Reviewerly/exHarmony huggingface dataset:
Concatenation hashing: A relative position preserving method for learning binary codes

Hashing methods perform the efficient nearest neighbor search by mapping high-dimensional data to binary codes. Compared to projection-based hashing methods, hashing methods that adopt the clustering technique can encode the complex relationship of the data into binary codes. However, their search performance is affected by the boundary of the cluster. Two similar data points may be assigned to two different clusters and then encoded into two much different binary codes. In this paper, we propose a new hashing method based on the clustering technique and it can alleviate the effect from the cluster boundary. It is from an observation that the relative positions of any two close data points to each cluster center are close. An alternating optimization is developed to simultaneously discover the cluster structures of the data and learn the hash functions to preserve the relative positions of the data to each cluster center. To integrate the information in each cluster, the corresponding binary code of each data point is obtained by concatenating the substrings learnt by the hash functions in each cluster. The experiments show that our method is competitive to or better than the state-of-the-art hashing methods.
Augmented Interactive Wall as a Technology-based Art Learning Media

Abstract Art plays an important role in stimulating the development of the individual right brain. Art learning can improve the intelligence of individual expression, increasing sensitivity and concentration, and fostering brilliant creativity. Formal education institutions in Indonesia have not provided an appropriate portion for art learning. This causes the development of visual art in Indonesia tends to be slow and hampered. This research will reveal the potential of the Augmented Interactive Wall as a medium for expressing ideas using digital technology. This is qualitative research using observation and interviews to obtain data. The result shows that collaboration between technology and art will produce innovations that will help individuals to explore and express their ideas better, and bring them into various forms of physical, social and cultural interactions.
Combining Tendon Vibration and Visual Stimulation Enhances Kinesthetic Illusions

In recent years, human augmentation has attracted much attention. One type of human augmentation, motion augmentation makes perceived motion larger than in reality, and it can be used for a variety of applications such as rehabilitation of motor functions of stroke patients and a more realistic experience in virtual reality (VR) such as redirected walking (RDW). However, as augmented motion becomes larger than the real motion, a variety of senses that accompany will be more inconsistent with those perceived from somatic sensations, which will cause a severe sense of discomfort. To address the problem, we focus on kinesthetic illusions that are psychological phenomena where a person feels as if his or her own body is moving. Kinesthetic illusions are expected to fill the gap between the intended augmented motion and perceived physical motion. However, it has not been explored if and how large kinesthetic illusions are produced while a user is moving their limbs voluntarily in VR. To expand the knowledge on kinesthetic illusions, we have conducted two user studies on the impact of tendon vibration and visual stimuli on kinesthetic illusions. First experiment confirmed that the perceived elbow angle becomes larger than the actual angle when presented with tendon vibration. Second experiment revealed that the increase of the perceived elbow angle was about 20 degrees when both tendon vibration and visual stimuli were presented whereas it was about 10 degrees when only visual stimuli were presented. Through these experiments, it has been confirmed that combining tendon vibration and visual stimulation enhances kinesthetic illusions.
Simple, Private, and Accurate Distributed Averaging

Some distributed optimization applications require privacy, meaning that the values of certain parameters local to a node should not be revealed to other nodes in the network during the joint optimization process. A special case is the problem of private distributed averaging, in which a network of nodes computes the global average of individual node reference parameters in a distributed manner while preserving the privacy of each reference. We present simple iterative methods that guarantee high accuracy (i.e. the exact asymptotic computation of the global average) and high privacy (i.e. no node can estimate another node's reference value to any meaningful degree). To achieve this, we assume that the digraph modeling the communication between nodes satisfies certain topological conditions. Other related methods in the literature also achieve high accuracy and privacy, but under topological conditions more restrictive than ours. Moreover, our method is simpler because it does not require any initial scrambling phase, it does not inject any noise or other masking signals into the distributed computation, it does not require any random switching of edge weights, and it does not rely on homomorphic encryption.
Adathm: Adaptive Gradient Method Based on Estimates of Third-Order Moments

Deep learning has been widely used in the field of data aggregation and fusion. As a significant aspect of deep learning, stochastic optimization algorithm affects the operating efficiency and final effect. Adaptive optimization methods such as Adagrad, RMSprop, Adam, which have been proposed to achieve a rapid training process with an element-wise scaling term on learning rates. Nevertheless, unstable and extreme learning rates may fail to converge to an optimal solution (or a critical point in nonconvex settings). In order to reduce the impact of unsuitable learning rate, we proposed a new method that we apply third-order moments to Adam, which is called Adathm. We also introduce the ideal of dynamic bounds on learning rates and endow the proposed method with "long-term memory" of past gradients. Our preliminary experimental results show that our proposed algorithm can fix the convergence issues and compares favorably to other stochastic optimization methods in some real applications.
Robust Visual Tracking via Multilayer CaffeNet Features and Improved Correlation Filtering

For problems related to the robust tracking of visual objects in various challenging tracking conditions, a robust visual tracking method based on multilayer convolutional features and correlation filtering is proposed.To solve the problems of mean deviation and insufficient discrimination ability in traditional convolutional neural networks (CNN), this study proposes randomized parametric rectified linear units (RPReLU) as the activation function.Meanwhile, the zero-setting operation of weights in the traditional dropout process occurs randomly and fails to discriminate the features with different weights, which leads to a low learning efficiency.Therefore, this study proposes an improved dropout method based on a support vector machine (SVM), which provides a selective dropout rate to increase the manual orientation and improve the learning efficiency of the dropout process.In addition, traditional CNN trackers only employ the output of the last layer, which can effectively capture semantic features but not spatial features.To solve this problem, we propose to use the rich features of the multiple convolution layers of CaffeNet as the target representation.Furthermore, we propose an improved correlation filter to further improve the tracking performance and improve the tracker's capability of dealing with scale changes, which effectively solves the problem of adaptive estimating of target size.The extensive experimental evaluations have been carried out through the OTB2015, VOT2016 and VOT2018 datasets, proving that the proposed method is very effective in dealing with a variety of challenging factors.
Detection and Parameters Estimation of Moving Objects via Video Surveillance

The article proposes an algorithm for processing video information obtained from traffic monitoring in order to automatically detect moving objects and evaluate some of their parameters. The algorithm automatically detects passing vehicles, and evaluates their dimensions, speed and direction of movement. The algorithm does not require large computational resources and can work in real time. It is proved on the real video traffic records and the results of the examination becomes very close to the real ones. It is applicable in smart traffic management systems with CCTV. In the future, the algorithm will be extended with capabilities for cars recognition and their license plates identifying.
BlockBD

Big Data is changing the perspective on how to obtain valuable information from data stored by organizations of all kinds. By using these insights, companies can make better decisions and thus achieve their business goals. However, each new technology can create new security problems, and Big Data is no exception. One of the major security issues in a Big Data ecosystem is what level of trust in data and data sources stakeholders can have: without reliable data, the results of data analysis lose value. In this paper, we propose a security pattern to improve traceability and veracity of data through the use of Blockchain technologies. In this pattern, Blockchain will be used as a distributed ledger where all operations performed on the data will be registered. Therefore, the veracity of the data will increase, as will the confidence in the insights obtained from the analysis. The purpose of this paper is to help Chief Security Officer and Big Data architects incorporate this mechanism to improve the security of their environment.
User authentication on smartphones using keystroke dynamics

These days, mobile devices have very sensitive and personal data that needs to be secured. Mobile devices use authentication techniques to protect data from unauthorized access. Consequently, many authentication mechanisms were proposed and many techniques were applied. One of these mechanisms is the analysis of the typing rhythm. It is also known as keystroke dynamics which enhances the password-based authentication by identifying the users based on their typing rhythms. This paper proposes a new authentication mechanism using keystroke dynamics. The dataset which is used in this research consists of 71 features for 42 users with 2142 records. The proposed method consists of two stages; firstly, applying some statistical methods on the 71 features in order to result with valuable new features, then the second stage is to use the existing features with the resulted features with the Random Forest machine learning algorithm. Experimental results showed an accuracy of 94.26%.
GAN for Recommendation System

Abstract In our day there are many approaches for recommendation generation, however several unsolved problems still exist. In our work we study the potential of Generative Adversarial Networks to generate relevant recommendations and compare the proposed approach with another recommendations system based on neural networks. We found that recommendation system based on GANs can successively compete with state-of-the-art network in the field of recommendation generation and suggest, that GAN recommendation system have high potential.
A Collaborative Previsualization Tool for Filmmaking in Virtual Reality

Previsualization is a process within pre-production of filmmaking where filmmakers can visually plan specific scenes with camera works, lighting, character movements, etc. The costs of computer graphics-based effects are substantial within film production. Using previsualization, these scenes can be planned in detail to reduce the amount of work put on effects in the later production phase. We develop and assess a prototype for previsualization in virtual reality for collaborative purposes where multiple filmmakers can be present in a virtual environment to share a creative work experience, remotely. By performing a within-group study on 20 filmmakers, our findings show that the use of virtual reality for distributed, collaborative previsualization processes is useful for real-life pre-production purposes.
Herding Behaviour in Cryptocurrency Market: CSSD and CSAD Analysis

Cryptocurrencies get substantial attention of investors as recently created innovations. This chapter focuses on herding behaviour in the cryptocurrency market, considering CSSD and CSAD approaches for cryptocurrencies in the CCI 30 Index. The analysis focuses on the cryptocurrency index and cryptocurrencies, which have existed since the arbitrarily set starting date of the index. In addition to the CCI 30 Index, as a proxy for market, Bitcoin, Litecoin, Stellar, Monero, Dogecoin and Dash are used for empirical analysis. To the best of the author's knowledge, the CCI 30 Index is used for the first time as a proxy for market return. Despite the growing literature on cryptocurrencies, there is still a gap in herding behaviour in the cryptocurrency market. Results indicate no evidence of herding behaviour in the cryptocurrency market in both CSSD and CSAD approaches. The findings of both approaches are in line with the findings of the previous literature regarding the herding behaviour in cryptocurrencies such as Bouri et al. (Financ Res Lett 29:216–221, 2018) and Vidal-Tomás et al. (Financ Res Lett doi: 10.1016/j.frl.2018.09.008, 2018).
Cryptocurrency Derivatives: The Case of Bitcoin

The effects of digitalization on the business ecosystem and business models are increasing day by day. Businesses and individuals intensely benefit from the services that FinTech platforms offer. As a result, there are significant changes in the structure of financial institutions and financial instruments. Blockchain technologies play an important role in the transformation of business ecosystems. In particular, cryptocurrencies are recognized by individuals, institutions, and governments as an economic asset. However, the high price volatility of cryptocurrencies shows that they have significant risks. Cryptocurrency derivatives are used to hedge against and benefit from price movements. This study aims to provide a basic framework for cryptocurrency derivatives. In this study, the most traded cryptocurrency type, Bitcoin derivatives, are used.
Articles Classification in Myanmar Language

Article classification is a problem of text classification to assign the articles to their corresponding class or topic. According to this work, there are two main barriers to classify Myanmar text in deep learning model, to find the proper way of determining the word boundaries and to build the datasets for Myanmar text classification. This paper shows the empirical evidence on article classification in Myanmar language for both syllable-level and word-level by fine-tuning Convolutional Neural Networks. They are denoted as Syllable-Level Convolutional Neural Networks (SL-CNN) and Word-Level Convolutional Neural Networks (WL-CNN). Although there are few publicly available general-purpose pre-trained vectors for Myanmar language that can be further applied to transfer learning, it is still needed to construct large-scale datasets for classifying Myanmar articles. We construct six datasets to classify Myanmar articles and evaluation is measured by the comparative analysis of these vectors on SL-CNN and WL-CNN with Recurrent Neural Networks for both syllable and word level, SL-RNN and WL-RNN.
Load balancing in cloud computing using water flow-like algorithm

Load Balancing is one of the main challenges in cloud computing. A good load balancing algorithm should reduce the response time, increase the throughput, and maximize the utilization of resources. Cloud computing provides many services which are delivered over the internet. In cloud computing, load balancing is implemented in a server cluster and is used to manage a variety of services. In this paper, a new load balancing algorithm for cloud computing is introduced. The proposed algorithm uses the Water Flow-like Algorithm (WFA) that mimics the behavior of water to develop an efficient load balancer for a cloud computing environment. The proposed WFA load balancing (WFA-LB) algorithm evaluated in terms of task migration, average response time and throughput and compared to Genetic Algorithm (GA), Round Robin (RR) and Min_Min algorithms. The results indicated that the proposed WFA-LB has a better performance than the compared algorithms.
Generative And Encoded Anomaly Detectors

We present two fully unsupervised deep learning approaches for hyperspectral anomaly detection. In one approach we formulate the anomaly detection problem as an adversarial game where a generator network learns the distribution of the hyperspectral background pixels comprising a single hyperspectral image and the output of the corresponding discriminator network yields a detection statistic. The other approach formulates the detection statistic as the error between an input hyperspectral pixel and the reconstruction of that pixel by an autoencoder network trained on the image. Both methods leverage a sub-sampling scheme that allows for unsupervised training and testing on the same data set. Our approaches are validated on a four-class synthetic hyperspectral data set and compared to a statistical approach (RX) and a geometric approach (skelton kernel principal component analysis). The proposed Generative Anomaly Detector algorithm achieves top performance on the data set while the autoencoder detection scheme also demonstrates performance gains relative to the comparison algorithms. Benefits and drawbacks of the approaches are discussed and highlight the many potential directions for future work.
A limited-size ensemble of homogeneous CNN/LSTMs for high-performance word classification

In recent years, long short-term memory neural networks (LSTMs) have been applied quite successfully to problems in handwritten text recognition. However, their strength is more located in handling sequences of variable length than in handling geometric variability of the image patterns. Furthermore, the best results for LSTMs are often based on large-scale training of an ensemble of network instances. In this paper, an end-to-end convolutional LSTM Neural Network is used to handle both geometric variation and sequence variability. We show that high performances can be reached on a common benchmark set by using proper data augmentation for just five such networks using a proper coding scheme and a proper voting scheme. The networks have similar architectures (Convolutional Neural Network (CNN): five layers, bidirectional LSTM (BiLSTM): three layers followed by a connectionist temporal classification (CTC) processing step). The approach assumes differently-scaled input images and different feature map sizes. Two datasets are used for evaluation of the performance of our algorithm: A standard benchmark RIMES dataset (French), and a historical handwritten dataset KdK (Dutch). Final performance obtained for the word-recognition test of RIMES was 96.6%, a clear improvement over other state-of-the-art approaches. On the KdK dataset, our approach also shows good results. The proposed approach is deployed in the Monk search engine for historical-handwriting collections.
Texture-Dependent Optimal Fractional-Order Framework for Image Quality Enhancement Through Memetic Inclusions in Cuckoo Search and Sine-Cosine Algorithms

In this contemporary era, technological dependencies on digital images are indispensable. Quality enhancement is an obligatory part of image pre-processing, so that desired information can be harvested efficiently. The varying texture in any image contributes to the information about the structural arrangement of surface content of the captured scene. Fractional-order calculus (FOC) and its related optimally ordered adaptive filtering are quite appreciable. Especially for texture preserved image quality enhancement, analytical strength of FOC is latently too valuable to be casually dismissed. No any closed-form free-lunch theory survives for evaluating the required fractional-order for overall quality enhancement because non-linear features of images from diverse domains require highly adaptive on-demand processing. Hence, texture preserved image quality enhancement can be considered as an NP-hard problem, where there isn’t an exact solution that runs in polynomial time. Thus, by the virtue of evolutionary algorithms along with their associated swarm intelligence, a near-exact solution can be attained. Memetic hybridization of cuckoo search optimizer (CSO) and sine-cosine optimizer (SCO) for this purpose is the core contribution in this chapter. In this chapter, to support the theoretical discussion in the context of the fundamentals behind CSO and SCO, their mathematical beauty of convergence is also highlighted which itself has resulted from the balance exploration and exploitation behavior. A novel texture-dependent objective function is also proposed in this chapter for imparting the patch-wise overall texture preserved image quality enhancement. Finally, the comparative analysis of results illustrates the superior capability of the proposed approach.
An Improved Association Rule Mining Algorithm Based on Ant Lion Optimizer Algorithm and FP-Growth

Discovering knowledge from the amount of data plays an important role in the era of big data and FP-Growth algorithm is one of the most successful methods for learning association rules. Though the FP-Growth algorithm only needs scan two times, it has a poor efficiency for large datasets. There are already efforts have been made to solve the problem by using some Meta-heuristic optimization algorithms, such as particle swarm optimization algorithm (PSO), immune algorithms etc, which outperform the traditional FP-Growth algorithm and shows strong performance. However, PSO is easy to trap in the local optimums. A novel algorithm ant lion optimizer (ALO) was proposed and with the advantages of global optimization, good robustness, and high convergence accuracy, which was applied to many engineering fields like antenna array synthesis, integrated process planning, scheduling and so on. In the paper, a novel association rule extraction algorithm is put forward based on the ant lion optimization algorithm. A new fitness schema based on confidence and support has been used in this approach, which avoids part of unnecessary searching processes of the FP-Growth algorithm and leads the method of searching the optimization solution more effectively. In order to evaluate the effectiveness of our approach, experiments on various datasets are carried out and experimental results are compared with some other classical meta-heuristic algorithms, experimental results testify the performance of the proposed method.
Modeling the Relationship Between Identifier Name and Behavior

This paper presents the features of a model that relates the natural language found in identifiers with program semantics. The model takes advantage of part of speech information and static-analysis-based program models to understand how different types of statically-derived semantics correlates with the natural language meaning of identifiers.