Miracle-dz/UniFAR-FTUs huggingface dataset:
Altered mRNA expression levels of vaspin and adiponectin in peripheral blood mononuclear cells of systemic lupus erythematosus patients.

OBJECTIVES Increasing studies have indicated the association between adipokines and multiple autoimmune diseases. This study aimed to evaluate the mRNA expression levels of vaspin, adiponectin and adrenomedullin in peripheral blood mononuclear cells (PBMCs) of patients with systemic lupus erythematosus (SLE), as well as their clinical associations. METHODS A total of 46 SLE patients and 51 normal controls were recruited. The three adipokines expression levels in PBMCs from SLE patients were measured by qRT-PCR, and their associations with major clinical and laboratory parameters of SLE patients were also analysed. RESULTS Compared with normal controls, vaspin expression level in PBMCs was significantly decreased (p<0.001), whereas adiponectin expression level was significantly higher in SLE patients (p<0.001). There was no significant difference in adrenomedullin expression level between SLE patients and normal controls. Vaspin and adrenomedullin expression levels in more active SLE were significantly lower than those in less active SLE (p=0.012, p=0.046, respectively). No significant difference in these adipokine expression levels was observed between SLE patients with and without lupus nephritis (LN). There was also no significant association between mRNA levels of these adipokines and major clinical and laboratory parameters. CONCLUSIONS Altered vaspin, adiponectin expression levels, and the associations between vaspin, adrenomedullin levels and disease activity in SLE patients suggested that these adipokines might play a role in SLE.
Neural Dynamics for Cooperative Control of Redundant Robot Manipulators

In this paper, a neural-dynamic distributed scheme is proposed for the cooperative control of multiple redundant manipulators with limited communications. It is guaranteed that, with the communication network being connected, all manipulators can jointly reach the same desired motion. The proposed distributed scheme is rearranged as a time-varying quadratic program and solved online by a Zhang neural network. Then, theoretical analyses show that, without noise, the proposed distributed scheme is able to execute a given task with exponentially convergent position errors. Moreover, an explicit bound relationship between the control input noise and the end-effector position error is analytically derived. Furthermore, numerical comparisons substantiate the superiority, effectiveness, and accuracy of the proposed distributed scheme.
Classification accuracy of the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) Effort Index (EI) and Effort Scale (ES) relative to the Test Of Memory Malingering (TOMM) in a mixed clinical sample

Abstract Performance Validity Testing has become an integral part of neuropsychological assessment, and a variety of embedded screening measures for performance validity have been proposed. Several performance validity indexes have been developed for the Repeatable Battery for the Assessment of Neuropsychological Status. Although the RBANS Effort Index (EI) and Effort Scale (ES) have garnered some empirical support, other research has raised questions regarding their accuracy in a number of assessment contexts. We evaluated the classification accuracy of the EI and ES against a standalone performance validity measure (Test of Memory Malingering; TOMM) in a mixed clinical patient sample. Our results showed limited utility for the ES in a mixed sample and modest classification accuracy for the EI, raising concerns about the appropriate scope of use for these scales in general clinical practice, which suggested that an alternate EI cutoff score of >0 may be most appropriate.
Effect of Blocked Versus Random Practice on Physical Therapy Students’ Manual Force Modulation

This study examined (a) the effect of practice organization on learning to modulate manual force, and (b) the effect of force magnitude on ability to accurately reproduce force. I randomly assigned 52 novice physical therapy students to either blocked or random practice schedules as they learned to apply different manual forces. I tested students immediately after training and one week later, using a three-way mixed design analysis of variance to assess the effects of practice organization (random vs. blocked), test (immediate posttest vs. retention test), and force (high vs. low) on ability to modulate manual forces. I found that blocked versus random practice, led to more accurate force application at the posttest and retention test, F(1, 50) = 5.57, p < . 05; both practice groups performed more accurately at the posttest than at the retention test, F(1, 50) = 5.6, p < . 05, and both performed more accurately at the lower, compared with the higher, force level, F(1, 50) = 60.9, p < . 05. These findings support use of a blocked' practice schedule for this motor training, though relevant contextual interference moderators may include such variables as nature of the task, learners’ skill levels, and practice time. In addition, higher forces resulted in greater errors and must be considered in preparing for and engaging in clinical practice.
Testing Cryptographically Secure Pseudo Random Number Generators with Artificial Neural Networks

We present a new way of testing Random Number Generators (RNGs). Our approach allows to test Pseudo Random Number Generators (PRNGs) including Cryptographically Secure Pseudo Random Number Generators (CSPRNGs). The paper describes how to use machine learning for this. To construct a tester we compare the properties of three most common learning techniques to find the one most suitable one for testing RNGs. By analyzing the system during training and regarding the expected behavior of random numbers, we define an optimizer for learning RNGs. Based on the results and regarding the behavior of the machine learning algorithm, we define a rating for RNGs. On a state-of-the-art GPU cluster, we evaluate the full tester for multiple PRNGs. Additionally, we compare the results with the results from the commonly used test suite dieharder. The results prove that the developed tester is suitable for testing random numbers. In comparison to dieharder, it is even more powerful and able to replace it. Our tester could disclose weaknesses in PRNGs that are wrongly considered as CSPRNG. This could increase the security of many cryptographic protocols based on random numbers.
Multiobjective Optimization Design of a Double-Rotor Flux-Switching Permanent Magnet Machine Considering Multimode Operation

The multimechanical-port double-rotor machine, as a core component in hybrid electric vehicles, has attracted considerable attention due to the merits of compact structure, control flexibility, and diverse performances. However, since there often exist two electric or mechanical ports in one compound machine frame, traditional design theory cannot be applied to it directly. Besides, because of the relatively complex machine structure and multiple design parameters, design objectives often influence even conflict with each other. In this paper, a new multiobjective optimization design approach is proposed to design and optimize a flux-modulated double-rotor flux-switching permanent magnet machine, where multiple driving modes are considered. To verify the feasibility of the double-rotor machine and the effectiveness of an optimization design method, the electromagnetic characteristics of the machine are investigated in detail. Finally, a prototype machine is built and tested. Both theoretical analysis and experimental results verify the validity of the proposed machine and multiobjective optimization design approach.
Titanium Dioxide-Based Memristive Thin Film: A Correlation Study Between the Experimental Work and Simulation Program With Integrated Circuit Emphasis Hyperbolic Sine Models

This paper presents a correlation study between experimental results of titanium dioxide (TiO2)-based memristors and various hyperbolic sine function models. The current–voltage (I-V) characteristics of sol-gel-derived TiO2 thin film annealed at 250°C were correlated with the reported hyperbolic sine function simulation program with integrated circuit emphasis memristor models. The correlation study showed that the existing models were not fitted well with our experimental data. The models with the lowest root means square errors were then combined together to achieve better fitting result. Further, experimental results of TiO2 thin films fabricated by varying the annealing temperature at 350 °C and 450 °C were correlated to the proposed model to further elucidate the device operation. The parameters were then analyzed by multiplying and dividing the simulation results by two. It was found that both the Schottky and tunneling mechanisms had a significant impact in shaping the I-V characteristic of annealed TiO2 thin film and device conductivity. The state variable derivative on the other hand caused changes in device threshold voltage. The knowledge gained from the fitting parameters of the proposed model enables us to predict the performance of fabricated memristive device and engineer the process parameters for desired memristive behavior.
Psychological disturbance and cognitive functioning in TBI patients

Abstract The current study examined psychopathology and neuropsychological test performance in a traumatic brain injury (TBI) patient sample. Previous research has found that the Minnesota Multiphasic Personality Inventory – Second Edition (MMPI-2) indices of psychological disturbance were related to specific neuropsychological tests of attention and memory. Furthermore, higher scores on clinical scale 2 (Depression) and higher scores on the content scales of Fears and Bizarre Mentation of the MMPI-2 were related to poorer performance in, most notably, Attention and List Learning factors. The present study sought to extend the research by adding a neuropsychological measure of conceptual reasoning. A sample of 116 TBI patients referred for evaluation at a private practice clinic were administered a battery of neuropsychological measures of list learning, working memory, verbal memory, visuographic memory, and conceptual reasoning, as well as the MMPI-2 to assess emotional functioning. Regression analysis indicated that an overall model of MMPI-2 indices significantly predicted poorer performance on List Learning. More specifically, higher scores on the content scales of Obsessions and Fears were most robustly associated with poorer neuropsychological test performance for List Learning and Working Memory respectively. Findings suggest psychopathology is correlated with performance on neuropsychological measures for TBI patients.
The role of neighbourhood greenspace in children's spatial working memory

Background Exposure to nature may be particularly beneficial for the brain regions that support spatial working memory, a strong correlate of academic achievement. Aims To explore whether children living in greener neighbourhoods (wards) have better spatial working memory. Sample Drawn from the UK's Millennium Cohort Study, the sample was 4,758 11‐year‐olds living in urban areas in England. Methods We fitted two‐level regression models, with children nested in wards, before and after adjustment for confounders, including poverty, parental education, sports participation, neighbourhood deprivation, and neighbourhood history. Spatial working memory was measured using the Cambridge Neuropsychological Test Automated Battery Spatial Working Memory task. Greenspace was measured as the percentage of greenery in the child's ward. Results Even after controlling for confounders, lower quantity of neighbourhood greenspace was related to poorer spatial working memory. Importantly, neighbourhood deprivation did not modify this relationship. Therefore, lower quantity of greenspace was related to poorer spatial working memory similarly in deprived and non‐deprived neighbourhoods. Conclusions Children living in greener urban neighbourhoods have better spatial working memory. If this association is causal, then our findings can be used to inform policy decisions about both education and urban planning.
An Overview of Core Coding Tools in the AV1 Video Codec

AV1 is an emerging open-source and royalty-free video compression format, which is jointly developed and finalized in early 2018 by the Alliance for Open Media (AOMedia) industry consortium. The main goal of AV1 development is to achieve substantial compression gain over state-of-the-art codecs while maintaining practical decoding complexity and hardware feasibility. This paper provides a brief technical overview of key coding techniques in AV1 along with preliminary compression performance comparison against VP9 and HEVC.
Ultrahigh Sensitivity to Strain of Cracked Thin Films Based on Metallic Nanoparticles in a Dielectric Matrix

Platinum nanoparticle-based strain gauges are elaborated by means of atomic layer deposition on flexible polyimide substrates. Their electromechanical response is tested under mechanical bending at strain levels up to 1. Sudden changes in electrical resistance are observed above 0.6 strain when straight cracks are formed in the nanostructured thin film. Scanning electron microscope (SEM) experiments reveal that these cracks are preferentially formed next to the interface with aluminum top electrodes. After crack formation, a giant gauge factor of 2 104 is measured at a strain level of 0.3. This high sensitivity to strain is attributed to the gradual opening (or closing) of the cracks when mechanical strain is applied. Finally, we demonstrate the feasibility of a flexible heart rate monitor, which integrates a cracked Pt nanoparticle-based strain gauge.
Optimal Mode Selection Algorithms in Multiple Pair Device-to-Device Communications

Device-to-Device (D2D) communications have been proposed to improve spectrum and energy efficiency of cellular networks. One of the most critical issues in D2D networks is the selection of the communication modes for multiple D2D pairs in a cell. In this article, relay-assisted mode, in addition to basic cellular and direct D2D modes, is taken into account to improve the link performance of D2D pairs. We investigate the optimal mode selection schemes to maximize cell-wise capacity based on the reuse of cellular uplink channels in different multi-pair D2D communication scenarios. We discover that the optimal mode selection problem can be transformed into a linear programming problem with polynomial computational complexity. The exact analytical solutions for optimal mode selection are derived and can be useful to cellular operators to maximize the cellwise capacity.
Video Quality Assessment Metrics for Infrared Video Frames using Different Edge Detection Algorithms

Infrared signal processing algorithms and architectures are important for the development of a high performance infrared imaging systems. Infrared signal processing deals with two types of processing sensor signal processing and infrared image processing for contrast enhancement and target detection. The sensor signal processing primarily deals with non uniformity correction for infrared sensors. Assessing the quality of the Infrared video frame is a complex and firm process since human’s opinion is affected by physical and psychological parameters. Infrared Video frame quality assessment plays an important role in the field of video processing. Structural Similarity Index has become a standard among image quality metrics. It is a framework for quality assessment based on the degradation of structural information of video frame. In this paper SSIM values are computed and compared for Infrared video frames by applying different edge detection approaches by using different color models to assess the quality of the frames. Experimental results comparisons demonstrate the effectiveness of the proposed method.
Customized Machine Learning-Based Hardware-Assisted Malware Detection in Embedded Devices

The emerging embedded systems, which account for a wide range of applications are often highly resource-constrained challenging the conventional software-based methods traditionally deployed for detecting and containing malware in general purpose computing systems. In addition to the complexity and cost (computing and storage), the software-based malware detection methods mostly rely on the static signature analysis of the running programs, requiring continuous software update in the field to remain accurate in capturing emerging malware, which is not affordable for embedded systems with limited computing and communication bandwidth. Hardware-assisted Malware Detection (HMD) though found to be more efficient, limited computing power and resources in embedded systems as well as the small number of available Hardware Performance Counter (HPC) registers that can be simultaneously captured, make accurate runtime malware detection in embedded devices a challenging problem. In response, this work proposes a lightweight customized HMD approach which takes advantage of HPC features to effectively detect and further classify various malware classes at runtime. To realize a runtime solution that relies on limited available HPCs and to enhance the accuracy of malware detection, we use customized HMD for individual class of malware that utilizes various Machine Learning (ML) classifiers to detect malware using the four most important HPC features.
A Hybrid Control Strategy for Dual-arm Object Manipulation Using Fused Force/Position Errors and Iterative Learning

We report on the development of a force/position hybrid control strategy for collaborative object-handling tasks in a dual-arm system composed of two position-controlled commercial manipulators. A Kalman filter (KF) is used to fuse the measured position and force errors to generate the fused force error as the input to a force PID controller that generates the associated position command that is accepted by the embedded controller of the commercial manipulators. A spring -inerter-damper model is also embedded within the KF, simulating the interface dynamics and providing the smooth force interaction between the manipulator hand and the grasped object. In addition, the iterative learning control that uses the fused position error serves as the additive position-based compensation of the manipulator. To verify the proposed strategy, the system is validated experimentally. The experimental results show that the proposed control strategy can properly alter the balance between the force and position errors. The results also show that adding the KF can prevent severe trembling of the robot's arms.
New University Students and Blended Learning Experience

Blended Learning demands have been discussed and examined by academicians at the Faculty of Informatics and Management, University of Hradec Kralove for more than two decades. If blended learning is applied appropriately, it con-tributes to the increase in the efficiency of the educational process, to the decrease in the amount of face-to-face instruction and to the strengthening the dimension of self-study and project activities. The point is whether students coming from secondary schools are ready to use the offered eLearning tools and the potential to the full. The objective of the paper is to present trends in areas that are crucial for further blended learning use for university study purposes.
Appendix - Recommended Statistical Significance Tests for NLP Tasks

Statistical significance testing plays an important role when drawing conclusions from experimental results in NLP papers. Particularly, it is a valuable tool when one would like to establish the superiority of one algorithm over another. This appendix complements the guide for testing statistical significance in NLP presented in \cite{dror2018hitchhiker} by proposing valid statistical tests for the common tasks and evaluation measures in the field.
Blockmania: from Block DAGs to Consensus

Blockmania is a byzantine consensus protocol. Nodes emit blocks forming a directed acyclic graph (block DAG) that is subsequently interpreted by each node separately to ensure consensus with safety, liveness and finality. The resulting system has communication complexity $O(N^2)$ even in the worse case, and very low constant factors --- as compared to $O(N^4)$ for PBFT; it is leaderless; and network operations do not depend on the composition of the quorum or node stake. This makes Blockmania very efficient (leading to over 400K transactions per second on a wide area network), and ideal for dynamic membership and flexible and non-interrupted proof-of-stake protocols. A X-Blockmania variant, has $O(N)$ communication cost but also higher latency $O(\log N)$.
Enhancing Data Protection in a Distributed Storage Environment Using Structure-Wise Fragmentation and Dispersal of Encrypted Data

We present a simple and effective way of fragmentation and dispersal of data encrypted with block ciphers. The method takes into consideration the block structure of the ciphertext, as well as the chaining properties of some of the operation modes. It considerably increases the difficulty of a brute-force search aiming at recovering data from less than the totality of the fragments. It can easily be integrated as an additional step inside known fragmentation techniques (such as Secret Sharing Made Short or AONT-RS) protecting data in a situation of key exposure. Comparative experimental results show clear performance and scalability improvement over relevant works.
Malignant Narcissism in Relation to Clinical Change in Borderline Personality Disorder: An Exploratory Study

Background: Normal and pathological narcissism have been the focus of considerable theoretical discussion and empirical research in recent years in personality psychology and psychopathology. Kernberg [1-4] has argued that there is a particularly dysfunctional and impairing variant of narcissistic disturbance known as malignant narcissism. This exploratory study sought to develop, using established assessment methods, a dimensional measure of malignant narcissism that incorporates the key features of grandiose narcissism, paranoid propensities, psychopathic features, and proclivity for a sadistic and aggressive interpersonal style. Method and Sampling: This study examined 57 subjects, diagnosed with borderline personality disorder that were treated using 3 different empirically supported treatments in a previous study [5], for possible deviance on the proposed malignant narcissism index. It also evaluated 2 important clinical domains of change in relation to malignant narcissism. To wit, it was predicted, based on Kernberg’s [3, 4] clinical model, that elevated levels of malignant narcissism would be significantly associated with slower rates of improvement in both general psychosocial/psychological functioning and anxiety among treated individuals. Results: Higher levels of malignant narcissism were associated, as predicted, with slower rates of improvement in both global functioning and anxiety. The proposed malignant narcissism index was a more powerful predictor of slowed improvement in global functioning than simple narcissistic personality disorder features. Conclusions: The heuristic potential of the malignant narcissism construct is discussed and the utility of a dimensional approach to this construct is explored, especially in reference employing personality traits/processes to better understand pathological configurations and personality disturbance.