Miracle-dz/UniFAR-FTUs huggingface dataset:
Plagiarism Detection of Multi-Threaded Programs via Siamese Neural Networks

Widespread intentional or unintentional software plagiarisms have posed serious threats to the healthy development of software industry. In order to detect such evolving software plagiarism, software dynamic birthmark techniques of better anti-obfuscation ability serve as one of the most promising methods. However, due to the perturbation caused by non-deterministic thread scheduling in multi-threaded programs, existing dynamic approaches optimized for sequential programs may suffer from the randomness in multi-threaded program plagiarism detection. Some thread-aware birthmarking methods have been then proposed to address this issue, which nevertheless largely rely on manual feature engineering and empirical observations without any ground-truth training, and thus require domain knowledge, making them inflexible to be deployed in the wild. Inspired by the success of self-guided optimization using deep neural networks and their superior feature learning ability, in this article, we transform multiple execution traces for each multi-threaded program under a specified input to the plain feature matrix, and feed it to the deep learning framework to learn latent representation as thread-aware birthmark that enjoys better semantic richness and perturbation resistance; instead of empirically determining the plagiarism over direct birthmark similarity metric, we further build up sophisticated siamese neural networks to supervise birthmark construction, similarity measurement, and decision making. Integrating our proposed method, a system called NeurMPD is developed to perform Neural network-based Multi-threaded program Plagiarism Detection. The experimental results based on a public software plagiarism sample set demonstrate that NeurMPD copes better with multi-threaded plagiarism detection than alternative approaches.
Human Activity Recognition: From Sensors to Applications

Human activity recognition (HAR) being a dynamic research topic in recent decades due to its high demand in countless applications, for instance, in healthcare, gaming, security and surveillance, and sports. Despite the amount of work contributed by the researcher to this well-researched field, there are still many challenging aspects and open issues that should be addressed in future works. In this paper, the current state-of-the-art in HAR from three holistic aspects is surveyed: sensors, models, and open challenges. First, we summarize the existing sensory systems, including sensor-based, vision-based sensors, and multimodal solutions. Next, the recent advances in HAR algorithms – from hierarchical fusion methods to handcrafted features to deep features, traditional machine learning algorithms to deep learning techniques – are discussed. Finally, the principal issues and challenges that should be addressed in future research are discussed.
ND-SMPF: A Noisy Deep Neural Network Fusion Framework for Stock Price Movement Prediction

There has been a recent surge of interest on development of news-oriented Deep Neural Network (DNN) architectures to predict stock trend movements. Limited focus is, however, devoted to reliability fusing different available information resources. In this regard, this paper proposes a Noisy Deep Stock Movement Prediction Fusion framework (ND-SMPF) for stock price movement prediction. The proposed ND-SMPF predictive framework uses information fusion to combine twitter data with extended horizon market historical prices to boost the accuracy of the stock movement prediction task. More specifically, Noisy Bi-directional Gated Recurrent Unit (NBGRU) is utilized coupled with a Hybrid Attention Network (HAN) to extract news level temporal information. A two level attention layer is used to identify relevant words with highest correlation and effects on the stock trends, which are then fused with historical price data to perform the prediction task. A real dataset is incorporated to evaluate performance of the proposed ND-SMPF framework, which illustrates superior performance in comparison to its recently developed counterparts.
A Management Technique for Concurrent Access to a Reconfigurable Accelerator

Embedded devices with increasing requirements on performance and severe restrictions on area and power have challenged designers to improve their efficiency. While ASICs are one alternative because of their low power and area, they lack generality and programmability. Reconfigurable architectures, on the other hand, bridge the gap between the benefits brought by specialized hardware and the generality provided by traditional general-purpose processors. Nevertheless, reconfigurable architectures, notably Coarse-Grained Reconfigurable Arrays (CGRAs), many times suffer from under-utilization when the workload consists of wide and diverse applications, as in modern embedded devices. Therefore, this work proposes a time-sharing management technique that enables multicore access to a single CGRA, increasing its efficiency (i.e. the tradeoff between performance and area) when compared to traditional architectures, which have a private CGRA for each core. Our set of experiments shows that our mechanism can achieve performance improvements of up to 2.02x over a traditional multicore system without any accelerator. It also increases efficiency up to 89.73% when compared to traditional reconfigurable systems composed of privately coupled CGRAs, occupying up to 3.92x less area at performance costs of up to 62.61%.
Median Access Control Protocols for Sensor Data Collection: A Review

Median Access Control (MAC) protocols are designed to mitigate collisions and enhance the energy efficiency for sensor data collection. This paper reviews two basic categories of MAC protocols. The first class is the contention-based protocols, where nodes randomly compete for channel access. The second class is the schedule-based MAC protocols, in which nodes access the channel on the basis of the predetermined schedules. We focus on discussing the Time Division Multiple Access (TDMA) protocols and classify different TDMA schedulings into three categories according to the communication patterns in the network, i.e., the link scheduling, aggregate scheduling and non-aggregate scheduling. Link scheduling deals with the peer to peer communication pattern, where there is no central node in the network. In comparison, both the aggregate and non-aggregate schedulings handle the convergecast communication pattern, in which all traffic are destined to the sink. This survey provides a comprehensive overview of these three categories and provided a detailed briefing on how the TDMA schedules handle the network traffic dynamics in the network. Compared with other surveys in this domain, this review does not confine itself to deal with collecting a particular form of data, but provides a unified framework to integrate the data semantics in a broader sense into the design of TDMA scheduling algorithms.
Quantitative Study on Impact of Node Selfishness on Performance of MANETs

Mobile ad hoc networks (MANETs) are kinds of wireless networks that are various features like dynamic mobility, constraint resources, fully decentralized etc. The communication among the nodes highly depends on the cooperation between nodes. Data forwarding is the main task during the communication process. Because of constraint of limited resources, it is rational that some nodes behave selfishly and refuse to burden forwarding tasks for others. In this paper, we quantitatively investigate the impacts of node selfishness in MANETs from various aspects, including mobility, presence, and combination of selfish nodes. The network performance is assessed in terms of average packet delivery ratio and average end-to-end delay. We carried out extensive simulations on OMNeT++ platform and analyzed the results. Our results quantitatively showed that node selfishness has significant impacts on the communication performance in MANETs. As a result, our experimental results imply that the impact of node selfishness should be evaluated in an integrated way of mobility, presence and combination.
Traffic Signal Classification with Cost-Sensitive Deep Learning Models

Deep learning has many successful real-world applications including traffic signal recognition, which are used in driver assistance systems and autonomous vehicles. Accurate detection of traffic signal indications is critical to ensure safety under autonomous driving. Many past studies have been completed on traffic signal recognition including detection, classification and tracking with datasets which are typically highly imbalanced due to the nature of traffic signal displays. However, most studies simply ignored the minority classes and did not consider cost-sensitive information inherent to traffic signal indications. This paper evaluated several cost-sensitive techniques applicable to deep learning models in traffic signal classification. A convolutional neural network (CNN) was used in the evaluation as the baseline model. Cost-sensitive techniques including cost-proportionate rejection sampling and the use of cost-sensitive loss function was then applied to the baseline CNN model to evaluate and compare the effects of using cost information in traffic signal classification. Arbitrary cost information was assumed in this evaluation, but the resulting models did not improve accuracy in prediction. Future studies may consider more carefully crafted cost information and/or other cost-sensitive techniques.
Towards IoT-enabled Multimodal Mental Stress Monitoring

Stress is a body’s natural way of responding to any kind of demand or challenge that everyone experiences from time to time. Although short-term stress typically does not impose a health burden, exposure to prolonged stress can lead to significant adverse physiological and behavioral changes. Coping with the impact of stress is a challenging task and in this context, stress assessment is essential in preventing detrimental long-term effects. The public embracement of connected wearable Internet of Things (IoT) devices, as well as the proliferation of Artificial Intelligence (AI) and Machine Learning (ML) technologies, have generated new opportunities for personalized stress tracking and management. Despite the advantages of this paradigm shift – including availability and accessibility, cost-effective delivery, and proactive intervention – still, many challenges need to be addressed to be able to develop ubiquitous solutions. In this paper, we present a comprehensive and generalizable IoT-based stress-level detection method with the following key attributes: (i) Connected: deploying vigilant IoT-based wearables and sensing technologies for continuous stress-related data collection; (ii) Data-driven: combining multimodal and heterogeneous data sources from sensor readouts; (iii) Hierarchical: consisting of device/sensor, data, intelligence, and service layers. Experimental results based on real-life stress datasets highlight the accuracy of the proposed approach for assessing the stress-level compared to state-of-the-art solutions.
On-chip Josephson junction-based sensor for temperature control of a cryocooled quantum standard

In this paper we show how an overdamped Josephson junction can be exploited as temperature sensor in a closed-loop circuit to control the operating point of a cryocooled device, e.g. a quantum standard. The resistance of a Josephson junction is measured at the desired temperature with a commercial ac resistance meter, that then uses this value to regulate the chip temperature via its own PID control functionality. Experimental setup and measurement methods are described here. Moreover, the temperature sensitivity of an ideal junction has been determined for different bias-current values.
Artificial Intelligence in Digital Media: The Era of Deepfakes

The recent practical advances realized by artificial intelligence, have also given rise to the phenomenon of deepfakes, which can be considered as a form of fake news. Deepfakes is the phenomenon of creation of realistic digital products, and a plethora of videos has emerged over the last two years in social media. Especially, the low technical expertise and equipment required to create deepfakes, means that such content can be easily produced by anyone and distributed online. The societal implications are significant and far-reaching. This article investigates the deepfakes via multiangled perspectives that include media and society, media production, media representations, media audiences, gender, law, and regulation, as well as politics. Some key implications of these viewpoints are identified and critically discussed. The results indicate that as a society, we are not ready to deal with the emergence of deepfakes at any level. That we have not witnessed any severe impacts so far is due to their early stage of development, which shows imperfections to address the issue, a combination of technology, education, training, and governance is urgently needed.
Research on the UBI Car Insurance Rate Determination Model Based on the CNN-HVSVM Algorithm

With the support of Internet of Vehicles technology, UBI (Usage Based Insurance) car insurance rate determination has certain guiding significance for achieving accurate pricing of car insurance rates and satisfying the personalized needs of users. Based on the CNN (Convolutional Neural Networks) algorithm and SVM (Support Vector Machine) algorithm, this paper establishes a rating model for UBI car insurance rates. The model first performs a series of operations such as convolutions, pooling and nonlinear activation function mapping using the CNN algorithm so that it can extract the features from the driving behavior data of UBI customers. Then, it introduces the Hull Vector to optimize the operating efficiency of the SVM algorithm. The HVSVM (Hull Vector Support Vector Machine) algorithm classifies customers according to their driving behavior, and thus obtains UBI customer car insurance rate grades. Therefore, this paper proposes a UBI car insurance rate grade determination model based on the CNN-HVSVM algorithm. The empirical results of the model show that the CNN-HVSVM algorithm has higher discrimination accuracy in the risk rating process of UBI customer driving behavior than the CNN algorithm, BP neural network algorithm and SVM algorithm; and when dealing with large training sets, it has a speed advantage over the CNN-SVM algorithm. Furthermore, it is easy to realize in the process of establishing the UBI car insurance rate determination model and it has good robustness, which can adapt to diverse data sets, thus achieving better results in the car insurance rate determination process. Therefore, the CNN-HVSVM model can predict the grade of UBI car insurance users more accurately and efficiently, and the prediction results are more consistent with the actual situation, which has strong applicability and flexibility. The UBI car insurance premium rate determination model based on the CNN-HVSVM algorithm can determine driver behavior more fairly and reasonably, and has certain practical significance for promoting car insurance rate market reform, which can better promote future UBI research work.
Performance and Variability Trade-offs of CMOS PTAT Generator Topologies for Voltage Reference Applications

This work presents the analysis, design, and performance evaluation of three usual CMOS proportional to absolute temperature (PTAT) voltage generators, with emphasis on variability effects. Minimization or compensation of the main error sources, such as fabrication variability and intrinsic non-linearities, is an important design challenge required to increase the precision and robustness of a voltage reference. The CMOS PTAT topologies are analytically described and design methodologies for PTAT circuits in subthreshold are presented. The compromises between design conditions and resulting performance are evaluated through simulation for these three PTAT generators, including linearity with temperature, temperature coefficient (TC), and variability impact. Monte-Carlo simulations demonstrated the sensitivity of each topology to fabrication variability, showing that the self-cascode MOSFET structure presents the best accuracy of TC, nominal PTAT voltage, and linearity with temperature.
Dynamic iFogSim: A Framework for Full-Stack Simulation of Dynamic Resource Management in IoT Systems

Complex Internet of Things (IoT) applications such as Healthcare IoT include a variety of compute, data, and communication kernel intensities and have diverse sensitivities of QoS requirements including latency, throughput, availability, accuracy, etc. Ensuring QoS requirements for the applications requires a comprehensive tool to perform efficient full-stack analysis. Per our observation, the literature lacks a simulator capable of supporting a full-stack communication-computation co-simulation of an IoT system. Furthermore, IoT system behavior can dramatically change during run-time due to variation in status and context. Therefore, such a system must be dynamically controlled over time. In this paper, for the first time, we propose a full-stack framework to co-simulate communication and computation aspects of an IoT system in a dynamic scenario. We integrate a Transmission Control Protocol (TCP) latency model with the iFogSim simulator. We conduct a health-care IoT-based case study to evaluate the framework. The framework is open-sourced and available on GitHub in the following repository: https://github.com/HealthSciTech/Dynamic iFogSim.
Outcome of Postcardiac Surgery Acute Myocardial Infarction and Role of Emergency Percutaneous Coronary Interventions

Background. Cardiac surgery carries a well-known risk of perioperative myocardial infarction (MI), which is associated with high morbidity and both in-hospital and late mortality. )e rapid haemodynamics deterioration and presence of myocardial ischemia early after cardiac surgical operations is a complex life-threatening condition where rapid diagnosis and management is of fundamental importance. Objective. To analyse the factors associated with mortality of patients with postcardiotomy MI and to study the role of emergency coronary angiography in management and outcome. Methods. We retrospectively enrolled adult patients diagnosed to have postcardiotomy MI and underwent emergency coronary angiography at our tertiary care hospital between January 2016 and August 2019. Results. Sixty-one patients from consecutive 1869 adult patients who underwent cardiac surgeries were enrolled in our study. )e studied patients had a mean age of 49± 16.2 years with a mean BMI of 29.5± 6.6 and 65.6% of them were males. As compared to the survivors group, the nonsurvivors of perioperative MI had significant preoperative CKD, postoperative AKI, longer CPB time, frequent histories of previous PCI, previous cardiotomies, pre and postoperative ECMO use, higher median troponin I levels, higher peak and 24 hours median lactate levels. Regression analysis revealed that reoperation for revascularization (OR: 23; 95% CI: 8.27–217.06; P � 0.034) and hyperlactataemia (OR: 3.21; 95% CI: 1.14–9.04; P � 0.027) were independent factors associated with hospital mortality after perioperative MI. Hospital mortality occurred in 25.7% vs 86.7% (P< 0.001), AKI occurred in 37.1% vs 93.3% (P< 0.001), haemodialysis was used in 28.6% vs 80% (P � 0.002), and mediastinal exploration for bleeding was performed in 31.4% vs 80% (P � 0.006) in the PCI and reoperation groups, respectively, while there were no significant differences regarding gastrointestinal bleeding, cerebral strokes, or intracerebral bleeding. )e median peak troponin level was 795 (IQR 630–1200) vs 4190 (IQR 3700–6300) (P< 0.001) in the PCI and reoperation groups, respectively. Absence of significant angiographic findings occurred in 18% of patients. Conclusions. Perioperative MI is associated with significant morbidities and hospital mortality. Reoperation for revascularization and progressive hyperlactataemia are independent predictors of hospital mortality. Emergency coronary angiography is helpful in diagnosis and management of perioperative MI.
Independent fact-checking platforms in Spain and Italy : Features , organisation and method

Resumen Abstract La verificación de la información es una de las actividades periodísticas que más relevancia ha adquirido en los últimos años, como consecuencia del incremento de la circulación de las noticias falsas y la facilidad para difundirlas a través de plataformas como las redes sociales. Esta investigación analiza la estructura, composición y funcionamiento de las plataformas independientes de verificación de la información que hay en España e Italia. Para alcanzar este propósito se han utilizado diferentes técnicas cualitativas, como el estudio de caso aplicado a cada una de las iniciativas, las entrevistas semiestructuradas en profundidad a sus cofundadores o responsables y el análisis de contenido web. Se trata de una triangulación de técnicas que ha permitido extraer conclusiones y aportar ejemplos de interés a la investigación. Los resultados revelan que estos proyectos cuentan con una amplia comunidad de seguidores en las redes sociales y se definen por una plantilla multidisciplinar que verifica principalmente las declaraciones políticas y las informaciones que circulan por la Red y cuyo modelo de negocio se aleja del convencional del periodismo. In recent years, fact-checking has become an extremely important activity in journalism as a result of the increased circulation of fake news and the ease with which it can be disseminated on platforms like social networks. This study analyses the structure, composition and operation of independent fact-checking platforms in Spain and Italy. To this end, three different qualitative techniques have been used: the case study technique applied to each initiative, in-depth semi-structured interviews with its co-founders or managers, and website content analysis. This triangulation of techniques has allowed us to draw conclusions and provide examples of relevance to the research. The results reveal that the projects have a large community of followers on social networks, a multidisciplinary team of staff engaged mainly in checking political statements and information circulating on the Internet, and a business model that differs substantially from the conventional model in journalism.
Investigating Mesh Quality on Overset Gridding of a Flapped Aerofoil

Overset gridding can significantly enhance the speed and flexibility of grid generation for complex geometries, particularly where there are moving components, such as aircraft control surfaces and store release from aircraft. This study uses a plainflapped 6% two-dimensional circular-arc aerofoil to study the effect of the gap size between the aerofoil and flap and the relative mesh size between the background and donor grids in the overlap region.