Miracle-dz/UniFAR-FTUs huggingface dataset:
Feature Extraction with SHAP Value Analysis for Student Performance Evaluation in Remote Collaboration

In recent years, group discussions are becoming an important part of corporate recruitment examinations in Japan. Developing a remote teaching support system for group discussion will help reduce the burden of teachers. As a part of our project, this study aims to support teachers who need effective teaching method in remote group discussions by analyzing the video images. In this study, we used the features obtained from the videos. Students performances in group discussion were assessed automatically by classification, and important features were selected for teaching from the SHapley Additive exPlanations(SHAP) values.
Telework, new business models and virtual campuses: a longitudinal analysis

Teleworking has become an emergency solution for numerous companies in the face of the confinement situation caused by COVID 19. This almost immediate implementation, without training or training employees, has generated numerous dysfunctions in companies and in teleworkers themselves. It is time to implement teleworking as a regular work activity for many workers and companies and to do so by maximizing the benefits and minimizing the inconveniences generated by teleworking. All of this leads us to understand that the hierarchical structure of companies must be changed and based on new models based on the network company. In this research we indicate the evolution of teleworking in Spanish companies and its acceleration due to confinement, with the aim of indicating the need to create online training programs in companies themselves on teleworking and its tools.
Normal Metal-Insulator-Superconductor Tunnel Junctions With Pulsed Laser Deposited Titanium Nitride as Superconductor

Here we report the fabrication of normal metal – insulator – superconductor (NIS) tunnel junctions using superconducting titanium nitride grown by pulsed laser deposition (PLD). The films for NIS junction fabrication were deposited on two different substrates: silicon nitride film and magnesium oxide. TiN films were characterized by means of electrical transport measurements, and films with superconducting transition temperatures above the liquid helium boiling point were chosen for fabrication of NIS junctions. Tunnel junction devices were successfully fabricated using electron beam lithography and shadow evaporation techniques. The insulator layer formation was performed using two different approaches: the tunnel barrier was either formed by direct oxidation of TiN, or by fabrication of an additional aluminum oxide layer. Devices fabricated by direct oxidation showed much more transparent barriers and slightly higher subgap currents, but both types of devices could be used for thermometry. Further optimization of the direct oxidation process may allow electric cooling applications in the future.
The Framework and Thinking of the Future Primary and Secondary School Information Support Environment

With the development and changes of the society, continuous technological innovation and improvement of people’s learning needs have promoted the continuous transformation of schools, thus deriving the concept of future schools. In the future school construction process, the design of information support environment is of great significance. This paper analyzes the structural design of the future school information support environment, proposes the school-based, regional and public information structure design, and provides insights and reflections on the construction process. Expecting to provide references for further research and application of future school construction.
Energy-Efficient Spatially-Correlated Hardware Impaired Massive MIMO FD Relaying

Most of the existing massive multi-input-multi-output (mMIMO) relaying works assume ideal transceiver hardware and spatially uncorrelated channels. A practical mMIMO relay, however, due to limited antenna spacing and the use of cost-effective equipment, usually experiences spatially-correlated channels and hardware impairments, respectively. We consider a hardware-impaired two-way full-duplex (FD) spatially-correlated mMIMO relaying with multiple MIMO FD user-pairs, and derive a closed-form spectral efficiency (SE) expression which is valid for a practical number of finite relay antennas. We use this expression to develop a quadratic transformation approach to optimize the non-convex global energy efficiency (GEE) and weighted sum energy efficiency (WSEE) metrics, which are fractional functions of optimization variables. The proposed approach first converts the fractional problem into its equivalent fraction-free counterpart, and then uses novel transformations to make the problem concave, and solve it using an iterative algorithm. We use this optimization to i) investigate the impact of spatial correlation and hardware impairments on GEE; ii) decide hardware impairment values for a tolerable reduction in GEE; and iii) investigate the impact of weights in WSEE on the users energy efficiency.
Accurate Gridless Indoor Localization Based on Multiple Bluetooth Beacons and Machine Learning

In this work we present an indoor location method using smartphones as a source of location information. The proposed method uses the Received Signal Strength Indicator (RSSI) value from Bluetooth Low Energy Beacons scattered around interior spaces. We present the results of our model using machine learning, which was developed based on measurements of RSSI values from Beacons inside a lab environment occupying a space of 31m2. Measurements were fed to the open-source TensorFlow framework to develop an estimator of the distance between the mobile phone and the beacon. Next, based on the cross-sections of peripheral lines having as a center the location of the Beacons and radius the predicted distances we compute the intersection points from all circles and base our position estimation on the Geometric median of intersection points. Through experiments, we show that our system has an average accuracy of 69.58cm and can predict position with an accuracy of less than a meter in 80% of the cases.
A Feature Selection Based DNN for Intrusion Detection System

The goal of networking has the idea of “resource sharing” and “communication” in a convenient way. However, more convenience services are provided, more problems of security and privacy issues may occur. In order to prevent these problems, an IDS (Intrusion Detection System) is designed to enhance the network security and to observe abnormal behavior. Model accuracy and the training time required to build the model are affected greatly if we use the unselected features and irrelevant data. This is the reason why the selection of features is a significant process in building an Intrusion Detection System (IDS). This paper aims to boost the Deep Neural Network (DNN) capabilities by selecting the feasible features before processing networking data. This research employed the KDD Cup 99 dataset which is considered as one of the representative datasets for intrusion detection. Based on our experimental results, it is concluded that the selection of the proper features has effects on the improvement of IDS compared to the method without feature selection. This research has proved that the improvement of DNN for IDS can reach up to 99.4% for accuracy, 99.7% for precision, 97.9% for recall, and 98.8 for F1 score.
Modeling User Interests With Online Social Network Influence by Memory Augmented Sequence Learning

Online social networks, such as Facebook and Twitter, enable users to share their shopping/travel experiences with their friends. However the influence on users’ decision-making on next visit/buy has sparse research exposure, by accurately modeling long-term user behaviors from historical data. The existing methods do not fully take advantage of the underlying social networks to model user interests, nor they have not modeled long-term transitional behavior patterns. In this paper, we propose a novel Social Influence aware and Memory augmented Sequence learning (SIMS) model, on what a user will likely buy/visit next. Specifically, SIMS first learns a representation for the visiting/purchasing sequence of each user using the sequence-to-sequence learning method. Then it predicts the interest of a user by integrating the representation of his/her own sequence, with another representation of the corresponding social influence, which is learned using an autoencoder-based model. In addition, we leverage an emerging memory augmented neural network, Differentiable Neural Computer (DNC), to further improve prediction accuracy. We conduct extensive experiments to evaluate the proposed model using three real-world datasets, Yelp, Epinions and Ciao. When compared with 10 other baselines and state-of-the-art solutions, the experimental results show that 1) the proposed model significantly outperforms all other methods in terms of various accuracy-related metrics; 2) the proposed social influence modeling and memory augmentation do lead to the performance gain.
Indium Tin Oxide Thin-Film Sensor for Detection of Volatile Organic Compounds (VOCs)

ABSTRACT Indium tin oxide (ITO) (In2O3 + 17% SnO2) thin films were grown on glass substrate by direct evaporation method. Two thick gold pads were deposited to take out contacts. The response of these films at different operating temperatures, when exposed to various volatile organic compounds (VOCs) such as methanol, ethanol, butanol, and acetone in the concentration range 200–2500 ppm was evaluated. Additionally, the effect of film thickness on the response charateristics of methanol and acetone was studied. The linearity and sensitivity of the sensors were measured. The ITO thin-film sensors showed a sensitivity of 0.256 ohms/ppm to acetone vapors, which was almost linear in the range 200–2500 ppm. In order to improve sensitivity and selectivity, a thin layer of various metal and metal oxides such as Cu and PbO was deposited on the sensor surface to work as catalytic layer and the effect on the performance of the sensor was studied. The response and recovery times of the sensor were determined for acetone vapors and were found to be 155 sec and 110 sec, respectively.
Joint Iterative Optimization-Based Low-Complexity Adaptive Hybrid Beamforming for Massive MU-MIMO Systems

This paper proposes a joint iterative optimization based hybrid beamforming technique for massive MU-MIMO systems. The proposed technique jointly and iteratively optimizes the transmitter precoders and combiners, aiming to approach the global optimum solution for the system sum-rate maximization problem. The proposed technique develops an adaptive algorithm exploiting the stochastic gradients (SG) of the local beamformers and provides low-complexity closed-form solutions. Furthermore, an efficient adaptive scheme is developed based on the proposed adaptive algorithm and the closed-form solutions. The proposed algorithm requires the signal-to-interference-plus-noise ratio (SINR) feedback from each user and a limited size transition vector to be exchanged between the transmitter and receivers at each step to update beamformers locally. Analytic result shows that the proposed adaptive algorithm achieves low-complexity when the array size is large and is able to converge within a small number of iterations. Simulation result shows that the proposed technique is able to achieve superior performance comparing to the existing state-of-art techniques. In addition, the knowledge of instantaneous channel state information (CSI) is not required as the channels are also adaptively estimated with each coherence time which is a practical assumption since the CSI is usually unavailable or have time-varying nature in real-time applications.
NLC and SFO Control Technique Based Multilevel Inverter fed 3-φ Induction Motor Drive

In this work, hybrid control technique based 3$-\varphi$ induction motor drive fed from a cascaded multilevel inverter is presented. In the past decades regular two level inverters are, however these are having output voltage with harmonic content. To improve the solar photovoltaic system power quality and voltage rating, the multilevel inverters are employed along with several lower voltage dc sources is employed. In multilevel inverters power conversion is by synthesizing a staircase voltage waveform. The presented inverter configuration is derived from a module of addition and subtraction of sources. It helps to reduce the number of components - number of sources, switching drive, capacitors, diodes and majorly power switches. In this work, performance analysis and operating modes of 3$-\varphi$ cascaded hybrid control technique based multilevel inverter for both symmetrical and asymmetrical topologies are explained. Further, the nearest level control (NLC) and switching frequency optimal (SFO) control techniques are employed in this work. To operate the power switches in this topology the switching signals are generated by using hybrid SFO-NLC technique. The enhanced features of this topology with the perspectives of power quality and optimized switching count are presented. The inverter fed drive system with proposed control technique is simulated by MATLAB/Simulink for the verification of the performance of the technique under both steady state and dynamic conditions.
From many monomers to many polymers: Soft ellipsoid model for polymer melts and mixtures

We present an extremely efficient and rather general model in which whole polymer chains are represented as soft particles. The particles are characterized by their overall sizes and shapes, as given by the conformations of the underlying chains. The probability of occurrence of a particle with a given size determines its internal free energy. The density of monomers within each particle is calculated from all conformations that have the same size. The interaction between two particles is taken to be proportional to the spatial overlap of their monomer density distributions. When a large number of such particles are brought into contact, as is the case for a polymer melt, the interactions between the particles force them to shrink and modify the equilibrium size distribution. We show by simulations that this model leads to a Gaussian statistics of the chains in melt. Since the internal degrees of freedom of a chain are integrated out, a large number (of order 104) of long (e.g., N=100) chains can be simul.
High Response Formic Acid Gas Sensor Based on MoS2 Nanosheets

In this study, we prepare dispersed MoS2 nanosheets by water bath ultrasonic stripping method uniformly. The MoS2 nanosheets are characterized by SEM, TEM and XRD, and then the gas formic acid is detected in detail. The experimental results show that the sensor patterned with MoS2 nanosheets has high response to formic acid gas with the response time of 11s, and the recovery time of 17s. The sensor exhibits excellent selectivity, repeatability and stability under a linear relationship between the resistance of the sensor and the gas concentration.
A case of gamification in virtual environments with RV/RA

This article describes a gamified educational experience using different tools and techniques, including virtual reality and augmented reality in higher education. Gamification in education has demonstrated to be a good strategy to motivate students promoting their active participation. It is possible to introduce gamification techniques into virtual environments and in traditional classrooms. A current successful game in 2020 is Among Us, and one of the gamified activities was based on its mechanics. Besides, this work presents different gamification techniques combining different virtual environments during the remote adaptation teaching due to the pandemic.
Improving the Prediction of Heart Failure Patients’ Survival Using SMOTE and Effective Data Mining Techniques

Cardiovascular disease is a substantial cause of mortality and morbidity in the world. In clinical data analytics, it is a great challenge to predict heart disease survivor. Data mining transforms huge amounts of raw data generated by the health industry into useful information that can help in making informed decisions. Various studies proved that significant features play a key role in improving performance of machine learning models. This study analyzes the heart failure survivors from the dataset of 299 patients admitted in hospital. The aim is to find significant features and effective data mining techniques that can boost the accuracy of cardiovascular patient’s survivor prediction. To predict patient’s survival, this study employs nine classification models: Decision Tree (DT), Adaptive boosting classifier (AdaBoost), Logistic Regression (LR), Stochastic Gradient classifier (SGD), Random Forest (RF), Gradient Boosting classifier (GBM), Extra Tree Classifier (ETC), Gaussian Naive Bayes classifier (G-NB) and Support Vector Machine (SVM). The imbalance class problem is handled by Synthetic Minority Oversampling Technique (SMOTE). Furthermore, machine learning models are trained on the highest ranked features selected by RF. The results are compared with those provided by machine learning algorithms using full set of features. Experimental results demonstrate that ETC outperforms other models and achieves 0.9262 accuracy value with SMOTE in prediction of heart patient’s survival.
Myanmar POS Resource Extension Effects on Automatic Tagging Methods

Part-of-speech (POS) tagging is the process of assigning the part-of-speech tag or other lexical class marker to each word in a sentence. It is also one of the most important steps in Natural Language Processing (NLP) task pipeline. There are several research works in Myanmar POS tagging implemented with different approaches. However, there is only one publicly available tagged corpus named myPOS corpus. The size of this corpus is only 11 thousand sentences. It is not enough to train downstream NLP tasks, such as machine learning. For this reason, we manually extended the original myPOS corpus as myPOS version 2.0 and the size of the extended corpus becomes approximately triple size of the original myPOS corpus. To evaluate the effects of the extended corpus versus the original corpus, the accuracies of four supervised tagging algorithms, namely, Conditional Random Fields (CRFs), Hidden Markov Model (HMM), Ripple Down Rules based (RDR), and neural sequence labeling approach of Conditional Random Fields $(\mathrm{NCRF}^{++})$ are compared. The results showed that the extended myPOS version 2.0 improved the accuracies of automatic POS tagging methods compared with the original myPOS.
Compact Flat Fabric Pneumatic Artificial Muscle (ffPAM) for Soft Wearable Robotic Devices

A pneumatic artificial muscle (PAM) has been widely adopted in a wide range of wearable applications, but the bulkiness and the slow response of the actuator have remained a challenge. In this work, we developed a flat fabric PAM (ffPAM), achieving high compactness, rapid response, and low hysteresis error. Our ffPAM exerts a maximum force of 118.0 N <inline-formula><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula> 0.1 N at 172 kPa, an average maximum contraction ratio of 23.84% <inline-formula><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula> 0.06%, and a maximum hysteresis error of 6.2%. Using the thin fabric as a base material, the actuator's total weight is 34.8 g. Based on dynamic response experiments, the ffPAM was verified to have a response time of 0.0318 s <inline-formula><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula> 0.0012 s for contraction, and the bandwidth of more than 4 Hz, regarding the contraction length with respect to input pressure. Moreover, a maximum contraction length was maintained during 10 000 cycles. Additionally, we embedded a fabric-based soft contractile sensor for the measurement of the contraction. We validated that the capacitance of the embedded capacitive contractile sensor has a linear relationship with the contraction length at constant pressures. To emphasize the compactness of the ffPAM in practical wearable applications, the actuator was placed between the legs, and its compactness was compared with that of previous PAMs.
Deep Learning Approach for Detecting Malicious Activities Over Encrypted Secure Channels

Nowadays, most cyber attackers exploit secure communication channels to hide malicious activities and imitate the behaviors of a legitimate user. These attacks over a secure channel make networked systems more vulnerable to new threats and increase the possibility of significant damage to other end users. Traditional TCP/IP-level traffic inspections do not suffice in investigating a secure sockets layer (SSL) conversation because the SSL conversation data is encrypted by a public key system and the SSL uses its own data unit of an SSL record. In this paper, we propose a novel malicious SSL traffic detection method, which reassembles SSL records from captured IP packets and inspects the characteristics of SSL records using a deep learning method. After an SSL record is reassembled from a single or multiple IP packets, the proposed method extracts unencrypted contents of the reassembled record and generates a sequence of unencrypted data from successive SSL records for deep learning-based classification. The sequences of SSL records are encoded using a long short-term memory autoencoder, and then an encoded feature map is generated for each SSL flow. These feature maps are forwarded to the convolutional neural network-based classifier to determine whether the SSL flow is malicious or not. The experiment shows that our proposed approach has a great separability between benign and malicious traffic flows on an encrypted SSL channel.
Presale Strategy for a Dual-Channel Retailer Considering Sales Effort

We study the presale strategy and decision-making problems for a retailer selling products. Based on the penetration and skimming pricing strategies, we consider the characteristics of consumer market segmentation and valuation heterogeneity, focus on the implementation conditions and optimal decision results of different pricing strategies before and after the introduction of an online presale channel, and conduct a multidimensional comparative study on the impact of the introduction of the online presale channel. We find that the introduction of an online presale channel can always improve the retailer’s profitability, but its pricing strategy and sales effort should be adjusted to align with the consumer market segmentation. When the proportion of online consumers is high or their preference for the spot channel is high, the retailer should always adopt a penetration pricing strategy. If online consumers also show a high consumption preference for the presale channel, the retailer should reduce its sales effort to save costs after introducing the presale channel; otherwise, the retailer should increase its sales effort to increase demand in the offline spot channel. When the proportion of online consumers is low and their preference for the spot channel is low, the retailer should always adopt a skimming pricing strategy and keep sales effort unchanged. When the proportion of online consumers and their consumption preference for the spot channel are moderate, the retailer should adjust its pricing strategy from penetration to skimming after introducing the online presale channel and appropriately reduce the level of sales effort.