Miracle-dz/UniFAR-FTUs huggingface dataset:
An Affective Interaction System using Virtual Reality and Brain-Computer Interface

Affective Computing is a multidisciplinary area of research that allows computers to perform human emotion recognition, with potential applications in areas such as healthcare, gaming and intuitive human computer interface design. Hence, this paper proposes an affective interaction system using dry EEG-based Brain-Computer Interface and Virtual Reality (BCI-VR). The proposed BCI-VR system integrates existing low-cost consumer devices such as an EEG headband with frontal and temporal dry electrodes for brain signal acquisition, and a low-cost VR headset that houses an Android handphone. The handphone executes an in-house developed software that connects wirelessly to the headband, processes the acquired EEG signals, and displays VR content to elicit emotional responses. The proposed BCI-VR system was used to collect EEG data from 13 subjects while they watched VR content that elicits positive or negative emotional responses. EEG bandpower features were extracted to train Linear Discriminant and Support Vector Machine classifiers. The classification performances of these classifiers on this dataset and the results of a public dataset (SEED-IV) are then evaluated. The results in classifying positive vs negative emotions in both datasets (~66% for 2-class) show promise that positive and negative emotions can be detected by the proposed low cost BCIVR system, yielding nearly the same performance on the public dataset that used wet EEG electrodes. Hence the results show promise of the proposed BCI-VR system for real-time affective interaction applications in future.
Explaining Factor Ascription

Explanation and justification of legal decisions has become a highly relevant topic in light of the explosion of interest in the use of machine learning (ML) approaches to predict legal decisions. Current suggestions are to use the established factor based explanations developed in AI and Law as the basis for explaining such programs. We, however, identify factor ascription as an important aspect of explanation of case outcomes not currently covered, and argue that explanations must also include this aspect. Finally, we outline our proposal for a hybrid system approach that combines ML and Abstract Dialectical Framework (ADF) layers to engender an explainable process.
Characterization of systolic and diastolic pressure time series in pregnant women with preeclampsia through symbolic dynamics

Preeclampsia (PE) is one of the leading causes of maternal mortality worldwide. Although clinical strategies to prevent the early onset of PE have been proposed, the ultimate solution is to end the pregnancy. Therefore, patients’ identification with major PE risk is important towards the prevention and better management of a severe manifestation of the illness. This study aims to analyze the systolic blood pressure (SBP) and diastolic blood pressure (DBP) time series through a nonlinear perspective using symbolic dynamics and to incorporate a multi-scale assessment in the first trimester of pregnancy, previous to the clinical manifestation of PE. The study group of normotensive women who developed and were diagnosed with PE included 14 pregnant women, a normotensive throughout pregnancy control group (N) consisting of 14 participants, and a group of 14 normotensive women during pregnancy without comorbidities (S) were matched with PE by age, body mass index, gestational age and comorbidities. The preliminary results of this study showed a decreased complexity of SBP, assessed by multiscale symbolic entropy in the first trimester in PE patients, in comparison with normotensive pregnant women.Clinical relevance— This work shows how nonlinear analysis of systolic and diastolic blood pressure time series are useful to detect preeclampsia in the first trimester of pregnancy.
3D Body Parts Tracking of Mouse Based on RGB-D Video from Under an Open Field

The mouse is a valuable animal model to address the neural mechanism of higher brain function and test the pharmacodynamics of new drugs. The development of novel behavioral analysis to detect subtleties of emotion is valuable for the evolution of neuroscience research and drug discovery. 3D pose estimation is expected to contribute significantly to them. Several methods for 3D pose estimation of the mouse using optical motion capture with markers and multiple cameras have been proposed, but these methods have problems such as preparing marker sets and the influence of the markers on mouse behavior. A low-cost and simple method for markerless 3D pose estimation of the mouse using a single RGB-D (Depth) camera is proposed. As a result, the proposed method improved the accuracy of limbs tracking compared to existing limbs tracking methods. In addition, this method could track other body parts (nose, base of tail) and the center of gravity.Clinical Relevance—This study could contribute to the development of neuroscience research and drug discovery by clarifying the relationship between subtle changes in mouse behavior and emotional movements.
PERSONNEL MANAGEMENT IN THE CONDITIONS OF SOCIAL RESPONSIBILITY OF THE ENTERPRISE УПРАВЛІННЯ ПЕРСОНАЛОМ В УМОВАХ СОЦІАЛЬНОЇ ВІДПОВІДАЛЬНОСТІ ПІДПРИЄМСТВА

Peculiarities of personnel management at enterprises, means of influencing the employee in the development of labor relations are determined. The comparative characteristics of the most important socially responsible business models are considered. Attention is paid to the importance of personnel responsibility policy. The role of the concept of social responsibility of personnel management, which is based on the integration and consideration of the bilateral relationship between social responsibility and personnel management, is indicated. It is concluded that the creation of purposeful principles of personnel management involves a clear management mechanism for goals and results, taking into account modern requirements and capabilities of consumers, the socio-psychological state of staff in the constant development of scientific and technological progress.
Neural Encoding of Reaches in a Linear Cortical Model

To effectively control the arm, motor cortical neurons must produce complex patterns of activation that vary with the position and orientation of the arm and reach direction. In order to better understand how such a finely tuned dynamical system could arise and what its basic organizing principles are, we develop a model of the motor cortex as a linear dynamical system with feedback coupled to a two-joint model of the macaque arm. By optimizing the connections between neural populations with respect to an objective function that penalizes error between hand and target, as well as neural and muscular energy use, we show that certain properties of the motor cortex, such as muscle synergies, can naturally be obtained. We also demonstrate that the optimization process produces a stable neural system in which targets in the physical space are mapped to attracting fixed points in the neural state space. Finally, we show that this optimization process produces neural units with complex spatial and temporal activation patterns.
Stability of General Dynamical Networks via Majorization of Phases

In this paper, we study the stability of a linear time-invariant dynamical network with both node and edge dynamics. It is demonstrated that at each frequency, the phases of the eigenvalues of the loop-gain transfer function are bounded from above (below, respectively) by the sum of the maximal (minimal, respectively) phases of the node and edge dynamics. Based on this property and the generalized Nyquist stability criterion, a sufficient condition for closed-loop stability is derived in terms of the phases of node and edge dynamics. This serves as an interesting starting point in developing a more general “small phase theorem”.
Assessing Vision Quality in Retinal Prosthesis Implantees through Deep Learning: Current Progress and Improvements by Optimizing Hardware Design Parameters and Rehabilitation

Retinal prosthesis (RP) is used to partially restore vision in patients with degenerative retinal diseases. Assessing the quality of RP-acquired (i.e., prosthetic) vision is needed to evaluate RP impact and prospects. Spatial distortions caused by electrical stimulation of the retina in RP, and the low number of electrodes, have limited the prosthetic vision: patients mostly localize shapes and shadows rather than recognizing objects. We simulate prosthetic vision and evaluate vision on image classification tasks, varying critical hardware parameters: total number and size of electrodes. We also simulate rehabilitation by re-training our models on prosthetic vision images. We find that electrode size has little impact on vision while at least 400 electrodes are needed to sufficiently restore vision (more than 65% classification accuracy on a complex visual task after rehabilitation). Argus II, a currently available implant, produces a low-resolution vision leading to low accuracy (21.3% score after rehabilitation) in complex vision tasks. Rehabilitation produces significant improvements (accuracy improvement of up to 30% on complex tasks, depending on the number of electrodes) in the attained vision, boosting our expectations for RP interventions and motivating the establishment of rehabilitation procedures for RP implantees.
Interpretation of Deep Neural Networks in Speech Classiﬁcation

The growing problem of the popularity of using deep neural networks is their black box representation. The lack of transparency is raising questions about their reliability, credibility, or vulnerability to adversarial attacks. This caused rising demand for neural network explainability. The goal of this paper is to replicate existing experiments on a gender classiﬁcation model and extend these experiments to analyze and uncover vulnerabilities of a network trained for gender classiﬁcation on audio signal spectrograms. The easiest way to explain something is through visualization. For this, a layer-wise relevance propagation technique was chosen in this work because it produces easy-to-understand heatmaps of features relevant to a neural network. The heatmaps are produced by back-propagating relevances through a network from the output to the input layer. Two neural network models with AlexNet and ResNet architecture were used. Experiments with AlexNet model show that the network’s predictions are highly dependent on a small number of time-frequency (TF) bins. By augmenting the training data using obtained relevance maps, I managed to lower the dependency on these bins. As a result, the prediction accuracy, when these bins were not present, was increased by 15% . The proposed approach can potentially lead to increased robustness of models, preventing or reducing the impact of adversarial attacks. Interpretation of ResNet model showed dependencies on lower frequencies and time. Producing interpretable heatmaps of the ResNet model required the implementation of more robust LRP rules.
Wireless Multi-sensor Physio-Motion Measurement and Synchronization System and Method for HRI Research

There is a strong demand for acquisition, processing and understanding of a variety of physiological and behavioral signals from the measurements in human-robot interface (HRI). However, multiple data streams from these measurements bring considerable challenges for their synchronizations, either for offline analysis or for online HRI applications, especially when the sensors are wirelessly connected, without synchronization mechanisms, such as a network-time-protocol. In this paper, we presented a full wireless multi-modality sensor system comprising biopotential measurements such as EEG, EMG and inertial parameter data of articulated body-limb motions. In the paper, we propose two methods to synchronize and calibrate the transmission latencies from different wireless channels. The first method employs the traditional artificial electrical timing signal. The other one employs the force-acceleration relationship governed by Newton’s Second Law to facilitate reconstruction of the sample-to-sample alignment between the two wireless sensors. The measured latencies are investigated and the result show that they could be determined consistently and accurately by the devised techniques.
Comparative Analysis of Tissue Reactions during Implantation of Endoprostheses made of Polytetrafluoroethylene in the Anterior Abdominal Wall

Objectives: The objective of this research was a comparative study of the reaction of the tissues of the anterior abdominal wall to the PTFE-endoprosthesis with a DLC-coating at long-term experimental stages. Material and Methods: The study was performed on 110 white laboratory rats divided into two experimental groups. In the first group, a 1.0–1.0 cm PTFE-endoprosthesis fragment was implanted under the skin of the anterior abdominal wall in animals. In the second group, a DLC-coated PTFE-endoprosthesis fragment was implanted into animals. A morphological study of the preparations was carried out after 180 days of the experiment. Results: A morphological study revealed significant differences in tissue reactions to two types of PTFE-endoprosthesis after 180 days of the experiment. Both groups showed no acute inflammatory changes, and the differences were related to the delimiting tissue reactions. The giant cell reaction was observed to a greater extent when uncoated PTFEendoprostheses were used, and type IV collagen fibronectin was accumulated in them. The presence of a DLC coating significantly reduced the degree of this reaction. Also, a significant difference is that the initial PTFE endoprosthesis with micropores is impregnated with plasma and matrix proteins and subsequently grows with collagen fibers. DLC-coating reduces the absorption of proteins in the thickness of the endoprosthesis. Conclusion: The study found that this surface modification of PTFE-endoprostheses significantly affects the nature of the inflammatory and restrictive reactions that determine the bio-integration of the endoprosthesis.
An Approach to the Assessment of Detectability of Subsurface Targets in Polar Ice From Satellite Radar Sounders

A satellite mission onboard a radar sounder for the observation of the earth's polar regions can greatly support the monitoring of the cryosphere and climate change analyses. Several studies are in progress proposing the design and demonstrating the performance of such an earth-orbiting radar sounder (EORS). However, one critical aspect of the cryospheric targets that are often ignored and simplified in these studies is the complex geoelectrical nature of the polar ice. In this article, we present a performance assessment of the polar ice target detectability by focusing on their realistic representation. This is obtained by simulating the orbital radargrams corresponding to different regions of the polar cryosphere by leveraging the data available from airborne campaigns in Antarctica and Greenland. We propose novel performance metrics to analyze the detectability of the internal reflecting horizons (IRHs), the basal interface, and to analyze the nature of the basal interface. This performance assessment strategy can be applied to guide the design of the signal-to-noise ratio (SNR) budget at the surface, which can further support the selection of the main orbital instrument parameters, such as the transmitted power, the two-way antenna gain, and the processing gains.
Testing Of Digital Circuits with Conserved Power Utilization: Jumbled Test Sequence Approach

Reduction of power dissipation during combinational circuit testing can be a challenging task in VLSI circuits. This is accomplished by jumbling up the evaluation vector application sequence so that the current collection gives less changing activity during testing. This subsequently reduces the power dissipation of the Circuit Under Test (CUT). Jumbled test sequence approach is applied utilizing Graph Theory concept. Testing power is reduced by this algorithm without affecting the fault coverage. Fresh outcome of the proposed algorithm with ISCAS85 benchmark circuits reveal the switching activity might be decreased up to 72 times when compared to the previous research paper of this work and existing work.
Calcium Extract from Chicken Eggshells as Dietary Calcium Source in some Juices

The aim of this study to extract calcium from organic chicken eggshell and white chicken eggshell, Calcium determination in each, then fortifying fresh natural juices. Calcium was extracted from eggshells by using fresh lemon juice and lemon salt, and fortifying fresh natural juices (with extracted calcium at three levels of supplementation as follows 5, 10 and 15%). Study the effect of extracted calcium supplementation on each of calcium content, pH value and sensorial properties of juices were studied. The results showed that the higher calcium content was in the organic eggshell (30706.97mg/100gm) compared with white eggshell (28543.365mg/100gm), and calcium extract by lemon juice (973.6mg/100ml) higher than extract calcium by lemon salt was (965mg/100ml). In conclusion, addition calcium extract of eggshell led to a pronounced increase calcium content in the supplemented of juices compared to control. And a decrease in the pH values of juices during fortification in all treated samples compared to the control. There were, no significant differences (p<0.05) in all sensory characteristics of juices with 5%, 10% of extract calcium as compared to control sample. There were, significant differences (p<0.05) in odor, taste and flavor of juices fortified with 15% as compared to control sample.
Filter Bank Sinc-ShallowNet with EMD-based Mixed Noise Adding Data Augmentation for Motor Imagery Classification

Motor imagery-based brain computer interface (MI-BCI) is a representative active BCI paradigm which is widely employed in the rehabilitation field. In MI-BCI, a classification model is built to identify the target limb from MI-based EEG signals, but the performance of models cannot meet the demand for practical use. Lightweight neural networks in deep learning methods are used to build high performance models in MI-BCI. Small sample sizes and the lack of multi-scale information extraction in frequency domain limit the performance improvement of lightweight neural networks. To solve these problems, the Filter Bank Sinc-ShallowNet (FB-Sinc-ShallowNet) algorithm combined with the mixed noise adding method based on empirical mode decomposition (EMD) was proposed. The FB-Sinc-ShallowNet algorithm improves a lightweight neural network Sinc-ShallowNet with a filter bank structure corresponding to four sensory motor rhythms. The mixed noise adding method employs the EMD method to improve the quality of generated data. The proposed method was evaluated on the BCI competition IV IIa dataset and can achieve highest average accuracy of 77.2%, about 6.34% higher than state-of-the-art method Sinc-ShallowNet. This work implies the effectiveness of filter bank structure in lightweight neural networks and provides a novel option for data augmentation and classification of MI-based EEG signals, which can be applied in the rehabilitation field for decoding MI-EEG with few samples.
Inbreeding Depression in Captive-raised Arma chinensis

Arma chinensis is an excellent predator that can prey on more than 40 species of lepidoptera, coleoptera and hymenoptera pests. At present, A. chinensis are mostly reared in insectary, which can result in inbreeding depression. A series of life characteristics of A. chinensis population in inbreeding condition were determined, which can support insectary rearing of the predator. A. chinensis populations were inbred or outbred continuously in the insectary, and the differences between the inbred and outbred populations in egg hatching rate, developmental duration of nymphal stages, body weight and sex ratio in the sixth generation were analyzed. Compared with those of the outbred population, egg hatching rate of the inbred population (43.66%) was 22.62% lower, egg hatching duration was 1.7 days longer. Developmental duration of each instar and the nymphs of the inbred population was marginally different from that of the outbred population. No significant difference was observed in weight between the outbred and inbred A. chinensis. The inbred population was significantly male-biased in comparison with the outbred population. Overall, the inbred population was significantly inferior to the outbred population in egg hatching rate and adult acquisition rate, while there was no significant difference in 192 中 国 生 物 防 治 学 报 第 35 卷 developmental duration and body weight. Therefore, outbreeding A. chinensis should be supplemented into the insectary rearing A. chinensis population to avoid inbreeding depression.
Comparison of return-to-zero phase shift keying and on-off keying in long haul dispersion managed transmissions

RZ-DPSK in DWDM dispersion managed transmission is studied experimentally and compared with OOK in a 10 Gb/s system. OOK out-performs PSK in a low spectral efficiency system. At 10 Gb/s transmission with 50 GHz channel separation, however, RZ-OOK and RZ-DPSK performances are comparable. RZ-PSK is advantageous in a high spectral efficiency system and results are shown of numerical studies of several PSK systems with high spectral efficiencies and long system reach.
Proprioceptive Gaming: Making Finger Sensation Training Intense and Engaging with the P-Pong Game and PINKIE Robot

Proprioceptive deficits are common after a stroke and are thought to negatively impact motor learning. Despite this, there is a lack of practical robotic devices for assessing proprioception, as well as few robotic rehabilitation techniques that intensely and engagingly target proprioception. This work first presents the design of a simple robotic device, PINKIE, developed to assess and train finger proprioception. PINKIE uses low-cost actuators and sensors and is fabricated completely from 3D printed, laser cut, and off-the-shelf components. We then describe the design and testing of a gamified proprioceptive training technique, Proprioceptive-Pong (P-Pong), implemented with PINKIE. In P-Pong, players must continuously make game decisions based on sensed index and middle finger positions, as the game robotically moves their fingers instead of screen pixels to express the motion of the ball and paddle. We also report the results of a pilot study in which we investigated the effect of a short bout of P-Pong play on proprioceptive acuity, and quantified user engagement and intrinsic motivation of game play. We randomly assigned 15 unimpaired human participants to play 15 minutes of P-Pong (proprioceptive training group) or a similar but video-only version of Pong (control group). We assessed finger proprioception acuity before and after game play using the Crisscross assessment previously developed by our laboratory, engagement using the User Engagement Scale, and motivation using the Intrinsic Motivation Inventory survey. Following game play, there was a significant improvement in proprioceptive acuity (2.2 ± 2.6 SD mm, p = 0.023) in the proprioceptive training group but not the control group (0.5 ± 0.9 SD mm, p = 0.101). Participants rated P-Pong highly on all survey subscales, and as highly as visual Pong, except in the Perceived Usability and Competence subscales, a finding we discuss. To our knowledge, this work presents the first computer gaming approach for providing intense and engaging finger proprioception training, by splitting the feedback of game elements between the visual and proprioceptive senses. The pilot experiment indicates that the human sensory motor system has the ability to at least temporarily improve proprioception acuity with such game-based training.