Reviewerly/exHarmony huggingface dataset:
A Secure Protocol for Remote-Code Integrity Attestation of Embedded Systems: The CSP Approach

No doubt, a person of modern society relying on Embedded Systems (ESs) has increased rapidly and the era of digital machines is gaining popularity among users and also systems providers. At the same time, such instruments face substantial security challenges because they usually operate in a physically unprotected environment, and thus attract the attackers to gain unauthorized access for utilizing the system functions. Accordingly, system integrity is important and hence there is a need to propose a technique/tool to verify that the original/pure systems codes have been used in those devices. In this research, our main objective is to design a system architecture with a secure communication for code integrity attestation of an ES. Indeed, the study presents the proposed system architecture for ESs integrity attestation which includes two main phases: fetching an ES code at a server site and examining the ES at a remote site (using a designed user application). Essentially, the hash function (SHA-2) with a random key to calculate a unique digest value for a targeted system have been utilized. Also, the study used timestamps and nonce values, two secure keys, and public key algorithm to design a secure protocol in-order to prevent potential attacks during data and the associated values transfer between the server and the remote user application. As many researchers state that the formal methods are very precise and accurate for presenting system specifications; this study modeled and analyzed the proposed attestation protocol using the Communicating Sequential Processes (CSP) formal method approach. Besides, the Compiler for the Analysis of Security Protocols (Casper) has been used to translate the protocol description into the corresponding process algebra CSP model. Then, the researcher used the Failures Divergences Refinement (FDR) to evaluate the proposed protocol. Those formal method tools are considered as a reliable verification measurement in-order to figure-out potential flaws and correct them. Overall, the final output of checking all the defined secrecy and authentication assertions using FDR 4.2.0, and thus all the secrecy and authentication specifications defined in the developed Casper script are passed.
SMS (Social-Media-Speak) as/for/in creative practice

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Research on Evaluation Index System for Software Vulnerability Analysis Methods

Due to the diversity of existing software vulnerability analysis methods, a unified index system is required if we are to accurately and comprehensively evaluate the analytic effects in order to further improve the level of analysis. Accordingly, this paper first proposes the principles and process necessary to construct an evaluation index system for software vulnerability analysis. Moreover, by focusing on the problems identified in previous relevant research, an evaluation index system for software vulnerability analysis methods that contains four aspects-accuracy, coverage, value and performance-is proposed; this system is more comprehensive and complete than existing achievements. Finally, using the vulnerability analysis testing framework, the indexes of the three different versions of cppcheck are measured. The results show that the proposed evaluation index system can be used for the comparative evaluation of different vulnerability analysis methods.
Implementation of Automatic Adjustment of Font Size System on Smartphone

Adjusting the font size is a function of the smartphone for improving readability. This smartphone is easier to use than other function but is less useful due to the psychological factors experienced by users, such as annoyance or fear of malfunction. Adjusting the font size is difficult when you are on the move or cannot use both hands to hold the smartphone. Therefore, we need a system that can automatically adjust the font size under any circumstances to improve readability. In this paper, we implement a system, in which the font size is automatically adjusted according to the distance between the user and the smartphone, without additional device operations. By automatically adjusting the font size, this system can increase readability while reducing difficulties in operation.
Productivity-based Features from Article Metadata for Fuzzy Rules to Classify Academic Expert

Since modeling expertise is necessary in an expert recommendation system, this paper addressed the issue to obtain researcher expertise in the academic field on certain topic interest. The profile considers productivity and dynamicity of an expert. The productivity of research activities through published articles as research output determine expertise that changes over time to indicate the dynamicity aspect. Here, the resulted expertise status on certain topic interest augments the expert profile. However, the expertise status is unavailable in the expert finder dataset. This paper discussed on approaches to classify the status from features of productivity and dynamicity in the form of fuzzy rules, which can be applied later in the expert recommendation system. Then, the approaches include of determining topics, mapping expertise candidates, extracting features, and labeling expertise status for training to generate fuzzy rules. Because of unavailable expertise status, to get better labels, the results of linear model and clustering were compared. Based on the empirical experiments, rules trained from scaled data with expertise labels from fuzzy clustering gave better results. After simplifying the rules, if-then forms with two features were representable enough for identifying the status of specialist or thriving experts on a topic interest.
CARGo: A Prototype for Contextual Annotation and Reconciliation of Goal Models

We propose a tool prototype that allows the evolution of goal models in changing business environments. Like jUCMNav, the tool supports annotated goal models where goals, tasks, and resources may be annotated with specific contexts. Softgoal context annotations and their interpretations are quite complex and left out for the time being. The tool performs a qualitative evaluation for identifying conflicts between annotations. It also performs reconciliation of each conflict and presents the user with conflict-free goal model alternatives.
Study on the Mobility Performance of Wheelchair sing Smartphone Accelerometer

Abstract Wheelchair is very important as a means of transport by a person who is unable to walk as a result of illness or injury. On the other hand, smartphones have become common device that allow users to remotely control many electrical and electronic appliances. This paper presents a study on the mobility performance of wheelchair using smartphone accelerometer. The powered wheelchair is presented by a two-wheeled robot car which emphasized on the control layout platform for the smartphone’s tilting directions with suitable tolerance values. A basic motion’s algorithms for the movement of robot car have been developed, and its mobility performances on the cornering and straight-line direction are analysed.
A Novel Block Based Triangulation method to improve Video Stabilization

In Videos the motion based estimation technique the main factor and more complex process is the feature extraction. This is done by comparing the features in the adjacent frames in order to find the similarity Depending on the area of the image the complexity increases and the key feature extraction is complex and time consuming. The complexity of key point extraction is proportional to the area of the images (frames). This paper, we develop an block based feature extraction technique. Reducing the key features will reduce the accuracy of the image. Similarity is measured by analyzing the all edges of the image (frame with adjacent frame). The main challenge is the moving object while extracting features. This can be overcome by inter block feature matching. The local motion objects can be minimized by selecting three point in each image and its adjacent image. Mismatch is always inevitable in motion estimation no matter how powerful the feature point locator is. To get rid of this challenges, we propose a novel idea of forming a triangle based on three best features. The pixel values of the triangle formed in two images is accumulated and matched. It will guarantees the removal of faulty features. This will suit for only clear images not for images with defects like blur or shakes or overlapped or collapsed ones. we have best matched features, using that we calculate the global motion vectors. Videos are stabilized with the smoothened image which is done with the available filter in order to remove interferences and noise.
A Sampling-Based Framework for Transductive Classification in Information Networks

Knowledge extraction from large information networks has received increasing attention in recent years. Among existing methods for knowledge extraction, transductive classification is a well-known semi-supervised learning method, where both labeled and unlabeled vertices are used in the learning process. However, transductive classification tasks become impractical in large information networks and the use of network sampling techniques in the transductive classification setting is not a trivial task, since it is required that all the vertices of the original network be classified during the transductive learning - and not only the vertices of the sample. In this paper, we present a framework called TCSN (Transductive Classification for Sampled Networks). TCSN allows the use of various network sampling techniques, as well as enables the use of various methods of transductive classification for information networks. We present a variation of the Chernoff Bounds method to calculate the minimum size of a sampled network, thereby bounding sampling error within a pre-specified tolerance level. Moreover, TCSN extends the concept of evidence accumulation to combine the results of several rounds of transductive classification into a final classification. Experimental results from different information networks reveals that TCSN statistically outperformed the classification performance in the whole original network. These promising results show that the TCSN enables transductive classification in large information networks without loss of quality in the knowledge extraction process.
Design of Predictive Control Structures to Track Trajectories for Multi-rotor Unmanned Aerial Vehicle

This paper presents the synthesis of four model predictive controllers for a multi-rotor unmanned aerial vehicle to track trajectories. Three control structures are used to control the state variables: x, y, z positions and the yaw angle. For the first structure, a centralized control loop is used and two control strategies are proposed. The first strategy uses the outputs described above, while the second one increases the outputs with the state variables: roll and pitch angles. The second and third control structures use a non-centralized control loop. The second one splits the control loop into a cascade structure with a master and a slave loops. The last structure combines a master model predictive control strategy with a slave PD-P control combination. In both cases, the slave loop controls the new reference state variables roll and pitch angles, given by the master loop. The development of each controller is accomplished by changing the set points and is later tested by tracking a square trajectory. For a 1 [m] step change in x or y position, the system response has a setting time of around 1.44 [s], 1. 45[s], and 2.74 [s], with an overshooting of approximately 1.4%, 0% and 2.2% for each structure, respectively. For a 1 [m] change in z position, the setting time is 1.94 [s] and the overshoot is 2.88% for the first two structures. While for the last one, the setting time is 3 [s] without overshoot. For a half turn change of the yaw angle, the setting time is 1.74 [s] for the first two structures and 4.16 [s] for the last one, all of them without overshoot. Finally, disturbances are included to test the robustness of the control strategies tracking a square trajectory. Based on these results, the conclusion is that the first and second structures have the best performance.
A Deep Neural Network Model for Rating Prediction Based on Multi-layer Prediction and Multi-granularity Latent Feature Vectors

Recommender systems have attracted abundant research in the past decades. Side information is generally used besides the rating matrix to alleviate the data sparsity problem for recommendation models. To achieve better performance, in recent years deep learning (DL) technique has been introduced to recommendation models. It can be noted that most existing recommendation models incorporating DL technique only use one layer as the learned features; and the learned features for all users/items have the same dimension despite the fact that different users/items have different numbers of ratings. The aforementioned issues have negative impact on the performance of these recommendation models. To address the issues, in this paper we propose a Deep neural network model based on Multi-layer prediction and Multi-granularity latent feature vectors (DMM model). The DMM model has two features: (1) A user or an item is represented by multiple latent vectors with different granularity, which can better describe the relationships between users and items. (2) Each layer in the DMM model produces a predicted rating for given user and item, then the overall rating is calculated by combining all these predicted values, which ensures fully use of the information in rating matrix and side information and thus may result in better performance. Experimental results on three widely used datasets demonstrate that the proposed DMM model outperforms the compared models.
Semiotic analysis of narrative legal texts using asymmetric document divergency

Abstract In the paper the case is studied then semiotic signs can be represented as language constructs in the same language as the text for the interpretation. The goal is to obtain estimates of the depth of interpretability with the respect to each of the signs by finding the projections of the narrative on these language constructs. That is, we find the probability of the presence of each of the signs. By use the embedding of linguistic constructions into a multidimensional numerical vector space and the grid layout, which is the image of the system of signs. At the stage of the inference, our model computes the nodes of the grid, which are the most adequate to the input text by the method of new proposed asymmetric similarity. The constructed model was studied using Russian criminal, civil and labour legislation. We suggest using the models called the constellation as for the grid of normative acts and the narrative together with the corresponding divergence. In this paper, exanimated the pre-trained models known in NLP as doc2vec and Fast Text. The evaluation of the quality of models was carried out using open databases of court decisions.
Exact Algorithms for the Bounded Repetition Longest Common Subsequence Problem

In this paper, we study exact, exponential-time algorithms for a variant of the classic Longest Common Subsequence problem called the r-Repetition Longest Common Subsequence problem (or r-RLCS, for short): Given two sequences X and Y over an alphabet S, find a longest common subsequence of X and Y such that each symbol appears at most r times in the obtained subsequence. Without loss of generality, we will assume that $$|X| \le |Y|$$ from here on. The special case of 1-RLCS, also known as the Repetition-Free Longest Common Subsequence problem (RFLCS), has been studied previously; e.g., in [1], Adi et al. presented an (exponential-time) integer linear programming-based exact algorithm for 1-RLCS. However, they did not analyze its time complexity, and to the best of our knowledge, there are no previous results on the running times of any exact algorithms for this problem. In this paper, we first propose a simple algorithm for 1-RLCS based on the strategy used in [1] and show explicitly that its running time is bounded by $$O(1.44225^{|X|}|X||Y|)$$. Next, we provide a DP-based algorithm for r-RLCS and prove that its running time is $$O((r+1)^{|X|/(r+1)}|X||Y|)$$ for any $$r \ge 1$$. In particular, our new algorithm runs in $$O(1.41422^{|X|}|X||Y|)$$ time for 1-RLCS, which is faster than the previous one.
Role of Crime and Criminal Tracking Network System (CCTNS) in Crime Control: A Study of Haryana State

The dawn of Information and Communication Technology (ICT) has altered the planet, therefore it had been compulsory that such technologies ought to be utilized as a catalyst to manage the changing and growing character of crimes. It had been launched with the intention to enhance policing and criminal justice system via e-governance and also to make a comprehensive and integrated platform for strengthening the efficacy and effectiveness of policing through e-governance from the nation.
The geometric properties of an infinitary line and plane languages

Formal grammar is an area of discrete mathematics dealing with syntactic techniques of generation of words and their properties. The concept of Parikh vector (PV) and Generalized Parikh vector (GPV) play important role in the combinatorial properties of words under formal grammars. The concept of Generalized Parikh Vector introduced by Siromoney et al. gives the positions of symbols in linear strings. It has been proved that GPVs of the strings of the same length lie on a hyper plane. As any formal language over binary letters are represented geometrically, the study done in this paper explore geometrical properties of lines induced by corresponding languages. The study is extended over three alphabet set giving rise to plane objects.
Examination of Higher Education Management Systems in the Digital Era

Abstract This article deals with application of the exam information system in higher education. The guarantee system over quality in higher education implemented by referring to customer satisfaction, National Standards on Education and demands for the development of the digital era. The solution offered is developing thesis and dissertation exam information system. Research method: Reseach and Development approach by applying Borg and Gall model (2006). The system manages the document from the time the document is created or received until it is set up to be saved. This system done as an effort to improve services to college students, appropriate decision making by the manager and also improved performance on procedures and better methods. Effectiveness and efficiency become the principle of performance measurement of the most important archiving and access systems. The results of the development show the product performance can be utilized for the management of thesis and dissertation exams throughout higher education.
Research on Entity Recognition Method in Knowledge Base Question Answering

Based on the analysis of the existing knowledge base question answering (KBQA) system, it is found that the open domain KBQA in Chinese lacks support for multi-relational question answering (MRQA). Based on the knowledge base (KB) provided by NLPCC-ICCPOL 2016, MRQA in Chinese domain is explored in this paper. The KBQA process is divided into three steps: entity recognition, entity relation extraction and answer retrieval. Entity recognition is the focus of this paper. A deep leaning model is introduced to identify multiple entities in the question, and the method of similarity calculation is used to link and disambiguate the identified entities with terms in the KB. The experimental results show that the method can support MRQA with a higher average F1 score.
Multi-agent modeling of the learning process using nested colored Petri nets

Abstract A model of the educational process of the university in the form of a two-level multi-agent system is implemented in the form of an embedded colored Petri nets using the CPN Tools software. The system allows a statistical modeling of the development of the educational program as a single student or group of students by evaluating the success of training. The average grade point and dropout rate are assessed both for passing one academic subject and the entire educational program. The initial data for modeling are taken on the basis of the experience of teaching students with a close profile of training and expert assessments of specialists. The structure of the system and the functioning of Petri nets are considered. An example is given illustrating the operation of the system.
Visual Attribute Manipulation Using Natural Language Commands

In this paper, a novel setting is tackled in which a neural network generates object images with transferred attributes, by conditioning on natural language commands. Conventional methods for object image transformation have used visual attributes, which are components that describe the object's color, posture, etc. This paper builds on this approach and finds an algorithm to precisely extract information from natural language commands, which transfers the attributes of an image and completes this image translation model. The effectiveness of our information extraction model is experimented, with additional tests to see if the change in visual attributes is correctly seen in the image.
A Software Testing Strategy Based on a Software Product Quality Model

Getting software testing to meet the needs of the organization is not an easy task, it goes beyond identifying problems. It also implies achieving a common understanding of the domain on the part of those involved in the project, and being able to face the current technological changes as part of the context. To this end, a strategy was defined for the evaluation of a software product based on the ISO 25000 quality standard and program tests directed by use cases. For this work, surveys were carried out with experts in the domain. The quality model proposed by the ISO 25010 standard was extended in accordance with current technological requirements, such as the cloud computing paradigm. In addition, metrics and indicators were defined in order to carry out a study, analysis and evaluation of the problem being studied: migration to the cloud of the business processes of a company in the environment, case study for the practical validation of the proposed strategy.
Implementation of a Quality Measurement Software for Virtual Reality Content on a head mount display

In a VR streaming content, an application software is necessary to control both a content quality on a streaming service and a display quality on a head mount display (HMD) according to loads for head mount display, computer facilities and network considering a user's requests. In this paper, we focus about a quality measurement software for Virtual Reality (VR) content to control display quality on a HMD and discussabout its implementation.
Polymeric Integrated Optics for Measuring Quantum-Entangled Photon States

Quantum-entangled photons have drawn considerable attention for application in quantum cryptography and quantum computers. The optical systems used for measuring polarization-entangled states mostly consist of free-space optical components, which are bulky and sensitive to environmental changes and requires persistent optical alignment. In this study, we propose a solution for the stable measurement of the quantum entanglement of photons based on polymeric waveguide integrated optics. The proposed system consists of two quarter-wave plates inserted in a polymeric optical waveguide, an asymmetric X-junction optical waveguide, four birefringence modulators, and two polarizing beam splitters. The basic performance of the integrated optic device has been confirmed using a 1550-nm distributed feedback laser. High polarization conversion and splitting efficiencies are demonstrated, which are important for the measurement of entangled photon states.