Reviewerly/exHarmony huggingface dataset:
CLUST

Currently most applications are data-intensive in nature and require the ability to process large data sets across a cluster of nodes. The Hadoop-an open-source implementation of MapReduce (MR) architecture has become the de facto processing platform for these applications. Even though Hadoop is considered as an ideal solution to analyse and gain insights from massive data, it has its own limitations when the data to be processed exhibits interest-locality (i.e. the data required for any query execution follows grouping behaviour wherein only a part of big data is accessed frequently). Since Hadoop data placement does not consider interest-locality, the dependent blocks required for execution may be concentrated within fewer computing nodes, resulting in severe degradation in MR performance. Hence in this paper, CLUST- Optimal data placement strategy based on grouping semantics is proposed, so that the query can be solved earlier. This paper harnesses the Hierarchical agglomerative clustering techniques in data placement for achieving improved MR performance during execution of interest-based queries. It has been validated by executing complex interest-based queries on NCDC weather dataset, distributed in two scalable heterogeneous Hadoop clusters deployed on the cloud. The CLUST significantly reduces execution time, improves data locality, CPU utilisation and proves to be an efficient solution for big data processing.
The rubber hand illusion is influenced by self-recognition

Susceptibility to the Rubber Hand Illusion (RHI) demonstrates that body ownership can be modulated by visuotactile inputs. In contrast to body-like images, other objects cannot be embodied suggesting that crossmodal interactions on body ownership are based on a ‘goodness-of-fit’ mechanism relative to one’s own body. However, it is not clear whether visual self-recognition influences susceptibility to the RHI, although evidence for individual differences in the perceptual body image on the RHI suggests that this may be the case. We investigated the role of self-recognition on the subjective experience of the RHI and measured proprioceptive drift and onset time of the RHI between two groups, one with the ability to identify an image of their own hand and the other without this ability. A typical RHI response was found overall with no group difference in the subjective experience of the RHI. However, a larger proprioceptive drift and an earlier onset time for the RHI was found for the non-recognisers than the self-recognition group. Our findings provide evidence for a link between a visual representation of one’s own body in long-term memory and plasticity of the body representation.
Correctness of Proof Strategy for the Sisal Program Verification

The Sisal-program verification system is being developed in IIS SB RAS. The experiments show that the proof strategies are very helpful if we aims at automatic verification. Obviously, correctness of such strategies is crucial. We would like to discuss here a strategy which can be applied to verification of definite iterations. This strategy is sequence of formula transformation. Either equivalent formula or more stronger formula is the result of this transformation. Consequently it is enough to prove for each transformation that its result is either equivalent to or stronger than source formula. This proof is a new result presented in this paper.
Integrating Meta-Path Similarity with User Preference for Top-N Recommendation

Recommendation Systems (RSs) are effective tools to find interesting items for users in explosive information on the network. However, traditional RSs still suffer from limitations. The first problem is feature extraction; lack of item information leads to the difficulty of semantic discrimination between items. The second one is the cold-start problem that happens when we do not have much information related to user ratings. This paper aims to alleviate the drawback by exploiting benefits from LOD - a data source using structured data presents its data. The method is tested on MovieLens 1M dataset and DBpedia - the subset of LOD to enhance semantically. In contrast to previous work, we use PathSim to capture semantic between items. Furthermore, we use five-star rating that uses a 5-star scale instead of binary rating that uses 0 and 1 to present user preference for LambdaMart model. Overall, our approach outperforms SPrank, a hybrid recommendation algorithm proposed in the literature.
Design of Binary Neurons with Supervised Learning for Linearly Separable Boolean Operations

Though the Artificial Neural Network is used as a potential tool to solve many of the real-world learning and adaptation problems, the research articles revealing the simple facts of how to simulate an artificial neuron for most popular tasks are very scarce. This paper has in its objective presenting the details of design and implementation of artificial neurons for linearly separable Boolean functions. The simple Boolean functions viz AND and OR are taken for the study. This paper initially presents the simulation details of artificial neurons for AND and OR operations, where the required weight values are manually calculated. Next, the neurons are added with learning capability with perceptron learning algorithm and the iterative adaptation of weight values are also presented in the paper.
Integrate Dragonfly Key Exchange (IETF - RFC 7664) into Arithmetic Circuit Homomorphic Encryption

This is an extension of an ongoing research project on Fully Homomorphic Encryption. Arithmetic Circuit Homomorphic Encryption (ACHE) [1] was implemented based on (TFHE) Fast Fully Homomorphic Encryption over the Torus. Just like many Homomorphic Encryption methods, ACHE does not integrate with any authentication method. Thus, this was an issue that this paper attempts to resolve. This paper will focus on the implementation method of integrating RFC7664 [2] into ACHE. Next, the paper will further discuss latency incurred due to key generation, the latency of transmission of public and private keys. Last but not least, the paper will also discuss the key size generated and its significance.
Open Distance Learning and Immersive Technologies: A Literature Analysis

Open and distance learning (ODL) education provides an opportunity for prospective students who require flexibility in education, enabling learning without traditional face-to-face lecture sessions. This chapter aimed to explore how new immersive technology can be used to enhance the experience of distance learning. Immersive technologies refer to Augmented and Virtual reality technologies. Augmented reality allows users to overlay their reality using a device such as a mobile phone to view 3D objects on their reality. Virtual reality allows users to be immersed into a simulated world of 3D views through head mounted gear. Employing a systematic literature review, findings were presented in the form of a thematic discussion looking at journal articles between the year 2000 to October 2018. Following a review of 40 articles which were included in the qualitative synthesis, this study investigated the challenges experienced in open distance learning experience, in an attempt to form the basis of interlinking the use of virtual and augmented reality technologies to enhance the distance learning experience. Findings revealed ODL presently employs various Information and Communication Technology (ICT) tools which are comprised of technological resources that assist the facilitation of lecturer and student interaction. However, these ICTs do not fully assist in enhancing the DL experience. Results from the review found that the adoption of AR and VR in higher education contributed to learner engagement and enhanced learning outcomes and thus could benefit ODL. Moreover, literature revealed that immersive learning is best utilized as a complement to traditional learning as opposed to a replacement.
Performance Evaluation of Wavelet SVD-Based Watermarking Schemes for Color Images

Digital image watermarking techniques have enabled imperceptible information in images to be hidden to ensure the information can be extracted later from those images. For any watermarking scheme, there are four main requirements which are imperceptibility, Robustness, capacity and security. Recently, hybrid Singular Value Decomposition (SVD) based watermarking schemes in the transform domain have significantly gained a lot of attention. This is due to the characteristics of SVD and the wavelet. Most of these schemes were tested under different conditions using grey images only. However, due to the growth of digital technology and the huge use of the colour images, it is important to consider the colour images in the watermarking area. Three different SVD-based image watermarking schemes with different wavelet transforms are selected in this paper to be tested and evaluated for colour images. Two colour models are used to represent the colour images to perform the embedding and the extraction watermarking process to study these colour models' performances and effectiveness in the watermarking area. These colour models are RGB and YCbCr. All these colour models' channels are used as an embedding channel and then are evaluated under different attacks types. The experimental results of the selected Wavelet SVD-based watermarking schemes proved that the embedding in the RGB and YCbCr colour channels are achieved high imperceptibility. These colour channels also showed good robustness against different attacks such as cropping, cutting, rotation and JPEG compression.
POI Recommendation Based on Locality-Specific Seasonality and Long-Term Trends

This work deals with time-aware recommender systems in a domain of location-based social networks, such as Yelp or Foursquare. We propose a novel method to recommend Points of Interest (POIs) which considers their yearly seasonality and long-term trends. In contrast to the existing methods, we model these temporal aspects specifically for individual geographical localities instead of globally. According to the results achieved by the experimental evaluation on Yelp dataset, locality-specific seasonality can significantly improve the recommendation performance in comparison to its global alternative. We found out that it is helpful mostly within recommendations for highly-active users (it has a smaller influence for the novice users) and as expected, in localities with a strong seasonal weather variation. Another interesting finding is that in contrast to seasonality, we did not observe an improvement in case of locality-specific long-term trends.
Smallest Graphs Achieving the Stinson Bound

Perfect secret sharing scheme is a method to distribute a secret information s among participants such that only predefined coalitions, called qualified subsets of participants can recover the secret, while any other coalitions, the unqualified subsets, cannot determine anything about the secret. The most important property is the efficiency of the system, which is measured by the information ratio. It can be shown that for graphs the information ratio is at most (δ + 1)/2 where δ is the maximum degree of the graph. Blundo et al. constructed a family of δ-regular graphs with information ratio (δ + 1)/2 on at least c · 6 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">δ</sup> vertices. We improve this result by constructing a significantly smaller graph family on c · 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">δ</sup> vertices achieving the same upper bound both in the worst and the average case.
Analyzing Sentiments of German Job References

Filling a vacancy takes a lot of (costly) time. Automated pre-processing of applications using artificial intelligence technology can help to save time, e.g., by analyzing applications using machine learning algorithms. We investigated whether such systems are potentially biased in terms of gender, origin and nobility. For this purpose we created a corpus of common German reference letter sentences on which we performed sentiment analysis using the cloud services by Amazon, Google, IBM and Microsoft. We established that all tested services rate the sentiment of the same template sentences very inconsistently and biased at least with regard to gender.
Human-Machine Collaboration for Face Recognition

Despite advances in deep learning and facial recognition techniques, the problem of fault-intolerant facial recognition remains challenging. With the current state of progress in the field of automatic face recognition and the in-feasibility of fully manual recognition, the situation calls for human-machine collaborative methods. We design a system that uses machine predictions for a given face to generate queries that are answered by human experts to provide the system with the information required to predict the identity of the face correctly. We use a Markov Decision Process for which we devise an appropriate query structure and a reward structure to generate these queries in a budget or accuracy-constrained setting. Finally, as we do not know the capabilities of the human experts involved, we model each human as a bandit and adopt a multi-armed bandit approach with consensus queries to efficiently estimate their individual accuracies, enabling us to maximize the accuracy of our system. Through careful analysis and experimentation on real-world data-sets using humans, we show that our system outperforms methods that exploit only machine intelligence, simultaneously being highly cost-efficient as compared to fully manual methods. In summary, our system uses human-machine collaboration for face recognition problem more intelligently and efficiently.
Optimized Distributed Subgraph Matching Algorithm Based on Partition Replication

At present, with the explosive growth of data scale, subgraph matching for massive graph data is difficult to satisfy with efficiency. Meanwhile, the graph index used in existing subgraph matching algorithm is difficult to update and maintain when facing dynamic graphs. We propose a distributed subgraph matching algorithm based on Partition Replica (noted as PR-Match) to process the partition and storage of large-scale data graphs. The PR-Match algorithm first splits the query graph into sub-queries, then assigns the sub-query to each node for sub-graph matching, and finally merges the matching results. In the PR-Match algorithm, we propose a heuristic rule based on prediction cost to select the optimal merging plan, which greatly reduces the cost of merging. In order to accelerate the matching speed of the sub-query graph, a vertex code based on the vertex neighbor label signature is proposed, which greatly reduces the search space for the subquery. As the vertex code is based on the increment, the problem that the feature-based graph index is difficult to maintain in the face of the dynamic graph is solved. An abundance of experiments on real and synthetic datasets demonstrate the high efficiency and strong scalability of the PR-Match algorithm when handling large-scale data graphs.
Pedestrian Proximity Detection using RGB-D Data

This paper presents a novel method for pedestrian detection and distance estimation using RGB-D data. We use Mask R-CNN for instance-level pedestrian segmentation, and the Semiglobal Matching algorithm for computing depth information from a pair of infrared images captured by an Intel RealSense D435 stereo vision depth camera. The resulting depth map is post-processed using both spatial and temporal edge-preserving filters and spatial hole-filling to mitigate erroneous or missing depth values. The distance to each pedestrian is estimated using the median depth value of the pixels in the depth map covered by the predicted mask. Unlike previous work, our method is evaluated on, and performs well across, a wide spectrum of outdoor lighting conditions. Our proposed technique is able to detect and estimate the distance of pedestrians within 5m with an average accuracy of 87.7%.
Rebalancing in Acyclic Payment Networks

In this paper, we propose a technique for rebalancing link weights in acyclic payment networks, which are peer-to-peer networks with payment channels or links between users. Payment networks such as Ripple and Stellar offer a range of services such as real-time payment transfers, including cross-currency transactions, and are growing in popularity. They incentivize users to use them by offering much lower transaction fees, and quick transfer times as compared to traditional bank wire transfers. The link weights are positive integers, and once two users connected by a link spend their link weight, the link gets exhausted, and no more transactions can be done over the link. In order to allow users to do any more transactions, the link weight must be increased, a process known as rebalancing link weights. Existing methods of rebalancing require users to perform expensive blockchain transactions of closing and refunding the channel; we consider the problem of rebalancing a payment channel in real time in an efficient way. Our decentralized technique of rebalancing will help users in acyclic payment channel networks to rebalance their link weights on an as-needed basis, and with minimal computational cost, and only off-chain transactions.
GA-based Velocity Planning Using Jerk as the Encoding Method for Autonomous Vehicles

A technique for the optimal velocity planning using the genetic algorithm for autonomous vehicles is proposed in this paper. A distance-time graph where the dynamic obstacles occupy the corresponding space will be established. Through gene coding method using jerk of genetic algorithm, the feasible distance-time curve is obtained. A fitness function comprehensively evaluates the safety, smoothness, economy, and speed performance of the curve. Through the reproduction and natural selection of several generations, the best individual is selected as the final velocity curve result. The simulation results based on PreScan show that the final velocity curve has good performance. This paper provides an intelligent, convenient and reliable solution for autonomous vehicle velocity planning.
Throughput-Optimal Hardware Implementation of LZW Decompression on the FPGA

The main contribution of this paper is to present a throughput-optimal FPGA implementation of LZW decompression algorithm. Since LZW decompression creates a dictionary table by reading codes of a compressed file one by one, its parallelization is not an easy task. However, in this work, continuously given LZW-compressed codes, the proposed circuit can decompress them with no interruption. In the same time, the circuit starts outputting the decompressed byte-data in several clock cycles after the first code of the input is provided. We have the proposed circuit on Xilinx Virtex-7 XCVX485T-2 and evaluated the performance. The experimental results show that our proposed circuit can decompress a LZW-compressed TIFF image of size 4096x3072 in 42.82 ms.
Adapted Course Content Format for Flipped Mobile Learning

Today, learning methods and approaches are experiencing great development through technology. Among these methods, we found mobile learning. This type of learning is based on mobile learners who learn in many contexts and environments. Hence the name of context-aware mobile learning systems. In addition, flipped learning is also a new method of learning that consists of inverting the classroom tasks with those done at home. In this paper, we present a context-aware mobile learning approach that provides learners with an adapted course content format. This approach is applied in the context of the flipped classroom.
Impact of Neural Network Architectures on Arabic Sentiment Analysis

Sentiment Analysis (SA), commonly known as opinion mining, during last couple of years, it becomes the fastest growing research areas in computer science. Conventionally, it helps to automatically detect if a text express is a positive, negative or neutral opinion. It enables us to identify and extract subjective information in a piece of writing, and this leads to gain an overview of wider public opinions or attitudes toward topics, products or services. Many researches have been done in this area, but most of them have focused on English and other Indo-European languages. Insufficient studies have actually accosted Sentiment Analysis in morphologically rich language such as Arabic. Regardless, given the increasing number of Arabic users and the exponential growth of online content, SA in this language has gained the attention of many researches last years, since Arabic raises many challenges because of its derivational, inflectional and agglutinative morphology. The objective of this paper is to promote the performance of Arabic Sentiment Analysis (ASA) by using Deep learning techniques. For that we implement Multi-Layer perceptron model in order to process and classify a dataset (Tweets). In fact, the experimental results prove that MLP as a deep learning model has a better performance for ASA than classical approaches.
Scale Adaptation of Text Sentiment Analysis Algorithm in Big Data Environment: Twitter as Data Source

The research of text sentiment analysis under big data is of great significance to the researchers of information science. Through this kind of text sentiment analysis based on big data, it can greatly optimize the efficiency and cost. The data set used in this paper is sentiment140 produced by Stanford University. In this paper, based on the traditional emotional analysis, a text emotional analysis based on big data environment is proposed, and the accuracy and generalization ability of the algorithm are compared. The experimental results show that the efficiency advantage of text emotion analysis based on big data is more obvious. In the aspect of resource utilization of system environment, with the increase of the number of system cores and nodes, the operation speed of the whole system is also accelerated.
Wavelets Teager–Kaiser Hilbert approach for AM–FM signal demodulation: application in the field of speckle metrology

In this work, we propose a combination of the Teager–Kaiser energy operator (TKEO) and the spiral phase transform (SPT) for robust instant energy estimation of amplitude-modulated and frequency-modulated (AM–FM) signals, where the energy extraction is followed by a high-frequency component, generally considered as noise. We demonstrate that this noise component can be subtracted mathematically using the SPT transformation applied to the AM–FM signal. The improvement in demodulation is tested using a simulated AM–FM image and evaluated by the image quality index. An experimental speckle fringe pattern obtained by digital speckle pattern interferometry on a hard disk is denoised using a multiband approach and demodulated using the proposed method.
scikit-hubness: Hubness Reduction and Approximate Neighbor Search

This paper introduces scikit-hubness, a Python package for efficient nearest neighbor search in high-dimensional spaces. Hubness is an aspect of the curse of dimensionality, and is known to impair various learning tasks, including classification, clustering, and visualization. scikit-hubness provides algorithms for hubness analysis ("Is my data affected by hubness?"), hubness reduction ("How can we improve neighbor retrieval in high dimensions?"), and approximate neighbor search ("Does it work for large data sets?"). It is integrated into the scikit-learn environment, enabling rapid adoption by Python-based machine learning researchers and practitioners. Users will find all functionality of the scikit-learn neighbors package, plus additional support for transparent hubness reduction and approximate nearest neighbor search. scikit-hubness is developed using several quality assessment tools and principles, such as PEP8 compliance, unit tests with high code coverage, continuous integration on all major platforms (Linux, MacOS, Windows), and additional checks by LGTM. The source code is available at https://github.com/VarIr/scikit-hubness under the BSD 3-clause license. Install from the Python package index with $ pip install scikit-hubness.