Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Text Summarization by Mean-absolute Constrained Convex Optimization

In this paper we propose a new algorithm for extractive text summarization. The proposed method is based on convex minimization and the properties of the l1 norm. The algorithm has some advantages, like the extensibility and the ability to easy take into account additional information and constraints. It provides very good results, but its execution time is usually larger than that of other similar procedures.
Concept Extraction with Convolutional Neural Networks

For knowledge management purposes, it would be interesting to classify and tag documents automatically based on their content. Concept extraction is one way of achieving this automatically by using statistical or semantic methods. Whereas index-based keyphrase extraction can extract relevant concepts for documents, the inverse document index grows exponentially with the number of words that candidate concpets can have. To adress this issue, the present work trains convolutional neural networks (CNNs) containing vertical and horizontal filters to learn how to decide whether an N-gram (i.e, a consecutive sequence of N characters or words) is a concept or not, from a training set with labeled examples. The classification training signal is derived from the Wikipedia corpus, knowing that an N-gram certainly represents a concept if a corresponding Wikipedia page title exists. The CNN input feature is the vector representation of each word, derived from a word embedding model; the output is the probability of an N-gram to represent a concept. Multiple configurations for vertical and horizontal filters were analyzed and configured through a hyper-parameterization process. The results demonstrated precision of between 60 and 80 percent on average. This precision decreased drastically as N increased. However, combined with a TF-IDF based relevance ranking, the top five N-gram concepts calculated for Wikipedia articles showed a high precision of 94%, similar to part-of-speech (POS) tagging for concept recognition combined with TF-IDF, but with a much better recall for higher N. CNN seems to prefer longer sequences of N-grams as identified concepts, and can also correctly identify sequences of words normally ignored by other methods. Furthermore, in contrast to POS filtering, the CNN method does not rely on predefined rules, and could thus provide language-independent concept extraction.
Modelling and Simulation of High-viscosity, Non Iso-thermal Fluids with a Free Surface

With the aim of using efficient control and/or diagnostic methods, more and more companies in the process engineering industry are using mathematical models to describe the underlying physical processes in sufficient detail. Against this background, the modeling and simulation of the behaviour of a non-isothermal, highly viscous fluid flow is examined in this paper. The behaviour of the fluid is decribed by a system of partial differential equations, which includes the incompressible Navier-Stokes equations as well as the thermal energy equation. With regard to the numerical calculation of the process variables, a combination of the Marker and Cell (MAC) method and a temperature calculation on a curvilinear grid is presented. The MAC method is used to identify the free surface by inserting particles without masses over the initialized fluid area and moving them with the calculated velocities. A characteristic feature of the typical use of the MAC method is that the defining partial differential equations are discretized spatially on a rectangular grid. However, this leads to the problem that a large part of the grid nodes lies within the obstacles which are surrounded by the fluid. In the present model, on the other hand, a curvilinear grid is used. The main advantage of this is that the outer grid nodes lie directly on the surrounding obstacles, resulting in a reduced system of differential-algebraic equations.
Equivalent Mutant Problem and its Problem-Solving Techniques: A Retrospective View

Mutation testing is a fault simulating testing technique mainly used to assess the quality of the test suite. During the execution of mutants, some of the mutants remain undetected are known to be equivalent. They also make the mutation testing process costly and time-consuming. Due to the similar behavior of the original program and equivalent mutants, they are unable to be identified by any of the test suites. Researchers have been working in the field to identify and detect equivalent mutants manually and automatically. In this paper, a comprehensive study is done to analyze the problem of equivalent mutants. During the course of this review, several well-known databases were searched for the studies discussing the equivalent mutant problem. A total of 36 studies were found to be relevant to the topic and were evaluated. It is concluded that handling equivalent mutants is the most critical problem; it negatively impacts the test adequacy score. The research work can be categorized into three categories: equivalent mutant detection, how to avoid the generation of equivalent mutants and analyzing the impact of equivalent mutants on different parameters of testing. Compiler optimization techniques and higher order mutation techniques are found to be the most prevailing to distinguish the equivalent mutants from a pool of mutants.
A Comprehensive Approach for Maximizing Flexibility Benefits of Electric Vehicles

Increasing variability and uncertainty coming from both sides of the power system equilibrium equation, such as wind energy on the generation side and increasing share of new consumers such as electric vehicles on the demand side, entail higher reserve requirements. While traditional approaches of assigning conventional generation units to maintain system stability can increase operational costs, greenhouse gas emissions, or give signals for new investments, utilizing intelligent control of distributed sources might mitigate those negative effects. This can be achieved by controllable charging of domestic electric vehicles. On the other hand, increasing number of public charging stations gives final users the opportunity to fast charge, making their vehicles an additional source of uncertainty rather than a provider of flexibility. This paper brings a full system assessment of combined effect of slow home charging of electric vehicles together with fast charging stations (both with and without integrated energy storage systems), cast as mixed integer linear programming unit commitment model. The contributions of this paper look into optimal periods when fast charging is beneficial for the system operation, as well as assess the benefits of integrating battery storage into fast charging stations to mitigate the negative effects to power system operation.
Design of Rotor Excited Axial Flux-Switching Permanent Magnet Machine

In this paper, a new structure of permanent magnet axial flux-switching machine with segmented rotor has been introduced. The 12-pole stator has been installed between two 8-pole segmented rotors, and the excitation system has been connected to the rotor. This topology enhances the design of flux-switching permanent magnet machines. With this new topology, an increase in the torque density, the reduction the total harmonic distortion, simple construction, and the reduction of cogging torque have been achieved. The harmonic spectrum of the back electromagnetic force has also been improved. This topology provides an optimum power for the electric vehicles because of the short axial length and the high power density along with the low vibration and noise due to lower torque ripple and cogging torque. The three-dimensional time-stepping finite-element analysis is used to determine the flux linkage, back electromagnetic force, the output torque, and the cogging torque. A comparative analysis is made of the proposed topology and related topology in the literature.
ICS: Interrupt-Based Channel Sneaking for Maximally Exploiting Die-Level Parallelism of NAND Flash-Based Storage Devices

The performance bottleneck of NAND flash-based storage devices (NFSDs) is mainly due to the slow NAND flash memories (NFMs). One of the well-known techniques for overcoming the bottleneck is an interleaving technique. This technique aims to maximize the utilization of high-speed channels by allowing slow NFMs to operate in parallel. Typically, NFSDs hierarchically apply the multilevel interleaving technique—channel, way, and die-level. While channel/way-level interleaving has already matured, die-level interleaving faces practical difficulties. The most critical issue is that it is not easy to identify individual die status because multiple dies in a package share a status pin, because of the form factor and cost issues of NFSDs. For this reason, NFSD has to issue a read status (RS) command to check the die status, which requires nonmarginal performance overhead. Moreover, the RS overhead attenuates the advantages of channel sneaking (CS) introduced in this brief to accelerate interleaving. To tackle this issue, we propose interrupt-based CS (ICS) that maximizes the impact of die-level interleaving while paying marginal overhead for identifying the die status. ICS performs on-demand die-status monitoring with minor modification of the NFM interface. We prove its effectiveness by conducting experiments in which ICS improves the performance by 19.8% over the typical scheme.
Optimum Flux-Weakening Characteristics for Five-Phase Surface Mount Permanent Magnet Synchronous Machines

This paper aims to explore the effect of varying the motor drive parameters on the optimum flux-weakening performance of the Multi-Frequency (MF) five-phase surface mount permanent magnet synchronous motor drive (MF five-phase SPM). A lossless, steady-state per-unit, DQ model is presented. The flux-weakening performance of the Single Frequency (SF) five-phase surface mount permanent magnet synchronous motor (SF five-phase SPM) is considered first because it represents a simplified case of the MF five-phase SPM drive and gives an insight into the operation of the MF five-phase SPM motor drive. Only the fundamental and the third harmonics are assumed to be in the spectrum of the back electromotive force (EMF) and are considered as the torque producing terms in the first plane and the second plane respectively. A new DQ normalisation for an MF five-phase SPM drive, based on the rated quantities of the first DQ plane, is developed to be used in the optimisation process necessary for studying the effect of varying the motor parameters on the flux weakening performance. The non-linear optimisation problem that aims to maximise the electromagnetic torque of the MF five-phase SPM machine under the voltage and current constraints, for operation in the linear modulation region, is formulated.
Hips Do Lie! A Position-Aware Mobile Fall Detection System

Ambient Assisted Living using mobile device sensors is an active area of research in pervasive computing. Multiple approaches have shown that wearable sensors perform very well and distinguish falls reliably from Activities of Daily Living. However, these systems are tested in a controlled environment and are optimized for a given set of sensor types, sensor positions, and subjects. In this work, we propose a self-adaptive pervasive fall detection approach that is robust to the heterogeneity of real life situations. Therefore, we combine sensor data of four publicly available datasets, covering about 100 subjects, 5 devices, and 3 sensor placements. In a comprehensive evaluation, we show that our system is not only robust regarding the different dimensions of heterogeneity, but also adapts autonomously to spontaneous changes in the sensor’s position at runtime.
A Predictive Routing Algorithm for WBSN Based on Kalman Filter Iterations

Deteriorating health status due to modern life style along with increased life expectancy seeks a potent health monitoring system in everyday living. Wireless body sensor networks (WBSN) are a result of this necessity. WBSNs require a new set of routing protocols as human body is an entirely different communication medium for electromagnetic waves. The primary quality of service that every routing protocol tries to achieve in a WBSN is reliable delivery of packets along with optimized life time. This paper presents a new link connectivity prediction-based protocol for routing in WBSN. Simulation results show that the proposed solution has better lifetime and packet delivery ratio than the existing predictive routing protocols.
Towards Cooperative Self-adapting Activity Recognition

Activity Recognition (AR) aims at deriving high-level knowledge about human activities and the situation in the human’s environment. Although being a well-established research field, several basic issues are still insufficiently solved, including extensibility of an AR system at runtime, adaption of classification models to a very specific behaviour of a user, or utilising of all information available, including other AR systems within range. To overcome these limitations, the cooperation of AR systems including sporadic interaction with humans and consideration of other information sources is proposed in this article as a basic new way to lead to a new generation of “smart” AR systems. Cooperation of AR systems will take place at several stages of an AR chain: at the level of recognised motion primitives (e.g. arm movement), at the level of detected low-level activities (e.g. writing), and/or at the level of identified high-level activities (e.g. participating in a meeting). This article outlines a possible architectural concept, describes the resulting challenges, and proposes a research roadmap towards cooperative AR systems.
Design and Analysis of Booth Multiplier with Optimised Power Delay Product

In most of the VLSI systems, multiplier being the vital part consumes nearly 15-20% of total IC power and is quiet slow in overall operation of the system. Thus it is essential to have an efficient design for the multipliers to improve the overall performance of the system. Booth multiplier reduces the number of partial products, taking into account two bits of the multiplier at a time, resulting in speed advantage over other multiplier architectures. With this advantage, Booth Multiplier is widely used in multiplication process for various digital and DSP circuits. The objective of this paper is to implement an optimized Booth Multiplier (8*8) with improved Power consumption and Delay Product (PDP). The sign extension is implemented using a single inverter and the addition operation is implemented by using custom designed Carry Skip Adders with IOT Full Adder. The design implementation and the simulations are done in Cadence Virtuoso V13.0 under 45nm technology.
Simultaneous Procurement of Demand Response Provisions in Energy and Spinning Reserve Markets

Demand Response (DR) is an important tool for Independent System Operators (ISO) for reliable operation of electricity markets, and there has been considerable interest from load-side market participants to offer their services for DR provisions. In this environment, the ISO needs to develop effective mathematical models for procurement of DR so as to maximize its benefits, such as reducing the peak demand, regulating electricity price shocks, and mitigating market power. In this paper, a new mathematical model for a Locational Marginal Price based, loss included, day-ahead, cooptimized, energy and spinning reserve market including DR provisions, is proposed. The proposed model includes a new proposition for load-side participants to submit price responsive demand (PRD) and load curtailment based DR bids simultaneously, in a unified market framework. Case studies considering DR providers participating in energy market only, in both energy and spinning reserve markets, and in spinning reserve market only, are presented, which examine the impact on system operation, market prices, and DR provider's benefits. The proposed model is tested on the IEEE Reliability Test System to demonstrate its functionalities and the results clearly justify the merits of simultaneous consideration of PRD bids and load curtailment based DR offers in a cooptimized energy and spinning reserve market.
Linking of Open Government Data

The use of Linked Open Data plays an important role in the process of opening government and public administration. Although the possibilities of Linked Data can provide numerous benefits in governmental processes; the adoption of the new technology involves a number of barriers, because its effective use requires complex knowledge and skills drawn from many disciplines. To promote the spread of Linked Open Data in the government there is a definite need for well-developed methodologies and guidelines of Linked Open Data technology. Our main objective is to promote the use of open Linked Data technology in the governmental sector. In this context the publication reveals the most important definitions related to Linked Open Government Data, proposes a model for its lifecycle, analyzes the necessary Linked Open Data procedures and methodologies from technological point of view focusing on governmental usage and collects the different architectural design models behind Linked Data publishing and applications.
dpMood: Exploiting Local and Periodic Typing Dynamics for Personalized Mood Prediction

Mood disorders are common and associated with significant morbidity and mortality. Early diagnosis has the potential to greatly alleviate the burden of mental illness and the ever increasing costs to families and society. Mobile devices provide us a promising opportunity to detect the users' mood in an unobtrusive manner. In this study, we use a custom keyboard which collects keystrokes' meta-data and accelerometer values. Based on the collected time series data in multiple modalities, we propose a deep personalized mood prediction approach, called dpMood, by integrating convolutional and recurrent deep architectures as well as exploring each individual's circadian rhythm. Experimental results not only demonstrate the feasibility and effectiveness of using smart-phone meta-data to predict the presence and severity of mood disturbances in bipolar subjects, but also show the potential of personalized medical treatment for mood disorders.
DBStore: A TrustZone-backed Database Management System for Mobile Applications

ARM TrustZone technology has been widely use to enhance the security of mobile devices by allowing for the creation of Trusted Execution Environments (TEE). However, existing TEE solutions tend to struggle with a trade-off between security and functionality: they either expose a larger attack surface to favor dynamic code loading inside the TEE, or depend on the static deployment of trusted services inside the TEE which is more cumbersome and error-prone to maintain. This paper proposes the deployment of a trusted service which aims to serve a broad range of applications by offering secure database storage capability inside the TEE. We present DBStore, a TrustZone-backed database management system for mobile applications. Applications can create and operate DBStore databases inside a TEE that provides confidentiality and integrity protection of databases and respective SQL queries without depending on the integrity of the mobile OS. We present a case study where DBStore is used in order to thwart existing attacks in HCE-based mobile ticketing applications.
Estimating Transdiagnostic Symptom Networks: The Problem of “Skip Outs” in Diagnostic Interviews

Network models of the symptoms of psychological disorders provide a novel lens for examining comorbidity. Viewing symptoms as causal entities in their own right, researchers can attempt to identify specific symptoms that “bridge” diagnostic entities, providing a more granular perspective on comorbidity than the one provided by analysis at the syndromal level. Such analyses could help identify transdiagnostic targets for both research and clinical interventions. Although promising conceptually, extant work using this approach often uses structured diagnostic interview data that employ “skip outs,” branching logic conditional on responses to gating questions (which may be criterial or risk markers). We demonstrate that skip outs, where screening items are asked for each disorder before assessing the remaining symptoms, can produce significant problems in interpretation of comorbidity between symptoms and, hence, transdiagnostic network models. The nature and extent of this problem is explored, and suggestions for future network studies of comorbidity are provided.
Real-Time Non-linear Noise Reduction Algorithm for Video

Noise is an essential issue for images and videos. Recently, a range of high-sensitivity imaging devices have become available. Cameras are often used under poor lighting conditions for security purposes or night time news gathering. Videos shot under poor lighting conditions are afflicted by significant noise which degrades the image quality. The process of noise removal from videos is called noise reduction (NR). Although many NR methods are proposed, they are complex and are proposed as computer simulations. In practical applications, NR processing of videos occurs in real-time. The practical real-time methods are limited and the complex NR methods cannot cope with real-time processing. Video has three dimensions: horizontal, vertical and temporal. Since the temporal relation is stronger than that of horizontal and vertical, the conventional real-time NR methods use the temporal relation to reduce noise. This approach is known as the inter-frame relation, and the noise reducer comprises a temporal recursive filter. Temporal recursive filters are widely used in digital TV sets to reduce the noise affecting images. Although the temporal recursive filter is a simple algorithm, moving objects leave trails when it reduces the high-level noise. In this paper, a novel NR algorithm that does not suffer from this trail issue and shows better performance than NR using temporal recursive filters
A simplified velocity estimation method for monitoring the damaged pavement by a multistatic GPR system YAKUMO

To identify the slight spatial variability in the electromagnetic(EM) signal propagation velocity and thickness of an asphalt layer caused by the presence of thin cracks within the layer, a practical interferometric approach using the multistatic ground-penetrating radar system YAKUMO was introduced in this study. It was demonstrated that several parallel distributed common midpoint (CMP) datasets can be acquired at each position while moving the system in real time, which makes it possible to apply this method to large-scale inspections. the proposed method can provide much higher-resolution estimations of slight deviations in the velocity and thickness from their corresponding reference values in the undamaged asphalt through the comparison of two CMP datasets, and these deviations can be obtained analytically instead of graphically extracted from the velocity spectrum. In this work, a short-term monitoring dataset is acquired at an airport taxi-way model during it was damaged artificially. This dataset is analyzed with proposed method in order to recognize the conditions of damaged pavement at different stages. The results show that the performed method can indicate the damaged part at different stages. This method provides a novel way to inspect partially damaged pavement when the damage is difficult to observe using the reflected signal only.