Reviewerly/exHarmony huggingface dataset:
Recommender Systems for an Enhanced Mobile e-Learning

In the last years we have been witnesses of the increasing use of on-line educational systems known as e-learning. Every year there are more teaching centers, both public and private ones, which provide their students with web-based access to Learning Management Systems (LMS). Also, it is important to mention platforms for Massive Open On-line Courses (MOOC) which are a type of on-line educational system recently developed according to the design and participation akin to the presential courses at most prestigious universities. These systems provide to all kind of students with didactic resources as well as activities. In another hand the Adaptive and Intelligence Web-based Educational Systems (AIWBES) are made in order to solve the problem of to automate the adaptation of the system to the user features and needs. One more recent alternative is implementing Recommender Systems, which might offer their users customized suggestions to find activities and educational content. Such systems filter user information, for instance, preferences known by a user community for forecasting preferences for the new user, this approach is known as collaborative Filtering. With this proposal we are trying to model and represent the domain knowledge about the learner and learning resources discovering the learners’ learning patterns.
Optimal Decision Tree with Noisy Outcomes

A fundamental task in active learning involves performing a sequence of tests to identify an unknown hypothesis that is drawn from a known distribution. This problem, known as optimal decision tree induction, has been widely studied for decades, and the asymptotically best-possible approximation algorithm has been devised for it. We study a generalization where certain test outcomes are noisy, even in the more general case where the noise is persistent, i.e., repeating a test gives the same noisy output. More generally, we introduce a problem, Submodular Function Ranking with Noise (SFRN), that generalizes the above problem. We design new approximation algorithms for both the non-adaptive setting, where the test sequence must be fixed, and the adaptive setting, where the test sequence depends on the outcomes of prior tests. Previous work in the area assumed that at most a logarithmic number of noisy outcomes per hypothesis and provided approximation ratios that depended on parameters such as the minimum probability of a hypothesis. Our new approximation algorithms provide guarantees that are nearly best-possible and work for the general case of a large number of noisy outcomes per test or per hypothesis where the performance degrades smoothly with this number.We numerically evaluated the performance of our algorithms in two natural applications with noise: toxic chemical identification and active learning of linear classifiers. Despite our theoretical logarithmic approximation guarantees, our methods give solutions with cost very close to the information theoretic minimum, demonstrating the effectiveness of our methods.
Image Steganography Using the Fusion of Quantum Computation and Wavelet Transformation

In the advancement of information technology the role of security of information is very significant. Many organization including research and educational institute, IT companies exploring the area of quantum computation due to which the field is advancing very quickly. The mechanism of quantum computer provides greater security for quantum data. So in this research we present a new high security steganography system using hybridization of quantum computation and wavelet transformation. The proposed algorithm provides dual security for the embedded data as we transform the normal signal of the high frequency information of wavelet transformation into quantum signal before embedding the secret information. The message extraction process depends on the corresponding coefficient value of low frequency information which acts as a pseudo random key. To assess the accomplishment of our proposed algorithm we have used three parameters MSE, PSNR and RS for different image formats. All these metrics showed that there is very less distortion of the original image.
Prevention of Crimes Made with the Use of the Internet Network as One of the Directions to Ensure the Cybersecurity of Russia

The article defines the range of socially dangerous acts committed using the information and telecommunications network “Internet”, which are currently prohibited by the criminal law of Russia. Based on official data of the Federal State Statistics Service, the current state of counteraction to these crimes is analyzed. In order to effectively counter these negative phenomena, measures have been formulated to prevent crimes committed using the Internet information and telecommunications network—legal, organizational, and software-technical. In addition, as part of the elimination of circumstances conducive to the commission of cybercrime, we can not neglect the measures of software and hardware confrontation to attackers.
Target Approach for an Autonomous Mobile Robot Using Camera Images and Its Behavioral Acquisition for Avoiding an Obstacle

An autonomous mobile robot has to possess a locomotion way suitable for the mobile environment and to autonomously move. This research aims to operate an autonomous mobile robot by determining the control input from the features of acquired camera images. Some advantages of this method are the following points: i.e., there is no need for self-localization, so that the amount of computations is small, and it can be controlled by a human-like method. In conventional studies, avoiding of static obstacles was realized by using edge detection and fuzzy control. However, there remain several problems, i.e., it is difficult to deal with moving obstacle, there is no system for approaching a target point while avoiding obstacles, etc. Therefore, this paper proposes a method for approaching a target point while avoiding obstacles, by adding potentials around the target when constructing the potential field. In addition, a collision prediction system is constructed using a convolutional neural network (CNN) and a method of stopping a robot in the vicinity of the target point while avoiding a moving obstacle is proposed by switching control based on such a system. The effectiveness of the proposed method is verified by simulation experiments. The experimental results show that the robot avoids a moving obstacle and stops at the target by using the proposed method.
DLBench: An Experimental Evaluation of Deep Learning Frameworks

Recently, deep learning has become one of the most disruptive trends in the technology world. Deep learning techniques are increasingly achieving significant results in different domains such as speech recognition, image recognition and natural language processing. In general, there are various reasons behind the increasing popularity of deep learning techniques. These reasons include increasing data availability, the increasing availability of powerful hardware and computing resources in addition to the increasing availability of deep learning frameworks. In practice, the increasing popularity of deep learning frameworks calls for benchmarking studies that can effectively evaluate the performance characteristics of these systems. In this paper, we present an extensive experimental study of six popular deep learning frameworks, namely TensorFlow, MXNet, PyTorch, Theano, Chainer, and Keras. Our experimental evaluation covers different aspects for its comparison including accuracy, speed and resource consumption. Our experiments have been conducted on both CPU and GPU environments and using different datasets. We report and analyze the performance characteristics of the studied frameworks. In addition, we report a set of insights and important lessons that we have learned from conducting our experiments.
Headlight Range Estimation for Autonomous Driving using Deep Neural Networks

When driving at night, a good illumination of the road ahead is crucial. With autonomous driving at close temporal proximity, this not only concerns human drivers but also autonomous systems capable of controlling the car. To achieve fully autonomous driving, a variety of sensors are integrated into the vehicles. Cameras act as one of the major sensors. However, due to their passivity, cameras cannot see well in the dark. To mitigate this shortcoming, modern cars are equipped with powerful headlights that provide proper illumination of the road ahead while avoiding the dazzling of other traffic participants. To use the headlights' full potential and to also provide advanced light functionality like glare-free high beam, they need to be properly adjusted. After the initial calibration during production, this setting is prone to undesirable degradation, primarily due to mechanical reasons. We present a completely new application of computer vision and machine learning to automatically detect wrongly adjusted headlights by estimating their pitch angle from the images of a vehicle-attached camera for advanced driving assistance systems (ADAS). We show that we can achieve high performance in terms of accuracy and robustness by training a deep neural network in an end-to-end fashion. To demonstrate the benefits of our proposed approach, an additional handcrafted baseline method is implemented.
Improving Fuzzy C-Means Algorithm using Particle Swarm Optimization and Fuzzy C-Means++

The aim of the paper is to improve fuzzy particle swarm optimization. Our modification consists of modifying the hybrid fuzzy particle swarm optimization by replacing the random initialization of centers in the c-means fuzzy section with the FCM++ algorithm. We experimentally compared our modification with other nature-inspired meta-heuristic algorithms for fuzzy clustering.
Internet histories and computational methods: a “round-doc” discussion

This conversation brings together leading experts within the field of computational methods. Participants were invited to discuss “Internet histories and computational methods”, and the debate focused on issues such as why scholars of internet histories should consider using computational methods, what scholars should be looking out for when they use these methods, how the process of collecting influences computational research, what impedes the use of computational methods, to what an extent internet historians should learn to code (or conversely, if developers should learn about historical methods), what are the most defining moments in the history of computational methods, and, finally, the future of using computational methods for historical studies of the internet.
Explicit Discourse Argument Extraction for German

We present an approach to the extraction of arguments for explicit discourse relations in German, as a sub-task of the larger task of shallow discourse parsing for German. Using the Potsdam Commentary Corpus, we evaluate two methods (one based on constituency trees, the other based on dependency trees) to extract both the internal and the external argument, for which our best results are 86.73 and 77.85 respectively. We demonstrate portability of this set of heuristics to another language and also put these scores into perspective by applying the same method to English and compare this to published results.
Oil Tank Detection Based on Linear Clustering Saliency Analysis for Synthetic Aperture Radar Images

As the significant position of oil tanks in oil storage, oil tank detection plays an important role in auto target detection for SAR images. This paper presents a linear clustering saliency analysis based detection model. Firstly, a linear iterative clustering of which the feature vector consists of three texture features and 2-D coordinates is used to over segment the input image. At the same time, multi intensity saliency maps constrain the shape of the superpixel using an adaptive balance weight. Secondly, feature vectors generated from the cluster centers are scattered as far as possible via Principal Component Analysis and sent to the MeanShift model to coarsely locate the candidate area. Finally, strong scattered points on the roof of tanks are utilized to locate the top of the targets. The whole method is evaluated in two aspects: the evaluation of saliency analysis and the accuracy rate of the top location. Experiment shows the efficiency and superiority of our algorithm with fewer interferences and more accurate location of oil tanks.
A Comparison of Hybrid and End-to-End Models for Syllable Recognition

This paper presents a comparison of a traditional hybrid speech recognition system (kaldi using WFST and TDNN with lattice-free MMI) and a lexicon-free end-to-end (TensorFlow implementation of multi-layer LSTM with CTC training) models for German syllable recognition on the Verbmobil corpus. The results show that explicitly modeling prior knowledge is still valuable in building recognition systems. With a strong language model (LM) based on syllables, the structured approach significantly outperforms the end-to-end model. The best word error rate (WER) regarding syllables was achieved using kaldi with a 4-gram LM, modeling all syllables observed in the training set. It achieved 10.0% WER w.r.t. the syllables, compared to the end-to-end approach where the best WER was 27.53%. The work presented here has implications for building future recognition systems that operate independent of a large vocabulary, as typically used in a tasks such as recognition of syllabic or agglutinative languages, out-of-vocabulary techniques, keyword search indexing and medical speech processing.
Dynamics of goal characterization in students’ exams-preparation systemic activity transition processes

This study examined the influence of students 'goal characterization on their goal-formation processes, as characterized by their goal-classification, the systemic consideration of their activity-strategies and decision-outcomes when preparing for an examination. Using the Structural Equation Modeling approach, a functional analysis underlined by systemic principles was conducted. Firstly, the issue of whether the influence of students' considerations of activity strategies on their decision outcomes is truly moderated by their activity goal formations, if they set highest-goals, is determined. Secondly, the issue of whether the influence of students' considerations of activity strategies on their decision outcomes is truly mediated by their activity goal formations, if they set best-goals, is also determined. Based on the findings, the conscious goal-directed processes associated to the emergence of an individual's thoughtfully mastered learning activity and its consequence on future design of systemic structural activity of individuals will be established.
Modelling Large Scale Datasets Using Partitioning-Based PCA

In this study, we propose an efficient approach for modelling and compressing large-scale datasets. The main idea is to subdivide each sample into smaller partitions where each partition constitutes a particular subset of attributes and then apply PCA to each partition separately. This simple approach enjoys several key advantages over the traditional holistic scheme in terms of reduced computational cost and enhanced reconstruction quality. We study two variants of this approach, namely, cell-based PCA for image datasets where samples are spatially divided into smaller blocks and the more general band-based PCA where attributes are partitioned based on their values distribution.
Visual Exploration of Topics in Multimedia News Corpora

The increasing availability of digital multimedia content has led to the need of new approaches for the analysis of large databases containing video and associated data, for example, subtitles. Visualization provides valuable insights of such dataset, complementing approaches solely based on techniques for knowledge discovery in databases and information retrieval. Hence, visual analytics, combining automatic processing with interactive data visualization, has proven to be an effective means to explore and interpret such data. The analysis of news corpora represents a typical task for such a scenario. Domain experts such as journalists and social science scholars require an overview of important topics, the temporal coherence of events, and they should be able to compare different topics. We present a visual analytics approach that aims to support these tasks with automatic video preprocessing, topic extraction, clustering, and dimensionality reduction. Coordinated linked views support the flexible inspection of the dataset and the processed results. We further discuss the application of our approach in a usage scenario, inspecting the dataset of a daily news broadcast of the year 2015.
Bidirectional Single-Stream Temporal Sentence Query Localization in Untrimmed Videos

Given an untrimmed video, we consider localizing specific temporal segments according to the text description. Existing methods employ the computationally-expensive sliding window fashion to generate proposals and use simple operations which are not expressive enough to combine the video and language information. To address these two issues, we propose a novel model, namely Bidirectional Single-Stream Temporal Localization (BSSTL) which effectively and effi-ciently localizes language queries in videos. We make two contributions: (1) Our BSSTL can run continuously in a video stream, without dividing input videos into short overlapping temporal windows for batch processing. (2) We propose a novel Attentive Cross-modal Fusion approach to jointly project sentence queries and video segments. For evaluation, We evaluate BSSTL on ActivityNet Captions [1] and TACoS datasets [2] and our model achieves state-of-the-art performance.
Pseudo-Relevance Feedback Based on Locally-Built Co-occurrence Graphs

In Information Retrieval (IR), user queries are often too short, making the selection of relevant documents hard. Pseudo-relevance feedback (PRF) is an effective method to automatically expand the query with new terms using a set of pseudo-relevant documents. However, a main issue in PRF is the selection of good expansion terms that allow improving retrieval effectiveness. In this paper, we present a new PRF method based on locally-built term co-occurrence graphs. We use a context window-based approach to construct our term co-occurrence graphs over top pseudo-relevant documents. For expansion terms selection, we propose an adapted version of the BM25 model, which allows to measure term-term similarity in co-occurrence graphs. This measure has the advantage of selecting discriminant expansion terms that are semantically related to the query as a whole. We evaluate our PRF method using four TREC collections, including the standard TREC Robust04 collection and the newest TREC Washington Post collection. Experimental results show that our proposal outperforms competitive state-of-the-art baselines and achieves significant improvements.
VeIGAN: Vectorial Inpainting Generative Adversarial Network for Depth Maps Object Removal

The recent precision increase in image-based depth estimation encourages to use this type of data for mapping. Recent work proposes different approaches to deal with the problem of occlusion generated by different scene perspectives of stereo cameras. However, there is less attention to depth estimation and inpainting for object removal and object occlusion. In this paper, we study recent inpainting approaches for RGB images and apply these methods on depth maps. We propose a Generative Adversarial Network (GAN) for depth feature extraction to estimate the depth inside a masked area, in order to remove objects on disparity images. Our results show that using depth features on the loss function and on the network architecture, increase the result precision and give to the generated image a depth distribution close to the real data. Our main contribution is a GAN, which estimates depth information in a masked area inside a disparity image.
Single-Shot Detector with Multiple Inference Paths

In this paper, we investigate the problem of resource-constrained object detection using deep learning, which is a challenging problem in real-world applications. To address this problem, we propose a single-shot detector with multiple inference paths based on a multi-scale DenseNet. Experiments are carried out on the PASCAL VOC and COCO datasets, and results show that, with significant computation reduction, our detection network obtains comparable performance corresponding to the single-shot detectors, such as YOLO and SSD.
Dynamic Spatial Predicted Background for Video Surveillance

Video foreground-background separation is considered as a basic step for many computer vision applications. Common approaches excel in handling background variances while trying to keep the computational load low. We propose a novel method that models the scene as a superposition of illumination effects while predicting each pixel's value with a linear estimator comprised by a few other pixels of the scene. By doing so, we are able to achieve real-time performance using minimal hardware, which is a crucial consideration for embedding such a system on surveillance cameras. Experimental results on two common datasets show our method's potential by comparing it to state-of-the-art methods.
Long and Short Memory Balancing in Visual Co-Tracking Using Q-Learning

Employing one or more additional classifiers to break the self-learning loop in tracing-by-detection has gained considerable attention. Most of such trackers merely utilize the redundancy to address the accumulating label error in the tracking loop, and suffer from high computational complexity as well as tracking challenges that may interrupt all classifiers (e.g. temporal occlusions). We propose the active co-tracking framework, in which the main classifier of the tracker labels samples of the video sequence, and only consults auxiliary classifier when it is uncertain. Based on the source of the uncertainty and the differences of two classifiers (e.g. accuracy, speed, update frequency, etc.), different policies should be taken to exchange information between two classifiers. Here, we introduce a reinforcement learning approach to find the appropriate policy by considering the state of tracker in a specific sequence. The proposed method yields promising results in comparison to the best tracking-by-detection approaches.
Path Planning of Omnidirectional Mobile Vehicle Based on Road Condition

This paper concerns with the vehicle path planning problem considering the actual natural environment and social environment, which may influence the reliability and stability. The basic path planning problem is determined to solve the optimal path problem. In here, the surface properties and terrain has been taken into consideration, based on improved ant colony algorithm(ACA). Unlike the assumptions of basic ACA, in this case the terrain slope and the surface condition are analyzed and described in order to set up terrain table and vehicle capacity table. Therefore, coordinating the terrain and the obstacles condition, the reliability is enhanced. For instance, this paper optimizes the initial pheromone distribution, path point selection strategy, pheromone updating method, upon the shortage of basic ACA, such as low initial search speed, poor convergence, the problem of local optimum. Eventually, the results of simulation are exhibited, which shows the model and algorithm's efficiency.