Reviewerly/exHarmony huggingface dataset:
The research trends in recommender systems for e-learning

Purpose A recommendation algorithm is typically applied to speculate on users’ preferences based on their behavioral characteristics. The purpose of this paper is to provide a systematic review of recommendation systems by collecting related journal articles from the last five years (i.e. from 2014 to 2018). This paper aims to study the correlations between recommendation technologies and e-learning systems. Design/methodology/approach The paper reviews the relevant articles using five assessment aspects. A coding scheme was put forward that includes the following: the metrics for the e-learning system, the evaluation metrics for the recommendation algorithms, the recommendation filtering technology, the phases of the recommendation process and the learning outcomes of the system. Findings The research indicates that most e-learning systems will adopt the adaptive mechanism as a primary metric, and accuracy is a vital evaluation indicator for recommendation algorithms. In existing e-learning recommender systems, the most common recommendation filtering technology is hybrid filtering. The information collection phase is an important process recognized by most studies. Finally, the learning outcomes of the recommender system can be achieved through two key indicators: affections and correlations. Originality/value The recommendation technology works effectively in closing the gap between the information producer and the information consumer. This technology could help learners find the information they are interested in as well as send them a valuable message. The opportunities and challenges of the current study are discussed; the results of this study could provide a guideline for future research.
A new harmonic continuous simplified swarm optimization

This paper introduces two novel concepts, the one-variable update mechanism (UM1) and the harmonic step-length strategy (HSS), to improve the continuous simplified swarm optimization (SSO). The proposed UM1 and HSS are able to balance the exploration and exploitation ability of the continuous SSO in solving high-dimensional multivariable and multimodal numerical continuous benchmark functions. The proposed new update mechanism UM1 updates only one variable and it is total completely different to that in the SSO without needing to update all variables. In the proposed UM1, the HSS enhances exploitation capacity by decreasing the step-length based on a harmonic sequence. Numerical experiments are performed on 18 high-dimension functions to confirm the efficiency of the proposed approach.
Outsourcing Proofs of Retrievability

Proofs of Retrievability (POR) are cryptographic proofs that enable a cloud provider to prove that a user can retrieve his file in its entirety. POR need to be frequently executed by the user to ensure that their files stored in the cloud can be fully retrieved at any point in time. To conduct and verify POR, users need to be equipped with devices that have network access, and that can tolerate the (non-negligible) computational overhead incurred by the verification process. This clearly hinders the large-scale adoption of POR by cloud users, since many users increasingly rely on portable devices that have limited computational capacity, or might not always have network access. In this paper, we introduce the notion of outsourced proofs of retrievability (OPOR), in which users can task an external auditor to perform and verify POR with the cloud provider. We argue that the OPOR setting is subject to security risks that have not been covered by existing POR security models. To remedy that, we propose a formal framework and a security model for OPOR. We then propose a generic procedure for transforming a public POR into an OPOR and we show the security of the resulting OPOR in our proposed security model. We demonstrate the transformation on two different instantiations of public POR schemes due to Shacham and Waters (Asiacrypt'08)-one based on BLS signatures and one using RSA signatures. A shortcoming of this transformation is that the generated OPOR inherits the high computational overhead from the underlying public key cryptography. Consequently, we propose afterwards an OPOR that is build from a private POR by Shacham and Waters. We implement a prototype based on our solutions, and evaluate their performance in a realistic cloud setting. Our evaluation results show that our proposals minimize user effort, and incur negligible overhead on the auditor.
Naive Bayesian classifier for hydrophobicity classification of overhead polymeric insulators using binary image features with ambient light compensation

Dispersion nature of water droplets over the insulator surface is used for hydrophobicity classification. Stochastic nature of water dispersions makes naive Bayesian classifier a preferable choice, which has been investigated in this work. About 12 features describing the characteristics of water droplets are extracted from the binary image using binary large objects analysis. Ambient light intensity is a significant factor that affects the binary image quality. As these insulators are installed in the outside environment, variations in ambient light intensity are inevitable. An adaptive threshold technique is proposed to compensate for ambient light variations. Six classes of various ambient light intensities have been considered in this study, and the proposed adaptive threshold technique can produce quality binary image consistently. Features extracted from the binary image are ordered according to their principal components (PCs) using PC analysis. Improvement in classification accuracy with the accumulation of ordered features is analysed. Results illustrate the use of the first eight features provides a reliable classification accuracy of 97.6% for test image samples. In comparison to the other existing classifiers, the proposed classifier illustrates optimal performance in terms of classification accuracy and computational time.
A Deep CNN-Based Ground Vibration Monitoring Scheme for MEMS Sensed Data

Ground vibration monitoring with microelectromechanical systems (MEMS) sensors is very effective and promising for alerting geological disasters. In this letter, explicitly considering and effectively addressing several specific issues related to practical MEMS sensors, we develop a novel ground vibration monitoring scheme for MEMS sensed data based on a deep convolutional neural network (CNN). Experiments are then conducted on the synthetic and real data sets. Experimental results on both data sets demonstrate that the proposed scheme significantly outperforms the other comparable schemes. For the synthetic data set, the proposed scheme achieves a very high overall accuracy of 98.82%. Also, for the real data set, the proposed scheme achieves a high overall accuracy of 81.64%, which is about 7% higher than that reported in the literature.
Human-Robot Interaction with Smart Shopping Trolley Using Sign Language: Data Collection

The paper presents a concept of a smart robotic trolley for supermarkets with a multimodal user interface, including sign language and acoustic speech recognition, and equipped with a touchscreen. Considerable progress in hand gesture recognition and automatic speech recognition within the last years has brought to life many human-computer interaction systems. At the moment the level of voiced speech and isolated/static hand gesture automatic recognition quality is quite high. However, continuous or dynamic sign language recognition still remains an unresolved challenge. There exists no automatic recognition system for Russian sign language nowadays. There are also no relevant data for model training. In the present research, we try to fill in this gap for the Russian sign language. We present a Kinect 2.0 based software-hardware complex for collection of multimodal sign language databases with an optical video camera, infrared camera and depth sensor. We describe the architecture of the developed software as well as some details of the collected database. The collected corpus is meant for further development of a Russian sign language recognition system, which will be embedded into a robotic trolley for supermarkets with gestural and speech interfaces. The architecture of the developed system is also presented in the paper.
DSC: Dense-Sparse Convolution for Vectorized Inference of Convolutional Neural Networks

The efficient applications of Convolutional Neural Networks (CNNs) in automotive-rated and safety critical hardware-accelerators require an interplay of DNN design optimization, programming techniques and hardware resources. Ad-hoc pruning would result in irregular sparsity and compression leading in very inefficient real world applications. Therefore, the proposed methodology, called Dense-Sparse Convolution, makes use of the right balance between pruning regularity, quantization and the underlying vectorized hardware. Different word length compute units, e.g. CPU, are used for low latency inference of the spares CNNs. The proposed open source CPU-kernel scales along with the vector word length and the number of cores.
Convolutional Neural Network for Hand Gesture Recognition using 8 different EMG Signals

The following paper presents the implementation of a versatile convolutional neural network architecture (CNN) for the recognition of 6 different commands by means of hand gestures using electromyographic signals. For this, a database consisting of 2880 multi-channel feature maps is built, that is, each dataset is composed of the processed signals of the 8 sensors of a Myo Armband, making use of power spectral density maps. The database is divided into 3 sets of equal size for training, validation and testing. With this, the architecture is trained, obtaining 98.4% accuracy in the validation and 99% in the tests, as well as the verification of the processing time that the network takes to obtain a result, this being 4 ms, demonstrating the ability of a shallow CNN to support multiple channels belonging to different sensors, achieving a high performance and having a reduced execution time that gives the possibility of being implemented in an application in real time.
I2P Anonymous Traffic Detection and Identification

Nowadays, network users are more and more aware of privacy protection. Anonymous communication technology has attracted much attention because it can hide the identity information of both sides of communication to achieve secure communication. Invisible Internet Project (I2P) is the most widely used open source anonymous communication tool besides The Onion Router (TOR) and Java Anon Proxy (JAP). It detects and identifies I2P traffic in the network, which plays an important role in network security monitoring and protection. Based on the analysis of the implementation mechanism of I2P anonymous service, this paper achieves the discovery and collection of I2P internal resources, and further analyses the NTCP communication protocol of I2P to realize the detection and identification of I2P network traffic. In addition, based on four traffic classification algorithms, the identification and classification of data traffic in I2P network are realized at anonymous service level, and the correlation between the selection of network traffic characteristics and classification performance is analyzed. The results show that Random Forest has the best classification performance compared with the other three classification algorithms. The classification performance improves with the decrease of the number of network traffic features selected.
Experimental demonstration of quantum finite automaton

Abstract In quantum information science, a major task is to find the quantum models that can outperform their classical counterparts. Automaton is a fundamental computing model that has wide applications in many fields. It has been shown that the quantum version of automaton can solve certain problem using a much smaller state space compared to the classical automaton. Here we report an experimental demonstration of an optical quantum automaton, which is used to solve the promise problems of determining whether the length of an input string can be divided by a prime number P with no remainder or with a remainder of R . Our quantum automaton can solve such problem using a state space with only three orthonormal states, whereas the classical automaton needs no less than P states. Our results demonstrate the quantum benefits of a quantum automaton over its classical counterpart and paves the way for implementing quantum automaton for more complicated and practical applications.
Training and Meta-Training Binary Neural Networks with Quantum Computing

Quantum computers promise significant advantages over classical computers for a number of different applications. We show that the complete loss function landscape of a neural network can be represented as the quantum state output by a quantum computer. We demonstrate this explicitly for a binary neural network and, further, show how a quantum computer can train the network by manipulating this state using a well-known algorithm known as quantum amplitude amplification. We further show that with minor adaptation, this method can also represent the meta-loss landscape of a number of neural network architectures simultaneously. We search this meta-loss landscape with the same method to simultaneously train and design a binary neural network.
Context Deployed Sentiment Analysis Using Hybrid Lexicon

Sentiment analysis refers to the study of attitudes or opinions. Sentiment mining is the drawing out of polarity of text, its features and the time at which the attitude was conveyed. Lexicon dependent techniques involve drawing out of polarities of term from the lexicon and aggravation of the obtained scores to determine the comprehensive sentiment of textual data. Lexicon based approaches plays vital role with respect to the large coverage of terms. The unsupervised machine learning methods rarely takes into account the appearance of emoticons, modifiers, negation terms, general purpose lexicon and domain specific lexicon while analyzing the polarity of text. In this paper, the lexicon based approaches plays an active role regarding the aforementioned aspects. Here we focus on handling of contextual polarity of text wherein which the prior polarity of the term expressed in the lexicon may be different from the polarity expressed in the text. Experimental results give evidence in the performance improvement of the proposed system in terms of accuracy, recall and precision when compared with the existing systems.
Three-dimensional face recognition using variance-based registration and subject-specific descriptors

Face recognition underpins numerous applications; however, the task is still challenging mainly due to the variability of facial pose appearance. The existing methods show competitive performance but they are still short of what is needed. This article presents an effective three-dimensional pose invariant face recognition approach based on subject-specific descriptors. This results in state-of-the-art performance and delivers competitive accuracies. In our method, the face images are registered by transforming their acquisition pose into frontal view using three-dimensional variance of the facial data. The face recognition algorithm is initialized by detecting iso-depth curves in a coordinate plane perpendicular to the subject gaze direction. In this plane, discriminating keypoints are detected on the iso-depth curves of the facial manifold to define subject-specific descriptors using subject-specific regions. Importantly, the proposed descriptors employ Kernel Fisher Analysis-based features leading to the face recognition process. The proposed approach classifies unseen faces by pooling performance figures obtained from underlying classification algorithms. On the challenging data sets, FRGC v2.0 and GavabDB, our method obtains face recognition accuracies of 99.8% and 100% yielding superior performance compared to the existing methods.
The Case of Self-transitions in Affective Dynamics

Affect dynamics, the study of how affect develops and manifests over the course of learning, has become a popular area of research in learning analytics. Despite some shared metrics and research questions, researchers in this area have some differences in how they pre-process the data for analysis [17]. Specifically, researchers differ in how they treat cases where a student remains in the same affective state in two successive observations, referred to as self-transitions. While most researchers include these cases in their data, D’Mello and others have argued over the last few years that these cases should be removed prior to analysis. While this choice reflects the intended focus in their research paradigm on the transitions out of an affective state, this difference in data preprocessing changes the meaning of the metric used. For around a decade, the community has used the metric L to evaluate the probability of transitions in affect. L is largely believed to have a value of 0 when a transition is at chance, and this is true for the original use of the metric. However, this paper provides mathematical evidence that this metric does not have a value of 0 at chance if self-transitions are removed. This shift is problematic because previously published statistical analyses comparing L values to the value at chance have used the wrong value, incorrectly producing lowered p values and in many cases reporting transitions as significantly more likely than chance when they are actually less frequent.
Implementation of CMAC Authentication Algorithm on FPGA for Satellite Communication

In communication systems, data encryption and authentication algorithms are utilized for secure communication. The utilization of encryption and authentication algorithms depends on the field of application and required level of security. Recently, researchers are working on encryption based authentication algorithms, as both services of encryption and authentication are utilized in highly secure systems such as satellite communication. The Cipher based Message Authentication Code (CMAC) finds its relevance in many applications. CMAC algorithm has been implemented previously on software and hardware. But by assessment of previous work on hardware implementation it seems that competence could be enhanced by efficiently utilizing hardware resources. In this paper, an improved and efficient hardware implementation of CMAC algorithm on FPGA is proposed for satellites application. Our results showed improvement in consumption of FPGA area and time utilization as compared to previous implementations.
User Identity De-anonymization Based on Attributes

Online social networks provide platforms for people to interact with each other and share moments of their daily life. The online social network data are valuable for both academic and business studies, and are usually processed by anonymization methods before being published to third parties. However, several existing de-anonymization techniques can re-identify the users in anonymized networks. In light of this, we explore the impact of user attributes in social network de-anonymization in this paper. More specifically, we first quantify the significance of attributes in a social network, based on which we propose an attribute-based similarity measure; then we design an algorithm by exploiting attribute-based similarity to de-anonymize social network data; finally we employ a real-world dataset collected from Sina Weibo to conduct experiments, which demonstrate that our design can significantly improve the de-anonymization accuracy compared with a well-known baseline algorithm.
Salient object detection using background subtraction, Gabor filters, objectness and minimum directional backgroundness

Salient object detection is the process of identifying essential objects in an image. This paper solves this problem using background subtraction, Gabor filters, minimum directional backgroundness, and objectness. The first step is to calculate a backgroundness score for each region by calculating the difference between the feature vector of image boundary and image regions. This backgroundness map is then used for calculating the minimum directional background difference. The image is segmented using Gabor filters, and then the objectness criterion is used to choose the segment containing the salient object. The normalized foreground saliency map is then used to refine the selected segment. Further enhancement of this intermediate output is done using morphological operations, and boundary correction is done using the method of lazy snapping. The algorithm is tested on eight publicly available datasets and is compared against five algorithms. The performance is evaluated by PR-curve, F-Measure curve, and Mean Absolute Error.
Performance Comparison of Dual Axis Solar Tracker with Static Solar System in Ural Region of Russia

Worldwide, solar power has become the third biggest source of energy after hydro and wind, thus, catching a global attention. This study draws a comparison between the performance of Dual Axis Solar Tracker (DAST) and Static Solar System (SSS) or a solar system with fixed axis to assess the solar power potential of Ural region located in Russian Federation. By using various sun angles such as zenith, solar altitude etc., solar irradiance was calculated during a day either cloudy or sunny, whereas the cumulative energy generated during the whole month was also measured. Moreover, for a clearer understanding, efficiency differences during a day as well as during the whole month were considered too. The results obtained in the form of energy gain and efficiency of proposed tracking unit presented in the form of graphs and tables suggest that the performance of DAST is significantly better than SSS.
Quadruple bordered constructions of self-dual codes from group rings

In this paper, we introduce a new bordered construction for self-dual codes using group rings. We consider constructions over the binary field, the family of rings Rk and the ring $\mathbb {F}_{4}+u\mathbb {F}_{4}$. We use groups of order 4, 12 and 20. We construct some extremal self-dual codes and non-extremal self-dual codes of length 16, 32, 48, 64 and 68. In particular, we construct 33 new extremal self-dual codes of length 68.
Directed color transfer for low-light image enhancement

Underexposed, low-light, images are acquired when scene illumination is insufficient for a given camera. Camera limitation originates in the high chance of producing motion blurred images due to shaky hands. In this paper we suggest to actively use underexposing as a measure to prevent motion blurred images to appear and propose a novel color transfer as a method for low light image amplification. The proposed solution envisages a dual acquisition, containing a normally exposed, possibly blurred image and an underexposed/low-light, but sharp one. Good colors are learned from the normal exposed image and transferred to the low light one using a framework matching solution. To ensure that the transfer is spatially consistent, the images are divided into luminance perceptual consistent patches called frameworks and the optimal mapping is piece-wise approximated. The two image may differ by colors and subject to improve the robustness of the spatial matching, we added supplementary extreme channels. The proposed method shows robust results from both an objective and a subjective point of view.