Reviewerly/exHarmony huggingface dataset:
Grid-search wolf pack optimization algorithm for two-dimensional OTSU image segmentation

Abstract Compared with One-Dimensional OTSU, Two-Dimensional OTSU includes the relationship about the pixels of the image and improves the anti-noise ability of the algorithm. However, as a result of too much computation caused by considering the relationship about pixels of the image to be processed, Two-Dimensional OTSU always takes much time. To keep the good performance of original Two-Dimensional OTSU as well as reduce the time spent; this paper proposes Grid-Search Wolf Pack Optimization Algorithm (GSWA) for Two-Dimensional OTSU Image Segmentation. Firstly, it is based on the thought of adaptive shrinking grid search chaos wolf optimization algorithm with adaptive standard-deviation updating amount (ASGS-CWOA) that traditional wolf pack optimization algorithm was improved to enhance its performance, which includes Grid Search and Opposite-Middle Raid(OMR). Grid Search can enhance the wolf pack’s summon and siege capability. Moreover, discrete step-size was adopted to adapt to discrete solution space of Image-Segmentation. Experimental results indicate that GSWA-OTSU not only reduces the segmentation time, but also takes the segmentation quality into account.
Reforming Russian legislation for crimes in the digital economy

The aim of this research work is to analyze Russian criminal legislation on punishment for computer crimes. The growth in the number and intensity of cyber-attacks throughout the world also leads to an increase of costs for companies and society as a whole to provide protection against cyber-attacks and to prevent losses from them. The financial sphere of the economy suffers especially. The article provides statistical and expert data on the potential damage from the suspension of the financial institution’s activities, including lost profits and costs for restoring websites after cyber-attacks. A significant increase in the number of crimes committed with the help of digital devices and a multiple increase in the amount of damage from them were revealed on the basis of an empirical study. On the part of the business community, the demand for the state to toughen penalties for computer crimes is increasing. To this end, in 2018 novels were introduced into the Criminal Code of the Russian Federation, which toughened criminal liability for embezzlement of funds from bank accounts or electronic money. It is shown that the changes introduced by the legislator in the criminal legislation of Russia take into account modern threats to economic security and increase the level of protection of the financial interests of citizens, credit organizations and the state as a whole.
Requirement Engineering as a Software Development Process

The paper introduces a novel requirements management tool, incorporating software development practices into requirements engineering. It is motivated by an expectation that using well-established techniques should provide benefits such as increase of requirements quality, better alignment of tooling to organization needs and support for Agile project management techniques. Our aim, therefore, is to provide an open architecture for various requirements engineering activities, as till now, many of existing requirements management tools do not follow the latest developments in software development practices. In this paper, we propose a mapping of various aspects of software development based on Continuous Integration to requirements engineering and describe a prototype requirements management tool built to validate the proposed concept.
Comparison of Various Approaches for Dependency Parsing

This paper presents the comparison results of dependency parsing for two distinct languages: Kazakh and English, by using a various discrete and distributed feature-based approaches We apply graph/transition-based methods to train these models and to report the typed and untyped accuracy. Different comparisons are made for comparing these models by utilizing discrete or dense features. Experimental results show that discrete feature-based approaches (graph-based) perform well than others when the size of data-set is relatively small. For a large data set, the results of those approaches are very competitive with each other, and no significant difference in performance can be observed. In terms of training speed, the results show that discrete feature-based parsers take much less training time than the neural network-based parser, but with comparable performances.
Augmenting Spacecraft Maneuver Strategy Optimization for Detection Avoidance With Competitive Coevolution

Optimization in the spacecraft detection avoidance problem is computationally cost prohibitive given the size of the state and action spaces available to both players. Competitive coevolution can be used to augment strategy optimization by reinforcement learning in a manner that results in dynamic search spaces. An evading spacecraft and a pursuing sensor compete directly with each other and reciprocally drive one another to increasing levels of performance and complexity. This is accomplished by gradually increasing the size and complexity of the strategies. Using coevolution provides significant computational cost savings compared to traditional optimization methods while ensuring a globally optimal result. It is found using competitive coevolution that a spacecraft is able to successfully evade a tasked sensor nearly half of the observation window time with only one 19 m/s maneuver every 5 days.
Practical Public-Key Encryption Scheme Tightly Secure in the Random Oracle Model

Chosen-ciphertext security is a central goal in designing a secure public-key encryption scheme, and it is also important that the chosen-ciphertext security is tightly reduced to some well-established hard problem. Moreover, it is more important to have a tight reduction in the multi-user multi-challenge setting, since a tight security reduction in the single-user single-challenge setting generally does not imply a tight reduction to the multi-user multi-challenge setting. We propose the first fully tightly secure and practical public-key encryption scheme which is chosen-ciphertext secure in the multi-user multi-challenge setting in the random oracle model. The scheme is proven secure under the decisional Diffie-Hellman assumption in a pairing-free group. The ciphertext overhead of our scheme is two group elements and two exponents.
Performance Analysis of Homomorphic Cryptosystem on Data Security in Cloud Computing

Most cloud users and officialdoms have a great interest in adoption and use cloud services like cloud storage to store their data on cloud environment. With the speedy growing in cloud computing technology, user's data security and privacy in cloud computing technology is the utmost dangerous issues because data owner stores their sensitive data onto remote cloud storage. However, successful retrieval of confidential data and computation on confidential data is hard to succeed by traditional cryptographic algorithm. Hence, to warrant the cloud security we are using the cryptographic algorithms that supports for computation over encrypted data. In this, Homomorphic cryptographic algorithm is one such technique that has interested homomorphic cryptographic algorithm used to secure user data in cloud server. It performs computation on cipher texts data can be operated directly without affecting the confidentiality of the encryption algorithms. We have established a framework for security of data on a cloud storage by using fully homomorphic cryptographic algorithm. Indeed, In this study, we have evaluated complexity of three homomorphic cryptographic algorithm those are SDC FHE, Paillier and RSA with encryption time, decryption time, throughput and memory usage.
Lossy compression of climate data using principal component analysis

Enormous size of climate data has posed a difficulty in terms of storage since a long time. Principal Component Analysis is a well known method used for data compression. This paper gives a brief idea about the compression of climate data using Principal Component Analysis by modifying the data obtained from the weather station. A minor modification in handling data led to a high compression ratio. This compressed file can then be processed to retrieve the data again with a significant accuracy. The data obtained from the retrieval using compressed file almost matched the real time data.
Evaluation of Stochastic Gradient Descent and Relevance Vector Classifier for Improvement in Spoken Language Understanding in Terms of Performance and Data Sparsity

Spoken Language Understanding (SLU) is a part of spoken dialogue system (SDS) which represents speech data into semantic form required to understand user's intention. The human-computer interaction system is most commonly placed in noisy environment. This introduces error in Automatic Speech Recognizer (ASR). SLU takes into account ASR errors. Thus robustness of SLU plays important role in efficient working of human computer interaction system. The SLU consist of semantic decoder and belief tracker. Recent line of research models semantic decoder as a classification task or sequence to sequence learning. The SLU models incorporate learning models for classification. The learning model requires lot of labelled data for training. The process of obtaining labelled data is expensive and time consuming. Thus it is desirable that SLU must be able to maintain its accuracy even if sparse training data is available. The objective of this research is to evaluate robustness in terms of accuracy and data sparsity of SLU incorporating Relevance Vector Classifier (RVC) and Stochastic Gradient Descent (SGD) classifier. The framework of SLU incorporates semantic tuple classifier (STC). The STC performs dialogue act classification and slot filling by using classifiers. The classifiers that were incorporated in semantic decoder are Support Vector Machine (SVM), SGD and RVC. The dataset used is DSTC II. The SVM classifier showed maximum robustness and better accuracy as compared to other classifiers. For SVM, classifier the Inter Cross Entropy (ICE) and accuracy were 1.0 and 0.91, respectively. For RVC, ICE and accuracy were 4.055 and 0.52.
Shift-inequivalent decimations of the Sidelnikov–Lempel–Cohn–Eastman sequences

We consider the problem of finding maximal sets of shift-inequivalent decimations of Sidelnikov–Lempel–Cohn–Eastman (SLCE) sequences (as well as the equivalent problem of determining the multiplier groups of the almost difference sets associated with these sequences). This is an open problem that was originally posed in Cohn et al (IEEE Trans Inf Theory 23:38–42, 1977) and that was mentioned more recently as being open in Akiyama (Acta Arithmetica LXXV.2:97–104, 1996). We derive a numerical necessary condition for a residue to be a multiplier of an SLCE almost difference set. Using our necessary condition, we show that if p is an odd prime and S is an SLCE almost difference set in $${\mathbb {Z}}_p^*,$$ then the multiplier group of S is trivial. Consequently, for each odd prime p, we obtain a family of $$\phi (p-1)$$ shift-inequivalent balanced periodic sequences (where $$\phi $$ is the Euler-Totient function) each having period $$p-1$$ and nearly perfect autocorrelation.
Design and Analysis of the Frame of Smart Wheelchair

In today's world the rate of differently abled people is increasing day by day. According to the CENSUS 2011 it is showed that out of all disabilities, 20% are facing disability in movement. Wheelchairs have been the basic and most widely used aid for such people. But there are people suffering with partial paralysis or quadriplegia which affect their limbs. Thus, they need another person in order to assist the movement of the chair. A smart wheelchair is the best option in this case. A smart wheelchair is a powered wheelchair to which computers, sensors, and assistive technology are attached. We are proposing a smart wheelchair in which we use a head gear with a Gyro sensor to detect the head movement and will generate signals accordingly to move the chair. When head tilts towards the left the chair will move in left direction and similarly in forward, backward and right direction according to the movement of the head with the help of motors powered by a battery thereby eliminating the need for another person. This prototype will help such people to get rid of all the joysticks and controls and provides a convenient and economical solution bringing a sense of independency into their lives. Necessary precautions will be incorporated to ensure the safety of the person.
Assessing the Value of 3D Software Experience with Camera Layout in Virtual Reality

Preproduction is a critical step in creating 3D animated content for film and TV. The current process is slow, costly, and creatively challenging, forcing the layout director (LD) to interpret and create 3D worlds and camera directions from 2D drawings. Virtual reality (VR) offers the potential to make the process faster, cheaper, and more accessible. We conducted a user study evaluating the effectiveness of VR as a preproduction tool, specifically focusing on prior 3D modeling experience as an independent variable. We assessed the performance of experienced 3D software participants to those with no experience. Participants were tasked with laying out a camera shot for an animated scene. Our results revealed that the experienced 3D software participants did not significantly outperform their non-experienced counterparts. Overall, our study suggests that VR may provide an effective platform for animation pre-production, "leveling the playing field" for users with limited 3D software experience and broadening the talent pool of potential LDs.
Inferential Semantics, Paraconsistency, and Preservation of Evidence

Proof-theoretic semantics provides meanings to the connectives of intuitionistic logic without the need for a semantics in the standard sense of an attribution of semantic values to formulas. Meanings are given by the inference rules that, in this case, do not express preservation of truth but rather preservation of availability of a constructive proof. Elsewhere we presented two paraconsistent systems of natural deduction: the Basic Logic of Evidence (BLE) and the Logic of Evidence and Truth ($$LET_{J}$$). The rules of BLE have been conceived to preserve a notion weaker than truth, namely, evidence, understood as reasons for believing in or accepting a given proposition. $$LET_{J}$$, on the other hand, is a logic of formal inconsistency and undeterminedness that extends $$BLE$$ by adding resources to recover classical logic for formulas taken as true, or false. We extend the idea of proof-theoretic semantics to these logics and argue that the meanings of the connectives in BLE are given by the fact that its rules are concerned with preservation of the availability of evidence. An analogous idea also applies to $$LET_{J}$$.
Segmentation of Prostate Peripheral Zone based on Multi-scale Features Enhancement

According to research statistics that there are about 70% of Prostate Cancer (PCa) has been occurred in Peripheral Zone (PZ). Automatic segmentation of the prostate peripheral zone on T2 weighted (T2w) images is a necessary clinical applications for prostate cancer diagnosis. However, the low contrast, blur contour, and significantly varies of shape serious challenges to accurate segmentation of peripheral zone. In this paper, we first present a deep learning method to segment the peripheral zone automatically. For extracting and encoding multi-level features about peripheral zone more effectively, multi-scale dilated convolution (MDC) and pooling block (MPB) were embedded in the baseline model U-Net. Then, we integrated a soft attention mechanism to focus on the salient features useful for segmentation of PZ. In addition, the joint adversarial loss could enhance the model performance of recognition. Experimental results show that the proposed method yield satisfactory segmentation. The mean DICE coefficient arrived at about 88% as compared to the ground truth. The quantitative and qualitative evaluation demonstrates that the modified generative adversarial network is more effective.
A Practical Image Encryption Algorithm for Privacy Protection

In this paper, we propose a practical image encryption algorithm for privacy protection and analyze its security. This algorithm will generate a permutation on the pixels of the image and need not share a permutation list for the sender and receiver anymore. The parameters of the algorithm are correlative to the size of the image and can be generated with the image. The image encryption algorithm is suitable for image outsourcing on cloud computing platform, image secret transmission in Wireless Sensor Network (WSN) and image transmission in Unmanned Aerial Vehicle (UAV) photography.
Comparison of Dynamic Bayesian Network Tools

In this paper we review and compare several state-of-the-art Dynamic Bayesian Network (DBN) software tools. Bayesian networks are probabilistic graphical representations used to build models from data and/or expert opinion. DBNs are extensions of Bayesian networks with temporal support to model systems with dynamic behavior. DBNs are utilized in a wide range of applications including robotics, data mining, speech recognition, digital forensics, protein sequencing, and bioinfor-matics. Existing DBN software tools differ in terms of features support, ease of use, documentation, users community, etc. We establish various metrics for selecting the proper software tools for creating and simulating DBNs, such as cost, licensing, GUI, built-in support for inference algorithms, structural learning, data types, etc. We provide a comprehensive evaluation and comparison of these tools for building DBNs based on the above set of user centered criteria.
Multi-script Text Detection from Images: A Survey

Textual information in images contains small but crucial information. Text detection is first necessary step to extract text from images. It has applications including intelligent car driving system, translation, information retrieval and many more. Therefore as a result, it is broadly studied. Detecting texts from images is a complex problem due to background, varied font size, style, orientation and transparency of text. Detecting multilingual text from images adds further complexity to computer vision problem that is primary to a collection of prominent applications. Since India is a multilingual country, multi script texts appears almost everywhere. The universal features for multi script environment are unknown due to different format, height and strokes of languages. Therefore detecting multi script text from images is an important yet unsolved problem. This paper provides a survey on available techniques to detect multi script text from images.
Interactive Text Graph Mining with a Prolog-based Dialog Engine

On top of a neural network-based dependency parser and a graph-based natural language processing module we design a Prolog-based dialog engine that explores interactively a ranked fact database extracted from a text document. We reorganize dependency graphs to focus on the most relevant content elements of a sentence, integrate sentence identifiers as graph nodes and after ranking the graph we take advantage of the implicit semantic information that dependency links bring in the form of subject-verb-object, “is-a” and “part-of” relations. Working on the Prolog facts and their inferred consequences, the dialog engine specializes the text graph with respect to a query and reveals interactively the document’s most relevant content elements. The open-source code of the integrated system is available at https://github.com/ptarau/DeepRank .
Cochran Q-Test for Criteria Validation of PeGI Framework

Abstract The research objective is to find out the valid criteria of the PeGI framework in evaluating the application of e-Government in Indonesia. The method used was a survey with the Cochran Q-Test approach involving 5 respondents. The results showed that there were 3 invalid PeGI criteria because the Cochran value was greater than the Chie table so that they were eliminated from the list of criteria thus leaving only 32 valid of PeGI criteria. The implications of the research can provide recommendations for government agencies to use the more relevant PeGI framework.
A Mean Field Games Approach to Cluster Analysis

In this paper, we develop a Mean Field Games approach to Cluster Analysis. We consider a finite mixture model, given by a convex combination of probability density functions, to describe the given data set. We interpret a data point as an agent of one of the populations represented by the components of the mixture model, and we introduce a corresponding optimal control problem. In this way, we obtain a multi-population Mean Field Games system which characterizes the parameters of the finite mixture model. Our method can be interpreted as a continuous version of the classical Expectation–Maximization algorithm.
Integrating collaborative topic modeling and diversity for movie recommendations during news browsing

Purpose Online news websites provide diverse article topics, such as fashion news, entertainment and movie information, to attract more users and create more benefits. Recommending movie information to users reading news online can enhance the impression of diverse information and may consequently improve benefits. Accordingly, providing online movie recommendations can improve users’ satisfactions with the website, and thus is an important trend for online news websites. This study aims to propose a novel online recommendation method for recommending movie information to users when they are browsing news articles. Design/methodology/approach Association rule mining is applied to users’ news and movie browsing to find latent associations between news and movies. A novel online recommendation approach is proposed based on latent Dirichlet allocation (LDA), enhanced collaborative topic modeling (ECTM) and the diversity of recommendations. The performance of proposed approach is evaluated via an online evaluation on a real news website. Findings The online evaluation results show that the click-through rate can be improved by the proposed hybrid method integrating recommendation diversity, LDA, ECTM and users’ online interests, which are adapted to the current browsing news. The experiment results also show that considering recommendation diversity can achieve better performance. Originality/value Existing studies had not investigated the problem of recommending movie information to users while they are reading news online. To address this problem, a novel hybrid recommendation method is proposed for dealing with cross-type recommendation tasks and the cold-start issue. Moreover, the proposed method is implemented and evaluated online in a real world news website, while such online evaluation is rarely conducted in related research. This work contributes to deriving user’s online preferences for cross-type recommendations by integrating recommendation diversity, LDA, ECTM and adaptive online interests. The research findings also contribute to increasing the commercial value of the online news websites.