Reviewerly/exHarmony huggingface dataset:
Apla Generic Constraint Matching Detection and Verification

The core of object-oriented programming (OOP) is the object, while the core of generic programming (GP) is the type requirement. Generic programming can improve the reusability, security and development efficiency of programs. Generic constraints can prevent run-time crashes of generic programs. Generics constraints and their matching mechanism in the mainstream programming languages are studied. The abstract degree of the current mainstream languages is not high enough to describe the complex dynamic semantic requirements. Abstract generic programming language Apla is used as the host language to propose a generic constraint method based on the type requirements of complete GP, including static syntax generic constraint and dynamic semantic generic constraint. A general generic constraint description language is given, a matching detection algorithm is designed to determine whether the static syntax generic constraints meet the requirements, and a matching verification mechanism is designed for dynamic semantic generic constraint verification. Finally, the whole process of matching detection and verification is demonstrated by taking Kleene algorithm as an example. The generic constraint mechanism can improve the security of the program. It further implements the GP concept with the type requirement as the core in the full sense.
A Bag of Constrained Visual Words Model for Image Representation

We propose a bag of constrained visual words model for image representation. Each image under this model is considered to be an aggregation of patches. SURF features are used to describe each patch. Two sets of constraints, namely, the must-link and the cannot-link, are developed for each patch in a completely unsupervised manner. The constraints are formulated using the distance information among different patches as well as statistical analysis of the entire patch data. All the patches from the image set under consideration are then quantized using the Linear-time-Constrained Vector Quantization Error (LCVQE), a fast yet accurate constrained k-means algorithm. The resulting clusters, which we term as constrained visual words, are then used to label the patches in the images. In this way, we model an image as a bag (histogram) of constrained visual words and then show its utility for image retrieval. Clustering as well as initial retrieval results on COIL-100 dataset indicate the merit of our approach.
Comparison of Skewness Normalization Methods on Javanese Script Documents Input

The scan of a text document that is visually quite perpendicular to its axis is very important. Correction operations on image slope will make the image visually perpendicular. This paper will compare the method of tilt detection both moment-based, center of gravity, and hough transformation on a printed image of a modern Javanese text document. The results of this study will be very useful in the line segmentation of document images which are part of the transliteration of Javanese script text documents. The data used in this study is the result of scanning Javanese literary books entitled “Hamong Tani”. On each page of the scan, the slope angle of the coordinate axes is tested using the three selected slope detection methods. The next step is the line segmentation process using the vertical projection method to see the success of each method in making slope corrections. Based on the result of comparation shown that the Hough Transform methode work better more than two methods else.
Expert Knowledge Elicitation for Skill Level Categorization of Attack Paths

Attack graphs deduce the attack paths based on the identified vulnerabilities, the existing network topology, and the applied network access controls. The exploitation likelihood of the paths derived from the Common Vulnerability Scoring System (CVSS) values of the vulnerabilities provides an important input to risk assessments. This paper focuses on the identification of attacker skill levels required for exploiting the attack paths. First, we elicited expert knowledge for the determination of skill level categories and their detailed descriptions. Second, we systematically applied the elicited knowledge to the attack graphs. This skill level categorization method can provide a significant contribution to the design of hands-on offensive cyber games as it enables to balance the skills of participants and difficulty of game tasks. It also improves the threat analysis capability of organizations by demonstrating the possible infiltration ways of threat actors depending on their skill levels.
Length Limiting of Quantum Key Distribution at Two-Stage Synchronization

The synchronization subsystem is investigated for the quantum key distribution (QKD), where photon pulses are used as sync signals. The analyzed two-stage synchronization algorithm is based on the fact that the pulse-repetition period and the duration of the optical sync signals are known. Analytical expressions are obtained that establish the functional relationship between the energy parameters and probability characteristics of the single-photon synchronization subsystem, and the parameters of an optical fiber, a transmitting optical module, and a single-photon avalanche photodiode (SPAD). The requirements for the choice of the steps number in the search stage and the allowable tests number that guarantee the maximum length of the communication line are formulated. It is shown that for actually used single-photon avalanche photodiodes, the length of the fiber-optic line is limited due to the frequency of generation of dark current pulses (DCP)by a value of 51 km with a synchronization error probability of 0.05.
Salient Object Detection via Light-Weight Multi-path Refinement Networks

Recently, deep learning-based saliency detection has achieved fantastic performance over conventional works. However, repeated subsampling operations in deep CNNs lead to difficulties for full resolution prediction and accurate prediction at boundaries of salient regions. In this paper, we use Light-Weight Multi-Path Refinement Networks (RefineNet) for image saliency detection task, an encoder-decoder architecture that explicitly exploits all the information available along the down-sampling process to enable full resolution prediction using long-range residual connections. The squeeze and excitation residual network (SE-ResNet) is adopted as our baseline network to better extract multi-level and multi-scale feature maps according to the resolutions. Furthermore, we proposed our end-to-end network architecture and reduced the parameters by more than 50% while maintaining the same performance. We carry out comprehensive experiments and set new state-of-the-art results on four public datasets. By contrast, our method is highly competitive.
TraceFilter: An Exploitability Analyzer of Vulnerabilities in Binary Code

Although the exploitation of vulnerabilities is a common method of attack, it is not always possible for program developers to eliminate all vulnerabilities before deployment. Fortunately, critical security behaviors such as gaining control of programs are enabled only by the input-associated vulnerabilities, which are used by experts in analyzing the severity of vulnerabilities. However, traditional methods based on dynamic analyses inherently suffer from performance problems. This paper introduces a new method of reverse-taint analysis to check if a vulnerability is related to user inputs. By adopting a static analysis as well as the simplified dynamic analysis, our analysis performs better than conventional analysis methods in this area.
Executive information system lecturer mobility and scientific reputation of higher education

Abstract Lecturer mobility and scientific reputation is one of the key indicators for the establishment of world class university. The total number of papers published in the academic journals indexed by Elsevier’s Scopus database per scholar, and scholarly publications are one of the parameters for universities in the world are ranked. Those are challenges for the Executives Boards in universities to develop strategies of the existing conditions in order to plan programs to support the vision of universities. Executive Information System is required to display information strategically based on the existing data conditions. The present research identified a web-based Executive Information Systems called SIMORI. It is the system development method using System Development Life Cycle (SDLC) approach through system investigation step with feasibility study, system analysis, design analysis, and implementation. Critical factors for the success of the system are good data governance, while the data currently displayed still need to be completed and improved. The strategy to be developed will be managing mobility data of scientific reputation by involving all Faculties and the Departments of university to up-date data through Academic Management Information System, to be verified by the executive and will be presented as strategic information for the university communities.
Modelling Social Networks Using Modified Hopfield Neural Network and Identify Leadership Traits

This paper aims at studying human behaviour in a group using centrality measures applying Modified Hopfield Neural Network, and analyse the change in group dynamics when chronological event sequences are altered. Further, to understand the personality traits unique to leaders and how to influence people with the right marketing strategy. The simulation considers the intragroup dynamics of centrality where every individual is considered to have a unique personality. Individuals are represented as a node, and the effect of the change in intragroup dynamics are simulated using modified Hopfield Neural Network and Hebb's rule. Our results show that every chronological change in the events can affect a groups opinion and influence the choice of leaders. Also, that the leaders (node with the highest centrality measures) have unique personality trait which comprises a rare combination of adaptability α, retention ρ, and forgetfulness γ.
Non-Canonical Topography of the z-Plane Discretized due to Quantization of the IIR Digital Filter Coefficients

It is known that all possible positions of the zeros and poles of IIR digital filters with finite word length form some discrete structure in z-plane. In this paper, this structure is called the z-plane topography. Discretization of the z-plane is explained by the fact that, due to quantization of the filter coefficients, not any of its points can be a zero or a pole. In their previous publications, the authors showed that for practically implemented digital filters, zeros and poles are elements of the algebraic number set. It was also shown that the topography of the sampled z-plane is completely determined by the degree of algebraic numbers and the bitness of fractional part of the transfer function coefficients. The corresponding topography is proposed to be called canonical. At the same time, structures are known for which the topography differs from the canonical one. For them, the name of non-canonical topography is suggested. These structures include the structure known as the coupled form, the normal form, the structure of Gold and Raider. This article is devoted to the study of such structures. It may seem that this is a completely new topography, not connected with the canonical one. However, this is not true. This paper establishes the relationship between the canonical topography and the topography of the coupled and other structures. It is shown that the coefficients of such structures are not independent. They are related by some additional equations. As a result, the number of possible zeros and poles is reduced. But the remaining zeros and poles are elements of the set that make up the canonical topography.
Intrinsic Decomposition by Learning from Varying Lighting Conditions

Intrinsic image decomposition describes an image based on its reflectance and shading components. In this paper we tackle the problem of estimating the diffuse reflectance from a sequence of images captured from a fixed viewpoint under various illuminations. To this end we propose a deep learning approach to avoid heuristics and strong assumptions on the reflectance prior. We compare two network architectures: one classic ‘U’ shaped Convolutional Neural Network (CNN) and a Recurrent Neural Network (RNN) composed of Convolutional Gated Recurrent Units (CGRU). We train our networks on a new dataset specifically designed for the task of intrinsic decomposition from sequences. We test our networks on MIT and BigTime datasets and outperform state-of-the-art algorithms both qualitatively and quantitatively.
A Semantic-Based Short-Text Fast Clustering Method on Hotline Records in Chengdu

As one of the main channels for citizens to reflect urban management issues, the urban hotline can collect many problems and occurrences about the city, such as noise nuisance and illegal buildings. However, traditional manual statistical methods have been adopting to analyze the topics of the urban hotline records, and few automatic analysis models or software on topic mining of urban hotline records has been documented. In order to automatically analyze the massive amount of information on urban hotline, we propose a semantic-based short-text fast clustering method to cluster short texts of semantic similarity to form long texts according to the similarity of semantic-based keywords set, and the latent dirichlet allocation (LDA) model is then applied to mine the topics distribution of urban hotline records. Experiments on 87,055 urban hotline records from 2017 to 2018 in Chengdu show that our approach can achieve a significantly better performance both in accurate and topic coherence than LDA method.
Object-oriented pseudo-random data generator realizations

Abstract With the development of computational science and computer simulations, the randomness source has become a necessary part of every complex system and testing environment. Most programming languages, software frameworks and application libraries do not provide all different types of randomness generators and do not even implement high-quality algorithms for secure data generation. This paper presents an object-oriented solution for providing all the main classifications of randomness via random data generator classes that offer frequently used formats in information technology and in statistical hypothesis testing. It also presents usages for secure data generation and confirms the importance of true randomness in cryptography. The purpose of the developed practical experiments is to evaluate and show the differences in the sample generation quality of the sub-classifications of software randomness generators using the developed object-oriented components.
User Identification Cross Multiple Social Media Platform with Revised Input Output Network Embedding Framework

The rapid development of various online applications has brought the network into the era of socialization, which facilitate people's life. Plenty of people own multiple accounts on diverse social media network (SMN), like Facebook, Twitter and Instagram. In order to grasp the user's behavior characteristics comprehensively, it is necessary to identify the target user across SMNs. In this paper, we develop a model to matchup accounts from different SMNs, based on the Input-Output Network Embedding Algorithm. We firstly represent the nodes from SMNs into the embedding space with the follower-ship and followee-ship of users. Second, with the hard constraint from anchor links and soft constraint from the candidate anchor users across SMNs, we align nodes from two networks in the embedding space. We also use Non-negative Matrix Factorization (NMF) to generate empirical counterparts of the node representation required in Input Output Network Embedding (IONE) framework without using additional features of users besides their relationship in SMN. To reduce time complexity, we set learning rate automatically adjusted in stochastic gradient descent. Based on the data collected from real world SMNs, we proceed several experiments and the results show the improvement of the user identification efficiency the model provided.
Graph-Based Scale-Aware Network for Human Parsing

Recent work has made considerable progress in exploring contextual information for human parsing with the Fully Convolutional Network framework. However, there still exist two challenges: (1) inherent relative relationships between parts; (2) scale variation of human parts. To tackle both problems, we propose a Graph-Based Scale-Aware Network for human parsing. First, we embed a Graph-Based Part Reasoning Layer into the backbone network to reason the relative relationship between human parts. Then we construct a Scale-Aware Context Embedding Layer, which consists of two branches to capture scale-specific contextual information, with different receptive fields and scale-specific supervisions. In addition, we adopt an edge supervision to further improve the performance. Extensive experimental evaluations demonstrate that the proposed model performs favorably against the state-of-the-art human parsing methods. More specifically, our algorithm achieves 53.32% (mIoU) on the LIP dataset.
Design a fuzzy inference system to determining superior commodities for create a village medium-term development plan

Abstract The Village RPJM, also known as the Village Medium Term Development Plan, is something that must be prepared very carefully. The Village RPJM, especially the Sukadamai village, currently uses intuition alone, even the data held by the village head is still not updated. So that the Village RPJM does not provide maximum results. Therefore, it is necessary to make an intelligent system that can help the village head in determining the potential commodities in Sukadamai village. The latest data with high precision was obtained with the help of the IPB PSP3 using drones and participant assistance. This intelligent system uses a Fuzzy Inference System (FIS) to determine the commodities that have potential in the town of Sukadamai. The input variables used in this study are 10. For the potential of the land and market potential, each of the 27 rules and for the potential of the citizens to produce 64 rules. In the next FIS process, it produces 27 rules to get a potential commodity, so the total rules obtained are 145 rules. Hopes that with this intelligent system, in making the Village RPJM, the village government can be more focused on developing superior commodities in the Sukadamai village.
Hybridisation of oppositional centre-based genetic algorithms for resource allocation in cloud

Cloud computing is an attractive computing model since it allows for the provision of resources on-demand. In cloud computing, resource allocation is one of the challenging problems; because when the clients want to allocate the resource to particular task while attaining minimum cost. To overcome the problem, in this work we introduce a novel technique for resource allocation in cloud computing using oppositional centre-based genetic algorithm. For optimisation, we hybridise the centre-based genetic algorithm with opposition-based learning (OBL), where OBL is improving the performance of the centre-based genetic algorithm while optimising the bi-objective function. The main aim is to assign the corresponding resources to each subtask within the minimum cost. The generated solution is competent to the quality of service (QoS) and enhances IaaS suppliers' believability. For experimentation, we compare our proposed hybrid algorithm with GA and CGA algorithm.
Selection of the Threshold for the First-Order Cyclostationarity Test

Automatic modulation classification has significant commercial and military applications and is one of the most challenging problems of the cognitive radio. To demodulate a captured signal, it is necessary to know the parameters of the signals and the preliminary information about the type of modulation. However, in most cases, the signal has no prior knowledge of the modulation type. Even more, there may not be a modulation pool available for possible modulation types. Cyclostationary-based features can be employed to blindly identify the unknown modulation type. In this study, a detailed theoretical analysis of the first order cyclostationarity was performed on FSK and AM signals, which are known to be first-order cyclostationary. Theoretical relationship between the threshold and the constant false alarm rate detector is derived for the first order cyclostationarity test. Theoretical analyses are validated by simulation results.
A Note on the Issue of Cohesiveness in Canonical Models

Abstract In their presentation of canonical models for normal systems of modal logic, Hughes and Cresswell observe that some of these models are based on a frame which can be also thought of as a collection of two or more isolated frames; they call such frames ‘non-cohesive’. The problem of checking whether the canonical model of a given system is cohesive is still rather unexplored and no general decision procedure is available. The main contribution of this article consists in introducing a method which is sufficient to show that canonical models of some relevant classes of normal monomodal and bimodal systems are always non-cohesive.
Formant Text To Speech Synthesis Using Artificial Neural Networks

Speech is a collection of words formed by voice. Simulation of human speech can be done by assuming fundamental frequency as glottis (source of sound) and the formant frequencies as the exacting arrangement of the speech organs, that is, on the location of the jaw, the tongue and the oral cavity. In this way, the human speech production system can be viewed as a source-filter model of speech production. This research has focused on to generate formant frequencies of any Punjabi sound and create a speech from a Punjabi text. The aim of this research is to synthesize speech waveform corresponding to input Punjabi text using formant synthesis technique. Python is used to create the model of speech generations and synthesis. The features of phonemes are extracted from recorded wave files, and those extracted features are stored in a database and generated formant frequencies for Punjabi sounds. Total 722 phonemes are taken for this research and 104 recorded wave files. The performance of the model was tested on the various parameters which are Formants, Fundamental frequency, voicing amplitude, Duration, Pitch, and Intensity. All these parameters were compared for better results and results indicated that these parameters in combination give better results.
Formal Specification and Verification of Smart Contracts

Smart contracts can be regarded as one of the most popular blockchain-based applications. The decentralized nature of blockchain introduces vulnerabilities absent in non-distributed programs. Furthermore, it is very difficult, if not impossible, to patch a smart contract after it is deployed. Therefore, smart contracts must be formally verified before they are deployed on the blockchain. In this work, we study the formal specification and verification of smart contracts.
Real-time Route Re-planning based on Modified Particle Swarm Optimization Algorithm

In this paper, the problem of three-dimensional real-time route replanning for Unmanned Aerial Vehicl(UAV) is formally defined, and the route penalty degree is used to evaluate the route. A modified particle swarm optimization algorithm (M-PSO) is proposed to solve the real-time route replanning problem. The Simulation results show that the modified particle swarm optimization algorithm has better real-time and stability in solving the 3D real-time route replanning problem. It can quickly search for routes that can avoid all threat and make the individuals in the population reach convergence state.