Miracle-dz/UniFAR-FTUs huggingface dataset:
The Impact of DDoS on E-commerce

The research paper scope covers the impact of the distributed denial of service (DDoS) security breach on ecommerce. It shows some cases related to this impact and the control for each case. The DDoS happened because of different security threats and the real system vulnerabilities that may cause Loss of Confidentiality, Loss of Integrity, and Loss of Availability, among other ways of the DDoS impact. Preventing the service may be critical in war, health, transactions and other situations. There are no specific resources or actions to prevent the attack of the DDoS from happening, while there are some action which make it possible to reduce its impact. The paper presents an overview for this type of attack with some recommended actions to respond to it.
Knowledge Management to Support Learning Analytics in Higher Eduction

This paper argues based on evidence from the literature that learning analytics, when undertaken by higher education institutions, is not considered within a holistic knowledge management strategy, which could provide significant improvement to the outcomes of learning analytics. Particularly, a synthesis of knowledge extraction via learning analytics and appropriate handling of such knowledge via knowledge management is not typically implemented in higher education practices, but it constitutes a promising path to improving it, and eventually contributes to improving learning services. Essentially, knowledge management can support improvements and innovation in analytics tools, translate an organisation's strategic vision into action, and enable sharing of information among different actors. These are all necessary requirements for effective learning analytics.
Comparative Structural and Non-structural Properties of Ultra High-performance Steel-fiber-reinforced Concretes and High-Performance Steel-fiber-reinforced Concretes*

Both ultra high-performance steel-fiber-reinforced concretes (UHP-SFRC) and high-performance steel-fiber reinforced concretes (HP-SFRC) have been reported as promising construction materials owing to their superior structural properties, e.g., high compressive strength, high tensile strength and high ductility with strain-hardening performance and multiple micro-cracks. In addition, both these materials also have exhibited some none-structural properties such as self damage sensing, self-crack healing, self-compacting, high conductivity… In this paper, the authors would like to comparatively review the structural and non-structural properties of two mentioned materials based on the previous studies of the authors. The provided information is really useful for civil designers to properly apply these materials.
Variable-Scale Clustering

Human naturally analyze and decide a problem from different perspectives, hierarchies, and dimensions, that is referred to as scale transformation (ST). Clustering, as one of the most effective data analysis tools, should support this ST demand. Hence, this paper focuses on the ST problem among clustering analysis especially for decision making. We define the variable-scale dataset based on the rough set theory. What’s more, an algorithm of variable-scale clustering (VSC) is also proposed. A case study shows that compared to the k-modes, the clustering results of the VSC are more available and accessible to decision makers.
Playbook Oriented Cyber Response

Cyber analysts tend to respond to anomalous events manually, often using subjective judgment that can lead to responses that are less than optimal. Additionally, analysts tend to report on events and share cyber knowledge in unstructured, textual formats, which not only require more time to parse – thus taking more time to respond – but also lead to multiple conclusions from the same input. To remedy this, we have proposed a framework designed to provide an analyst with a set of timely and accurate courses of action in response to events, in some cases automating those responses. As part of this framework, we have created a playbook specification format that allows analysts to specify the right course of action to take in response to events, given certain risk conditions and mission context. In addition to providing the specification format, we have also created an initial ontology to help analysts build their playbook contents and have laid out a notional architecture that can operationalize these playbooks. Our playbook format can help standardize how analysts should respond to events, thus decreasing the time to response and enabling analysts to share key knowledge in a common format. Ultimately, this should increase the efficacy of security operations center personnel.
Self-Driving Cars Using CNN and Q-Learning

DrivingMatter is an experiment carried out to understand the deeper side of an autonomous car. In 1900s, idea was to drive car on Moon from Earth. This was initial motivation which grew from there and now expanding to complex system of roads in the real world. A book-sized Raspberry Pi based autonomous car is built to carry out the experiment on hardware. Software side was accomplished by developing a Python based library for controlling and communicating with car over a network or locally within the car. For environment learning two methodologies are practiced; Supervised learning: Drove the car on an environment/road and collected 3, 000+ data-points. Based on this a CNN model was trained which achieved 73 % test 89 % train accuracy. Reinforcement learning: Car is trained for three different road signs; Stop, No left, and Traffic light using DQN with existing CNN model. These road signs are detected in the environment using OpenCV cascade classifiers.
CutiQueue: People Counting in Waiting Lines Using Bluetooth Low Energy Based Passive Presence Detection

Queueing systems are used to estimate the attributes of waiting lines, for example the number of people in line or the waiting time. This information helps to reduce the time spent waiting by balancing people amongst multiple lines or aids in the decision making about opening a new line. However, current queueing systems are often mounted at fixed positions, require user participation, or need time consuming manual calibration after each layout change. In this work, we introduce CutiQueue, a flexible and portable queueing system that is battery powered, needs no manual calibration or user participation. CutiQueue is based on passive presence detection using the RSSI of low cost Bluetooth Low Energy transceivers. We analyze the performance of the system in experiments using a prototype implementation to estimate the number of people waiting as well as the length of the line. The experimental evaluation indicates an average accuracy of 97.96% when compared to counting people walking or standing in the waiting line using a laser barrier.
Adaptive Evolutionary Neural Network Gait Generation for Humanoid Robot Optimized with Modified Differential Evolution Algorithm

This paper introduces a novel approach for the biped robot gait generation which aims to control humanoid robot to walk more naturally and stably on a flat platform. The dynamic biped gait generator created by the novel adaptive evolutionary neural model (AENM) that is optimally identified with the proposed modified differential evolution (MDE) optimization algorithm. The comparison results with genetic algorithm (GA) and particle swarm optimisation (PSO) demonstrated the effectiveness of proposed MDE method. The prototype small sized humanoid robot is used to test the performance of the proposed MDE algorithm and other algorithms. The comparison results demonstrate that the new proposed neural AENM model proves an effective approach for a robust and precise biped gait generation.
Design and Analysis of a Compliant Gripper Integrated with Constant-Force and Static Balanced Mechanism for Micro Manipulation

There is a huge demand of manipulating delicate micro objects in various fields such as biotechnology, micro electro mechanical systems (MEMS). However, due to the uncertainty in sizes and stiffness of those micro objects, it makes this task more challenging. Those systems have rigorous requirements for the regulation of the output force from the gripping tool. Apparently, the use of sensors and feedback systems makes the mechanism both complicated and cost intensive. This paper presents a novel design concept of a gripper which can manoeuvre objects in various sizes but it still can preserve a constant gripping force by integrating a constant force mechanism. No sophisticated sensors and control systems are required for the design thanks to the usage of compliant mechanism. The passive type integrated compliant constant force mechanism exhibits its characteristics from combined compression and bending of beam structures. The design methodology of this compliant constant-force gripper mechanism (CFGM) using genetic algorithm shape optimization is presented. Finite element analyses are used to characterize the constant-force behavior of the gripper under static loading. A design formulation is proposed to find the CFGM shape for a specified gripping range with constant output force of the mechanism. The benefits of the monolithic nature of the compliant mechanism would also extend the miniaturization possibility of the CFGM as a micro-gripper for MEMS applications.
Feature Selection Applied to G.729 Synthesized Speech for Automatic Speaker Recognition

Performance of Automatic Speaker Recognition (ASR) over wireless communications knows a clear degradation due to the effect of the synthesized speech. In order to enhance the performance accuracy, we propose to apply a feature selection using Linear Discriminant Analysis (LDA) in the front end part of the ASR system. For simulation the G.729 codec is used. The speaker and session dependent i-vector distribution is modeled using a Gaussian probabilistic LDA (GPLDA). The obtained results, carried out by using the TIMIT corpus and MFCCs input parameters demonstrate that the LDA technique mainly dismissed the MFCC's derivatives, which enhances the recognition accuracy in both clean and synthesized speech.
Mining Emotions of the Public from Social Media for Enhancing Corporate Credit Rating

The proliferation of online social media has been changing the ways how individuals interact with corporations. Previous studies have examined how to extract investors' sentiments captured on social media to enhance stock prediction. However, little work was done to leverage public's emotions captured on social media to predict corporate credit risks. Our research fills the current research gap by developing a new computational method to extract public's emotions embedded in social postings to supplement common financial indicators (e.g., return-on-assets) for predicting corporate credit ratings. Grounded in Plutchik's Wheel of Emotions, the proposed computational framework can automatically extract the distribution of eight basic emotions from textual postings on online social media. In particular, one main contribution of our work is the development of the new emotion latent dirichlet allocation (ELDA) model for textual emotion analysis. In addition, we develop an ensemble learning model with random forest (RF) as the basis classifier to improve the performance of corporate credit rating. Based on the real-world data crawled from Twitter, our experimental results confirm that the proposed ELDA model can effectively and efficiently extract public's emotions from social postings to enhance the prediction of corporate credit ratings. To our best knowledge, this is the first successful research of developing a new computational model of extracting public's emotions from social postings to enhance corporate credit risk prediction.
Kazimir Malevich and the art of geometry

Kasimir Malevich was a Russian artist who pioneered a geometric style of painting that he called Suprematism. As well as creating non-objective paintings, he produced the designs for the 1913 futurist opera Victory over the Sun. This work analyzes Malevich's inspiration, methods and meanings.
The Sharing Economy for the Electricity Storage

The sharing economy has upset the market for housing and transportation services. Homeowners can rent out their property when they are away on vacation, car owners can offer ridesharing services. These sharing economy business models are based on monetizing under-utilized infrastructure. They are enabled by peer-to-peer platforms that match eager sellers with willing buyers. Are there compelling sharing economy opportunities in the electricity sector? What products or services can be shared in tomorrow’s smart grid? We begin by exploring sharing economy opportunities in the electricity sector, and discuss regulatory and technical obstacles to these opportunities. We then study the specific problem of a collection of firms sharing their electricity storage. We characterize equilibrium prices for shared storage in a spot market. We formulate storage investment decisions of the firms as a non-convex non-cooperative game. We show that under a mild alignment condition, a Nash equilibrium exists, it is unique, and it supports the social welfare. We discuss technology platforms necessary for the physical exchange of power, and market platforms necessary to trade electricity storage. We close with synthetic examples to illustrate our ideas.
Sliding Mode Control of Three-Phase Three-Level Two-Leg NPC Inverter with LCL Filter for Distributed Generation Systems

In this study, three-phase two-leg grid-connected neutral point clamped (NPC) inverter with LCL filter is proposed for distributed generation systems. Since the proposed inverter has only two legs, only two of the three-phase currents are controlled. Once the control of two currents is achieved, the control of third current is achieved automatically. The control objective is achieved by using sliding mode controller (SMC). Unlike the existing methods, the sliding surface function involves the inverter current and capacitor voltage errors. Hence, the reference signals of the inverter currents and capacitor voltages are essential in the SMC. In this study, these reference signals are generated by cascaded connected proportional resonant (PR) controllers without making use of filter parameters and derivative operations. The proposed control algorithm is validated with MATLAB/Simulink simulations. Simulation results show that the proposed three-level two-leg NPC inverter injects sinusoidal and balanced currents to the grid. In addition, the designed controller provides fast transient response.
Comparison of Meander Track Primary Topologies for EV Roadway Charging

Recent research on electric vehicle wireless charging has shifted from static to dynamic charging. The focus when considering design options for the primary of a dynamic charging system is to transfer power smoothly and yet also be interoperable with all the various secondary topologies being considered. This paper uses a published double-D pad primary as a benchmark, and conducts a comparative study between it and limited length track based options which have been proposed in the literature. Of particular interest in the paper is the performance of single-phase and two-phase meander track primary systems to evaluate the potential solutions that may help minimize the number of power supplies per meter. This performance evaluation is undertaken here using magnetic simulations using a bipolar pad secondary which can be configured to act as either a traditional rectangular or double-D secondary, or as a multicoil secondary. It is shown that under the same primary VA per meter the three primary topologies can deliver similar magnitude of power. Track topologies can offer larger charging zone and better interoperability while lumped DD topology emits lower magnetic leakage. Furthermore, a two-phase system can lower the requirement on rating of the electronic components on the secondary side.
Self-Organizing Map Using Classification Method for Services in Multilayer Computing Environments

The increasing amount of data running in cloud-computing environments has started inflating networks. To solve the problems caused by network inflation (e.g., latency and privacy), new types of computing environments with multiple layers have been proposed. However, service placement inside these multilayer computing environments has not been proposed. Nodes inside multilayer computing environments have different preferences, and the services deployed also have restrictions on deployment. Therefore, services must be placed carefully inside the computing environment. To place these services, we introduce a service classification method according to their properties and restrictions. However, when accommodating dynamic placement, rapid classification is needed to avoid serious damage caused by restriction changes. Therefore, we propose a classifying method using $\pmb{k}-$ Nearest Neighbor Classification ($\pmb{k}$ - NN) In addition, to accelerate the process, we use a dimension reduction method called Self-Organizing Maps (SOM) to preprocess the data. The proposed classification method is expected to be used as the primary step in service placement. The method will supply service placers with the identification of which layer services should be deployed.
The Effect of Air Gap Between ABS Sensor and Brake Disc to the Anti – lock Braking System Efficiency

Wheel speed sensors (WSS) are important components in anti-lock braking systems (ABS), they provide essential wheel speed information not only for ABS, but also for traction control and stability control systems. The WSS on most vehicles are magnetic and generate an alternating current (AC) signal that increases in frequency and amplitude with wheel speed. When the teeth on the tone ring rotate past the sensor tip, it changes the magnetic field and induces a current in the sensor windings. The result is a classic sine wave current pattern that changes with wheel speed. The strength of the signal can be affected by not only wheel speed but also the air gap between the sensor and tone ring, resistance inside the sensor, resistance in the external wiring and connector. A precise gap is necessary to produce a strong, reliable signal. But if the gap is too close, metal-to-metal contact between the sensor and tone ring would damage both. Conversely, an air gap that’s too wide may produce a weak or erratic signal or no signal at all. The paper presents the change of alternating current signal when changing sensor air gap. By using PULSE LabShop and SignalAnalyser software, sensor KMI15, we observe the waveform on the scope. A scope will show the voltage and frequency of the signal, as well as any missing or distorted humps in the sine wave pattern. From this, we can determine the ABS brake efficiency.
Modelling and Thermal Analysis of Advanced Insulating Layer of Electronic Applications

This paper proposes the replacement of a conventional Insulating-Metal-Substrate (IMS) dielectric layer with an electro-conductive adhesive which is applied onto a treated aluminium heat sink. The adhesive based structure results in a lower thermal resistance of the system compared to IMS based on Aluminium. The model covers the investigation of two IMS structure with a Copper conductor layer with a thickness of $200 {\mu } \mathrm {m}$, a dielectric layer of $50 \mu m$ with different thermal conductivities and an Aluminium base layer with a thickness of $920 {\mu } \mathrm {m}$. However, the IMS is placed onto a 100 x 100 x 33 mm Aluminium heat sink. The investigated adhesive based structure consists of the same Aluminium heat sink treated with an oxidation layer with a thickness of $50 {\mu } \mathrm {m}$ and an adhesive conductor layer with $30 {\mu } \mathrm {m}$ thickness. Furthermore, the use of forced convection to reduce the junction temperature of all three models is also considered.
Estimating Latent Relative Labeling Importances for Multi-label Learning

In multi-label learning, each instance is associated with multiple labels simultaneously. Most of the existing approaches directly treat each label in a crisp manner, i.e. one class label is either relevant or irrelevant to the instance. However, the latent relative importance of each relevant label is regrettably ignored. In this paper, we propose a novel multi-label learning approach that aims to estimate the latent labeling importances while training the inductive model simultaneously. Specifically, we present a biconvex formulation with both instance and label graph regularization, and solve this problem using an alternating way. On the one hand, the inductive model is trained by minimizing the least squares loss of fitting the latent relative labeling importances. On the other hand, the latent relative labeling importances are estimated by the modeling outputs via a specially constrained label propagation procedure. Through the mutual adaption of the inductive model training and the specially constrained label propagation, an effective multi-label learning model is therefore built by optimally estimating the latent relative labeling importances. Extensive experimental results clearly show the effectiveness of the proposed approach.
Space Vector Modulation for Packed-U-Cell Converters (PUC)

Generally, space vector modulation strategies employ the Three Nearest Vectors (NTV) to the reference voltage vector in order to achieve low distortion on the output voltages. When it comes to multilevel converters, the modulation technique must deal with a large number of redundancies and sometimes it must also perform the balance of the capacitor voltages, depending on the converter topology. This paper proposes a space vector modulation strategy applied to Packed-U-Cell Converters (PUC). It is proposed a cost function to be minimized comprising two objectives: (i) adjacent switching vectors distance minimization (ii) desired currents in order to keep the capacitors charged. The proposed cost function minimization results in a optimized switching sequence and balanced capacitor voltages. Simulation results demonstrate the good performance of the proposed modulation strategy,
Research on Energy Storage Capacity Configuration Method Based on Demand Response

Demand side response (DR) was incorporated into the energy storage system (ESS) configuration problem in this paper. First, the control principle of demand side response was studied, and then the loads at any time of a day were calculated after the application of time-of-use (TOU) electricity price in distribution network. Second, the model of ESS with capacity and charging/discharging power was established. Third, taking into account the mutual benefits, an optimization model of energy storage capacity configuration and TOU electricity price calculation has been established with the objective of maximizing economic efficiency and peak load shifting. Finally, using the actual data of a certain city grid as an example to calculate the energy storage capacity configuration and TOU power price, the calculation results show that the proposed method can better stabilize the fluctuation of system load, and the economic benefits are more assured.