Reviewerly/exHarmony huggingface dataset:
An Optimized Cloud Architecture for Integrity Verification

Cloud has become popular in today’s environment of IT industry where all types of services whether hardware, software, or storage can be availed at one place and can be used efficiently. When we use hardware and software then there is very less chance of risk but when we deal with storage then we are storing our personal data on the cloud which is not transparent to user. In proposed model we introduced hybrid technique which provides security and assurance beyond Service Level Agreement. We proposed a strong integrity verification mechanism at the time of recovering a file/data. Hardness/strength of the key or the shared file is increased up to maximum level i.e., 7.9. Integrity of the proposed system is compared with the standard cloud’s integrity and approximately we gain 60% higher level of integrity other than the standard cloud nodes.
Using Pre-trained Deeply Contextual Model BERT for Russian Named Entity Recognition

Named entity recognition is an important part of the Information Extraction process (extracting structured data from unstructured or semi-structured computer-readable documents). To highlight in the text of people, organizations, geographical locations, etc., many approaches are used. Although, well-known bidirectional LSTM neural networks, show good results, there are points for improvement. Usually, the word embedding vector are used as the input layer, but the main disadvantage of the last vector models (word2vec, GLOVe, FastText) is that they do not consider the context of documents. In this paper we present the effective neural network based on the deeply pre-trained bidirectional BERT model, which was introduced in the fall of 2018, in the task of named entity recognition for the Russian language. The BERT model, trained for a long time on large unannotated corpuses of texts, were used in our work in two modes: feature extraction and fine-tuning for the NER task. Evaluation of the results was carried out on the FactRuEval dataset and the BiLSTM network (FastText + CNN + extra) was taken as the baseline. Our model, built on fine-tuned deep contextual BERT model, shows good results.
Inside the Tool Set of Automation: Free Social Bot Code Revisited

Social bots have recently gained attention in the context of public opinion manipulation on social media platforms. While a lot of research effort has been put into the classification and detection of such automated programs, it is still unclear how technically sophisticated those bots are, which platforms they target, and where they originate from. To answer these questions, we gathered repository data from open source collaboration platforms to identify the status-quo of social bot development as well as first insights into the overall skills of publicly available bot code.
Alternating Electric Field-Based Static Gesture-Recognition Technology

Currently, gesture recognition based on electric-field detection technology has received extensive attention, which is mostly used to recognize the position and the movement of the hand, and rarely used for identification of specific gestures. A non-contact gesture-recognition technology based on the alternating electric-field detection scheme is proposed, which can recognize static gestures in different states and dynamic gestures. The influence of the hand on the detection system is analyzed from the principle of electric-field detection. A simulation model of the system is established to investigate the charge density on the hand surface and the potential change of the sensing electrodes. According to the simulation results, the system structure is improved, and the signal-processing circuit is designed to collect the signal of sensing electrodes. By collecting a large amount of data from different operators, the tree-model recognition algorithm is designed and a gesture-recognition experiment is implemented. The results show that the gesture-recognition correct rate is over 90%. With advantages of high response speed, low cost, small volume, and immunity to the surrounding environment, the system could be assembled on a robot that communicates with operators.
Handwritten digits recognition using convolution neural networks

Convolution Neural Networks (CNNs) have been successfully used to solve variety of problems in computer vision and pattern recognition applications. In this paper, we explore the use of CNN to prov...
An Improved Coarse-to-Fine Method for Solving Generation Tasks.

The coarse-to-fine (coarse2fine) methods have recently been widely used in the generation tasks. The methods first generate a rough sketch in the coarse stage and then use the sketch to get the final result in the fine stage. However, they usually lack the correction ability when getting a wrong sketch. To solve this problem, in this paper, we propose an improved coarse2fine model with a control mechanism, with which our method can control the influence of the sketch on the final results in the fine stage. Even if the sketch is wrong, our model still has the opportunity to get a correct result. We have experimented our model on the tasks of semantic parsing and math word problem solving. The results have shown the effectiveness of our proposed model.
Enhancing High Availability for NoSQL Database Systems Using Failover Techniques

NoSQL allows fast processing of real-time large data applications. It allows database professionals to make use of elastic scalability of database servers. Most of these databases are created to perform on full throttle despite the presence of low-cost hardware. Users are able to create the database more quickly due to the non-relational nature of NoSQL. There is no need of developing the detailed (fine-grained) database model. As a result, it will save lots of development time. The primary aim of this paper is to present a novel data integration method as well as workflow that executes the data integration of various source systems in such a way that user does not require any programming abilities. Middleware is considered as main part of the proposed technique which provides high availability of data in case of failures by utilizing the checkpointing. Experimental results reveal that the proposed technique provides efficient results, especially in case of any kind of failures in distributed NoSQL servers.
An Empirical Analysis of IFPUG FPA and COSMIC FFP Measurement Methods

The accuracy of functional size measuring is critical in software project management, because it is one of the key inputs for effort and cost estimation models. The functional size measurement (FSM) process is performed based on standardized methods; however, the accuracy of the FSM results is still based mostly on the knowledge of the measurers. In this paper, an empirical study was conducted to analyze the accuracy, reproducibility, and acceptance properties of the IFPUG FPA and COSMIC FFP functional size measurement methods. Results show that the performance of participants in measuring the requirement specifications using IFPUG FPA and COSMIC FFP did not differ significantly in terms of accuracy and reproducibility. Likewise, acceptance properties such as perceived ease of use, perceived usefulness, and intention to use did not present significant differences. Our results suggest that novice measurers could apply both methods with similar results.
Hydrothermal Minerals Mapping using based on Remotely Sensed Data from Sentinel 2 Sattelite: a Case Study in Vinh Phuc Province, Northern Vietnam

This paper presents the experiences obtained in the application of Principal Component Analysis (PCA) method to map hydrothermal minerals based on remotely sensed data. In this study, Sentinel-2B MultiSpectral Instrument (MSI) image is used to detect distribution of hydroxyl-bearing minerals in Vinh Phuc province, northern Vietnam. Four bands of Sentinel-2B image including blue band (band 2), Vegetation Red Edge band (band 8A) and SWIR bands (band 11 and 12) are used to calculate the Principal Components, then and then select the Principal Component, which containing provides information on the hydrothermal minerals information. The obtained results findings show that the methodology and data are effective in detecting and mapping hydrothermal mineralization.
Cryptography from Information Loss.

Reductions between problems, the mainstay of theoretical computer science, efficiently map an instance of one problem to an instance of another in such a way that solving the latter allows solving the former. The subject of this work is reductions, where the reduction loses some information about the input instance. We show that such reductions, when they exist, have interesting and powerful consequences for lifting hardness into useful hardness, namely cryptography. Our first, conceptual, contribution is a definition of lossy reductions in the language of mutual information. Roughly speaking, our definition says that a reduction C is t-lossy if, for any distribution X over its inputs, the mutual information I(X;C(X)) ≤ t. Our treatment generalizes a variety of seemingly related but distinct notions such as worst-case to average-case reductions, randomized encodings (Ishai and Kushilevitz, FOCS 2000), homomorphic computations (Gentry, STOC 2009), and instance compression (Harnik and Naor, FOCS 2006).We then proceed to show several consequences of lossy reductions:1. We say that a language L has an f-reduction to a language L' for a Boolean function f if there is a (randomized) polynomial-time algorithm C that takes an m-tuple of strings X = (x_1,…,x_m), with each x_i ∈ {0,1}^n, and outputs a string z such that with high probability, L'(z) = f(L(x_1),L(x_2),…,L(x_m)). Suppose a language L has an f-reduction C to L' that is t-lossy. Our first result is that one-way functions exist if L is worst-case hard and one of the following conditions holds: - f is the OR function, t ≤ m/100, and L' is the same as L - f is the Majority function, and t ≤ m/100 - f is the OR function, t ≤ O(m log n), and the reduction has no error This improves on the implications that follow from combining (Drucker, FOCS 2012) with (Ostrovsky and Wigderson, ISTCS 1993) that result in auxiliary-input one-way functions.2. Our second result is about the stronger notion of t-compressing f-reductions - reductions that only output t bits. We show that if there is an average-case hard language L that has a t-compressing Majority reduction to some language for t=m/100, then there exist collision-resistant hash functions.This improves on the result of (Harnik and Naor, STOC 2006), whose starting point is a cryptographic primitive (namely, one-way functions) rather than average-case hardness, and whose assumption is a compressing OR-reduction of SAT (which is now known to be false unless the polynomial hierarchy collapses).Along the way, we define a non-standard one-sided notion of average-case hardness, which is the notion of hardness used in the second result above, that may be of independent interest.
Generalized DCT and DWT hybridization based robust feature extraction for face recognition

Pose, expression and light variations are the major complications in recognition of face images. Hence, a robust technique is introduced in this paper which deals with variations in light in the face images. Hybridization of fractional discrete Cosine transform (FrDCT) and DWT are utilized as the feature extraction technique which helps in normalizing the light variations in the face images. DWT helps in obtaining the global behaviour of the light compensated face image. Also, use of DWT with FrDCT helps in obtaining the redundancy which is difficult to obtain with FrDCT alone. Therefore, it is obtained with DWT and after that, FrDCT is exploited for the local correlation. The proposed method is experimented and investigated on YALE, Yale B and CMU PIE face databases and very good performance outcome has been achieved as compared to the other state of art methods.
Traffic Lights Detection and Recognition Algorithm Based on Multi-feature Fusion

Detection and recognition of traffic lights is important for intelligent assisted driving. Traditional color space based traffic lights detection algorithms could be easily affected by other objects (such as buildings, car taillights) in the surrounding environment, and the detection accuracy and real-time performance are not ideal enough. Generally, the deep learning based methods have better advantages of real-time and accuracy performance for the normal scene with obvious traffic lights targets. However, the small traffic lights targets detection rate and accuracy in night-time of these methods are still can't be satisfactory. To solve this problem, this paper proposed a novel traffic lights detection and recognition algorithm based on multi-feature fusion, which can be implemented in two steps (detection and recognition). For the first step, the SLIC (simple linear iterative clustering) super-pixel segmentation algorithm is used for purposes reducing the image data processing complexity and improving the real-time performance. The mean-shift algorithm was used to cluster the HSV (Hue, Saturation, Value) color space components respectively for enhancing the target data and reducing the interference from other targets. For the second step, the feature information extracted by CNN (Convolutional Neural Network) and HOG(Histogram of Oriented Gradient) feature are fused. The SVM (Support Vector Machine) classifier is trained on a data set of traffic lights established by our own. To verify the proposed algorithm in this paper, amount of experiments were carried out in real traffic scenes. Experimental results show that this algorithm almost has the same real-time performance with YOLO_V3 neural network and a better accuracy.
High-Speed ASIC Implementation of Paillier Cryptosystem with Homomorphism

Recently, compatibility between privacy protection and information utilization has been considered to be a major challenge. Hence, researches on homomorphic cryptosystem which can realize secure computation draw growing attention. The Paillier encryption with additive homomorphism is widely used in fields such as electronic voting and secure biometrics. At the same time, the large amount of data in the database and the requirements of Paillier security require computationally expensive processes each authentication and comparison, so the improvement of computational efficiency is particularly important. In this research, in order to reduce the computation time of Paillier, we design an ASIC based hardware architecture dedicated to Paillier encryption, and realize high-speed encryption and decryption by using high radix arithmetic core and the parallelism in the decryption process.
Segmenting ECG and MRI data using ant colony optimisation

This paper proposes efficient medical data segmentation using ant colony optimisation (ACO) and modified intuitionistic fuzzy C-means (MIFCM) clustering. MIFCM is a variant of intuitionistic fuzzy ...
DATA MART DESIGN IN BKPP BANDUNG USING FROM ENTERPRISE MODELS TO DIMENSIONAL MODELS METHOD

The data warehouse is a single data storage that is complete and consistent with subject-oriented, integrated, non-volatile, and time-variant characteristics that can be used to support decisions. While Data mart is a subset of a data warehouse that supports the information needs of certain departments or business process functions. Badan Kepegawaian, Pendidikan Pelatihan (BKPP) of Bandung Municipality has a history of employee mutation transactions that have not been optimally utilized so it will be very useful if the data can be made into a data mart. This data mart development method uses the From Enterprise Models to Dimensional Models method as its design method and the Bottom-Up Approach as an approach in developing a data mart. This data mart developed using the PostgreSQL database and the PHP language. The purpose of this research is to develop human resource data mart by optimizing the utilization of historical data from employee mutation transactions. The results of this research are human resources data mart that accommodates historical data on employee mutation transactions and produces information that is useful for the Top Management to support the making decision.
“Computing With Words”- based Semantic Similarity Measure for Adjective Phrases

This paper addresses the semantic similarity computation between adjective phrases like ‘more or less tall and not very short’, ‘somewhat tall and not extremely tall’, ‘very short and not very very tall’ etc. The proposal finds application in scenarios where human perception based inputs are involved, such as in processing product reviews, feedbacks, survey responses, social media posts etc. To be able to adequately access the semantic spectrum, we employ the Computing with words (CWW) based technique in formulating the similarity measure. Particularly, we use the concepts of a linguistic variable and linguistic hedge. The implementation results report similarity scores that are in agreement with human reasoning.
The Right to an Explanation

The opacity of some recent Machine Learning (ML) techniques have raised fundamental questions on their explainability, and prompted the creation of a research subdomain, Explainable Artificial Intelligence (XAI). Opacity would be particularly problematic if those methods were used in the context of administrative decision-making, since most democratic countries grant to their citizens a right to receive an explanation of the decisions affecting them. If this demand for explanation were not satisfied, the very use of AI methods in such contexts might be called into question. In this paper, we discuss and defend the relevance of an ideal right to an explanation. It is essential both for the efficiency and accountability of decision procedures, both for public administration and private entities controlling the access to essential social goods. We answer several objections against this right, which pretend that it would be at best inefficient in practice or at worst play the role of a legal smokescreen. If those worst-case scenarios are definitely in the realm of possibilities, they are by no means an essential vice of the right to an explanation. This right should not be dismissed, but defended and further studied to increase its practical relevance.
Preventing Data Manipulation and Enhancing the Security of data in Fitness Mobile Application

In this internet era people are likely to use the mobile application very often for their daily needs like: Mobile recharge, Food delivery, Online shopping, Fitness tracking application and many more. It's necessary to make sure application is secure so that the user details are not exposed. This paper narrates the feature of fitness mobile application and explain about the security of Fitness mobile application in detail. As people are more concerned about their health and fitness, so people tend to take care of their heath using the fitness mobile application which monitors the health in the single application. Basically, the mobile device data base will not be secured, i.e., SQLite Data base of the mobile application is not secure enough. If the application which the user is using has some sort of security, then that security is used to secure the applications database. So, the fitness application defined in this paper has the following: activity tracker, calories finder, user position, emergency message sender, URL, and user details. Here these data are been stored in the SQLite, so if the security is not provided for the application then there will be a chance in manipulation of data in the server side. And the user data might be altered, and false data is been displayed for the user in the application. To make sure the user data is not altered there should be some encryption algorithm which has to be used to secure the application. There are plenty of encryption algorithm but from the study done in the paper, AES algorithm can be used to secure the application and to prevent the manipulation of the user data because time taken to secure the data is less when compared with the other algorithm.
Prediction of Anthropometric Data Based on Ladder Network

The inadequacy of training data is an acute contradiction in anthropometric data prediction. To address this problem, a novel anthropometric data prediction method based on Ladder network is proposed. On one hand, some volunteers in various pose are captured with RGB cameras, whose anthropometric data (e.g., chest circumference) are measured manually. On the other hand, a series of unlabeled sample data are collected through virtual human bodies. After segmenting human body silhouettes from the background, a semi-supervised network (e.g., Ladder network) can be trained to predict the ratio between anthropometric data and height. Therefore, anthropometric data of arbitrary human body can be inferred with height, which can be potentially used in 3D human modeling and clothing industry. Experiments show that our method can achieve acceptable results with limited training data.
Attacks on the Random Gamming Code

In 1917, Hilbert Vernam patented a top-secret encryption scheme, which at first was called a one-time notepad and later a Vernam cipher. At the time that Vernam proposed this scheme, there was no evidence that it was completely secret, since, in fact, at that time yet there was no idea what the perfect secret of the cipher was. However, about 25 years later, Claude Shannon introduced the definition of perfect secrecy (perfect cipher) and demonstrated that the random gamming cipher reaches this level of security. Cryptographers believe that there are no effective attacks for attacks of random gamming. In particular, there are no effective attacks for the Vernam cipher. Objective : to justify the fallacy of this proposition to build effective attacks. Methods : analysis of the relationship between the cipher key and the received encrypted text. Results : an attack on the plaintext of a random gamming cipher based on a given encrypted text was developed. In addition, there was a suggestion for another attack on the plaintext contents based on the encrypted text. For all attacks, parameters of their complexity are calculated. These results are new. Previously, an attack on the random gamma code was unavailable. The results disprove the opinion that there are no attacks on this cipher. Practical relevance : firstly, it has become possible to carry out attacks on the random gamming code. Secondly, when using this cipher, it is necessary to strictly limit the length of the message. Discussion : the idea that there is an effective attack on a random gamming cipher arose in 2002, due to the possibility of introducing a similar concept, in which in a definition of the perfect cipher the plaintext is changed for a key. The first idea in creating attacks is that when the key is long its elements are repeated. The second idea is that attacks on two plaintexts are encrypted with one key. And the main idea was that it is necessary to improve the mathematical model of the Shannon code. Therein, when interpreting the concept of the perfect cipher, we should talk about the cipher model perfection. The publication place : in the Yandex search engine a query "Perfect ciphers" resulted in 22 million links, on a query "schemes perfectly secret" there were 43 million links. Yandex on the query "random gamming code" gave 13 million results.