Miracle-dz/UniFAR-FTUs huggingface dataset:
Promoting Emotional Well-Being with Angry-Birds-like Gameplay on Pixel Image Levels

This paper describes the use of “Real Pixel Image Level Generator (RPILG)” along with a design of game control, called “enhancing play with smiling” to promote the players’ emotion. Our study is conducted on Science Birds, an Angry-Birds-like game. RPILG is a procedural content generation method that generates game levels with the appearance of a pixel image of things, such as a pumpkin. We apply RPILG in Science Birds. A facial expression tool is used to recognize smiles in the player. And in our design, the black bird, a kind of shooting object in the game having the power to explode blocks, is modified such that its explosion power can be enhanced according to the degree of smiling. The idea behind this game design is to encourage smiling. An experiment is conducted with fourteen participants where two modes of gameplay “smiling for enhancing the birds” and “baseline play” are compared. An online survey using Positive and Negative Affect Schedule is used to evaluate the players’ emotion after they have experienced both modes. Our experimental results provide an evidence that the former mode leads to statistically significantly higher positive affect and lower negative affect.
NPA: Neural News Recommendation with Personalized Attention

News recommendation is very important to help users find interested news and alleviate information overload. Different users usually have different interests and the same user may have various interests. Thus, different users may click the same news article with attention on different aspects. In this paper, we propose a neural news recommendation model with personalized attention (NPA). The core of our approach is a news representation model and a user representation model. In the news representation model we use a CNN network to learn hidden representations of news articles based on their titles. In the user representation model we learn the representations of users based on the representations of their clicked news articles. Since different words and different news articles may have different informativeness for representing news and users, we propose to apply both word-and news-level attention mechanism to help our model attend to important words and news articles. In addition, the same news article and the same word may have different informativeness for different users. Thus, we propose a personalized attention network which exploits the embedding of user ID to generate the query vector for the word-and news-level attentions. Extensive experiments are conducted on a real-world news recommendation dataset collected from MSN news, and the results validate the effectiveness of our approach on news recommendation.
Small Scale Characteristics Analysis on Highway V2V Channel for Intelligent Transportation Systems

Small scale characteristics are very important for the vehicle-to-vehicle (V2V) radio channel. In this paper, the Akaike information criterion (AIC), which aims to solve the problem of the optimal model selection, is used to select the best fading distribution for vehicle radio channel from Rayleigh and Rician distributions in a highway environment. The measurement results show that the Rician distribution has the best fitting for the V2V radio channel in a highway communication environment. Influence from surroundings on the small scale characteristics are analyzed. The Rician K-factors are extracted and modeled based on the measurement data. It can be found that the mean value of Rician K-factor is around 4.86 dB in this scenario.
Early Detection of Depression and Treatment Response Prediction using Machine Learning: A Review

The turn of the century saw tremendous development in using Machine Learning (ML) for medical diagnosis. Majority of the prediction models or the diagnostic models were based on imaging. These methods extensively rely on huge dataset to develop a deep learning model to effectively diagnose conditions like Alzheimer's, Arrythmia, Lung Cancer, Breast Cancer among others. Early detection of depression is a major hurdle in effective and timely treatment. This paper explores the existing corpora of work on application of ML for early detection of depression. Further, a review on treatment response prediction is also elucidated. Relatively, the apparent dearth of work in depression has been found to be due to the scanty imaging dataset available on depression. We have reviewed the existing body of work on depression that have used imaging biomarkers, mobility traces, multi-modal fusion of audio, video and text. Our hope in writing this paper is to spur and catalyze research in early detection of depression and effective prediction of response to its treatment.
Performance Comparison of (GaAS/GaAs) and (InGaP/GaAs) Tunnel Junction in Multi-Junction Solar Cells

This study presents a comparison of the performance of two multi-junction solar cells in terms of simulated short-circuit current density, open circuit voltage, efficiency and fill factor one of these cells with an InGaP / GaAs structure with a GaAs / GaAs junction tunnel and the other with the same structure but with an InGaP / GaAs hetrojunction tunnel. The silvaco-ticad software has been used to analyze and study these performances for both solar cells, the total thickness is specified to 0. 66μm of top solar cell (GaInP), and the thickness of bottom solar cell (GaAs) is 2.64 μm and the tunnel junction with thickness 0.040 μm. He result of the simulation exposures for these multiple junction solar cells gives a conversion efficiency maximum of 24.2343% (AM1.5) would be obtained by choosing the p-In0.5Ga0. 5P/n-GaAs tunnel heterojunction.
Wind farm water area path planning algorithm based on A* and reinforcement learning

In recent years, the scale of offshore wind farms is increasing because of the high efficiency and pollution-free wind power resources. However, the introduction of many facilities in the corresponding wind farm sea area has led to the increasing difficulty of ship navigation. Therefore, it is very important to plan safe and efficient driving path according to the corresponding starting and ending points for the navigation of ships in the increasing wind farm area. In this paper, a path planning algorithm based on the hybrid method of A* algorithm and reinforcement learning is proposed, which can plan an effective collision avoidance path for the sea area of wind farm. Then the method is used to simulate the ship’s path planning in a wind farm, which proves the feasibility of the method. Finally, it shows that the method has universal reference significance for ship navigation in the wind farm waters.
Effects of Intake of Apples, Pears, or Their Products on Cardiometabolic Risk Factors and Clinical Outcomes: A Systematic Review and Meta-Analysis

ABSTRACT Apples and pears contain nutrients that have been linked to cardiovascular health. We conducted a systematic review and meta-analysis to summarize related research. Medline, Cochrane Central, and Commonwealth Agricultural Bureau databases were searched for publications on apple or pear intake and cardiovascular disease (CVD)/ cardiometabolic disease (CMD). Studies in adults (healthy or at risk for CVD) that quantified apple or pear intake were included. Random-effects models meta-analysis was used when ≥3 studies reported the same outcome. In total, 22 studies were eligible including 7 randomized controlled trial, 1 nonrandomized trial, and 14 prospective observational studies. In RCTs, apple intake significantly decreased BMI, but made no difference in body weight, serum lipids, blood glucose, or blood pressure. In observational studies, apple or pear intake significantly decreased risk of cerebrovascular disease, cardiovascular death, type 2 diabetes mellitus, and all-cause mortality. No association was reported for cerebral infarction or intracerebral hemorrhage. In conclusion, apple or pear intake significantly decreased BMI and risk for CVD outcomes.
Spatial Interference Nulling Before RF Frontend for Fully Digital Phased Arrays

Fully digital arrays offer huge advantages in terms of flexibility and performance; however, they may suffer from dynamic range issues when used in the presence of in-band interferers. Higher dynamic range components can be used, but are more costly and power hungry, making the implementation of such technology impractical. This paper presents a way to mitigate those interferers by creating a spatial notch at the RF front-end with an antenna-agnostic circuit placed at the feeding network of the antenna array. This circuit creates a steerable null in the embedded element pattern that mitigates interferers at a specified incoming angle. A full mathematical model and closed form expressions of the behavior of the circuit are obtained and compared to simulated and measured results, where up to a 20 dB null in the embedded element pattern of an 1 $\times$ 8 array is achieved with less than 1.5 dB of insertion loss. Finally, a real case scenario is set up with a desired signal and an interferer, which is initially saturating the receiver. The receiver successfully demodulates the signal after the null is placed in the direction of the interferer.
ERP Acceptance Model for SMEs based on CSFs

More and more companies are implementing ERP nowadays. Due to the fact that almost all large companies have already adopted ERP, the ERP vendors are now focusing on SMEs. However, in Mauritius, the rate of adoption of ERP by SMEs is relatively low. In this study, the Critical Success Factors (CSF) affecting SME ERP adoption in developing countries have been analyzed. Technology adoption models including those proposed in the ERP landscape have been investigated. Based on these findings, an ERP Adoption Model (ERPAM) has been proposed which is based on CSF and existing technology adoption models. This model has been evaluated using SMEs in Mauritius using a survey-based approach and hypothesis testing. Data was collected by the means of questionnaires and a total of 104 responses were recorded for analysis. Regression and Correlation tests were performed to test the hypotheses. The model is then re-adapted based on the results of the evaluation.
Development and Characterization of a Recycled Plastic Based Ion-Selective Electrode (PB-ISE) Using CNT Ink as Ion-To-Electron Transducer

Environment care is currently a crucial priority due to the increase in worldwide pollution. Nanotechnology and recycled material could be used as a platform to know more easily some analytical ion concentrations. In this investigation, a recycled plastic-based ion-selective electrode (PB-ISE) to use as a potentiometric sensor was built using a transductor material as carbon nanotubes ink (CNT-ink) and a metalized polyester sheet (MPS) from packing residues. To make the electrode, CNT-ink is applied and spread onto the MPS surface adding cycles of 1 ml at 100°C until a maximal reduction in the electrical resistance and a stable roughness were observed. Once the conductive plastic is ready, it's covered with adherable plastic masks on both sides of MPS to protect it from liquids. The final step is the addition of the ion-selective membrane (ISM) for potassium determination on a circular orifice, which is located on the front mask. The results obtained were compared with a classical solid-contact ion-selective electrode (SC-ISE). Some analytical parameters as sensitivity, linear ranges, and limits of detection of the recycled plastic electrode were comparable with those obtained for the SC-ISE. The best performance was reported for the PB-ISE that afforded a near-N ernstian response $(\mathbf{52.08}\pm \mathbf{2.36}\ \mathbf{mV})$ with a linear range from 10−4 to 10−1M and a limit of detection of 0.0176 mM. The potentiometry technique presents advantages in comparison with other techniques as well as a reduction in the cost and instrumental equipment to measure a wide range of analytes. Also, the straightforward fabrication and robustness that embrace the ISE could create an advantage technique using residual material from food packaging.
Decentralized sliding-mode control of robotic manipulator with constraint workspace: a finite-convergent barrier Lyapunov approach

The aim of this manuscript is to develop a robust output-based controller for a robot manipulator (RM), under the presence of state constraints. A novel methodology is proposed to solve the tracking trajectory problem between the joints position of a RM and desired reference trajectories, using a barrier sliding mode (BSM) control. The controller design guarantees the tracking trajectory and the fulfillment of the state constraints considering the presence of perturbations. In this study, it is considered that only the angular position of each RM articulation is available. A decentralized adaptive super-twisting algorithm estimates the angular velocity of the corresponding RM. A class of barrier Lyapunov function (BLF) is used to analyze the finite-time stability of the sliding surface in a closed-loop with the proposed controller. An example of a numerical evaluation considering a two-link RM illustrates the advantages obtained with the proposed methodology compared to the non-barrier control design.
Regression-Based Three-Dimensional Pose Estimation for Texture-Less Objects

3-D pose estimation for texture-less objects remains a challenging problem. Previous works either focus on a template matching method to find the nearest template as a candidate, or construct a Hough forest, which utilizes the offset of patches to vote for the object location and pose. By contrast, in this paper, we propose a comprehensive framework to directly regress 3-D poses for the candidates, in which a convolutional neural network-based triplet network is trained to extract discriminating features from the binary images. To make the features suitable for the regression task, a pose-guided method and a regression constraint are employed with the constructed triplet network. We show that the constraint reaches the goal of creating the correlation between the features and 3-D poses. Once the expected features are obtained, the object pose could be efficiently regressed, by training a regression network with a simple structure. For symmetric objects, depth images are treated as an additional channel to feed the triplet network. Experiments on the LineMOD and our own datasets demonstrate our method with high regression precision and efficiency.
Effectiveness of Multi-ROI 3D-CNNs in Discrimination of Motor Imagery from Functional Brain Image

Brain-machine interfaces (BMIs) generate control-commands for specific operations by discriminating signals representing brain functions. In most of current BMIs, machine learning methods are employed as classifier. Their drawback is strong dependency on the individual characteristics of the datasets for each subject. To expand usability of BMIs as more conventional tools, it is desirable that the discriminator is valid even for functional brain data obtained from arbitrary subjects who are not included in the training sample. We proposed Multi-ROI 3D-CNNs such a “generalized” discriminator to classify the motor imagery status using task based fMRI data. As the result, it was suggested that our 3D-CNNs work for unknown subjects' data and finish training faster than our previous classifier (linear Support Vector Machine) did.
Smart Parking Space Allocation Based on a Cooperative System

Advanced research solutions for ‘smart parking’ involve the use of vehicular cooperation to share parking information via vehicular ad hoc network. Solutions can be opportunistic when looking for parking spaces, which can increase the competing level and in turn, increase the searching time. Considering the parking resource limitations, we need to take vehicular cooperation to a next level by relaxing the constraints of cooperating drivers and so expanding the feasibility of the search space. This paper presents smart parking model Based on a Cooperative System; a Reward System that allows better use of resources especially during peak hours, by relaxing the constraints of cooperating drivers and so contributing to parking assignment efficiency increase. Subscribed drivers in the Reward System will be able to manage their delays induced by the traffic, since a readjustment of the parking space allocation it will be made dynamically by the system, based on the driver remaining travel time to his destination. In order to work with large-scale parking optimization, a real case mobility scenario is simulated using a SUMO/MATLAB framework. Based on simulation results, compared with non-cooperative parking processes, our model reduces the average parking rate failure and the traveling cost, whereas the overall parking capacity is more efficiently utilized.
Exploring the Use of Tangible User Interfaces for Teaching Basic Java Programming Concepts: A Usability Study

Computer programming has become an essential technical proficiency needed by companies especially within the Information Technology industry. To prepare graduates with the required skills and knowledge, many university courses have integrated modules that teach students such concepts. However, learning programming languages was found to be challenging to students as concepts are abstract, making it difficult for students to understand. In order to address such challenges and to innovate from traditional learning techniques, tangible user interfaces (TUI) could potentially be used to enhance learning experience. However, limited work has been done to investigate the use of TUI for students to learn the programming languages. Due to this limitation, the usability of such systems has not been well studied even though this quality attribute is important to investigate so as to improve adoption of systems by end users. This study delves into the usability of a TUI system for teaching basic java programming concepts. To achieve this purpose, a prototype was developed using Processing and ReacTI Vision. The usability of the proposed prototype was evaluated through application of the System Usability Scale. Results revealed that the system is acceptable on the scale used although different limitations were identified.
Design of Shopper Support Function by Cooperation of Agent-based IoT Devices

The labor shortage for human life support is approaching epidemic proportions in the world. To resolve manpower shortage of the service industry, various Internet of Things (IoT) Devices that support a specific job are introduced (e.g, shopping mall). If they can combine each other, it is possible to improve a support service for a shopper and also provide a new service. We propose a mechanism to provide new support services for shoppers by combining various IoT devices with suppressing influence to their regular jobs. This paper describes concept of agent-based IoT (AIoT) and design of this mechanism, and we introduce a prototype system.
A Novel Two-Timescale Limited Feedback Hybrid Beamforming/Combining Design for Millimeter Wave Systems

Hybrid beamforming offers a low hardware complexity for millimeter wave massive multiple-input multiple-output systems, which is divided among the analog radio frequency precoding and digital baseband precoding. In order to provide channel state information for the base station, channel feedback is essential, especially in frequency division duplexing systems. However, in large antenna systems, the size of channel matrix is huge, and the overhead of feedback complete channel is not acceptable. To reduce the feedback overhead for hybrid beamforming, leveraging the property that the millimeter wave path angles of departure vary more slowly than the path gains, we propose a two-timescale hybrid beamforming/combining algorithm for downlink multi-streams millimeter wave systems. We analyze the performance of the proposed algorithm with perfect channel state information, and the analytical and simulation results show that proposed algorithm can achieve the same performance of full digital singular value decomposition algorithm. We also analyze the rate gap between the proposed algorithm with perfect channel state information and limited feedback. The rate gap caused by quantization of radio frequency and baseband precoder codebooks are decoupled, and the quantization error is analyzed. At last, we provide a closed-form expression of relationship between feedback bits and rate gap.
Keyword Spotting in Historical Bangla Handwritten Document Image Using CNN

In this paper, we propose a segmentation-free keyword spotting method for historical Bangla Handwritten Document images. For a given query keyword, the proposed method generates a set of candidate text regions within a document image using Histogram of Oriented Gradient (HOG) features. Next, the set of candidate text regions are fed to a trained Convolution Neural Network (CNN) model. The network finds the class label of each candidate text region and the regions whose class labels are same as the class label of the query word, are spotted in the document image. The proposed method is tested on three historical handwritten Bengali datasets and one historical English handwritten dataset. Detailed performance analysis demonstrates the goodness of the proposed method
MergeDTS

Online ranker evaluation is one of the key challenges in information retrieval. Although the preferences of rankers can be inferred by interleaving methods, the problem of how to effectively choose the ranker pair that generates the interleaved list without degrading the user experience too much is still challenging. On the one hand, if two rankers have not been compared enough, the inferred preference can be noisy and inaccurate. On the other hand, if two rankers are compared too many times, the interleaving process inevitably hurts the user experience too much. This dilemma is known as the exploration versus exploitation tradeoff. It is captured by the K-armed dueling bandit problem, which is a variant of the K-armed bandit problem, where the feedback comes in the form of pairwise preferences. Today’s deployed search systems can evaluate a large number of rankers concurrently, and scaling effectively in the presence of numerous rankers is a critical aspect of K-armed dueling bandit problems. In this article, we focus on solving the large-scale online ranker evaluation problem under the so-called Condorcet assumption, where there exists an optimal ranker that is preferred to all other rankers. We propose Merge Double Thompson Sampling (MergeDTS), which first utilizes a divide-and-conquer strategy that localizes the comparisons carried out by the algorithm to small batches of rankers, and then employs Thompson Sampling to reduce the comparisons between suboptimal rankers inside these small batches. The effectiveness (regret) and efficiency (time complexity) of MergeDTS are extensively evaluated using examples from the domain of online evaluation for web search. Our main finding is that for large-scale Condorcet ranker evaluation problems, MergeDTS outperforms the state-of-the-art dueling bandit algorithms.