Miracle-dz/UniFAR-FTUs huggingface dataset:
Smart energy tools for buildings energy management

Nowadays, due to population growth, the demand for energy resources has increased. In this respect, the consortium of the European project CitiSim has developed a platform for the smart city ecosystem. It will provide a powerful monitoring and control infrastructure for planners to make critical management decisions on tactical and strategic levels. Smart services included are focused on energy consumption and building management. We focused our work on developing two innovative solutions: Smart Energy for building management and Business Intelligence Tool. The Smart Energy for building service is a service designed to deal with energy-related data, using analytic and forecasting algorithms to provide KPIs useful for the strategical decision making within a building. The second service, Business Intelligence Tool, relies on energy production and consumption monitoring, energy production potential simulation, and simulation of the economic performance of energy efficiency projects for various investment scenarios.
Android Malware Detection Based on System Calls Analysis and CNN Classification

Android is the most used mobile operating system in the world with two billion monthly active users in May 2017 [1]. Provided by Google, to be used on a multitude of smartphones, tablets and other connected objects. It allows the installation of a variety of applications for messaging, calling, news or video games.This multitude of applications facilitate the user’s life and make the device a database rich in personal information such as phone number, emails, messages, confidential correspondence, etc., serving also as a good information repository for hackers. This gives rise to a wave of malignity, using suspicious applications, usually for profit (surcharged messages, sale of personal information or even tools of pressure and threat), or to satisfy personal desires (curiosity, vandalism …). Our solution proposes a behavioral dynamic analysis of the applications likely to be a source of malignancy. The application will be sent towards a distant server through a user-friendly and simple to use interface. It will be installed and executed with a simulation of a human use. After execution, system calls generated by the Linux kernel are collected, processed, and provided to the neural network model that will be used to predict whether the analyzed applications are malware or goodware. This model is built and refined using an APK database varied between goodware and malware.We used a neural network for automatic learning, and more precisely the Convolutional Neural Network (CNN). Our method uses matrix representation of collected system calls and input to the CNN model, a less expensive representation in memory space and therefore accelerate the process of learning.
Physical Internet Characterization

Over the last years, a new developed paradigm known as Physical Internet (PI) has been appeared, and studied in logistics management. The purpose of this global and open system is to deal with logistics grand challenge by setting up an efficient and sustainable Logistics Web. Methodology and approach: the purpose of this paper is to review of scientific articles dedicated to Physical Internet topic, and to provide a clustering strategy enabling to classify the literature on the Physical Internet, to follow its evolution, as well as to criticize it. The classification is based on three factors: Logistics Web, organization, and resources. Results: Several papers about Physical Internet have been classified and analyzed along the Logistics Web, resources and organization views at a strategic, tactical and operational level, respectively. A developed cluster analyses shows that Physical Internet needs more studies and more analyses to be adopted. Outcomes: The Physical Internet system has attracted several researchers in the last 4 years, and since then more articles have been published. However, this topic is still on papers and it needs more practice.
A Nine-Level Inverter for Low-Voltage Applications

A novel nine-level inverter topology for low-voltage applications is proposed in this paper. Each phase of the inverter is composed of a T-type three‐level cell and an active neutral point clamp three‐level cell. The operation principles and current conduction paths are analyzed in detail and phase-disposition sinusoidal pulsewidth modulation is used to balance voltages of flying capacitors and neutral point. The mathematical relationship between the capacitance and the desired ripple in the inverter output is established to select the intermediate capacitance value. Compared with other nine-level inverters suitable for low-voltage applications, the proposed inverter has advantages in a number of gate drivers, a number of dc sources, and efficiency. Simulation and experimental results under different settings of modulation index, power factor, output frequency, and load are presented to verify the effectiveness and performances of the proposed inverter.
The Acquisition of Generic Skills of Culturally-diverse Student Cohorts

The changing nature of higher education and the structure of graduate labour markets have increased emphasis on employability and graduate outcomes. Universities have responded to this changed environment by embedding generic skills in the curriculum. This paper examines the generic skills that students perceived they acquired in their accounting studies in preparation for graduate employment. Given the changed background profiles of students studying accounting degree in Australia, and the employment difficulties they encounter on graduation, the study specifically addresses the perceptions of students from diverse cultural backgrounds. The findings demonstrate that, overall, students believed that their accounting course assisted in developing generic skills, while differences in perceptions were identified between different cultural cohorts. The research highlights the need to develop educational practices which embed generic skills development in the curriculum in a way that maximises the opportunities for culturally-diverse student cohorts to enhance their employment outcomes on graduation.
Local Content Cloud based Cooperative Caching Placement for Edge Caching

Edge caching can improve the efficiency of content delivery by making full use of the limited storage capacity. Exploiting cooperation between base stations (BSs) could promote the diversity of contents in the whole network and alleviate backhaul traffic. However, prior works mostly overlook the effect of cooperation during caching placement period. In this paper, firstly we propose a local content cloud cooperation scheme to utilize the cache capacity of adjacent BSs. Considering the impact of the capacity of cooperative link and backhaul link, then we formulate an optimizing problem to minimize the average consumption, which consists of content downloading delay and transmission cost. To reduce the complexity and the expenditure of information exchange, we solve the NP-hard problem by the belief propagation method. Meanwhile, simulation shows that the proposed cooperation-based caching placement decreases the average consumption and enhance the hit ratio compared with other strategies.
Partial volume characteristics of ionization chambers in kilovoltage x-ray exposure measurements

Abstract The partial volume (spatial) response of four ionization chambers (Keithley) in kilovoltage X-ray beams, generated by the Philips Super 80CP X-ray unit, was assessed. The volume of the ionization chambers were of 10 cm3, 15 cm3, 150 cm3, and 600 cm3 used with Keithley electrometer Model 35040. The beam output was measured using a monitor chamber (Radcal 6.0 cm3) placed close to the collimator. The source to chamber distance was kept constant at 1 m. For the measurement of the response of ionization chambers of 15 cm3, 150 cm3, and 600 cm3, a slit of 2.0 mm width was made in a lead sheet of 3.2 mm thick and size of 30 × 30 cm2 and was placed on the ionization chamber. The measurements were made for 81 kVp, 400 mA, and 0.25 s and the slit was moved at an increment of 2.0 mm over the entire length of the chamber. For the measurements of the ionization chamber of 10 cm3 (CT chamber), the beams of 120 kVp, 200 mA and 0.2 s were generated, and a slit of 5 mm width was made in a similar lead sheet that was moved at an increment of 5.0 mm. From the result it appears that the sensitive volumes of the ionization chambers affect the response of the ionization chamber to incident radiation.
A Novel Recurrent Neural Network Architecture for Classification of Atrial Fibrillation Using Single-lead ECG

Atrial Fibrillation (AF) is a type of abnormal heart rhythm which may lead to a stroke or cardiac arrest. In spite of numerous research works, developing an automatic mechanism for accurate detection of AF remains a popular yet unsolved problem. In this paper, we propose a deep neural network architecture for classification of AF using single-lead Electrocardiogram (ECG) signals of short duration. We define a novel Recurrent Neural Network (RNN) structure, comprising two Long-Short Term Memory (LSTM) networks for temporal analysis of RR intervals and PR intervals in an ECG recording. Output states of the two LSTMs are merged at the dense layer along with a set of hand-crafted statistical features, related to the measurement of heart rate variability (HRV). The proposed architecture is proven on the open access PhysioNet Challenge 2017 dataset, containing more than 8500 single-lead ECG recordings. Results show that our methodology yields sensitivity of 0.93, specificity of 0.98 and F1-score of 0.89 in classifying AF, which is better than the existing accuracy scores, reported on the dataset.
Animal Disease Diagnosis Expert System using Convolutional Neural Networks

Livestock breeding farms are commonly found to be geographically distributed in remote areas with comparatively poor conditions of disease diagnosis. Usually, it is difficult to perform animal disease diagnosis quickly and concisely due to the technical expertise and experience required. But, the farms can diagnose these diseases precisely using the animal disease diagnosis expert system(ADDES) which could ascertain a proper enlargement of the stockbreeding industry. In this paper, we are trying to enhance the diagnosis accuracy and minimize the loss by proposing a method using Convolutional Neural Network(CNN). Our center of attention is on evolving an effective strategy for this diagnosis issue by improvising the existing techniques. By segmenting and extracting the features from the diseased images dynamically, our model is bound to produce an accurate result. We evaluate our algorithms via experiments and through the visualization of these rules and plots, one can accurately come up with an elaborate explanation of the disease and its cause and cure more effectively.
Surface Classification with Millimeter-Wave Radar Using Temporal Features and Machine Learning

Classification of surfaces using millimeter-wave radar commonly considers the use of polarization-based methods for road condition monitoring. When a surface consists of larger structures, one is instead often interested in monitoring the surface topography, which is typically not resolvable by the limited radar bandwidth. To alleviate this problem, we here consider several phase coherent radar measurments conducted during the motion of the radar, in order to capture not only the instantaneous depth measurement, but also the depth variation over time. Analysis of scattering from rough surfaces is highly complex and relies on prior knowledge of surface structure. By constructing a set of features based on a number of radar measurements over time, a machine learning classifier is proposed to distinguish grass target surfaces from asphalt, gravel, soil, and tiled surfaces. Six different classifier structures are evauated and presented in the paper. Using estimated autocovariances and average envelope shapes as features, a small, fully connected, neural network classifier is, using a leave-one-out strategy, shown to allow for accurate determination of the surface type. The proposed classifier can be implemented with limited hardware requirements, making it suitable for autonomous devices, such as, e.g., autonomous lawn mowers.
AC-DC Multi-port Traction Power Supply System with Power Electronic Transformer

In this paper, an AC-DC multi-port traction power supply system with power electronic transformer is put forward. Based on this structure, the system control method is proposed to realize energy optimization and system stabilization. The system control method is proposed to divide the system state and utilize DC bus voltage information, aiming at achieving optimal energy utilization and system stability. The system consists of PV (photovoltaic) source DC system, wind energy communication system, energy storage module, regenerative braking module, DC micro-grid power supply module and local load, which can run in grid or island mode. The system is divided into five working modes, which are the main control DC bus voltage of PV source DC system under balanced state, the main control of island operation energy storage module, the main control of regenerative braking module, and the main control and communication of regenerative braking module in grid mode. The control methods of the PV interface converter, the grid side interface converter and the energy storage interface converter are respectively given. The simulation and the test results proof the usefulness of the proposed method.
Performance Analysis of Several Relay Node Selection Schemes for Cooperative Communications

This paper considers a cooperative wireless network where a set of nodes cooperate to relay in parallel the information from the source node to the destination node employing a decode-and-forward approach. The proposed cooperation takes place in two phases. During the first phase, the source node broadcasts signals to the relay nodes and the destination node. During the second phase, some relay nodes are selected to forward the decoded signals to the destination node in light of the given relay node selection scheme. Within the same cooperative system framework, outage probability and diversity-multiplexing tradeoff (DMT) expressions are developed for several relay node selection schemes named as random relay node selection (RRS), best relay node selection (BRS), and all relay nodes selection (ARS), respectively, so as to measure and compare their corresponding performance. Numerical results demonstrate that both of ARS and BRS can achieve significant performance gains over RRS. Furthermore, ARS can also offer some performance gains over BRS at the cost of increasing some computational complexity.
A 625MS/s, 12-Bit, SAR Assisted Pipeline ADC with Effective Gain Analysis for Inter-stage Ringamps

Ring amplifier has been shown as an alternative to an OTA for its low power consumption and large output swing in low-voltage, deep nanoscale CMOS processes. The robustness of the ring amplifier is an important consideration for its application in products. A transient amplification analysis in this paper reveals the difference between the DC gain and the effective gain of the ring amplifier at different output voltages. This non-linear gain error varies with process corner, supply voltage and temperature (PVT) variation. To compensate this gain error, a calibration method is proposed based on the code density analysis of the output data from the back-end stages. Designed in a 1 V 28 nm CMOS process, a prototype SAR assisted pipeline ADC is proposed with a quick-start ring amplifier. The 12-bit ADC achieves 59.3 dB SNDR and 74.4 dB SFDR with a 6 MHz input and a sample rate up to 625 MS/s. The ADC has measured SNDR and SFDR of 57.2 dB and 67.8 dB, respectively, for a Nyquist frequency input and consumes 13.2mW. The measured SNDR and SFDR remains above 53.5 dB and 66.3 dB for a -6dBFs 50 MHz input with supply voltage and temperature variation.
Classification of Sweet Potato Variety using Convolutional Neural Network

Sweet potato (Ipomoea batatas) from being ‘a poor man’s crop,’ sweet potatoes it is now viewed as a good with high business assets also significance for a multitude of reasons. Due to its versatility and high nutritional value, it is now considered a cash crop, making it one of the fastest growing commodities on the market. However, variety classification on this commodity is considerably significant to ensure the quality of products before reaching production. This study aims to present a sweet potato classification using convolutional neural network method. The results of the study show that the convolutional neural network achieves 96.33 percent accuracy in classifying sweet potato variety. Thus, a convolutional neural network is appropriate in the classification of sweet potatoes. This paper could bring forward an approach of sweet potato classification. Practical implications and research directions are presented.
Resonant Inductance Design and Loss Analysis of a Novel Resonant DC Link Inverter

To realize a soft-switching inverter with the advantages of a simple structure, high efficiency, low-voltage stress, and easy to control, a novel parallel resonant dc-link inverter with the function of pulse current regeneration is proposed in this paper. All switches in the inverter main circuit can achieve zero-voltage switching (ZVS) turn-on and ZVS turn-off. In addition, all switches in the auxiliary commutation circuit (ACC) can achieve soft switching. The power in the ACC is small. Freewheeling diodes in the inverter turn off under zero current condition, which can reduce the reverse recovery loss of freewheeling diodes. In addition, the bulk capacitors are avoided in the circuit and there is no neutral point potential variation problem. At the same time, the zero-voltage duration is independent of the load current, and the time of transition can be selected freely. According to the equivalent circuits in different operation modes under the proposed modulation strategy, the operation principle, soft-switching implementation condition, and parameter design procedure of the proposed inverter are analyzed successively in this paper. Finally, a 10 kW, 10 kHz soft-switching inverter prototype with insulated gate bipolar transistor based switches is built. Experimental results are given to demonstrate the validity and features of the soft-switching inverter.
Impact of Subscription Fraud in Mobile Telecommunication Companies

Subscription Fraud (SF) is one of the hardest and most expensive revenue leakage to prevent. This fraud is the leading revenue leakage in telecommunication. The tough economic challenges and saturated telecommunication market in South Africa makes it difficult for telecommunication companies to invest in good fraud prevention systems since their main focus is to increase earnings before interest tax and amortization (EBITA). Most fraud analysts determine the revenue impact of SF on the companies, but less focus is given to the impact on the customer. In this paper, we determine the impact of SF on company’s revenue, churn and customers. The common use of machine learning techniques in telecommunication fraud detection and prevention has been very successful. We used decision tree model to predict SF using post-paid customers data, the model correctly predicted 98 percent of fraud cases and highlighted high monthly subscription fees and payment types as key attributes to the model for predicting fraud, the model further indicated that 30 percent of revenue loss is caused by SF. Information presented in this paper may be used to develop predicting models and also shows a need to develop a solution to vet the customers before contract can be approved.
FPGA/Python Co-Design for Lane Line Detection on a PYNQ-Z1 Board

This paper presents the implementation of lane line detection on FPGA and Python. Lane line detection consists of three functions, median blur, adaptive threshold, and Hough transform. We implemented only accumulation of Hough transform on FPGA. Although the Hough transform cannot be implemented on a low-end FPGA board if implemented directly, by reducing ρθ space, it was successfully implemented on a low-end FPGA board. The rest of the Hough transform was implemented using Python's NumPy and SciPy, and OpenCV. Although it was very easy to write, it did not become a bottleneck for the whole process because of its effectiveness. As a result, we could achieve a 3.9x speedup compared to OpenCV and kept the developing cost down. When implementing median blur and adaptive threshold on an FPGA, we could achieve a 6.34x speedup.
Computational Acceleration and Smart Initialization of Full-RANK Spatial Covariance Analysis

Full-rank} spatial covariance analysis (FCA) is a method for blind source separation. It is based on a model for observation mixtures with flexible source-related parameters, and an EM algorithm is known to optimize the parameters. FCA has the potential to obtain high-quality separations. However, the algorithm for FCA is computationally demanding and sensitive to initializations. This paper proposes two practical techniques to make effective use of FCA. The first one is to accelerate the execution of the algorithm by using single-instruction-multiple-data (SIMD) instructions run on a GPU. The second one is to initialize the parameters appropriately by scanning the observation mixtures. Experimental results show that high-quality separations were achieved for 6-second real-room speech mixtures (4 sources and 3 microphones) with a computational time of less than 8 seconds.
Towards Effective Mutation Testing for ATL

The correctness of model transformations is crucial to obtain high-quality solutions in model-driven engineering. Testing is a common approach to detect errors in transformations, which requires having methods to assess the effectiveness of the test cases and improve their quality. Mutation testing permits assessing the quality of a test suite by injecting artificial faults in the system under test. These emulate common errors made by competent developers and are modelled using mutation operators. Some researchers have proposed sets of mutation operators for transformation languages like ATL. However, their suitability for an effective mutation testing process has not been investigated, and there is no automated mechanism to generate test models that increase the quality of the tests. In this paper, we use transformations created by third parties to evaluate the effectiveness ATL mutation operators proposed in the literature, and other operators that we have devised based on empirical evidence on real errors made by developers. Likewise, we evaluate the effectiveness of commonly used test model generation techniques. For the cases in which a test suite does not detect an injected fault, we synthesize test models able to detect it. As a technical contribution, we make available a framework that automates this process for ATL.
Different responses of load models when facing short-circuits in a distribution system with intermittent distributed generation

The electric distribution systems topology are being increasingly altered by the inclusion of elements that may characterize a bidirectional power flow, which adds complexity to their operation. In this scenario, analyses of short-circuits in distribution systems with intermittent distributed generation should be given attention, due to the load model and weather conditions that can be better represented computationally. This paper proposes an analysis on the impact of considering different static load models and weather scenarios for the analysis of short-circuits applied to a radial distribution system with photovoltaic generation and battery energy storage system. Various sensitivity tests are performed to evaluate the actual importance of considering certain load models and scenarios in short-circuit analyses, allowing inference about model and most favorable conditions.