Miracle-dz/UniFAR-FTUs huggingface dataset:
DRE2: Achieving Data Resilience in Wireless Sensor Networks: A Quadratic Programming Approach

We focus on sensor networks that are deployed in challenging environments, wherein sensors do not always have connected paths to a base station, and propose a new data resilience problem. We refer to it as DRE2: data -resiliency in $\underline{e}$xtreme $\underline{e}$nvironments. As there are no connected paths between sensors and the base station, the goal of $\mathrm{D}\mathrm{R}\mathrm{E}^{2}$ is to maximize data resilience by preserving the overflow data inside the network for maximum amount of time, considering that sensor nodes have limited storage capacity and unreplenishable battery power. We propose a quadratic programming-based algorithm to solve $\mathrm{D}\mathrm{R}\mathrm{E}^{2}$ optimally. As quadratic programming is NP-hard thus not scalable, we design two time efficient heuristics based on different network metrics. We show via extensive experiments that all algorithms can achieve high data resiliences, while a minimum cost flow-based is most energy-efficient. Our algorithms tolerate node failures and network partitions caused by energy depletion of sensor nodes. Underlying our algorithms are flow networks that generalize the edge capacity constraint well-accepted in traditional network flow theory.
Modified Partial-Shading-Tolerant Multi-Input-Single-Output Photovoltaic String Converter

Photovoltaic (PV) energy systems rapidly penetrate global renewable energy market recently due to their direct energy conversion, environmental friendly and modularity features. Partial shading and irradiance mismatch create one of the major challenges that face commercial PV converters. Software based maximum power point tracking (MPPT) algorithms show limited performance as only the global peak power of the attached PV string can be captured. On the contrary, modified PV string converters can achieve true MPP acquisition, hence maximizing the system power yield and enhance the overall performance. In this article, a partial-shading-tolerant PV string system is proposed that feature (i) a multi-input multi-output (MISO) PV string converter and (ii) true MPP seeking event-driven based MPPT technique as well. The presented converter feature a single inductor buck configuration with multi-input ports attached to N number of PV panels offering reduced passive components and extended modularity feature in addition to single digital controller implementation. The presented MPPT technique exhibits decoupled tracking of the true power of each connected PV panel independently irrespective of the other attached panels even under extreme inhomogeneous irradiance distribution. Rigorous simulation results accompanying matched experimental validation assure the proposed system applicability. Dynamic modelling, in addition to various operating conditions assessments, verify the claimed effectiveness of the proposed system.
GSAN: Graph Self-Attention Network for Interaction Measurement in Autonomous Driving

Modeling the interactions among vehicles has been considered essential in improving efficiency and safety in autonomous driving, since the real traffic scenarios, such as merging lanes, intersection, and lane change, are full of complex interactions. In the literature, interaction is considered implicitly in individual tasks, which makes it hard to extract the interactions for other related downstream tasks. In this paper, we propose a novel Graph Self-Attention Network (GSAN) to quickly capture and quantify the influence of interactions among vehicles from historical trajectories, which can be used as a tool to introduce the impact of interactions into different downstream tasks and further analyze the dominating features affecting the interactions among vehicles. We conduct experiments on the trajectory prediction task as one example to illustrate how to use the spatial-temporal interaction vector to improve the performance of interaction related tasks. The experiment results demonstrate that the GSAN module outperforms the state-of-the-art solutions in terms of the trajectory prediction accuracy. Also, we visualize the effects from all surrounding vehicles on the ego vehicle by heat maps using the trained attention values from the GSAN module.
Multi-class segmentation of Diabetic Retinopathy lesions: effects of metrics, improvements and loss

Deep Learning (DL) is increasingly used in every medical imaging segmentation task with great results in terms of illness detection, but precise segmentation of lesions areas and contours from eye fundus images (EFI) is more challenging than the more common detection of whether there are lesions in images, for referral, or classification of lesion given a small window. We investigate the difficulties and how the choice of architecture, loss function and data augmentation can improve the quality segmenting precisely small and difficult to detect lesions, achieving results. Our best IoU scores were, for different classes, (BK:98%, MA: 16%, HA:28%, HE:61%, SE:51%, OD:91%). These results and discussion also show that research is still required to improve results further.
Real-Time Attack-Recovery for Cyber-Physical Systems Using Linear Approximations

Attack detection and recovery are fundamental elements for the operation of safe and resilient cyber-physical systems. Most of the literature focuses on attack-detection, while leaving attack-recovery as an open problem. In this paper, we propose novel attack-recovery control for securing cyber-physical systems. Our recovery control consists of new concepts required for a safe response to attacks, which includes the removal of poisoned data, the estimation of the current state, a prediction of the reachable states, and the online design of a new controller to recover the system. The synthesis of such recovery controllers for cyber-physical systems has barely investigated so far. To fill this void, we present a formal method-based approach to online compute a recovery control sequence that steers a system under an ongoing sensor attack from the current state to a target state such that no unsafe state is reachable on the way. The method solves a reach-avoid problem on a Linear Time-Invariant (LTI) model with the consideration of an error bound ε ≥ 0. The obtained recovery control is guaranteed to work on the original system if the behavioral difference between the LTI model and the system’s plant dynamics is not larger than ε. Since a recovery control should be obtained and applied at the runtime of the system, in order to keep its computational time cost as low as possible, our approach firstly builds a linear programming restriction with the accordingly constrained safety and target specifications for the given reach-avoid problem, and then uses a linear programming solver to find a solution. To demonstrate the effectiveness of our method, we provide (a) the comparison to the previous work over 5 system models under 3 sensor attack scenarios: modification, delay, and reply; (b) a scalability analysis based on a scalable model to evaluate the performance of our method on large-scale systems.
Affordable Robotics for Upper Limb Stroke Rehabilitation in Developing Countries: A Systematic Review

Stroke incidence is increasing more rapidly in Low- and Middle-Income Countries (LMICs) than in High-Income Countries (HICs), but LMICs are not well organized to provide adequate post-stroke rehabilitation because of physical, financial and personal barriers. The use of rehabilitation robotics can help bridge in healthcare disparities but they are available in LMICs in limited number due to their high cost and this limits the number of patients that can benefit from them. This article presents a literature review on the affordable robots for upper limb stroke rehabilitation that could be suitable in low-resources clinical settings with a focus on the different techniques implemented to reduce production costs. Different approaches can be carried out: i) developing end-effector and exoskeleton robots for the rehabilitation of proximal and distal joints respectively; ii) reducing the number of degrees of freedom of the robots at minimum (one or two) allowing the rehabilitation in a planar workspace; iii) avoiding the use of conventional materials that could make the robot bulky, expensive and difficult to transport. According to the mechanical properties required by the task, stell or metal alloys could be substituted for wood, aluminum, plastic, 3D-printed materials and by the adaptation of off-the-shelf elements.
Optimizing Coherence Traffic in Manycore Processors Using Closed-Form Caching/Home Agent Mappings

Manycore processors feature a high number of general-purpose cores designed to work in a multithreaded fashion. Recent manycore processors are kept coherent using scalable distributed directories. A paramount example is the Intel Mesh interconnect, which consists of a network-on-chip interconnecting “tiles”, each of which contains computation cores, local caches, and coherence masters. The distributed coherence subsystem must be queried for every out-of-tile access, imposing an overhead on memory latency. This paper studies the physical layout of an Intel Knights Landing processor, with a particular focus on the coherence subsystem, and uncovers the pseudo-random mapping function of physical memory blocks across the pieces of the distributed directory. Leveraging this knowledge, candidate optimizations to improve memory latency through the minimization of coherence traffic are studied. Although these optimizations do improve memory throughput, ultimately this does not translate into performance gains due to inherent overheads stemming from the computational complexity of the mapping functions.
A Design and Trial Production of EEGNet based EEG Pattern Recognition Processor using FPGA

In brain-machine interface (BMI) field, for further advancement of BMI technology, it is required to recognize patterns of multi-channel signals such as EEG in realtime. Aiming to apply to this field, we designed a dedicated processor based on EEGNet for pattern recognition of EEG. The designed EEGNet prediction processor corresponds to a maximum of 64ch and it can be implemented on one chip FPGA. In this paper, we report the architecture of the designed processor and the results of the three examinations (i.e. circuit scale, execution time, and data accuracy) using two datasets. As the result, it was confirmed that it can be implemented in one chip FPGA. From the results of logic circuit simulation, the execution times of the designed processor for one prediction were 58.84 μs and 115.85 μs for each dataset. These results were suggested that it can be expected to perform prediction while measuring in real-time. In addition, it was confirmed that the same accuracy as that of a PC was obtained. Moreover, from the results of an FPGA implementation, it was confirmed that the designed processor works correctly.
Deep Clustering Bearing Fault Diagnosis Method Based on Local Manifold Learning of an Autoencoded Embedding

In many practical fault diagnosis applications, the acquisition of fault data labels requires substantial manpower and resources, which are sometimes impossible to achieve. To address this, an unsupervised bearing fault diagnosis method based on deep clustering is proposed. In this method, an autoencoder is initially applied to the signal spectrum to learn the initial representation. Then, its potential manifold is further searched, and a Gaussian mixture model is finally used for clustering. Experiments conducted on the Case Western Reserve University bearing datasets show that the proposed method can find the optimal clusterable manifold. Moreover, its clustering performance is better than those of the current advanced baseline methods, and it is only slightly complex. Thus, the effectiveness of the proposed method is verified.
Using Graph Embeddings and Machine Learning to Detect Cryptography Misuse in Source Code

Cryptography is an essential aspect of software development. Nevertheless, software developers have limited knowledge of cryptography primitives, and support tools are limited. In this work, we present a comparison between graph embedding techniques, node2vec and Bag of Graphs, as embedding generators of source code graph representations. We combined these techniques with machine learning models in order to detect cryptography misuses in source codes. We show that Bag of Graphs outperforms node2vec in this task; also, both techniques outperform previously evaluated tools.
Data Augmentation of 3D Brain Environment Using Deep Convolutional Refined Auto-Encoding Alpha GAN

Learning-based methods represent the state of the art in path planning problems. Their performance, however, depend on the number of medical images available for the training. Generative Adversarial Networks (GANs) are unsupervised neural networks that can be exploited to synthesize realistic images avoiding the dependency from the original data. In this article, we propose an innovative type of GAN, Deep Convolutional Refined Auto-Encoding Alpha GAN, able to successfully generate 3D brain Magnetic Resonance Imaging (MRI) data from random vectors by learning the data distribution. We combined a Variational Auto-Encoder GAN with a Code Discriminator to solve the common mode collapse problem and reduce the image blurriness. Finally, we inserted a Refiner in series with the Generator Network in order to smooth the shapes of the images and generate more realistic samples. A qualitative comparison between the generated images and the real ones has been used to test our model’s quality. With the use of three indexes, namely the Multi-Scale Structural Similarity Metric, the Maximum Mean Discrepancy and the Intersection over Union, we also performed a quantitative analysis. The final results suggest that our model can be a suitable solution to overcome the shortage of medical images needed for learning-based methods.
Noble Inheritance Mechanism of Digital Content for "Digital-Ji-in" toward Sustainable Society

Various research and development efforts have been made to implement cyber-physical systems, aiming to maintain the associations between digital and physical identities (such as sensors, events, or people). In this study, we consider a scenario with the inheritance of digital content owned by a deceased person. People increasingly store their information (such as digital photos and videos) in cloud services, and this content may become inaccessible to the relatives in case of an unexpected death. In this paper, we propose a method for realizing a noble inheritance mechanism for secret digital content in "Digital-Ji-in" which maintains digital identities toward sustainable society. Specifically, we use a secure secret scheme to realize the noble inheritance mechanism. The "shares" of the encryption key are distributed to future heirs and the trusted third party such as cloud provider. The cryptographic key cannot be recovered until all the heirs’ shares are united and the trusted third party’s shares are obtained. This paper describes and analyzes the proposed method. Moreover, it outlines a preliminary scenario with "the right to be forgotten. " The proposed method can help maintain the connection between the real world and cyberspace in perpetuity.
Artificial Hydrocarbon Networks for Freezing of Gait Detection in Parkinso’s Disease

Freezing of gait (FoG) is one of the most impairing phenomenon experienced by Parkinson’s disease (PD) patients. This phenomenon is associated with falls and is an important factor that limits autonomy and impairs quality of life of PD patients. Pharmacological treatment is difficult and do not always help to deal with this problem. Robust FoG detection systems can help monitoring and identifying when a patient needs aid providing external cueing to deal with FoG episodes. In this paper, we describe a comparative analysis of traditional machine learning techniques against Artificial Hydrocarbon Networks (AHN) for FoG detection. We compared four supervised machine learning classifiers and AHN for FoG event detection using a publicly available dataset, obtaining 88% of F-score metric with AHN. We prove that AHN are suitable for FoG detection.
Mechanics, Dynamics, and Aesthetics Framework on Gamification at University

Gamification is a concept in which game elements are applied in a non-game environment. In this paper, we analyze the use of the concept of gamification in learning using the mechanics, dynamics, and aesthetic (MDA) framework. Using the literature between 2017 and 2020, the gamification model used at the university is detailed according to three categories of the MDA Framework. From this study, it was found that leaderboard and points are still the dominant game mechanics. The results of this study can be used as a basis for researchers to know the application model of gamification that has been implemented and the impact of the implementation of the model.
Readout and Control Circuit with non-contact temperature sensor for Multiple ISFETs with Microcontroller using I2C protocol

This paper presents readout and control circuit for plurality of ion sensitive field effect transistor (ISFET) sensors interfaced with ADG715 octal switch which is controlled by Inter-Integrated Circuit I2 C communication with ATMEGA328 microcontroller. This system offers advantage in ease of use in measuring pH or other ion concentration in a solution. The proposed system can read and control up to 32 ISFETs in a single bus line with two wire connection. In addition, non-contact temperature sensor and OLED display are connected by the same bus line. Temperature sensor allows temperature compensation capability of ISFET sensor. The system offers advantage in its simple connections and saving the usage of input and output pins of the microcontroller. The authors have tested the circuit in several pH ISFET sensors, though other ions of interest can be realized.
AdaTrust: Combinational Hardware Trojan Detection Through Adaptive Test Pattern Construction

As society becomes increasingly reliant on products and systems that make use of integrated circuits, the defense against potential hardware Trojan attacks by an untrusted foundry becomes an important part of any certification flow for critical components. The slew of recent proposals notwithstanding, a satisfactory solution is still wanting as the solutions offered heretofore either require impractical design/test pattern cost or deliver insufficient detection capabilities, primarily challenged by the noise induced by process variation. The methodology put forth by this proposal aims to remedy this, leveraging an adaptive approach that applies superposition to perform a fine-grained circuit analysis and expose any extant Trojan circuitry. Iterative test pattern modifications, circuit response analysis, and adaptive decision-making are deployed, all embedded within the design-for-test and test pattern cost paradigms of a common industrial circuit. We demonstrate the efficacy of this technique on standard Trust-Hub benchmark circuits with combinational Trojans inserted in sequential designs, showing significant improvement over prior techniques. We also explore the potential cost–benefit tradeoffs that exist within such a methodology, with the intent to provide an efficient solution for an array of potential product markets. This methodology provides a reliable and effective means for Trojan detection, addressing an important piece of the overall circuit certification puzzle.
Semantic Nonnegative Matrix Factorization with Automatic Model Determination for Topic Modeling

Non-negative Matrix Factorization (NMF) models the topics of a text corpus by decomposing the matrix of term frequency–inverse document frequency (TF-IDF) representation, X, into two low-rank non-negative matrices: W , representing the topics and H, mapping the documents onto space of topics. One challenge, common to all topic models, is the determination of the number of latent topics (aka model determination). Determining the correct number of topics is important: underestimating the number of topics results in a poor topic separation, under-fitting, while overestimating leads to noisy topics, over-fitting. Here, we introduce SeNMFk, a semantic-assisted NMF-based topic modeling method, which incorporates semantic correlations in NMF by using a word-context matrix, and employs a method for determination of the number of latent topics. SeNMFk first creates a random ensemble of matrices based on the initial TF-IDF matrix and a word-context matrix, and then applies a coupled factorization to acquire sets of stable coherent topics that are robust to noise. The latent dimension is determined based on the stability of these topics. We show that SeNMFk accurately determines the number of high-quality topics in benchmark text corpora, which leads to an accurate document clustering.
Toward URLLC: A Full Duplex Relay System with Self-Interference Utilization or Cancellation

Ultra-reliable low-latency communication (URLLC) is one of the key use cases of 5G wireless communications to facilitate specific application scenarios with stringent latency and reliability demands, such as industrial automation and Tactile Internet. A full duplex (FD) relay with simultaneous transmission and reception in the same frequency band is an effective approach to enhance the reliability of cell-edge user terminals by significantly suppressing self-interference (SI). However, the signal processing latency at FD relay due to SI cancellation, referred to as relaying latency, takes a significant part in the end-to-end latency, and therefore should be minimized, while guaranteeing high reliability. In this article, we first present an up-to-date overview of the end-to-end latency for an FD relay system, addressing physical layer challenges. We investigate the possible solutions in the literature to achieve the goal of URLLC. The efficient solution is to allow a simple amplify-and-forward FD relay mode with low-complexity SI radio frequency and analog cancellations, and process the residual SI alongside the desired signal at the base station in an adaptive manner, rather than being cancelled at relay in the digital domain. Also, the residual SI can be utilized at the base station to enhance the reliability and degree of freedom in signal processing, not necessarily being cancelled as much as possible. The FD relay assisted system with adaptive SI utilization or cancellation enables extended network coverage, enhanced reliability, and reduced latency compared to the existing overview work.