Reviewerly/exHarmony huggingface dataset:
An Expression Analysis Service based on Edge Cloud Computing

Facial Expression Analysis (FEA) is widely applied in many areas such as marketing, health care and on-line learning. For considering performance and cost, FEA currently is implemented as a cloud service. However, uploading the streaming of face images from the end of networks to clouds will probably result in that the network bandwidth in Internet is exhaustively consumed, and finally the quality of the FEA service will degrade to an unacceptable level due to high network latency. To avoid this problem, this paper is aimed at proposing an expression analysis service system based on the architecture of edge cloud computing. The proposed system can efficiently provide users with the services of expression analysis while it need not consume a large amount of network bandwidth for data transmission from user clients to data centers. Moreover, it can provide a higher precision of expression analysis than related works by a method combining MTCNN, OpenFace and BPNN, and it can effectively select edge servers for reducing the time of expression analysis.
Bistable Sigmoid Networks

It is commonly known that Hopfield Networks suffer from spurious states and from low storage capacity. To eliminate the spurious states Bistable Gradient Networks (BGN) introduce neurons with bistable behavior. The weights in BGN are calculated in analogy to those of Hopfield Networks, associated with Hebbian learning. Unfortunately, those networks still suffer from small storage capacity, resulting in high reconstruction errors when used to reconstruct noisy patterns. This paper proposes a new type of neural network consisting of neurons with a sigmoid hyperbolic tangent transfer function and a direct feedback. The feedback renders the neuron bistable. Furthermore, instead of using Hebbian learning which has some drawbacks when applied to overlapped patterns, we use the first order Contrastive Divergence (CD1) learning rule. We call these Networks Bistable Sigmoid Networks (BSN). When recalling patterns from the MNIST database the reconstruction error is zero even for high load providing no noise is applied. For an increasing noise level or an increasing amount of patterns the error rises only moderate.
Go for Parallel Neural Networks

Training artificial neural networks is a computationally intensive task. A common and reasonable approach to reduce the computation time of neural networks is parallelizing the training. Therefore, we present a data parallel neural network implementation written in Go. The chosen programming language offers built-in concurrency support, allowing to focus on the neural network instead of the multi-threading. The multi-threaded performance of various networks was compared to the single-threaded performance in accuracy, execution time and speedup. Additionally, two alternative parallelization approaches were implemented for further comparisons. Summing up, all networks benefited from the parallelization in terms of execution time and speedup. Splitting the mini-batches for parallel gradient computation and merging the updates produced the same accuracy results as the single-threaded network. Averaging the parameters too infrequently in the alternative implementations had a negative impact on accuracy.
OnMLM: An Online Formulation for the Minimal Learning Machine

Minimal Learning Machine (MLM) is a nonlinear learning algorithm designed to work on both classification and regression tasks. In its original formulation, MLM builds a linear mapping between distance matrices in the input and output spaces using the Ordinary Least Squares (OLS) algorithm. Although the OLS algorithm is a very efficient choice, when it comes to applications in big data and streams of data, online learning is more scalable and thus applicable. In that regard, our objective of this work is to propose an online version of the MLM. The Online Minimal Learning Machine (OnMLM), a new MLM-based formulation capable of online and incremental learning. The achievements of OnMLM in our experiments, in both classification and regression scenarios, indicate its feasibility for applications that require an online learning framework.
Security in Asynchronous Interactive Systems

Secure function computation has been thoroughly studied and optimized in the past decades. We extend techniques used for secure computation to simulate arbitrary protocols involving a mediator. The key feature of our notion of simulation is that it is bidirectional: not only does the simulation produce only outputs that could happen in the original protocol, but the simulation produces all such outputs. In asynchronous systems there are also new subtleties that arise because the scheduler can influence the output. Thus, these requirements cannot be achieved by the standard notion of secure computation. We provide a construction that is secure if \(n > 4t\), where t is the number of malicious agents, which is provably the best possible. We also show that our construction is secure in the universal composability model and that it satisfies additional security properties even if \(3t < n \le 4t\).
Big Data Analytics for Popularity Prediction

Abstract In this paper examines the prescient intensity of huge social knowledge in terms of offline and on-line behavior. We have a tendency to address the analysis question of however massive social knowledge from Facebook will anticipate the measure of watchers and TV evaluations inside the instance of the Danish National soccer Association (DBU). The correct and timely prediction of the recognition of programs is of nice worth to content suppliers, advertisers, and television stations. This data is also of profit to operators within the purchase call of TV programs and facilitate advertisers to formulate applicable advertising investment plans. Technically, an explicit program population prediction methodology enhance thecomplete telecast system, like the Content Delivery Network (CDN) methodology and in this way the store technique. Many predictive models supported YouKu, YouTube, and Twitter VOD knowledge are planned.In my planned system, a distance-based k-medoids formula (DTW = Dynamic Time Warping) is employed, that is applied to cluster programs and represents the evolution of recognition in trends. after, the trend-specific predictive models are created severally exploitation Random Forest Regression (RF). Consistent with the info sets removed from AN electronic program direct (EPG) and early survey conventions, freshly printed programs are classified into trends by a gradient enhancing call tree. By consolidating prescient qualities from pattern particular models and in this way the arrangement possibility, the arranged methodology accomplishes higher prescient outcomes.
An advanced comparison on big data world computing frameworks

Abstract Today’s internet world becomes really introducing a landmark between Peta to Exa byte which is significantly generating an enormous size of data while computing the digital things including the format of each datasets which signifies highly unstructured because which could be generating from different social sites, IOT, Google engine, Twitter, Yahoo, monitoring and controlling through sensors essentially called big data. Because of this fast era, we apply just contemporary techniques with common tools regarding having focused performing, smooth process and to execute computations on huge data. Though such tremendous universal data has some shortcomings for getting effective processing, analyzing the universe immense datasets and scalability techniques. Apache open free source Hadoop does the latest big data weapon which can process Zetta byte dimensions of databases by its most developed and popular components as hdfs and Map Reduce, to make up vast storage facility plus great administration in the sense to process zettabyte of datasets as powerfully, flexible. MR likes more famous software popular structure for handling big-data existing issues with full parallel, highly distributed and most scalable manner. However, public and unrestricted source tools on Hadoop, map reduces become major limitations like poor allocate process on needy resources working regarding stream-oriented processing, Shortage significant viewpoints like latency, dynamic manner execution, optimization, computing as online and diverse logical solutions. We consider significant various complex data computing orientated techniques. This study paper address Apache fastest spark tool, online-oriented tool public and unrestricted source and Flink are in Apache project are efficient frameworks to conquer that limitation.
Network Embedding by Walking on the Line Graph

In this paper, we propose to embed edges instead of nodes using state-of-the-art neural/factorization methods (DeepWalk, node2vec). These methods produce latent representations based on co-ocurrence statistics by simulating fixed-length random walks and then taking bags-of-vectors as the input to the Skip Gram Learning with Negative Sampling (SGNS). We commence by expressing commute times embedding as matrix factorization, and thus relating this embedding to those of DeepWalk and node2vec. Recent results showing formal links between all these methods via the spectrum of graph Laplacian, are then extended to understand the results obtained by SGNS when we embed edges instead of nodes. Since embedding edges is equivalent to embedding nodes in the line graph, we proceed to combine both existing formal characterizations of the line graphs and empirical evidence in order to explain why this embedding dramatically outperforms its nodal counterpart in multi-label classification tasks.
Recommendation System for Complementary Breastfeeding using Ontology Modelling and Naïve Bayes

Abstract Complementary breastfeeding is an additional food given to the baby started from six to 24 months. The giving of complementary foods is given gradually according to the age of the children and adapted to the condition of the children, such as allergies suffered or malnutrition suffered. This research aims to develop an ontology based decision support system that will help mothers in giving breastfeeding to their babies with keep regarding to their food preferences. This research successfully develops a content-based recommendation system by performing the Naïve Bayes Method and modified the existing ontology modelling and also develop a mobile application with the Android platform so that it can be more accessible to many people. Thirty-five users evaluated the system, and the result of the usability testing shows that the user satisfaction level using SUS (System Scale Usability) method is 79.57, which is in Grade A-. This grade indicates that the system can be well understood by the user and can help mothers in choosing breastfeeding recipes or menus.
LZ based Compression Benchmark on PE Files

The key element in runtime compression is the compression algorithm itself, that is used during processing. It has to be small in enough in decompression bytecode size to fit in the final executable, yet have to provide the best possible compression ratio. In our work we benchmark the top LZ based compression methods on Windows PE (both exe and dll) files, and present the results including the decompression overhead and the compression rates.
Dynamic security for multi-user access control in distributed environment

Abstract Cloud computing is an on-demand facilities that allows customers to access processing sources and services from anywhere and at any time. Information protection means defending the information from illegal individuals or online hackers. We propose Integrated Group Sharing Approach (IGSA) schema for multiple customer accessibility control in customer operations like information insertion, deletion and in customer cancellation immediately reasoning database integration. We look at the security of IGSA plan and assess with the current IGSA techniques which are used for information access immediately reasoning computing for achieving real-time applications in reasoning. Our experimental results show efficient information removal from multiple customer accessibility in search of information from reasoning server. In future, some protection systems are to be developed to provide protection to cloud.
Real-time robust complementary visual tracking with redetection scheme

Recently, correlation filter-based trackers have been widely investigated due to their high efficiency and robustness. However, most of them use a fixed cosine window to deal with boundary effects and ignore the reliability of tracking results, which results in poor tracking performance when the target endures severe occlusion and large appearance variation. In order to deal with these issues, we propose a tracking framework with an adaptive cosine window, which is composed of a reliability estimation module and a redetection module. First, we incorporate the object likelihood map into the traditional fixed cosine window to form an adaptive cosine window, which can enlarge the searching region and effectively cope with boundary effects. Second, the peak-to-sidelobe ratio of HOG-based correlation response map and the color score of each frame are adopted to estimate the reliability of tracking results. Third, we introduce the Siamese tracker to redetect targets in case of tracking failures. Finally, a target pyramid scheme is built to deal with scale variation. Extensive experiments on the OTB-2013, OTB-2015, TemplateColor, UAV123@10 fps, and VOT-2015 demonstrate that our proposed method outperforms favorably against the state-of-the-art methods with real-time tracking speed.
Crypto-Stego Technique for Secure Data Transmission

Abstract As days are passing, technology is taking its force towards its smartness. But there are some devices which are to be modified for better security results. Cryptography is a technique used for sending secret messages and Steganography is a process related to image. In this paper, we consider Cryptography and Steganography for secure transmission of secret message. The technique used in this paper is to place an image within another image. Encrypted data or Secret data is placed on an image based on pixels and that image is placed as background for other image. When the receiver receives that image he/she thinks it as a normal image but it contains a hidden message and decrypted for original image.
Towards Ubiquitous Computing Technology - A Survey

Robots cooperating with the physical world arrangement with models of material science. We advocate that robots cooperating with individuals need to plan with models of perception. This review outlines the experiences we have picked up in incorporating computational psychological models of individuals into mechanical technology arranging and control. It begins from a general game-theoretic detailing of communication, and breaks down how various approximations result in distinctive helpful coordination practices for the robot amid its communication with individuals. Customary mechanical robots are intensely subject to hard robotization that requires pre-indicated installations and tedious (re)programming performed by experienced administrators. In this work, instructing by human-as it were exhibition is utilized for lessening required time and skill to setup a robotized get together station. This is accomplished by the proposed structure upgrading the automated framework with cutting edge discernment and psychological capacities, gotten to through an easy to use Human Robot Interaction interface. The methodology is assessed on a little parts' gathering use case conveyed onto a communitarian modern robot tested. Tests show that the proposed approach enables unpracticed clients to proficiently show robots new get together undertakings.
Validation of Unimodal Non-Gaussian Clusters

We analyze the influence of the cluster shape on the performance of four cluster validation criteria: AIC, BIC, ICL and NI. First we introduce a method to generate unimodal and radially symmetric clusters whose shape can be interpolated between peaky long-tailed and flat distributions using a single parameter. Normally distributed clusters are obtained as a special case. Then we systematically study the performance of AIC, BIC, ICL and NI when validating clusters of arbitrary shapes. Using problems with two clusters, different inter-cluster distances and different dimensions, we show that, while BIC provides the best results for normally distributed clusters, in a general context with high dimensional data and unknown cluster distributions the use of ICL or NI may be a better choice.
Comparison of mineral valuations by international codes of CRIRSCO and GKZ for Kyrgyzstan

The authors compare valuations by GKS (RF State Commission of Reserves) and CRIRSKO for the transition of Kyrgyzstan to the international methods of mineral appraisal.
Nature-LKT

Teaching ‘Nature Science’ poses a great challenge to education professionals. In a search for alternatives that can facilitate student’s learning and attract their interest, a trial experience is described that was conducted in the class of BA Degree in Pedagogy at the University of Seville. It was based on the creation of motivational and attractive — Learning and Knowledge Technology (LKT) — materials in the discipline of Nature Science. The results show the advantages that prospective teachers find in the use of Information and Communication Technologies (ICTs) for teaching and learning, in this case, Nature Science.
Choose your country or region?

Internet has enabled new modes of communication and ways of facilitating commerce, and globalization also has cross-cultural implications for the fields and practices of Information Systems and Human-Computer Interaction. With the aim to seek authoritative sources and best practices regarding coded references to territories, the study has collected, enumerated and cross-examined the territory names provided by the Unicode Common Locale Data Repository (CLDR) and currently used by the Google Advanced Search. The findings of the study show, regarding the references to territories, that (1) the Unicode CLDR provides more comprehensive data points, that (2) the Google Advanced Search uses shorter names, and that (3) based on a closer look on Japanese, Korean, and Chinese (both traditional/orthodox and simplified), over 95% of the territory references are exactly the same. The findings indicate that theoretical frameworks and more evidence are needed to improve the authoritative sources and best practices regarding references to countries, territories and regions.
The true professionals. Reminiscences of authority bibliographers

On the occasion of the 100th anniversary of the National Library of Russia, the reminiscences by Gertruda Vasilyevna Gedrimovich, the oldest and prominent Russian bibliographer, of her first mentors in the profession. The portraits of Elena Vladimirovna Iуenish (1926–2008), Victor Isidorovich Gransky (1903–1970), and Lyudmila Veniaminovna Zilbermintz (1909–1972) – outstanding staff members of Saltykov-Shchedrin State Public Library (today – National Library of Russia), are given. Some interesting facts in their biographies are cited; their contribution to the library science and leadership are appraised. The author emphasizes their spacious intellect, principles of work and mentorship. Their work made the essence and way of their living. All of them were talented persons with their individual characteristic features. Special attention is given to their ability to achieve high goals and professional results. The author also focuses on the timeless value of the profession of bibliographer and expresses her hope that it will last and develop: today, professional and experienced bibliographers of the National Library of Russia, its general catalogs, group processing department, periodicals reading rooms, Central Reference Library, Library Studies Office, and E-library efficiently execute queries and orders.
Ensemble feature analysis classifier for sentiment analysis using convolutional neural networks

Abstract Text mining is the worldwide fast growing domain in research. Sentiment analysis is the one of the sub domain in the text mining to extract the sentiment from the various texts available in the internet and from other sources. Various existing systems are implemented to get the sentiment analysis with the migration of natural language processing algorithms (NLP) and artificial intelligence algorithms.Various issues identified in the text mining with sentiment analysis are solved very rarely. According to the previous research, deep-learning and artificial intelligencebased TSA prediction method that comprises of a stacked auto encoder (SAE) model that is used to learn generic linguistic and text semantic features But the system not reached up to the mark. In this paper, Ensemble Feature Analysis Classifier to incorporate the new domain dimension within the rating and text based sentiment analyzer. Implementation of this proposed prototype validates our claim and highlights our efficiency in supporting multiple dimensions during sentiment analysis.
Groundless Threat of Infringement of Patent

Research, innovation and development are the main characteristics of developing nation.India is one of them and is the main hub of innovation, research and development in the field of growth of intellectual Property Law.There are rights which are guaranteed and a law to protect the intellectual rights of a person when infringed.However, there occur instances where there exists a groundless threat of infringement.It may come in form of a letter, a suit or a summon copy.Such groundless threats are discouraged by law since it brings shame and damage to the aggrieved.This paper focuses on the concept of groundless threat under Indian Law in comparison with the Patent Law around the World.
Combining Common Sense Rules and Machine Learning to Understand Object Manipulation

Automatic situation understanding in videos has improved remarkably in recent years. However, state-of-the-art methods still have considerable shortcomings: they usually require training data for each object class present and may have high false positive or false negative rates, making them impractical for general applications. We study a case that has a limited goal in a narrow context and argue about the complexity of the general problem. We suggest to solve this problem by including common sense rules and by exploiting various state-of-the art deep neural networks (DNNs) as the detectors of the conditions of those rules.&#x0D; We want to deal with the manipulation of unknown objects at a remote table. We have two action types to be detected: `picking up an object from the table' and `putting an object onto the table' and due to remote monitoring, we consider monocular observation. We quantitatively evaluate the performance of the system on manually annotated video segments, present precision and recall scores. We also discuss issues on machine reasoning. We conclude that the proposed neural-symbolic approach a) diminishes the required size of training data and b) enables new applications where labeled data are difficult or expensive to get.