Miracle-dz/UniFAR-FTUs huggingface dataset:
Locus of control in offenders and alleged offenders with learning disabilities

Accessible summary • We looked at locus of control. • Locus of control means whether a person thinks they can control what they do or what happens to them. • The questionnaires that are used to find out how people think are sometimes hard to understand. • People with learning disabilities helped us make a new questionnaire that was easier to understand. • We used the questionnaire to find out if there was any difference in locus of control in people with learning disabilities who had been in trouble with the police and those who had not. Summary Locus of control can be a useful measure of treatment outcome in offenders from the general population. However, there is little information regarding locus of control and offenders with learning disabilities. Existing measures of locus of control use complex language and abstract ideas that may not be accessible to individuals in this group. A new 20-item measure, using written statements and cartoons, was developed. The tool was then used to compare locus of control in offenders and alleged offenders (n = 41) and non-offenders (n = 61) with learning disabilities. A significant relationship was found between locus of control and offender status if the offender was convicted (n = 22). Where there was offending behaviour but no conviction (n = 19), there was no significant difference between this group and the non-offender sample. Convicted offenders with intellectual disabilities appear more likely to have a greater external locus of control than alleged or non-offenders. The new locus of control scale may be a useful measure of treatment outcome in offenders with learning disabilities.
Migrating from SQL to NOSQL Database: Practices and Analysis

Big data and data analytics require migrating from relational databases (SQL) to NoSQL data structures to represent the data. Such transformation is challenging because of the lack of automatic transformation process and the requirement of guaranteeing both performance and accurate representation. In this paper, we analyze and implement commonly used mapping from SQL to NoSQL structures. We compare between these mappings in terms of retrieval time in an attempt to identify the best mapping. We use MySQL as DBMS for SQL structure and MongoDB for NoSQL structures. Our experiments showed promising results when using a mix of one level embedded document with a reference relationship with another document.
Successive Two-Way Relaying for Full-Duplex Users With Generalized Self-Interference Mitigation

In this paper, we propose a novel successive two-way relaying (STWR) system that uses a pair of conventional half-duplex (HD) relays to mimic a full-duplex two-way relay (FD-TWR). Although classical FD-TWR is spectral efficient and expands cell coverage, the proposed STWR utilizes the existing HD infrastructure to boost the FD implementation and offers bi-directional data exchange and low-complexity residual self-interference (RSI) mitigation. To formulate STWR, we develop a unified signal model to facilitate the mitigation of the generalized self-interference (GSI). GSI consists of back-propagating interference due to two-way relaying, RSI of FD sources and inter-relay interference caused by the pairs of HD relays. Because the GSI channel matrix has a distinct row linearity, we propose an efficient digital approach to remove the GSI and design two low-complexity algorithms. These algorithms avoid RSI channel estimation, full-rank matrix, and complex matrix computation. Our analysis and simulations show that: 1) the proposed STWR achieves the multiplexing gain of the true FD-TWR; 2) the distance between the two HD relays should be optimized to achieve the highest spectral efficiency; and 3) the STWR system with two algorithms can achieve a diversity order of one or two, respectively. Therefore, the STWR concept achieves a flexible tradeoff between performance and complexity, potentially enabling large-scale relay deployments.
Capacity Bounds and Interference Management for Interference Channel in Visible Light Communication Networks

In this paper, we investigate the channel capacity region of interference channel and develop both centralized and distributed interference management schemes for visible light communication (VLC) networks. For a typical multiuser and multi-LED scenario, we derive both discrete inner and outer bounds of the channel capacity region, and such a proposed inner bound is numerically shown to be the highest among the existing inner bounds. Moreover, with continuous input signals, we develop the channel capacity region bounds in a closed form, termed $\left ({{\alpha,\beta,\gamma } }\right)$ (ABG) inner bound and ABG outer bound, which are tight for the large amplitude-to-variance ratio. Then, based on the derived ABG inner bounds, we investigate a centralized beamforming design problem to minimize the total transmit power under three practical constraints: peak optical power, average optical power, and average electrical power. By utilizing semidefinite relaxation technique, we reformulate this NP-hard problem as a convex semidefinite program and obtain the optimal beamformers. Furthermore, to reduce the cost of channel station information exchange, we propose a distributed coordinated interference management scheme by adopting the alternating direction method of multipliers method. Finally, numerical results are presented to evaluate the performance of the proposed interference management schemes in VLC networks.
LDPC Decoder Based on Markov Chain Monte Carlo Method

Low-density parity check (LDPC) codes have attracted the attention of a large number of researchers with its near-Shannon performance and easy implementation features. As a data coding scheme under the 5G scenario, how to achieve efficient encoding/decoding for LDPC codes has become one of the important research topics. Under this condition, this paper 1) proposes a novel decoding algorithm for LDPC codes using the Markov Chain Monte Carlo (MCMC) method and 2) introduces two improved versions, MCMC-S and MCMC-L, which achieve better results. Simulation figures show that the improved methods outperform the traditional belief propagation (BP) decoding method in relatively short codes by more than 1 dB at the bit error rate (BER) of 10−3. Very large scale integration (VLSI) architecture of the proposed method is also given in this paper.
Do Google App Engine’s Runtimes Perform Homogeneously? An Empirical Investigation for Bonus Computing

Bonus computing is a new metacomputing form that takes advantage of free computing power from the public Cloud market. To maximize the value of free Cloud resources and facilitate dividing Bonus computing tasks, it would particularly be crucial to understand the performance of candidate Cloud services before using them in production. By offering free quotas in its standard environment, Google App Engine (GAE) has become a popular public Platform-as-a-Service (PaaS) for Bonus computing. Since GAE natively supports various programming languages with flexible configurations (e.g., region selection), it will be possible and valuable to squeeze GAE’s free computing power if there is an optimal choice of its different runtimes. Following the performance evaluation methodology DoKnowMe, we implemented several versions of the Fibonacci(-like) calculation as benchmarks to fundamentally investigate GAE’s standard environment. Our investigation results reveal that GAE does not support its runtime environments homogeneously in terms of their computation speed and memory efficiency. The heterogeneity could be related not only to the characteristics of different programming languages but also to the diverse GAE infrastructures. For example, Go runtime seems to be a well-trade-off to satisfy Bonus computing among all the options, while the GAE service located in southamerica-east1 and us-central1 performs dramatically worse than that in the other regions.
Secure and Efficient Multi-Authority Attribute-Based Encryption Scheme From Lattices

Attribute-based encryption (ABE) mechanism on lattices can against quantum attack and guarantees fine-grained access control of encrypted data, and it has been widely used in cloud computing and privacy protection. However, the high complexity of the trapdoor generation and delegation and preimage sampling are the main barriers for the development of ABE schemes in a post-quantum world. Thus, we proposed a security and efficient multi-authority ABE scheme from lattices in this paper. The scheme is based on the theory of MP12 and is proved to be secure under the standard model. In this scheme, we construct an optimized sampling algorithm to generate a $\mathbb {Z}_{q}$ -invertible matrix with a lower runtime. Based on it, combining the trapdoor generation and delegation algorithm in MP12, we designed multiple attribute authorities which can manage different attribute sets and generate private keys for the user independently. In addition, the Shamir’s secret sharing technique is introduced to support policies expressed in any monotone access structures. Compared with the existing related schemes, the proposed scheme can improve the functional agility and the flexibility of the access policy prominently, and it also can achieve a better performance with less lattice dimension, trapdoor storage occupation, and ciphertext expansion rate. The analysis shows that our scheme is feasible and superior in the large-scale distributed environment.
Data Information Fusion From Multiple Access Points for WiFi-Based Self-localization

In this letter, we propose a novel approach for fusing information from multiple access points in order to enhance WiFi-based self-localization. A common approach for designing WiFi-based localization systems is to learn location-to-signal strength mappings for each access point in an environment. Each mapping is then used to compute the likelihood of the robot's location conditioned on sensed signal strength data, yielding as many likelihood functions as mappings are available. Office buildings typically have from several tens to a few hundreds of access points, making it essential to properly combine all available likelihoods into a single, coherent, joint likelihood that yields precise likelihoods, yet is not overconfident. While most research has focused on techniques for learning these mappings and improving data acquisition; research on techniques to adequately fuse them has been neglected. Our approach for data information fusion is based on information theory and yields considerably better joint distributions than previous approaches. Furthermore, through extensive testing, we show that these joint likelihoods considerably increase the system's localization performance.
Input-to-State Stabilization of Uncertain Parabolic PDEs Using an Observer-Based Fuzzy Control

This paper presents an observer-based fuzzy controller for one-dimensional parabolic partial differential equations with distributed bounded disturbance using discrete point measurements. Sufficient conditions are derived to guarantee the input-to-state stability of the closed-loop system in terms of linear matrix inequalities. A numerical simulation example is given to demonstrate the efficiency of the results.
Electronic Characteristics of MoSe2 and MoTe2 for Nanoelectronic Applications

Single-crystalline MoSe<inf>2</inf> and MoTe<inf>2</inf> platelets were grown by Chemical Vapor Transport (CVT), followed by exfoliation, device fabrication, optical and electrical characterization. We observed that for the field-effect-transistor (FET) channel thickness in range of 5.5 nm to 8.5 nm, MoTe<inf>2</inf> shows p-type, whereas MoSe<inf>2</inf> with channel thickness range of 1.6 nm to 10.5 nm, shows n-type conductivity behavior. At room temperature, both MoSe<inf>2</inf> and MoTe<inf>2</inf> FETs have high ON/OFF current ratio and low contact resistance. Controlling charge carrier type and mobility in MoSe<inf>2</inf> and MoTe<inf>2</inf> layers can pave a way for utilizing these materials for heterojunction nanoelctronic devices with superior performance.
Secure Transmission in Multi-Pair AF Relaying Massive MIMO Networks Against Active Pilot Spoofing Attacks

This paper investigates the secure transmission in multi-pair massive multiple-input multiple-output amplify-and-forward (AF) relaying networks under active pilot spoofing attacks. The AF relaying beamforming and the artificial noise generation are designed based on the imperfect estimated channel state information. Exploiting a random matrix theory, we first derive the tractable analytical expressions for the end-to-end signal-to-interference-plus-noise ratios (SINRs) at both the intended user and the eavesdropper. Then, we provide the asymptotic results for derived SINRs with respect to the infinite number of relay antennas, unbounded transmit power, and relay power, which show that the SINRs at intended user and eavesdropper are both limited by specific bounds and the effects of relay noise or received additive noise will be eliminated in these extreme power cases. Moreover, power scaling laws are studied to reveal that only the scaled relay power is effective, while source power has no scaling properties for secure transmission in the considered system. To mitigate the severe information leakage, by superimposing a random detection pilot sequence on the training sequence, a simple method based on the minimum description length criterion is provided to detect the presence of the pilot spoofing attacks. The numerical results are provided to confirm the accuracy of our theoretical derivations and illustrate the effectiveness of the proposed scheme.
Mobile Data Offloading with Uniform Pricing and Overlaps

Mobile data offloading is an emerging technology to alleviate cellular network congestion and improve user service quality. In this paper, we investigate the economics of mobile data offloading through access points (APs) deployed by small cell service providers (SSPs), implementing uniform volume prices for all the mobile users (MUs) in each SSP's coverage including the overlapping area. In particular, we consider a data offloading game with a single mobile network operator (MNO) and two SSPs with overlapping coverage areas, where each SSP announces a uniform price for serving the cellular traffic within its coverage, and the MNO determines the traffic volumes to offload. We show that there is no pure Nash equilibrium (PNE) under such price competition, and determine the corresponding mixed strategy Nash equilibrium (MNE) using price randomization. As a practical solution, we propose a simple one shot auction mechanism that is easy to implement and has PNEs which is payoff equivalent with the MNE under price competition. We believe that this simple mechanism due to its simplicity of determining the equilibrium prices could be used in the negotiation between the SSPs and the MNO to determine the average service prices. Finally, we study the strategic topological infrastructure placement problem using a 1-dimension (1D, linear) user traffic flow model and a 2-dimension (2D) user traffic flow model when SSPs compete assuming uniform price competition as above. We show that the first mover in the placement problem will deploy its APs to cover more than half of the total flow volume and has an advantage to obtain a higher equilibrium payoff.
Altitude control of an underwater vehicle based on computer vision

It is common the use of the sonar technology in order acquire and posteriorly control the distance of an underwater vehicle towards an obstacle. Although this solution simplifies the problem and is effective in most cases, it might carry some disadvantages in certain underwater vehicles or conditions. In this work it is presented a system capable of controlling the altitude of an underwater vehicle using computer vision. The sensor capable of computing the distance is composed of a CCD camera and 2 green pointer lasers. Regarding the control of the vehicle, the solution used was based on the switching of two controllers, a velocity controller (based on a PI controller), and a position controller (based on a PD controller). The vehicle chosen to test the developed system was a profiler, which main task is the vertical navigation. The mathematical model was obtained and used in order to validate the controllers designed using the Simulink toolbox from Matlab. It was used a Kalman filter in order to have a better estimation of the state variables (altitude, depth, and velocity). The tests relative to the sensor developed responsible for the acquisition of the altitude showed an average relative error equal to 1 % in the range from 0 to 2.5 m. The UWsim underwater simulation environment was used in order to validate the integration of the system and its performance.
Locally Shared Features: An Efficient Alternative to Conditional Random Field for Semantic Segmentation

In recent years, semantic segmentation methods based on fully convolutional networks (FCNs) have achieved great success. However, these methods tend to produce inconsistency and isolated class labels, mainly because the end-to-end mapping of FCN essentially treats each pixel independently. As a post-processing approach, the conditional random field (CRF) has been widely used to alleviate this problem. However, the inference of CRF is usually very time-consuming in computation. To solve this problem, we present a new method, called locally shared features (LSFs), to model local dependence between pixels. The LSF encourages adjacent pixels to have similar features by making them share certain properties with each other. This is achieved by concatenating features around a pixel, including the pixel itself. Our experimental results indicate—the LSF approach delivers comparable or better performance than the CRF method with respect to the accuracy and local smoothness in segmentation output, while obtaining a significant gain in computational efficiency.
Human-in-the-loop for autonomous underwater threat recognition

In this paper, human expert operators and automated classification algorithms are charged with the task of analyzing sonar images collected during real mine countermeasures exercises in order to detect and classify targets. Images are collected using synthetic aperture sonar (SAS) and side scan sonar (SSS), covering a test area on the Belgian Continental Shelf, between the Thorton bank and the Goote Bank. A seafloor segmentation map of this area, calculated using lacunarity and representing how difficult or how benign the seafloor is for object-recognition, is used as a new strategy in order to divide the database between operator and computer. Results demonstrate the utility of considering the human operator as an integral part of the automatic underwater object recognition process, and demonstrate how automated algorithms can extend and complement human performances
Energy Efficiency of Secure Cognitive Radio Networks with Cooperative Spectrum Sharing

Energy-efficient and secure wireless communications have recently earned tremendous interests due to economic, environmental, and military concerns. This paper investigates the tradeoff between the secrecy throughput and the energy efficiency in cognitive radio networks (CRNs), where primary and secondary users with different priorities of spectrum access can either interfere or cooperate with each other. To gain an understanding of the intricate effects that system parameters have on underlay network's performance, we exclusively focus on characterizing several key aspects that may have potential impacts on secure underlay CRNs, including the transmission power, the number of interfering users, and the designed interference resistance coefficient. Based on the obtained analytical results, we further propose a cooperative spectrum sharing paradigm to improve both the secrecy throughput and the energy efficiency of primary users. The main idea is that primary users allow secondary users to simultaneously access the licensed spectrum and in return, the secondary transmitter acts as both a relay for primary transmissions and a friendly jammer against eavesdropping, in case the primary transmission fails. Both theoretical and numerical results reveal that: (i) When the interference from secondary transmitters is small, there is an optimal transmission power that maximizes the secrecy throughput for primary users compared to CRNs without the security issue. (ii) When the interference from secondary transmitters is large, the secrecy throughput increases with the transmission power for primary users. (iii) The transmission power that maximizes the energy efficiency is smaller than that maximizes the secrecy throughput for primary users. (iv) The number of interfering users has a slight impact on the secrecy throughput and the energy efficiency of primary users due to the secondary power control. (v) The proposed cooperative paradigm is an efficient approach to boost both the secrecy throughput and the energy efficiency of primary users compared with the traditional non-cooperative spectrum sharing, and provides an alternative method to compensate for the interference caused by secondary users.
Single Cell Electro-Thermal Model for Simulation of an Electric Vehicle

Battery operation is influenced by the temperature. Thus, thermal modelling is an important aspect for battery electric vehicles (EVs). Generally the electro-thermal parameters are identified for one single cell. In EVs the battery is divided into several modules (groups of cells side by side). The objective of this paper is to quantify the accuracy by using the electro-thermal parameters of one single cell to model the behavior of a module. The temperature of a cell inside a module and the voltage of this module are recorded during a real driving cycle in an instrumented commercial EV. Considering the module as a group of identical single cells leads to an error of 7% for module voltage and 4.8% on the self-heating.
High-Gain Fabry-Perot Antennas With Wideband Low Monostatic RCS Using Phase Gradient Metasurface

The design of two high-gain Fabry–Perot antennas with wideband low monostatic radar cross section (RCS) is presented in this paper. First, a phase gradient metasurface (PGM) without lossy layer is used to realize in-band RCS reduction and high aperture efficiency, simultaneously. The simulated results show that its peak gain is 19.2 dBi, and a high aperture efficiency of 58.6% is achieved. By utilizing the PGM structure, the monostatic RCS is reduced from 7 to 11 GHz, and the in-band co-polarization RCS is reduced by 13.1 dB. After that, a PGM with metamaterial absorber is designed to broaden the bandwidth of RCS reduction. A prototype antenna is fabricated and measured. The peak gain of 17.9 dBi is obtained, and the monostatic RCS is reduced in a wider frequency band of 7–15 GHz.
A Benchmark Dataset and Evaluation for Non-Lambertian and Uncalibrated Photometric Stereo

Recent progress on photometric stereo extends the technique to deal with general materials and unknown illumination conditions. However, due to the lack of suitable benchmark data with ground truth shapes (normals), quantitative comparison and evaluation is difficult to achieve. In this paper, we first survey and categorize existing methods using a photometric stereo taxonomy emphasizing on non-Lambertian and uncalibrated methods. We then introduce the 'DiLiGenT' photometric stereo image dataset with calibrated Directional Lightings, objects of General reflectance, and 'ground Truth' shapes (normals). Based on our dataset, we quantitatively evaluate state-of-the-art photometric stereo methods for general non-Lambertian materials and unknown lightings to analyze their strengths and limitations.