Reviewerly/exHarmony huggingface dataset:
Formalizing the dependency pair criterion for innermost termination

• Dependency pairs criterion and verification of termination of rewriting systems. • Automating termination of rewriting and functional programs. • Formalization of termination of first-order term rewriting systems. • Innermost reduction and termination of rewriting. • Formally proving the soundness of rewriting with the PVS proof assistant. Rewriting is a framework for reasoning about functional programming. The dependency pair criterion is a well-known mechanism to analyze termination of term rewriting systems. Functional specifications with an operational semantics based on evaluation are related, in the rewriting framework, to the innermost reduction relation. This paper presents a PVS formalization of the dependency pair criterion for the innermost reduction relation: a term rewriting system is innermost terminating if and only if it is terminating by the dependency pair criterion. The paper also discusses the application of this criterion to check termination of functional specifications.
Selection Driven Query Focused Abstractive Document Summarization

The current encode-attend-decode paradigm suffers from noisy encoder problem. We implemented a seq2seq model consisting of a novel selective mechanism for query focused abstractive document summarization using neural networks to solve this problem. Selective mechanism was used for the better representation of input (passage) sequence. We conducted experiments on Debatepedia dataset and have demonstrated that our model outperforms the state-of-the-art model in all ROUGE scores.
Gaussian Mixture PHD Filter with State-Dependent Jump Markov System Models

The Gaussian mixture probability hypothesis density (GM-PHD) filter with jump Markov system (JMS) models assume that mode transition probabilities are constant, irrespective of the target state. However, in some applications (e.g., air traffic control), the mode transition of the target is dependent on its state and thus the mode transition probabilities are a function of the target state. This paper proposes a multiple model GM-PHD filter with state-dependent mode transition probabilities which are represented as Gaussian probability density functions. The performance of the proposed algorithm is illustrated with an example in an air traffic control application.
A learning-based approach for rigid image registration accuracy estimation

This work presents the machine learning approach to estimate the accuracy of pixel (or intensity) based image registration algorithms. The considered method is applied to the problem of image-based navigation but can be used in a variety of registration tasks. Training was conducted on the basis of twenty one features that extracted from the pair of registered images. For creating the train data a Monte Carlo simulation under four different models of subpixel shifts is considered. The RMSE (root mean square error) of registration estimates (along two axes) is used as target. The fragments of real terrain images were used in experiments. The bias-variance analysis of registration errors is conducted. Proposed method was compared with one theoretical (Cramer-Rao bound) and one simulation (bootstrap) approaches. Comparison showed the superiority of the proposed method to the existing state-of-the-art-methods.
Optimizing the Digital Customer Journey – Improving User Experience by Persona-Based and Situation-Aware Adaptations

This paper discusses a novel approach for combining persona-based and situation-aware software adaptation methods for individualizing some of the touch points of the digital customer journey and thereby optimizing customer experience and effectiveness of e-commerce applications. Our approach uses emotion recognition, eye-tracking, and other individual tracking methods for adapting interactive web applications by accessing a flexible adaptation framework at runtime. The framework provides model-based support and allows for individualization at runtime by applying situation-aware adaptations. The approach is demonstrated with examples from a commercial beauty-products e-business portal.
Automatic extraction of Uyghur domain concepts based on multi‐feature for ontology extension

In the internet age, as a conceptual model of knowledge organisation, ontology has become a research hotspot. Ontology extension achieves the purpose of expanding ontology by adding new concepts and discovering the relationships between concepts in the existing ontology. In order to improve the automation and accuracy of ontology concept extraction in the Uyghur language, here the authors propose a new method to automatically extract concepts from text collection. For the characteristics of the Uyghur domain ontology concept, the text preprocessing is performed first, then the inter-word correlation of multi-feature fusion is calculated, such as Mi, Cd and Ea, and finally, the domain terminology and concept are automatically extracted, according to the term frequency–inverse document frequency algorithm. Experiment results show that, in terms of precision and recall rate, the multi-feature method proposed here represents an improvement over other methods. It also proves the feasibility and effectiveness of the authors’ method to extract the domain concept.
FEW-SHOT VIEWPOINT ESTIMATION

When an image is projected from 3D, the viewpoint of objects in the image, relative to the camera, must be determined. Since the image itself will not have sufficient information to determine the viewpoint of the various objects in the image, techniques to estimate the viewpoint must be employed. To date, neural networks have been used to infer such viewpoint estimates on an object category basis, but must first be trained with numerous examples that have been manually created. The present disclosure provides a neural network that is trained to learn, from just a few example images, a unique viewpoint estimation network capable of inferring viewpoint estimations for a new object category.
On the modulo degree complexity of Boolean functions

For each integer m≥2, every Boolean function f can be expressed as a unique multilinear polynomial modulo m, and the degree of this multilinear polynomial is called its modulo m degree. In this paper we investigate the modulo degree complexity of total Boolean functions initiated by Parikshit Gopalan et al. [9], in which they asked the following question: whether the degree complexity of a Boolean function is polynomially related with its modulo m degree. For m be a power of primes, it is already known that the module m degree can be arbitrarily smaller compare to the degree complexity (see Section 2 for details). When m has at least two distinct prime factors, the question remains open. Towards this question, our results include: (1) we obtain some nontrivial equivalent forms of this question; (2) we affirm this question for some special classes of functions; (3) we prove a no-go theorem, explaining why this problem is difficult to attack from the computational complexity point of view; (4) we show a super-linear separation between the degree complexity and the modulo m degree.
Dual Stage Normalization Approach Towards Classification of Breast Cancer

Breast cancer is a major concern among women that causes high risk of death. Early diagnosis of such cancer becomes challenging due to alterations in the color of the histopathological breast images. This study uses a publicly available dataset of breast cancer histopathology images. This paper introduces a dual stage normalization approach, to address the color variation problem of biopsy specimen collectively caused by incompatible staining in biopsy process and bizarre imaging quality. The dual stage normalization proposed here consists of a stain normalization unit and a light normalization unit. This system addresses the variations of both imaging and staining of specimen that are caused by a microscopic imaging setup. Later on, eight features have been extracted from the normalized images and used for the classification of breast cancer (benign and malignant). The overall accuracy of the back propagation algorithm (BPA) classifier is obtained as 81.8%. After comparison with other classifier accuracies, BPA classifier is found to be acceptable. Recall and precision values are approximately 89% and 90%, respectively, which is acceptable. The saturation-weighted hue statistics produces balanced and uniform color hues for stain normalization. This statistic is powerful against variations in model parameters and unsusceptible to image subjects and achromatic colors. This normalization technique retains all histological data with an enhanced performance.
Shibboleths in the studio: Informal demarcation practices among audio engineers

Recording engineers have an interest in maintaining their roles as skilled professionals as compared to external competitors, which increasingly include unpaid amateurs and automated software tools. They do this through a variety of material-semiotic demarcation practices. Formal modes of demarcation, such as unionization and professional attire, have largely eroded in recent decades, making informal practices increasingly important. These informal demarcation practices, which I term 'shibboleths', allow engineers to locally observe and perform differences between 'real' engineers and non-engineers (amateur and automated) while also controlling the visibility of these performances for various audiences. I situate the shibboleth concept within the existing literature on boundary objects and boundary-work, suggesting that it is useful for analyzing situations where collaboration and consensus temporarily break down. I consider two examples: electrical audio cable wrapping techniques and hearing the artifacts of digital vocal tuning software.
Aspect‐based requirements mining technique to improve prioritisation process: multi‐stakeholder perspective

Requirement prioritisation and selection is an essential activity in modern-day large software development. Optimal prioritisation process is critical for successful implementation and release planning in a software development project. Requirement prioritisation becomes more challenging in projects having large sets of requirements and stakeholders, having diverse perspectives resulting in irrelevancy and ambiguity during features extraction. This study aims to improve requirement prioritisation process using text mining and clustering techniques for accurate extraction of features and requirement prioritisation in multi-stakeholder context. The proposed framework developed to avoid incompleteness in requirements and disagreement among development teams and stakeholders. Thus, the proposed framework compared with other requirement prioritisation techniques (i.e. Analytical Heretical Process, Commutative Voting and Wiegers) to highlight the significance of the proposed framework while conducting an experimental study. The results show that the proposed framework outperformed the traditional techniques and enhanced the prioritisation process with complete semantic information of extracted features and taking into account the diverse perspective of stakeholders.
XSSD: A Cross-site Scripting Attack Dataset and its Evaluation

Cross-site Scripting (abbreviated as XSS) attacks are application level code injection attacks where a malicious user injects malicious scripts into the legitimate code of a Web application used by the victim. To defend against such attacks, a number of defense mechanisms have been proposed over the years. The evaluation of the efficiency and the accuracy of a defense mechanism requires the use of a suitable relevant dataset. The unavailability of such an XSS feature dataset is a bottleneck in conducting research. To overcome this problem, we propose a data preparation framework, the result of which is an XSS feature dataset, referred to as XSSD (XSS Dataset). The dataset preparation framework consists of three stages and four modules, all of which are essential to support extraction of several URL-based and script-based features. We evaluate the dataset we generate with the help of five benchmark classifiers, and validate classification results in terms of ROC.
Canonicalizing Knowledge Bases for Recruitment Domain

Online recruitment industry holds large amount of user-generated content in the form of job postings, resumes etc. This content finds its way in the knowledge bases (KB) causing duplicate and non-standard representations of entities (like company names, institute names, designations, skills etc.) These non-standard entity representations impact various applications such as search, recommendations and information retrieval. Therefore, KB canonicalization i.e, mapping multiple references of same entities into unique clusters is imperative for online recruitment platforms. Research suggests various approaches that use enriched semantic context or external context (from sources like Freebase) to perform KB Canonicalization. In fields where such external sources of context do not exist the problem remains challenging. To address these challenges, we propose a novel deep Siamese architecture with character-based attention and word embeddings that (a) estimates pairwise similarity between all entity mentions, and (b) then uses these similarity (scores) to create canonical clusters representing unique entity in the KB. Our experiments on recruitment domain dataset comprising of 62,288 unique entities of various types such as companies, institutes, skills, and designations demonstrate the effectiveness of our approach. We also provide insights on different network architectures, each of which encapsulate a different set of variation while performing canonicalization.
An Enhanced Data Compression Algorithm

Data in today's world is the most important asset one can have, but with it comes the issue of handling it properly. To increase data portability, facilitated by reducing the size of data to be stored due to limited storage space, a strong need for data compression has arisen. A lot of ways have been defined for data compression; since the 1950's we have been digging up and devising many algorithms to modify data compression techniques and over the years, many seemingly powerful algorithms have been developed. With a wide range of compression techniques available, there comes the need to choose the right method for text compression, which gives good results in lesser time. In this paper, we present a hybrid approach to compress text data. This hybrid approach encompasses various methodologies like Run Length Encoding (RLE), Infix Encoding and Bit reduction techniques. We aim to overlay different techniques to achieve the best possible compression ratio for large text files and dictionary data.
AI Surveillance UGV

Security and surveillance are the prime focus for any organization that chooses to be safe from any kind of physical threats. Installation of video surveillance systems can cost organizations a big portion of their financial budget and can lead to significant changes in their network infrastructure. The purpose of our research is to minimize the hurdles in setting up the video surveillance system into organizations that has open areas, that cannot afford to let any outsider to get into their networks and that need many costly cameras to cover their entire place. By installing cameras on an unmanned ground vehicle (UGV) that can move around in a specified area using geographic coordinate system, by smartly choosing its own path for its automatically generated destinations within the area. The UGV can also be manually controlled by the means of analog transmission within specified range. The video is transmitted using analog video transmitter and can be received over a specific channel using the analog video receiver, and can be observed using the developed software on any computer. Software developed for the ground control station can smartly identify an employee or an outsider using deep learning model trained on the faces of the employees and can alert the organization on its own when it detects any intrusion. Our research increased the range of wireless video surveillance and reduced the financial and architectural barriers for installing video surveillance into the organization.
A Multi-Stream Recurrent Neural Network for Social Role Detection in Multiparty Interactions

Understanding multiparty human interaction dynamics is a challenging problem involving multiple data modalities and complex ordered interactions between multiple people. We propose a unified framework that integrates synchronized video, audio, and text streams from four people to capture the interaction dynamics in natural group meetings. We focus on estimating the dynamic social role of the meeting participants, i.e., Protagonist, Neutral, Supporter, or Gatekeeper. Our key innovation is to incorporate both co-occurrence features and successive occurrence features in thin time windows to better describe the behavior of a target participant and his/her responses from others, using a multi-stream recurrent neural network. We evaluate our algorithm on the widely-used AMI corpus and achieve state-of-the-art accuracy of 78% for automatic dynamic social role detection. We further investigate the importance of different video and audio features for estimating social roles.
Role of sub-trajectories in online signature verification

In this paper, we have provided a partitioned based technique which can increase the efficiency of an existing technique in which each partition is called as a sub-trajectory. To implement it, eighty features are extracted from signature trajectories and categorized into four feature sets as static, kinematics, structural and statistical. We have used these four feature categories and their possible combinations on two different algorithms. An important outcome is observed as the EER decreases with increase in sub-trajectories to an optimum level and behaves in reverse direction afterwards which suggests that for any matching algorithm, the present technique can further reduce the error rate to an optimum level. These experiments are performed on the benchmark database SVC 2004 TASK 2 which contains forty genuine signatures of each forty writers and forty skilled forgeries from five different writers. The experiments are discussed in detail for change in the EER with change in each subsequent sub-trajectory level for all feature sets and the results prove that the technique using sub-trajectories improving the EER by a significant average amount of 1.18 with increase in one sub-trajectory level for all the eighty features and 1.5 for the features of categories kinematics and structural when taken together.
The Efficient use of Blockchain for Reducing Frauds in Parental Property Distribution

Blockchain has pulled in numerous individuals from different industry as the desire is it can address a few computing issues particularly in decentralized condition. The blockchain innovation presently can’t seem to be completely comprehended where usage, for example: Parental Property Distribution, Academic System, Travel Support Tracking System etc. This paper proposed property distribution approach using blockchain where all of the distribution will be automatic and immutable although modifiable with the desire of parents. The proposed approach is not being limited to property only but also can be applicable to all assets of family as well as power system.
SartCyber Security Awareness Measurement Model (APAT)

Critical Information Infrastructures are constantly under the threat from various forms of cyber-attacks. Over the past few years adversaries have found ways to develop malwares which are specifically designed to hit the target and have also been quite successful as well. This also shows that adversaries are much ahead in terms of their strategy and are technically skilled to outpace the traditional controls. An analysis of last few cyber-attack trend provides a very insightful information that most of the attacks are initiated by targeting the end user. Hence end user is seen as a weak link. Also in most of the organizations cyber security trainings are limited to security team members without giving much importance to the normal users. Therefore if employees are not trained enough to recognize a security threat, they cannot be expected to avoid it, report it or remove it. Employees need cybersecurity awareness and training to protect themselves and the company against new evolving cyber-attacks. By making employees aware of new forms of security threats and what measures to follow when a suspicious activity is identified, we can then strengthen the most vulnerable links in the chain.
Caveats in Eliciting Mobile App Requirements

Factors such as app stores or platform choices heavily affect functional and non-functional mobile app requirements. We surveyed 45 companies and interviewed ten experts to explore how factors that impact mobile app requirements are understood by requirements engineers in the mobile app industry.
I-Privacy Photo

The ever-increasing popularity of Online Social Networks (OSNs) sites for posting and sharing photos and videos has led to unprecedented concerns on privacy violation. The available Online social networking (OSNs) sites offer a limited degree of privacy protection solutions. Most of the solutions focus on conditional access control meaning, allowing users to control who can access the shared photos and videos. This research study attempts to address this issue and study the scenario when a user shares a photo and video containing individuals other than himself/herself (public-level photos and videos). For privacy-preserving, the proposed system intends to support an automated human face recognition and filtering for public-level photos and videos. Our proposed approach takes into account the content of a photo and makes use of face filtering as a strategy to increase privacy while still allowing users to share photos. First, the proposed system automatically identifies a person face frame from a digital image or video. Next, it compares the detected face features to each face vectors stored in the application database. After face recognition step completed, the proposed system filters all un-known persons in the image. Conventual Neural Network (CNN) has been used for face detection step, while deep learning facial embedding algorithms has been used for the recognition. Both have shown high accuracy results in addition to the capability of being executed in real-time. For face filtering, Gaussian algorithm has been used for face blurring as it has been considered a very fast real-time algorithm which allow the user to control the blurring degree. Based on the obtained results after testing the system using three different datasets, we can conclude that our system can detect and recognize the faces in photos and videos using the improved Conventual Neural Network (CNN) for face detection with 91.3% accuracy and K-Nearest Neighbor (KNN) for the face recognition with 96.154% accuracy using I-Privacy dataset.
Blockchain in Enterprise: An Innovative Management Scheme Utilizing Smart Contract

Currently, with the increasing scale of the enterprise, a common tendency is that more convenient and intelligent management system is demanded for high security and maximum transparency. One feasible option is via blockchain, which acts as a transparent ledger. This paper is based on a successful project, which primarily aims at the development of smart contracts used for car insurance application. According to this result, the potential benefit of utilizing blockchain in commercial area can be demonstrated, meanwhile, a possible scheme in enterprise management could be inspired. This management model would replace manual operation, for instance, salary distribution, with smart contracts, which could satisfy both security and intelligence demand. This paper will briefly descript the blockchain project mentioned above and raise original method and conception concerning with blockchain based on existed management issues, as well as advantages and disadvantages.