumption and latency. Herein, we propose a search algorithm called Evolution with shaving to find optimized configurations in these search spaces. We evaluate our approach using 3 state-of-the-art image classification DNNs on Nvidia Jetson-TX2 platform, demonstrating the benefit of exploring the proposed search spaces and the ability of the proposed algorithm to successfully perform the search. We show that our approach achieves optimized mapping and hardware configuration in <0.1% of search-space exploration.
CNNET: A Configurable Hardware Accelerator for Efficient Inference of 8-bit Fixed-Point CNNs

Fast, reliable, efficient, and low-cost hardware is essential for the development of autonomous vehicles. To enable safe decision making, artificial intelligence tasks are run to decode a car’s environment, including visual and auditive obstacles. Convolutional Neural Networks (CNNs) are the preferred choice due to their scalability and performance. This study focuses on designing a configurable CNN hardware accelerator in Verilog for 8-bit fixed-point networks. The accelerator, CNNET, is intended to improve an existing accelerator, IANET, by increasing the throughput while reducing power consumption and area utilization. IANET is a combination of two accelerators for a fixed data path, while the enhanced accelerator can be scaled to multiple CNN architectures. CNNET accelerator has been verified with a SystemVerilog testbench and Python model, and tested with audio and image CNNs specific to autonomous driving. Compared to IANET, CNNET demonstrates significant enhancements in computational speed, power consumption, area utilization, and memory footprint. It can process 30 images and one audio frame per second at 243 MHz.
Abnormal Scene Image Recognition Method for Intelligent Operation and Maintenance of Electrical Equipment in Substations

The utilization of robot inspections and accurate, rapid identification of fault targets in images through artificial intelligence technology represents an important research area in the field of intelligent operation and maintenance for substations. To address the issue of massive image data collected by robot inspections, an abnormal scene image recognition method is proposed for intelligent operation and maintenance of electrical equipment in substations, with consideration for both the accuracy and real-time performance of object detection. Firstly, the research status and development trend of intelligent operation and maintenance technology of substation electrical equipment is elaborated. Subsequently, a fault detection model for intelligent operation and maintenance of electrical equipment in substations is proposed based on YOLO v5 deep neural network. To improve the detection speed of the model, Cross Stage Partial (CSP) and Spatial Pyramid Pooling Fast (SPPF) structures are utilised to reduce model parameters. Finally, the effectiveness of the proposed model is validated through two application scenarios: substation foreign object suspension image recognition and thermal defect detection based on infrared images. The experimental results demonstrate that the model achieves a detection accuracy of 99.8% in foreign object suspension detection for electrical equipment, with an average frame rate of 149 frames/s. Moreover, the model has an accuracy of 98.9% for the detection of thermal defects in infrared images of electrical equipment, with an average frame rate of 123 frames/s. These results satisfy the application requirements for real-time monitoring.
Association based Feedback Aided Underwater Passive Target Tracking

Passive sonar target tracking is a challenging problem, especially due to the high ambient noise as well as multiple tracks originating from the same target. One of the features which is always available in a passive sonar system is the bearing of the target. So, passive bearing line tracking is a widely used tracking method in this field. Passive bearing line tracking is really challenged, in situations like low Signal to Noise Ratio (SNR) scenarios and when targets are spaced close to each other. This paper presents a track association system which associates sensor level tracks like passive bearing line tracks and multiple frequency line tracks, and provides feedback to sensor level tracking. This results in the performance enhancement of sensor level tracks like passive bearing line tracks. An enhanced multiple frequency line tracking (MFLT) method is also proposed in this paper, which will make the passive bearing line tracking more robust and accurate. The effectiveness of the proposed method is illustrated by various multi-target tracking simulation scenarios.
Consensus-based Distributed Variational Multi-object Tracker in Multi-Sensor Network

The growing need for accurate and reliable tracking systems has driven significant progress in sensor fusion and object tracking techniques. In this paper, we design two variational Bayesian trackers that effectively track multiple targets in cluttered environments within a sensor network. We first present a centralised sensor fusion scheme, which involves transmitting sensor data to a fusion center. Then, we develop a distributed version leveraging the average consensus algorithm, which is theoretically equivalent to the centralised sensor fusion tracker and requires only local message passing with neighbouring sensors. In addition, we empirically verify that our proposed distributed variational tracker performs on par with the centralised version with equal tracking accuracy. Simulation results show that our distributed multi-target tracker outperforms the suboptimal distributed sensor fusion strategy that fuses each sensor’s posterior based on arithmetic sensor fusion and an average consensus strategy.
Path Planning Based On Rapidly-Exploration Random Tree (RRT)

Path planning from an initial position to the target point is one of the critical challenges in autonomous mobile robot applications over diverse environment scenarios. The selection of the best route for safe and robust navigation depends mainly on the environmental model, the type of mobile robot, and the considered application. In this paper, an artificial intelligent (AI) control technique is employed for efficient robot path planning with collision avoidance. In particular, the bidirectional rapidly-exploration random tree (BiRRT) algorithm is proposed to significantly reduce the travel path distance compared with the reference RRT benchmark. This has a direct impact on minimizing the processing time response and consumed power resources. Simulation results of different environment map scenarios demonstrated the effectiveness of the BiRRT approach for mobile robotic applications.
Fall Detection System for Elderly People using IoT and Machine Learning technology

For the senior population, falls are a severe issue since they can result in life-threatening injuries or even death. The probability of a successful recovery can be considerably increased by prompt intervention following a fall, yet it can be difficult for caregivers to be aware of a fall as soon as it occurs. We describe a wearable gadget that employs a few sensors to detect falls and alert designated caregivers via a mobile app in order to solve this problem. Our system’s IoT design is built on sensors that transmit data to the cloud, where machine learning algorithms analyze the information to determine whether a fall has taken place. The device measures body acceleration and angular velocity, and the machine learning algorithms use this data to recognize when the wearer has fallen. If a fall is detected, the system sends relevant information to caregivers, such as the time of the fall, enabling a prompt and informed response. Our solution has several benefits compared to other fall detection methods. First, it is wearable, so it does not require the elderly individuals to carry a separate device or manually trigger an alarm. Second, it is automatic, which reduces the risk of false alarms and increases the likelihood of timely response. Finally, it uses machine learning algorithms to analyze the Big Data gathered from the different sensors, which enables it to adapt to the wearer’s individual movements and behavior, making it more accurate and reliable. Furthermore, our wearable device has the potential to significantly improve the quality of life for elderly individuals and provide peace of mind for their loved ones. By reducing the time between falls and responses, our device can increase the likelihood of successful recovery and help prevent potential fatalities.
CellIdentifier: Classification of Peripheral Blood Cell Images using Deep Learning

Diagnosis and Identification of cells and disease infected cells are considered as very important in medical science all over the world. There are health implications which can often be identified by observing the morphological changes of cells. The traditional methods of identifying such issues can be expensive and time consuming to the extent that only certain medical centers can perform the task at hand, or take days to receive a report of. However, deep Learning with convolutional neural networks (CNNs) can take over most of this tedious process. This paper aims towards creating a custom CNN model that can quickly classify different kinds of peripheral blood cells such as different white blood cell times and platelets. Such a model can be used in blood cell counts which can be used to identify cases like leukemia. Moreover, such a method can be extended into other fields such as red blood cell detection or even infected cell detection, which includes identifying diseases from Sickle Cell Anemia to cells affected by COVID19. For this research, a total of 32768 images were used. Eventually, the custom CNN model has performed exceptionally well, achieving accuracies as high as 99.1% and 98.9% in training and validation respectively, which is significantly higher than using pretrained models such as DenseNet or NasNet. The result also proves to be higher compared to the previously completed similar tasks to the best of our knowledge. The performance of the model has also been evaluated based upon confusion matrix and classification report.