Reviewerly/exHarmony huggingface dataset:
A novel fusion mixture of active experts algorithm for traffic signs recognition

Traffic sign recognition is an important problem in today’s applications. In this paper, by combining ensemble and active learning methods, a novel fusion mixture of active experts algorithm is proposed for this problem. The active learning algorithm is a popular method for reducing the number of samples. The primary goal of active learning is diminishing complexity, increasing the convergence rate, speeding up training process, and decreasing the cost of samples labeling. The active learning, hence, chooses informative samples to train. In addition, ensemble methods are a combination of simple classifiers for improving accuracy. Each classifier tries to learn a region of dataset better than other regions that all opinions are considered on ensemble methods as an ultimate decision. The mixture of experts is one of the most modern hybrid methods in which the training process takes a relatively long time, and it is a problem for large datasets. Our proposed Mixture of Active Experts tries to solve this problem. It decreases the training time process and increases the speed of convergence for finding optimal weights by selecting only informative samples in active learning phase. It is also applicable for online situations, in which the model should be trained continuously. The results of different experiments on German Traffic Sign Recognition Benchmark dataset demonstrate that the proposed method shows 96.69% accuracy and achieved the 6th rank among all the state of the art algorithms using smaller number (only 60%) of training samples.
QComms QKD Software Toolkit

The purpose of this software is to provide a set of interfaces, algorithms and tools to produce a full QKD system.The aim is to reduce the barrier to entry into QKD systems by developing a strong architecture with well defined interfaces.The separation of responsibility into components will allow researchers to concentrate on a specific component, the rest of the system being provided by existing algorithms or simulators.
Analogy-based Matching Model for Domain-specific Information Retrieval

This paper describes a new matching model based on analogical proportions useful for domain-specific Information Retrieval (IR). We first formalize the relationship between documents terms and query terms through analogical proportions and we propose a new analogical inference to evaluate document relevance for a given query. Then we define the analogical relevance of a document in the collection by aggregating two scores: the Agreement, measured by the number of common terms, and the Disagreement, measured by the number of different terms. The disagreement degree is useful to filter documents out from the response (retrieved documents), while the agreement score is convenient for document relevance confirmation. Experiments carried out on three IR Glasgow test collections highlight the effectiveness of the model if compared to the known efficient Okapi IR model.
Action Anticipation from Multimodal Data

The idea of multi-sensor data fusion is to combine the data coming from different sensors to provide more accurate and complementary information to solve a specific task. Our goal is to build a shared representation related to data coming from different domains, such as images, audio signal, heart rate, acceleration, etc., in order to anticipate daily activities of a user wearing multimodal sensors. To this aim, we consider the Stanford-ECM Dataset which contains syncronized data acquired with different sensors: video, acceleration and heart rate signals. The dataset is adapted to our action prediction task by identifying the transitions from the generic “Unknown” class to a specific “Activity”. We discuss and compare a Siamese Network with the Multi Layer Perceptron and the 1D CNN where the input is an unknown observation and the output is the next activity to be observed. The feature representations obtained with the considered deep architecture are classified with SVM or KNN classifiers. Experimental results pointed out that prediction from multimodal data seems a feasible task, suggesting that multimodality improves both classification and prediction. Nevertheless, the task of reliably predicting next actions is still open and requires more investigations as well as the availability of multimodal dataset, specifically built for prediction purposes.
Data Augmentation Using Part Analysis for Shape Classification

Deep Convolutional Neural Networks have shown drastic improvements in the performance of various Computer Vision tasks. However, shape classification is a problem that has not seen state-of-the-art results using CNNs. The problem is due to lack of large amounts of data to learn to handle multiple variations such as noise, pose variations, part articulations and affine deformations present in the shapes. In this paper, we introduce a new technique for augmenting 2D shape data that uses part articulations. This utilizes a novel articulation cut detection method to determine putative shape parts. Standard off-the-shelf CNN models trained with our novel data augmentation technique on standard 2D shape datasets yielded significant improvements over the state-of-the-art in most experiments and our data augmentation approach has the potential to be extended to other problems such as Image Classification and Object Detection.
Effect of Feedback Medium for Real-time Mastication Awareness Increase using Wearable Sensors

Increasing the number of mastications can help suppress obesity, but it is difficult to keep constant awareness of it in everyday life. Besides, the conventional mastication number measurement apparatus is large and such it is difficult to use it in daily life. This research proposes a system that supports chewing and utterance consciousness improvement in real-time. It is composed of a cheap and small bone conduction microphone to collect sound intra-body sound signal, and a smartphone that processes sound and provides feedback in real-time so that it can be used conveniently in daily life. First, the accuracy of mastication counting and utterance length estimation has been evaluated, confirming to be sufficient to provide real-time feedback. Second, the evaluation of the effect on chewing and utterance consciousness of different ways and medium of real-time feedback during a meal was carried out. It was possible to clarify the impact of real-time feedback, as well as to determine the factors that affect more efficiently the improvement of mastication number and utterance.
Conflict Handling Framework in Generalized Multi-agent Path finding: Advantages and Shortcomings of Satisfiability Modulo Approach

We address conflict reasoning in generalizations of multi-agent path finding (MAPF). We assume items placed in vertices of an undirected graph with at most one item per vertex. Items can be relocated across edges while various constraints depending on the concrete type of MAPF must be satisfied. We recall a general problem formulation that encompasses known types of item relocation problems such as multi-agent path finding (MAPF) and token swapping (TSWAP). We show how to express new types of relocation problems in the general problem formulation. We thoroughly evaluate a novel solving method for item relocation that combines satisfiability modulo theory (SMT) with conflict-based search (CBS). CBS is interpreted in the SMT framework where we start with the basic model and refine the model with a collision resolution constraint whenever a collision between items occurs. The key difference between the standard CBS and the SMT-based modification of CBS (SMT-CBS) is that the standard CBS branches the search to resolve the collision while SMT-CBS iteratively adds a single disjunctive collision resolution constraint. Our experimental evaluation revealed that although SMT-CBS performs better than CBS in small densely occupied instances of variants of MAPF, it is outperformed on large sparsely occupied environments. The performed analysis shows that individual paths in large environments of relocation instances can be found faster using simple A*-based algorithm than by the SMT solver. On the other hand the SMT solver performs better when many conflicts between items need to be resolved.
Bivariate direction finding using two perpendicular bi-directional (“figure-8”) sensors of (possibly) unequal orders

A "figure-8" sensor is so labeled because its spatial pattern resembles the character "8" with regard to the sensor's axis. This figure-8 pattern narrows as the sensor's order increases. Using two such figure-8 directional sensors of higher order, oriented perpendicularly to each other-this paper pioneers closed-form signal-processing algorithms to estimate an incident signal's azimuth-elevation bivariate direction-of-arrival. Monte Carlo simulations verify these proposed algorithms' efficacy and statistical closeness to the corresponding Cramér-Rao bounds.
Deep Reinforcement Learning with Decorrelation

Learning an effective representation for high-dimensional data is a challenging problem in reinforcement learning (RL). Deep reinforcement learning (DRL) such as Deep Q networks (DQN) achieves remarkable success in computer games by learning deeply encoded representation from convolution networks. In this paper, we propose a simple yet very effective method for representation learning with DRL algorithms. Our key insight is that features learned by DRL algorithms are highly correlated, which interferes with learning. By adding a regularized loss that penalizes correlation in latent features (with only slight computation), we decorrelate features represented by deep neural networks incrementally. On 49 Atari games, with the same regularization factor, our decorrelation algorithms perform $70\%$ in terms of human-normalized scores, which is $40\%$ better than DQN. In particular, ours performs better than DQN on 39 games with 4 close ties and lost only slightly on $6$ games. Empirical results also show that the decorrelation method applies to Quantile Regression DQN (QR-DQN) and significantly boosts performance. Further experiments on the losing games show that our decorelation algorithms can win over DQN and QR-DQN with a fined tuned regularization factor.
Personalized Neural Embeddings for Collaborative Filtering with Text

Collaborative filtering (CF) is a core technique for recommender systems. Traditional CF approaches exploit user-item relations (e.g., clicks, likes, and views) only and hence they suffer from the data sparsity issue. Items are usually associated with unstructured text such as article abstracts and product reviews. We develop a Personalized Neural Embedding (PNE) framework to exploit both interactions and words seamlessly. We learn such embeddings of users, items, and words jointly, and predict user preferences on items based on these learned representations. PNE estimates the probability that a user will like an item by two terms—behavior factors and semantic factors. On two real-world datasets, PNE shows better performance than four state-of-the-art baselines in terms of three metrics. We also show that PNE learns meaningful word embeddings by visualization.
Efficient Bayesian Optimization for Target Vector Estimation

We consider the problem of estimating a target vector by querying an unknown multi-output function which is stochastic and expensive to evaluate. Through sequential experimental design the aim is to minimize the squared Euclidean distance between the output of the function and the target vector. Applying standard single-objective Bayesian optimization to this problem is both wasteful, since individual output components are never observed, and imprecise since the predictive distribution for new inputs will be symmetric and have negative support. We address this issue by proposing a Gaussian process model that considers the individual function outputs and derive a distribution over the resulting 2-norm. Furthermore we derive computationally efficient acquisition functions and evaluate the resulting optimization framework on several synthetic problems and a real-world problem. The results demonstrate a significant improvement over Bayesian optimization based on both standard and warped Gaussian processes.
Student Performance Early Warning based on Data Mining

Student performance in higher education is related to many complicated factors and always has uncertainty, so early warning of it is a very difficult issue.In this study, a systematic review was first carried out on student performance prediction and early warning using data mining techniques, including basic data sources, evaluating factors, predicting methods, application tools, and practices.Then, insufficiencies of the related studies were discussed, including incomprehensive source data, inadaptable and unspecialized calculation methods, and lack of integrated methodology systems in practice.Finally, a solution design was proposed, consisting of learning situation big data, a systematic early warning model, and an integrated information support system.Preliminary experiment results showed that it could identify at-risk students in a timely manner and improve the overall efficiency and effectiveness of early warning education management in practice, so it is of both academic and practical significance in promoting the deep integration of information technology and early warning education.
The strategy of Two Stay Two Stray to improve EFL students’ reading skill

The objectives of this study are to figure out whether the use of Two Stay Two Stray (TSTS) strategy could improve Grade 8 EFL students’ reading skill in a junior high school in Banda Aceh and to find out the students’ perspective about the use of this strategy. One class was randomly chosen as the sample in this study. The data were collected by using tests and questionnaire. The data were both analyzed by using statistical formulas. The results show that the use of TSTS in teaching reading to the Grade 8 students was successful. It can be seen from the mean scores of the experimental class that was 84.70. Moreover, the t-score (6.81) was higher than t-table (2.021), and this indicates that the students in the experimental class who were taught using TSTS got better improvement than before they were taught using this strategy. The results from the questionnaire also revealed that the students’ have positive responses in relation to the use of TSTS because this strategy offered a positive relationship between teacher-students and students-students, and further increase their interest in learning. This led the students to escalate their self-confidence, social interaction, individual accountability, and group skills. A better understanding of the materials taught was also attained because they could learn together as a team.
A Stack Autoencoders Based Deep Neural Network Approach for Cervical Cell Classification in Pap-Smear Images

Background: Early detection of cervical cancer may give life to women all over the world. Pap-smear test and Human papillomavirus test are techniques used for the detection and prevention of cervical cancer. Objective: In this paper, pap-smear images are analysed and cells are classified using stacked autoencoder based deep neural network. Pap-smear cells are classified into 2 classes and 4 classes. Twoclass classification includes classification of cells in normal and abnormal cells while four-class classification includes classification of cells in normal cells , mild dysplastic cells, moderate dysplastic cells and severe dysplastic cells. Methods: The features are extracted by deep neural networks based on their architecture. Proposed deep neural networks consist of three stacked auto encoders with hidden sizes 512, 256 and 128, respectively. Softmax used as the outer layer for the classification of pap smear cells. Results: Average accuracy achieved for 2-class classification among normal and abnormal cells is 98.2 % while for 4-class classification among normal, mild, moderate and severe dysplastic cells is 93.8 % respectively. Conclusion: The proposed approach avoids image segmentation and feature extraction applied by previous works. This study highlights deep learning as an important tool for cells classification of pap-smear images. The accuracy of the proposed method may vary with the different combination of hidden size and number of autoencoders.
CAMeL: A Self-Adaptive Framework for Enriching Context-Aware Middlewares with Machine Learning Capabilities

Context-aware middlewares support applications with context management. Current middlewares support both hardware and software sensors providing data in structured forms (e.g., temperature, wind, and smoke sensors). Nevertheless, recent advances in machine learning paved the way for acquiring context from information-rich, loosely structured data such as audio or video signals. This paper describes a framework (CAMeL) enriching context-aware middlewares with machine learning capabilities. The framework is focused on acquiring contextual information from sensors providing loosely structured data without the need for developers of implementing dedicated application code or making use of external libraries. Nevertheless the general goal of context-aware middlewares is to make applications more dynamic and adaptive, and the proposed framework itself can be programmed for dynamically selecting sensors and machine learning algorithms on a contextual basis. We show with experiments and case studies how the CAMeL framework can (i) promote code reuse and reduce the complexity of context-aware applications by natively supporting machine learning capabilities and (ii) self-adapt using the acquired context allowing improvements in classification accuracy while reducing energy consumption on mobile platforms.
Periphery-Fovea Multi-Resolution Driving Model guided by Human Attention

Inspired by human vision, we propose a new periphery-fovea multi-resolution driving model that predicts vehicle speed from dash camera videos. The peripheral vision module of the model processes the full video frames in low resolution. Its foveal vision module selects sub-regions and uses high-resolution input from those regions to improve its driving performance. We train the fovea selection module with supervision from driver gaze. We show that adding high-resolution input from predicted human driver gaze locations significantly improves the driving accuracy of the model. Our periphery-fovea multi-resolution model outperforms a uni-resolution periphery-only model that has the same amount of floating-point operations. More importantly, we demonstrate that our driving model achieves a significantly higher performance gain in pedestrian-involved critical situations than in other non-critical situations.
A Systematic Literature Review in Blockchain: Benefits and Implications of the Technology for Business

Blockchain is a core, underlying technology with promising applications to disrupt the world of business. Using a systematic review, in this paper we provide an overview of the concept of blockchain technology, identify some advantages and barriers for business use and find that while some industries seem to benefit more from the technology (healthcare and those that depend on supply-chains like physical retail stores), others may suffer a blow (banks, energy and other service providers).
User Risky Behavior and Security Awareness through Lifespan

In the last decade, the Internet has become a crucial part of human life. Everyday usage of the Internet has enabled rapid development of social engineering. The weakest link in this chain are naive users with their risky online behavior who are unaware of Internet security risks. As knowledge, awareness and behavior depend on human characteristics, such as maturity, age and education, the main goal of this study was to investigate trendlines of user risky behavior and security awareness through the lifespan. Results have shown the growth of online risky behavior through the lifespan and the growth of knowledge and security awareness in middle adulthood. In general, we can conclude that people who are more knowledgeable about and more aware of potential security risks are at the same time more prone to risky behavior when using information systems.
Adults’ Digital Competence And Readiness For Online Learning: Preliminary findings on Latvian adult learners’ readiness to study online

Technological development of ICT in Latvia is high; however, Latvian adults are passive online learners.The relationship between Latvian adult learners' digital competence and experience with online learning has been researched in this paper.The author has investigated Latvian adults' digital competencies and their experience with online learning.The findings show that adults' digital competence and the interest to study online is rather low.The results of this research can be used as a basis for online course design adapted to needs of adults with low digital competence.
Patient-attentive sequential strategy for perimetry-based visual field acquisition

Perimetry is a non-invasive clinical psychometric examination used for diagnosing ophthalmic and neurological conditions. At its core, perimetry relies on a subject pressing a button whenever they see a visual stimulus within their field of view. This sequential process then yields a 2D visual field image that is critical for clinical use. Perimetry is painfully slow however, with examinations lasting 7–8 minutes per eye. Maintaining high levels of concentration during that time is exhausting for the patient and negatively affects the acquired visual field. We introduce PASS, a novel perimetry testing strategy, based on reinforcement learning, that requires fewer locations in order to effectively estimate 2D visual fields. PASS uses a selection policy that determines what locations should be tested in order to reconstruct the complete visual field as accurately as possible, and then separately reconstructs the visual field from sparse observations. Furthermore, PASS is patient-specific and non-greedy. It adaptively selects what locations to query based on the patient’s answers to previous queries, and the locations are jointly selected to maximize the quality of the final reconstruction. In our experiments, we show that PASS outperforms state-of-the-art methods, leading to more accurate reconstructions while reducing between 30% and 70% the duration of the patient examination.
Design of Students’ Spoken English Pronunciation Training System Based on Computer VB Platform

Spoken English communication is most commonly used in the international communication. However, the accuracy of spoken English pronunciation is the key factor to restrict English learners in China. For the current situation that spoken English proficiency is generally low in China, this paper aims to design a spoken English pronunciation training system that will provide guidance and help for English learners’ spoken pronunciation. The Visual Basic platform is used in the design of the system. This paper first conducts an in-depth study on the related theories of voice recognition, discusses the correction algorithm of voice scoring and pronunciation, and puts forward more practical and convenient AP-based scoring method, providing full theoretical support for the design of the system. Then through the function analysis and design of the spoken English pronunciation training system, this paper realizes the system design of scoring and correcting errors of English spoken pronunciation based on the VB platform. The system boasts the basic functions, including English phonetic symbols and word pronunciation to follow, real-time voice evaluation, and pronunciation error correction. According to the test, the similarity of the system with the experts is over 90% in scoring and its efficiency of pronunciation error correction reaches 80%, which plays a certain role in improving spoken English of English learners.