Reviewerly/exHarmony huggingface dataset:
Adaptive Threshold Detection Based on Current Demand for Efficient Utilization of Cloud Resources

Efficient utilization of Cloud resources is of utmost importance for Cloud Data Centers. The overall objective is to reduce energy consumption without compromising over quality of service. In order to achieve this goal different Virtual Machine (VM) Consolidation techniques have been proposed. Threshold detection for over utilized and underutilized hosts is an important phase of these techniques. In this paper we have proposed an adaptive model based on current load to optimize thresholds for detection of over and underutilized hosts. The proposed model uses Mean utilization of hosts and Standard deviation of VM utilization to calculate an acceptable range of utilization thresholds. An algorithm based on the proposed model is devised and tested through simulation experiments using Cloudsim. Four threshold detection policies namely, Static Threshold (ST), Inter-Quartile Range (IQR), Local Regression (LR), and Median Absolute Deviation (MAD) with Minimum Migration Time (MMT) as VM selection policy are compared with our proposed algorithm. Experiment results validate that the proposed algorithm reduces energy consumption, SLA violations, number of VM migrations and standard deviation of VM utilization in order to optimize resource utilization considerably.
Cooperation of Neuro-Fuzzy and Standard Cloud Web Brokers

Nowadays we cannot imagine the world without the World Wide Web which connects us with the surrounding world. The best way to maintain the availability and high quality of the Web services is to host Web systems in the cloud computing environment. The cloud-based Web systems use in their design numerous Web servers as well as appropriate strategies and mechanisms for the distribution of HTTP requests. In the article we discuss intelligent and non-intelligent distribution strategies and present our Fuzzy-Neural Request Distribution strategy. We also attempt to answer the question whether the cooperation of the non-intelligent and intelligent HTTP request distribution strategies eliminates the shortcomings of these strategies and increases the quality of service in the Web cloud system. We describe modern solutions, present the test-bed and the results of conducted experiments. In the end we discuss the results and present final conclusions.
The Usage of Grayscale or Color Images for Facial Expression Recognition with Deep Neural Networks

The paper describes usage of modern deep neural network architectures such as ResNet, DenseNet and Xception for the classification of facial expressions on color and grayscale images. Each image may contain one of eight facial expression categories: “Neutral”, “Happiness”, “Sadness”, “Surprise”, “Fear”, “Disgust”, “Anger”, “Contempt”. As the dataset was used AffectNet. The most accurate architecture is Xception. It gave classification accuracy on training sample 97.65%, on cleaned testing sample 57.48% and top-2 accuracy on cleaned testing sample 76.70%. The category “Contempt” is worst recognized by all the types of neural networks considered, which indicates its ambiguity and similarity with other types of facial expressions. Experimental results show that for the considered task it does not matter, the color or grayscale image is fed to the input of the algorithm. This fact can save a significant amount of memory when storing data sets and training neural networks. The computing experiments was performed using graphics processor using NVidia CUDA technology with Keras and Tensorflow deep learning frameworks. It showed that the average processing time of one image varies from 4 ms to 30 ms for different architectures. Obtained results can be used in software for neural network training for face recognition systems.
Motion Detection and Object Tracking Based on Frame Comparison

”Walls have ears” is a wise saying. Due to this rapid technological growth, ears were not enough, so we also needed the walls to have eyes. Then came the surveillance cameras, the “Electronic eyes” which helped to prevent many crimes and put a check to criminals. Now these electronic eyes present in places with high significances such as ATMs, Banks, Hospitals, Government organizations and crowded streets. Blooming up cloud surveillance and remote surveillance techniques have made it very easy for beginners to implement the full surveillance system in mere couple of days. So there requires a system to detect motion and track moving object so that it can prevent crimes and avoid surprise “storage full” issues. This algorithm deals on detecting motion of object with very less computational complexity possible and tracking it.
Muslim Ablution Eco Water Tap: From First Design Alpha Prototype to Second Design

Muslim in Indonesia uses a lot of water to do an ablution activity, and it is not caused by wasteful character of Indonesia's Muslim society, but because of the design of conventional water tap is not intended to conserve water in ablution activity. To solve the problem, a conceptional Muslim ablution eco water tap design was made. The concept of the product is, it has a mechanical based system to create an auto shutdown mechanism. Thus, the water usage for ablution is predicted can be decreased. However, after the first design alpha prototype is built, faults of the design are directly detected. Thus, this paper tries to analyze the first design alpha prototype and suggest a refinement to create the second design. In this paper, two deficiencies from the first design are identified and a second design is proposed.
A framework for integrating architectural design patterns into PCG

Asset generation has a huge time cost associated with it in games. In order to reduce this time cost, designers have adopted Procedural Content Generation (PCG) systems into their workflows. However, most PCG techniques may only give a modest workflow speed up. While some PCG techniques can give immense speed ups, they have extra time costs that might not be worth it. These techniques require users to encode their domain knowledge in a way these techniques can understand. This can be a costly process and the technique might not be able to be reused in another project. We propose a framework for encoding domain knowledge in a way to promote reusability. We provide an example that is based in architectural design philosophies.
Design and Development of Software Tool for Code Clone Search, Detection, and Analysis

Replicating a piece of code with or without making minor changes in their code during project development is a normal practice adopted by the developers to reduce their production time which results in code/program clones. Clones could also occur in their existing file without any prior knowledge of the programmer. Though code/program cloning helps to increase the productivity initially, later it may increase the maintenance cost. Hence code/program cloning needs to be identified thoroughly during the development in a frequent interval of time. The main objective of this work involves in depth analysis of source code produced by the programmer for code cloning and to indicate how much percentage of code is copied in that their file. The proposed work detects clones in terms of syntax, function, method, variables, data types, keywords, spaces, author name, imports, commented lines, number of lines, and last-modified-date and shows the similarity percentage in graphically.
Fusing non-conservative kinetic market models and evolutionary computing

This research establishes an identity between kinetic market models of econophysics and evolutionary algorithms of computer science. The fusion between the two approaches motivated a new market model with two basic operations: sampling and selection of states. The result is a non-conservative market that depends on the size of the sample set and the approach used to approximate the principle of energy conservation. This market exhibits complex dynamics with random walks for the sum of all the agents’ money and a scaling behavior for the money distribution in the population. Moreover, the fusion demonstrates how to add an evolutionary context to the kinetic market models and suggests a quasi-equilibrium version of those models. As a by-product, the work reveals a practical application as a new replacement rule for family competition evolutionary algorithms, which outperforms traditional ones in challenging combinatorial optimization problems.
Some results on ordinary words of standard Reed-Solomon codes

The Reed-Solomon codes are widely used to establish a reliable channel to transmit information in digital communication which has a strong error correction capability and a variety of efficient decoding algorithm. We usually use the maximum likelihood decoding algorithm (MLD) in the decoding process of Reed-Solomon codes. MLD algorithm lies in determining its error distance. Li, Wan, Hong and Wu et al obtained some results on the error distance. For the Reed-Solomon code $RS_q({\mathbb F}_q^*, k)$, the received word ${\bf u}$ is called an ordinary word of $RS_q({\mathbb F}_q^*, k)$ if the error distance $d({\bf u}, RS_q({\mathbb F}_q^*, k)) = n-\deg(u(x))$ with $u(x)$ being the Lagrange interpolation polynomial of ${\bf u}$. In this paper, we make use of the polynomial method and particularly, we use the KŚnig-Rados theorem on the number of nonzero solutions of polynomial equation over finite fields to show that if $q\geq 4, 2\leq{k}\leq{q-2}$, then the received word ${\bf u}\in{\mathbb F}_q^{q-1}$ of degree $q-2$ is an ordinary word of $RS_q({\mathbb F}_q^*, k)$ if and only if its Lagrange interpolation polynomial $u(x)$ is of the form $ u(x) = \lambda\sum\limits_{i = k}^{q-2}a^{q-2-i}x^i+f_{\leq k-1}(x) $ with $a, \lambda\in{\mathbb F}_q^*$ and $ f_{\leq k-1}(x)\in {\mathbb F}_q[x]$ being of degree at most $k-1$. This answers partially an open problem proposed by J.Y. Li and D.Q. Wan in [On the subset sum problem over finite fields, Finite Fields Appls. 14 (2008), 911-929].
Image Compression Using Polynomial Fitting

A new efficient method to compress images using polynomial curve fitting approximation techniques is presented in this paper. The polynomial curve fitting represents many pixels by a smaller number of polynomial coefficients. The presented method is based on two distinct scanning techniques of the image under compression. In the first technique, we scan the image row by row, and in this case, we use first or second order polynomial to represent the chosen number of pixels from the row. In the second case, we scan the image using a block of pixels with variable dimensions and represent each block by a first order two-dimensional polynomial. Experimentally the method gives an acceptable compression ratio with reasonable reconstructed image quality.
Detection of Fraudulent Tweets: An Empirical Investigation Using Network Analysis and Deep Learning Technique

Social media has become a powerful and efficient platform for information diffusion. The increasing pervasiveness of social media use, however, has brought about the problems of fraudulent accounts that are intended to diffuse misinformation or malicious contents. Twitter recently released comprehensive archives of fraudulent tweets that are possibly connected to a propaganda effort of Internet Research Agency (IRA) on the 2016 U.S. presidential election. To understand information diffusion in fraudulent networks, we analyze structural properties of the IRA retweet network, and develop deep neural network models to detect fraudulent tweets. The structure analysis reveals key characteristics of the fraudulent network. The experiment results demonstrate the superior performance of the deep learning technique to a traditional classification method in detecting fraudulent tweets. The findings have potential implications for curbing online misinformation.
DYRS: Bandwidth-Aware Disk-to-Memory Migration of Cold Data in Big-Data File Systems

Migrating data into memory can significantly accelerate big-data applications by hiding low disk throughput. While prior work has mostly targeted caching frequently used data, the techniques employed do not benefit jobs that read cold data. For these jobs, the file system has to pro-actively migrate the inputs into memory. Successfully migrating cold inputs can result in a large speedup for many jobs, especially those that spend a significant part of their execution reading inputs. In this paper, we use data from the Google cluster trace to make the case that the conditions in production workloads are favorable for migration. We then design and implement DYRS, a framework for migrating cold data in big-data file systems. DYRS can adapt to match the available bandwidth on storage nodes, ensuring all nodes are fully utilized throughout the migration. In addition to balancing the load, DYRS optimizes the placement of each migration to maximize the number of successful migrations and eliminate stragglers at the end of a job. We evaluate DYRS using several Hive queries, a trace-based workload from Facebook, and the Sort application. Our results show that DYRS successfully adapts to bandwidth heterogeneity and effectively migrates data. DYRS accelerates Hive queries by up to 48%, and by 36% on average. Jobs in a trace-based workload experience a speedup of 33% on average. The mapper tasks in this workload have an even greater speedup of 46%. DYRS accelerates sort jobs by up to 20%.
A Low-Cost Computer Vision System for Real-Time Tennis Analysis

This paper describes a low-cost vision-based system for real-time tennis game analysis. The system elaborates videos captured by four synchronized and calibrated cameras installed at the sides of the court in order to accurately localize ball and players, and track them in real-time. From this low-level data mid-level events, like shots, bounces, ball in net, and high-level events, like stroke type and line calling, are detected. All this data is made available to the players both on-court during the play or through a web device at the end of the session. Currently, system prototypes are undergoing a field test in three locations in Italy. In addition to positive comments of users, robustness and reliability of the system have been demonstrated with specific evaluation tests. Detection rate of shots is 99.7% while miss detection rate is less than 0.8%. Reliability of the stroke classification is 97.1% and of in/out evaluation is 99.5%. On average reaction time for line calling is 152 ms.
Usage Models Mapped to Programs

Model-based programming can replace classical programming based on compilation and systematic development of models as well on explicit consideration of all model components without hiding intrinsic details and assumptions. A key element of model-based programming is the proper definition and management of model suites, by which multiple, interrelated models can be transformed from one another and their consistency is ensured after modifications. A usage model is based on the specification of user roles and types, together with an interaction space described in a form of a storyboard, showing which activities are supported, in which order, by which actors. A workflow model is an extended, well-formed declaration of how specific processes should be carried out. It can directly be translated to program code, using a proper workflow or process engine. A novel way of programming is being opened up by usage modeling, which is being investigated in this paper: given a storyboard with supported usage scenarios, it is possible to derive a workflow model from it. We present our two translation methods using a working example, identifying guidelines as requirements for model refinement and normalization, rules for model translation, and propose considerations towards improved methods and model specifications.
Cyberaccounting for the Leaders of the Future

The new volatile, uncertain, complex and ambiguous economic context generates the evolution of a global, integrated, and permanently connected world, in which territoriality and temporality have almost disappeared. Fluctuations and changes in economic power poles, past financial crises, but also signs of new recession periods, rising capital, multiplying variables, and cause-effect factors outline the current economic environment. Simultaneity and interconnection of Industrial Revolution 4.0, Globalization 4.0, Artificial Intelligence, Internet of Things, Data Revolution, and Digital Enterprise have determined the emergence of new concepts such as: Digital Ecosystems, Cyber counting, Cybersecurity, Platform Architecture for Financial, Digital Interface, concepts directly related to business performance in the new economic environment: Digital Economy.
Comparative Exploration Of Prediction Algorithms For Sentiment Analysis Using NLP

Recently, enormous amount of online or e-commerce reviews that reveal the opinion of the clients, benefit designers and consumers. But, with excessive progress of product attributes and reviews which are getting more assorted, the conventional sentiment analysis about the general emotional tendency is unable to fulfill the requirements of customers and manufacturers. Customers expect to acquire analysis outcomes of goal features whereas the manufacturers expect to master weaknesses and strengths of products plus also the customers' taste. The proposed study takes the raw reviews, cleans them to remove the stop words and then runs various prediction algorithms, the results of which are then evaluated to come to a conclusion.
Automatic Creation of Large Scale Object Databases from Web Resources: A Case Study in Robot Vision

A fundamental ingredient in the success of deep learning for computer and robot vision is the availability of very large-scale annotated databases. ImageNet, with its 1000 object classes and 1.2 million images, tends to be the dominant data collection for creating pre-trained deep architectures. A less investigated avenue is how the possibility to create task-specific data collections on demand, with limited or without manual effort, would affect the performance of convolutional architectures. This would be useful for all those cases where contextual information about the deployment of the deep net is available, and it would be particularly relevant for robot vision applications, where such knowledge is usually available. The goal of this work is to present a protocol for the automated creation of task specific datasets starting from a pre-defined list of object classes, exploiting the Web as a source of information in an automated fashion. Our pipeline consists of (a) an algorithm for automatic Web crawling that searches for “image class seeds”, i.e., informative images of object classes of interest, (b) algorithms for figure-ground segmentation of the object of interest and pasting of the segmented item in contextual images close to where the agent is going to work, and (c) a tailored data augmentation routine for maximizing the informative content of the generated images. A thorough set of experiments on a public benchmark, as well as deployment to a robot platform, prove the value of the proposed approach.
The Orthographic Projection Model for Pose Calibration of Long Focal Images

Most stereovision and Structure from Motion (SfM) methods rely on the pinhole camera model based on perspective projection.From this hypothesis the fundamental matrix and the epipolar constraints are derived, which are the milestones of pose estimation.In this article we present a method based on the matrix factorization due to Tomasi and Kanade that relies on a simpler camera model, resulting in orthographic projection.This method can be used for the pose estimation of perspective cameras in configurations where other methods fail, in particular, when using cameras with long focal length lenses.We show this projection is an approximation of the pinhole camera model when the camera is far away from the scene.The performance of our implementation of this pose estimation method is compared to that given by the perspectivebased methods for several configurations using both synthetic and real data.We show through some examples and experiments that the accuracy achieved and the robustness of this method make it worth considering in any SfM procedure.
The Construction of Multi-wavelets with Matrix Dilation via the Lifting Scheme with Several Steps

In this paper, a multidimensional bi-orthogonal multi-wavelets with matrix dilation is constructed via the lifting scheme with several steps. We generalize the lifting scheme in the case of multidimensional bi-orthogonal multi-wavelets with matrix dilation. The lifing and dual Lifing scheme are discussed in the case of $$\left| \det (M) \right| \ge 2$$ , where M denotes a dilation matrix. The general interpolating multiscaling function is also presented and discussed. Finally, an example is given.
Research on Unknown Words Processing of Mongolian-Chinese Neural Machine Translation Based on Semantic Similarity

In this paper, the unknown words processing method in Mongolian-Chinese neural machine translation based on the similarity model and based on the Mongolian-Chinese alignment dictionary are studied. The unknown words processing method based on the similarity model uses the word vector to capture the characteristics of word semantics and grammatical information, calculates the semantic similarity between the unknown words and the words in the vocabulary, and selects the words with the closest semantics to replace all the unknown words. The unknown word processing method based on the Mongolian-Chinese alignment dictionary is to replace the unknown words with the word alignment information. Finally, the original corpus and the new corpus replacing the unknown words are merged and Training model. The final experiment showed that the translation's BLEU value was increased by 0.95 percentage points in the Mongolian-Chinese translation task.
Hyper-entanglement optical circuits for quantum communications

This paper discusses high-performance quantum optical circuits for processing polarization entanglement between correlated photon pairs. The primary application of this technology is secure communication links with quantum encryption that rely on the inherent properties of laser light and physical-layer processes in optical components. It can be used for generating identical pairs of encryption keys in quantum key distribution applications, for a variety of encryption protocols ranging from single-photon to multiple-photon, keyed communication in quantum noise, but we see a potential spectrum extending further to include multi-access systems. Success in these applications can be achieved by introducing additional degrees-of-freedom in each single-particle quantum state. We see hyper-entanglement as a very promising approach to do this. The concept is visualized by having multiple entangled photons with specifically assigned frequencies in the 100 GHz ITU grid and arriving simultaneously from multiple sources. Processing of such signals requires hyperspectral optical circuits capable of responding to non-classical features of quantum states. The function of these circuits is to direct the arriving signals along different processing paths to single photon detectors, which can be realized by a combination of different technologies, such as highly selective Lyot filters, dense wavelength division multiplexing, and others. Processing capabilities of these quantum circuits, while seemingly straight-forward in theory, still present a great implementation challenge. Practical operating conditions and characteristics of optical components must be taken into consideration to address the underlying design problems and make this technology feasible. This constitutes the main focus of our paper.