Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of a torque measurement unit for upper limbs industrial exoskeletons*

Exoskeletons have been introduced in occupational environments as devices to increase human performance and minimize loads on muscles and joints. The main goal of industrial exoskeletons is to prevent overload or repetitive stress injuries in workers. Most of these exoskeletons use adjustable passive mechanisms to provide support to joints and therefore, two important aspects in the design of industrial exoskeletons are robustness and safety. However, due to the lack of public detailed information about most of the commercial exoskeletons, it is necessary to further assess their load capacity and evolution over time, as their performance may change with use. In this paper, we present the design of a controlled device to measure the actuating torque of shoulder exoskeletons models and the construction of the proposed device considering mechanical actuation and sensor subsystems.
Effective Multi-Fault Localization Based on Fault-Relevant Statistics

Fault localization can facilitate software debugging and thus is a key technology in software maintenance. Spectrum-based fault localization (SBFL) has been known as an effective and lightweight approach. Nevertheless, the existence of multiple faults within the same system is still the bottleneck of SBFL. Inspired by feature selection in data mining, this paper improves the Relief algorithm and proposes fault-relevant statistics (FRS) to calculate the suspiciousness of program elements. Specifically, it takes test cases as samples, execution results as labels, and spectrum information as features, so that the problem of multi-fault localization can be viewed as a feature selection problem. Unlike the clustering method, this paper only takes into account the closest failing and successful case for each sampled test case when computing FRS. Experiments on open source software systems show that FRS improves the efficiency of fault localization with low computational cost.
Uma Discussão sobre a Análise de Descargas Parciais para o Prognóstico de Falhas de Cabos Isolados e Gerenciamento de Ativos

Resumo—Este trabalho apresenta uma criteriosa revisão bibliográfica e uma discussão sobre a avaliação do estado da isolação de cabos isolados de média e alta tensão baseada na análise de descargas parciais, além da formulação de planos de manutenção e de gerenciamento de ativos baseados neste mesmo fenômeno. São apresentados os possíveis métodos de detecção de descarga parciais e os sensores adequados para cada aplicação, além de métodos para a estimativa do tempo de vida restante de cabos isolados empregando-se o valor da descarga aparente medido. Por fim, são mostrados diferentes métodos e sistemas que aplicam as descargas parciais como um parâmetro de auxilio no planejamento da atividade de manutenção preditiva e gerenciamento de ativos.
ApproxiFuzzer: Fuzzing towards Deep Code Snippets in Java Programs

A real-world, complex software system can contain a number of code snippets. Many snippets are deep, surrounded by complicated triggering conditions and/or hidden in functions less frequently invoked. Fuzzing and symbolic execution are two mainstreams for exploring input spaces and increasing code coverage of complicated software systems. Meanwhile, it remains a challenge to determine whether a deep code snippet is reachable, and if it is reachable, which test(s) can reach it.This paper presents ApproxiFuzzer, an effective, demand-driven approach to fuzzing towards deep code snippets in Java programs. Given a program P, a target deep code snippet tcs, and a set of seeding test inputs, the key idea behind ApproxiFuzzer is to selectively mutate the test inputs and collect their execution traces such that the execution traces gradually approximate tcs; several measures are designed for measuring the distances between execution traces and the code snippet and directing the fuzzing process towards generating test inputs reaching tcs.We have implemented ApproxiFuzzer and evaluated it against Kelinci (an AFL-based fuzzer) and JDart (a concolic execution tool) on a set of real-world benchmarks. The evaluation clearly demonstrates the strengths of ApproxiFuzzer—ApproxiFuzzer outperforms Kelinci by 36× in efficiently generating test inputs, obtaining up to 18.2% higher code coverage; ApproxiFuzzer also outperforms JDart by 46.2∼96.2% in hitting deep code snippets.
Running Status Diagnosis of Onboard Traction Transformers Based on Kernel Principal Component Analysis and Fuzzy Clustering

The onboard traction transformer is a critical equipment of high-speed trains, its running state directly affects the safety and stability of a train’s operation. Given the complexity of the running condition of the onboard traction transformer, this paper proposes a running state diagnosis algorithm based on kernel principal component analysis (KPCA) and fuzzy clustering. To fully extract the status information of the onboard traction transformer, the aging characteristics of insulating oil and main insulation are analyzed under different running mileage as the first step. Thereby, to eliminate the signal redundancy, the status feature set of the onboard traction transformer is analyzed by KPCA combined with the characteristic quantities of the traditional dissolved gas analysis (DGA), and the eigenvalues with the contribution rate of over 95% are used as new eigenvectors. Finally, a status diagnosis model is established by using fuzzy clustering analysis, considering the limitations of fault data of onboard traction transformer. The results from field collected data show that the proposed method is effective in diagnosing the running status of the onboard traction transformer.
Bringing Opportunistic Networking to Smartphones: a Pragmatic Approach

Opportunistic networking allows mobile devices to communicate without any fixed infrastructure, using the store-carry-and-forward principle, based on D2D (device to device) transmissions. Although smartphones may appear as perfect candidates to implement opportunistic networking protocols and algorithms, it turns out that their ability to support D2D transmissions is quite constrained, which hinders the deployment of opportunistic applications at a large scale. Acknowledging this fact we present Ligo, a device that is meant to behave as a peripheral device of a smartphone, providing this smartphone with the opportunistic networking services it cannot implement natively. The hardware components of Ligo are detailed in this paper, as well as the different software elements that enable the communication in the opportunistic network and the interaction with the smartphone.
Investigation and Analysis of Novel Skewing in a 140 kW Traction Motor of Railway Cars That Accommodate Limited Inverter Switching Frequency and Totally Enclosed Cooling System

This study facilitated the improvement of no-load back electromotive force (back-EMF) wave form, total harmonic distortion (THD) of back-EMF, and torque ripple using a novel skew angle formula, considering the specific order of a no-load THD. In real usage environments, it is taken into consideration for the fully enclosed cooling system and limited inverter switching frequency of urban railway car traction motors. Since the most railway car traction motors use high-withstand voltage rectangular wires in slot-open structure, a no-load back EMF waveform includes large space slot harmonics, which should be smaller as possible. For 6-step control, the no-load back EMF waveform is important because switching for motor control is performed once after the rotor position is determined. To improve the no-load back EMF waveform and THD, two-dimensional and three-dimensional finite element analysis (FEA) were performed using a novel skew angle formula considering specific harmonic order reduction, while the fundamental amplitude was minimally reduced. A prototype with the novel skew was fabricated and verified. In addition, it was designed by calculating a low current density for a fully enclosed cooling system. A temperature saturation experiment was also performed, and successfully verified. Therefore, we suggest that the no-load back EMF characteristics and torque ripple are improved by applying the novel skew angle instead of a traditional skew angle.
Human Susceptibility to Phishing Attacks Based on Personality Traits: The Role of Neuroticism

The COVID19 pandemic situation has opened a wide range of opportunities for cyber-criminals, who take advantage of the anxiety generated and the time spent on the Internet, to undertake massive phishing campaigns. Although companies are adopting protective measures, the psychological traits of the victims are still considered from a very generic perspective. In particular, current literature determines that the model proposed in the Big-Five personality traits (i.e., Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism) might play an important role in human behaviour to counter cybercrime. However, results do not provide unanimity regarding the correlation between phishing susceptibility and neuroticism. With the aim to understand this lack of consensus, this article provides a comprehensive literature review of papers extracted from relevant databases (IEEE Xplore, Scopus, ACM Digital Library, and Web of Science). Our results show that there is not a well-established psychological theory explaining the role of neuroticism in the phishing context. We sustain that non-representative samples and the lack of homogeneity amongst the studies might be the culprits behind this lack of consensus on the role of neuroticism on phishing susceptibility.
FireBugs: Finding and Repairing Cryptography API Misuses in Mobile Applications

In this paper, we present FireBugs for Finding and Repairing Bugs based on security patterns. For the common misuse patterns of cryptography APIs (crypto APIs), we encode common cryptography rules into the pattern representations for bug detection and program repair regarding cryptography rule violations. In the evaluation, we conducted a case study to assess the bug detection capability by applying FireBugs to datasets mined from both open source and commercial projects. Also, we conducted a user study with professional software engineers at Mutual of Omaha Insurance Company to estimate the program repair capability. This evaluation showed that FireBugs can help professional engineers develop various cryptographic requirements in a resilient application.
A Fast Sub-Optimum Access Point Selection in Ultra-Dense Networks

Solving the main optimization problem, including a large number of access points and user equipment in ultra-dense networks (UDNs) using the Munkres algorithm, is a time-consuming solution. In this paper, we propose two new ideas for selecting the appropriate access points (APs) in UDNs that reduce the complexity order and find the suboptimum solution efficiently. Applying the first idea, all user equipment (UE) that are out of access points’ service area and all access points that cannot find any user equipment in their service area are removed. In this case, the solutions of the conventional and proposed version of the Munkres algorithm are the same, which offers a high total sum value of the signal to noise ratios (SNRs). Still, it does not guarantee minimum interference. Hence, as the second idea, we consider estimation of the signal to interference plus noise ratio (SINR), entitled by average-SINR and random-SINR, and solve the optimization problem, including a large number of access points and a variable number of user equipment densely distributed. The simulation results show the effectiveness of the proposed algorithms in the view of sum-rate, the number of successful user equipment, and computational complexity for an area, including 250 APs and a variable number of UEs.
Orthogonal Time-Frequency Space Modulation: A Promising Next-Generation Waveform

Sixth-generation (6G) wireless networks are envisioned to provide global coverage for the intelligent digital society of the near future, ranging from traditional terrestrial to non-terrestri-al networks, where reliable communications in high-mobility scenarios at high carrier frequencies would play a vital role. In such scenarios, the conventional orthogonal frequency division multiplexing (OFDM) modulation, that has been widely used in both the fourth-generation (4G) and the emerging fifth-generation (5G) cellular systems as well as in WiFi networks, is vulnerable to severe Doppler spread. In this context, this article aims to introduce a recently proposed two-dimension-al modulation scheme referred to as orthogonal time-frequency space (OTFS) modulation, which conveniently accommodates the channel dynamics via modulating information in the delay-Doppler domain. This article provides an easy-reading overview of OTFS, highlighting its underlying motivation and specific features. The critical challenges of OTFS and our preliminary results are presented. We also discuss a range of promising research opportunities and potential applications of OTFS in 6G wireless networks.
Optimum Pareto Method for Simultaneous Placement of Manual and Remote Controlled Switch Based on MILP Model

Manual and remote controlled switches are commonly used in distribution networks to improve the reliability of electricity supply. This article aims to determine the number and placement of network switches in order to optimize both the investment costs and the reliability of distribution systems. Based on a mixed integer linear programming model, this paper proposes a methodology to obtain the cost/reliability Pareto front relative the simultaneous placement of manual and remote controlled switches, with the reliability measured by the expected energy not served index. The methodology is applied on a standard reliability test system (RBTS4) and a Pareto front is obtained as a result, which defines a set of non-dominated solutions. Additionally, a sensitivity analysis is performed to evaluate the results obtained by changes in system parameters. The proposed method provides a tool for the planning of electrical distribution networks, that assists in the decision making between conflicting objectives such as cost and reliability.
Application of a Neural Network Classifier to Radiofrequency-Based Osteopenia/Osteoporosis Screening

This preliminary study reports application of a neural network classifier to the processing of previously collected data on low power radiofrequency propagation through the wrist with the goal to detect osteoporotic/osteopenic conditions. The data set used includes 67 subjects (23-94 years old, 50 females, 17 males, 27 osteoporotic/osteopenic, 40 healthy). We process the entire spectrum of the propagation coefficient through the wrist from 30 kHz to 2 GHz, with 201 sampling points in total. We found that the dichotomic diagnostic test of raw non-normalized radiofrequency data performed with the trained neural network approaches 90% specificity and ~70% sensitivity. These results are obtained without inclusion of any additional clinical risk factors. They justify that the radio transmission data are usable on their own as a predictor of bone density. With the inclusion of additional clinical risk factors, both specificity and sensitivity improve to 95% and 76% respectively. Our approach correlates well with the available DXA measurements and has the potential for screening patients at risk for fragility fractures, given the ease of implementation and low costs associated with both the technique and the equipment.Clinical Relevance— Dichotomic diagnostic test of raw non-normalized radiofrequency data performed with the trained neural network approaches 90% specificity and ~70% sensitivity. With the inclusion of other clinical risk factors, specificity and sensitivity increase to 95% and 76% respectively.
An Improved Pulse Voltage Injection Based Initial Rotor Position Estimation Method for PMSM

It’s essential to obtain the initial rotor position of PMSM to prevent the reversal of the rotor and to ensure the maximum electromagnetic torque output during the startup process. An improved pulse-voltage injection-based initial rotor position estimation method is proposed in this paper. Different from the conventional pulse-voltage-injection-based method that only utilizes the information about the virtual d-axis current for the voltage vector injected to PMSMs, the information about the virtual q-axis current is also utilized in the proposed method to reduce the estimation errors caused by the current sampling errors. The experimental results indicate that the estimation accuracy can be greatly improved.
Deep Learning Applied to Automatic Reclosers Detection in Power Grid

Brazilian energy distribution companies are investing in automatic circuit reclosers (ACRs) to optimize their energy grids. These devices often require specialists to maintain. Training electricians can be problematic, as there are very similar models and provide different commands for the same tasks. Based on this problem, an application was developed in this work that allows generalists to carry out maintenance on reclosers and make training less confusing. This paper describes the object detection module of this application, which employs Deep Learning to identify four different recloser models. Were tested some state of art neural networks implementations in object detection field in the recognition of the ACRs supported models and in the tests was reached the best neural network obtained approximately 89% in Mean Average Precision (mAP). This work aim apply the object detection in energy area, focused in maintenance and training scenarios, showing in effective to detect and differentiate ACRs similar models, thus helping general electricians to provide a more accurate service and reducing company costs.
Smart Home applied to historic buildings A real case study

Smart home is increasing in popularity within the IoT applications. This thanks to its ability to change house equipment into being more intelligent and interconnected and thus, delivering a connected life experience to the user. This paper investigates the literature and state-of-the-art on the smart home. Then, the paper presents a real case study that describes how smart home concepts have been applied to a heritage building, raised in 1922 and located in the City of Turin (Italy).
In Vivo Non-Thermal, Selective Cancer Treatment With High-Frequency Medium-Intensity Focused Ultrasound

Focused ultrasound (FUS) has proven its efficacy in non-invasive, radiation-free cancer treatment. However, the commonly used low-frequency high-intensity focused ultrasound (HIFU) destroys both cancerous and healthy tissues non-specifically through extreme heat and inertial cavitation with low spatial resolution. To address this issue, we evaluate the therapeutic effects of pulsed (60 Hz pulse repetition frequency, 1.45 ms pulse width) high-frequency (20.7 MHz) medium-intensity (spatial-peak pulse-average intensity ISPPA < 279.1 W/cm2, spatial-peak temporal-average intensity ISPTA < 24.3 W/cm2) focused ultrasound (pHFMIFU) for selective cancer treatment without thermal damage and with low risk of inertial cavitation (mechanical index < 0.66), in an in vivo subcutaneous B16F10 melanoma tumor growth model in mice. The pHFMIFU with $104~\mu \text{m}$ focal diameter is generated by a microfabricated self-focusing acoustic transducer (SFAT) with a Fresnel acoustic lens. A three-axis positioning system has been developed for automatic scanning of the transducer to cover a larger treatment volume, while a water-cooling system is custom-built for dissipating non-acoustic heat from the transducer surface. Initial testing revealed that pHFMIFU treatment can be applied to a living animal while maintaining skin temperature under 35.6 °C without damaging normal skin and tissue. After eleven days of treatment with pHFMIFU, the treated tumors were significantly smaller with large areas of necrosis and apoptosis in the treatment field compared to untreated controls. Potential mechanisms of this selective, non-thermal killing effect, as well as possible causes of and solutions to the variation in treatment results, have been analyzed and proposed. The pHFMIFU could potentially be used as a new therapeutic modality for safer cancer treatment especially in critical body regions, due to its cancer-specific effects and high spatial resolution.
A Constrained Multi/Many-Objective Particle Swarm Optimization Algorithm With a Two-Level Balance Scheme

Constrained multi-objective optimization problems are common in practical engineering and are more difficult to handle than unconstrained problems. In general, it is necessary to find a balance between the convergence and diversity of solutions, as well as its feasibility. For the constrained multi/many-objective optimization problem, a particle swarm optimization algorithm based on a two-level balance strategy is proposed. In contrast to existing views, the first level of the proposed algorithmic framework emphasizes convergence, while diversity and feasibility are considered together as the second-level scheme. An ensemble fitness ranking was used to improve the convergence of the proposed algorithm. To balance the diversity and solution feasibility, the solutions are selected by combining the angles between the solutions using the constraint dominance principle. A penalty-based boundary-crossing approach is used as a utility function to calculate the fitness of the populations, which is compared with six state-of-the-art constrained multi/many-objective evolutionary optimization algorithms on multiple constrained test suites, and the experimental results show that the proposed algorithm is highly competitive in most test problems. Furthermore, to illustrate the effect of different utility functions on the performance of the algorithm, the Chebyshev decomposition method is employed and compared with the former, and the results show that different utility functions need to be chosen to cope with problems of different characteristics.
The Role of Physiological Responses in a VR-Based Sound Localization Task

Virtual reality (VR) has recently emerged as a platform that can be employed in the context of e-health applications. Even though the majority of VR-based applications focus on visual stimuli as the main content, audio also plays a very important role. If someone has an issue with auditory processing, the comprehension of auditory information is compromised. This condition reflects negatively on one’s quality of life, given the impact of audio perception on one’s ability to communicate effectively or to differentiate and ignore “noise”. This work aims to design a VR application that can be used to: (a) optimize multimodal VR experiences based on user trials (b) extract user data continuously throughout the experiment (c) evaluate a user’s auditory processing ability. To accomplish this goal, participants are required to localise a sound source in space in the presence of multiple listening conditions with simple and complex sound stimuli configurations. Data collected from users consists of physiological and objective metrics. The results of this study highlight the relationship between user behaviour (head movement, fixation points) and performance in the sound localisation task. This information can be used to design future applications with the purpose of training one’s auditory localisation ability. In addition, the evaluation compared the impact of using two interaction methods to perform this task: using a pointer or eye gaze to indicate the location of the target source. The findings from this study show statistically significant differences in terms of physiological response when subjects are exposed to different interaction methods, with greater immersion and performance for the pointer group.