Reviewerly/exHarmony huggingface dataset:
Measuring Software Product Quality using ISO 9126: A Systematic Review

ISO 9126 software quality model of 2001 is the most comprehensive and widely accepted model.It is a generic quality model that is used in measuring quality of software across computing domains.This study seeks to access the level of application of ISO 9126 model in measuring software quality and its impact on different software domains.We employed a standard methodology for systematic literature review using automated search on four digital libraries for studies published between 2001 and 2016.The results of the systematic review reveal that the model as a whole has played a significant role in measuring software quality across different domains.Out of total 63 primary stud-ies, 30 applied the six characteristics, and 33 papers applied one or more characteristics for measuring software quality.Software component and database are the most evaluated domains, while usability characteristics are the most measured quality.This is the result of the fact that software vendors are moving towards quality user-centred design instead of technology driven designs.Ambiguity resulting from the lack of clear guideline and operational instrument for evaluation is the major shortcoming of this quality model, but were favored by many due to its flexibility to suit the growing software domains.
Route Planning of Unmanned Aerial Vehicle Based on Sparse A* Algorithm

Considering the matters of accuracy, real-time, task adaptability and dynamic re-planning for the route planning of unmanned aerial vehicle. When the unmanned aerial vehicle is planning its route on a largescale map, it needs to ensure better real-time performance. If new obstacles are encountered during flight, the route planning algorithm needs to be able to carry out dynamic planning to re-plan the route to avoid the new obstacles. This paper presented an optimized route planning of unmanned aerial vehicle algorithm, which mainly through the management of number of search nodes and stratification strategy while still maintaining a safe route. We also presented a dynamic route re-planning function when encountering new obstacles by updating the map. The algorithm has better real-time performance and robustness. It has great reference value for the practical application of unmanned aerial vehicle.
The Effects of Phonological rules Instruction Program Combining Reading and Spelling on the Literacy Improvement of Multicultural Children

본 연구의 목적은 읽기 및 철자쓰기를 통합한 음운규칙 프로그램이 다문화가정 아동의 읽기 정확도와 철자 정확도에 미치는 효과를 검증하는 데 있다. 이를 위해 초등학교에 재학 중인 네 명의 다문화가정 아동이 본 연구에 참여하였으며, 중다기초선 설계법의 응용인 지연된 중다기초선 설계를 적용하여 단일대상연구를 수행하였다. 본 연구에서는 한국어의 음운규칙 중 8개의 음운규칙을 12단계의 학습 내용으로 구성하여 체계적으로 중재하였다. 연구결과, 대상 아동들은 각 단계의 음운규칙에서 높은 습득도와 진전도를 보였으며, 음운규칙 단어의 읽기 및 철자 정확도가 큰 폭으로 향상되었다. 뿐만 아니라, 참여 아동들은 중재를 통해 학습한 음운규칙을 중재에 포함되지 않은 음운규칙 단어에도 일반화하였다. 이러한 중재 효과는 중재 종료 한 달 후에도 유지되었으며, 읽기 및 철자쓰기를 통합한 음운규칙 프로그램이 음운규칙 단어의 읽기 및 철자 정확도 향상에 효과적임을 보여주었다. 이와 같은 결과는 교육현장에서 다문화가정 아동들에게 본 연구의 프로그램이 유용하게 활용될 수 있음을 시사한다.The purpose of this study was to examine the effects of the Korean phonological rules instruction program combining reading and spelling on multicultural children s Korean reading and spelling accuracy. Because Korean pronunciation doesn t match with spelling, multicultural children have difficulty in reading and spelling the Korean words that changes according to phonological rules. As a systematical approach, the intervention consisted of 12 stages of 8 phonological rules. The intervention participants were four elementary multicultural children. Single subject research design based on delayed multiple baseline across participants was conducted for the study. The results of the study can be summarized as follows. First, the four elementary children participants showed high level of phonological rule acquisition and progression at each stage along with improved accuracy of reading and spelling Korean words that change according to the phonological rules. Second, applying Korean phonological rules were generalized to other words that were not included in the intervention program, and the ability to generalize the learned phonological rules revealed to be maintained even twenty-seven days after the termination of the intervention. The results of this study indicated that the phonological rules instruction program combining reading and spelling was effective in improving the accuracy of reading and spelling Korean words with phonological change. These findings provide useful evidence for teaching multicultural children who have problems with reading and spelling Korean words with phonological change. Based on the study results, some limitations of the current study and implications for the further study are discussed.
Action Recognition from Low-Resolution Infrared Sensor for Indoor use: A Comparative Study between Deep Learning and Classical Approaches

In recent years, automatic action recognition has attracted a lot of attention in the field of computer vision. In this paper, action recognition in an indoor environment using low-resolution sensor is considered. In this environment, several studies have been carried out in visible imagery. Despite impressive performances, the main pitfall faced by the techniques developed is a marked restraint of the users to be filmed. To cope with the problems of personal identity revealed during the surveillance, we opted within the CoCAPS project for using a low resolution (64 x 64 pixels) infrared sensor with ceiling position which guarantees the respect of the intimacy of the person. Defined in collaboration with the industrial partners involved in the CoCAPS project, the scenarios targeted concern office situations and 7 classes of action have been selected, namely: no action, restlessness, sitting down, standing up, turning sitting on a seat, slow walking, fast walking. Classical approaches such as those based on the computation of shape descriptors (such as geometric and Color Histogram of Oriented Phases (CHOP)) extracted from Motion History Image (MHI) are investigated to represent action video sequences. Within this first group of classical approaches, the performance of a proposed model based on statistical attributes constructed from the tracking of centers of gravity of segmented forms is also presented. The comparative study is then completed by considering other models extracted from deep learning literature (convolutional neural networks (3D-CNN), Long Short Term Memory (LSTM)). The results obtained from the comparative study show that the proposed model is very competitive and provides promising results (83% of f-score).
On the Capacity of Weakly-Private Information Retrieval

Author(s): Jia, Zhuqing | Advisor(s): Jafar, Syed Ali | Abstract: The problem of weakly-private information retrieval, is a variant of private informationretrieval, where the user wants to retrieve 1 out of K messages from a distributed storagesystem with N servers that stores all K messages, and is willing to leak some informationof the identity of the desired message. In this work, we study the problem of weakly-privateinformation retrieval. A novel information leakage metric is proposed, and the capacity forthe setting of N = 2 servers, and arbitrary number of messages K is characterized. Inparticular, in the capacity achieving scheme, designing the distribution of the non-uniformlydistributed noise Z turns out to be the key to achieve the capacity.
Speech-Driven Facial Animation by LSTM-RNN for Communication Use

The goal of this research is developing a system that a rich facial animation can be used in communication is generated from only speech. Generally, a source of the generating facial animation is a camera. Using cameras as an input source, it causes limitations of the angle of view of the camera or problems that cannot be aware of the human face, depending on the orientation of the face. Therefore, it is reasonable for developing a system for generating a facial animation using only voice. In this study, we generate facial expressions from only speech using LSTM-RNN. Comparing 3 patterns of speech analysis data, we showed that the proposed method using A-weighting is effective for facial expression estimation.
Multi-dimensional K-Means Algorithm for Student Clustering

K-Means is one of the popular methods for generating clusters. It is very well-known and commonly used for its convenience and fastness. The main disadvantage of these criteria is that user should specify the number of cluster in enhance. As a repetitive clustering strategy, a K-Means criterion is very delicate to the preliminary beginning circumstances. In this paper, has been proposed a clustering strategy known as Multi-dimensional K-Means clustering criteria. This algorithm auto generates preliminary k (the preferred variety of cluster) without asking input from the user. It also used a novel strategy of establishing the preliminary centroids. The experiment of the proposed strategy has been conducted using synthetic data, which is taken form LIyod’s K-means experiments. The algorithm is suited for higher education for calculating the student’s CGPA and extracurricular activities with graphs.
Architectural decision forces at work: experiences in an industrial consultancy setting

The concepts of decision forces and the decision forces viewpoint were proposed to help software architects to make architectural decisions more transparent and the documentation of their rationales more explicit. However, practical experience reports and guidelines on how to use the viewpoint in typical industrial project setups are not available. Existing works mainly focus on basic tool support for the documentation of the viewpoint or show how forces can be used as part of focused architecture review sessions. With this paper, we share experiences and lessons learned from applying the decision forces viewpoint in a distributed industrial project setup, which involves consultants supporting architects during the re-design process of an existing large software system. Alongside our findings, we describe new forces that can serve as template for similar projects, discuss challenges applying them in a distributed consultancy project, and share ideas for potential extensions.
Smart Contracts, Distributed Ledgers, and the Need for an Algorithmic Financial Contract Standard

Nick Szabo defined smart contracts as, “….a set of promises, specified in digital form, including protocols within which the parties perform on the other promises.” The essence of a smart contract is that it is self-executing and has a protocol to effect this, i.e. the mechanism for communicating with the smart contract. In addition, Szabo distilled four basic objectives of any contract that should be fulfilled by smart contracts. Two of these, verifiability and enforceability, are given more attention in this paper. Szabo, however, did not address what types of contracts would be most suited to be executed as smart contracts.This paper describes first the unique aspects of financial contracts that make them the most promising candidates for implementation as smart contracts. Secondly, we analyze the conditions under which the use of distributed ledgers and smart financial contracts are most likely to prove successful. This analysis concludes that unless a distributed ledger is combined with an algorithmic financial contract standard and a standard protocol, the potential benefits of smart contracts will not be realized. The corollary to this conclusion is that it is essential for FinTech to adopt such a standard in order to be able to realize its promise of a paradigm shift in finance. The combination of distributed ledger technology and an open, well documented and well tested algorithmic financial contract standard is the next logical step in the development of FinTech.
Multi-depth Dilated Network for Fashion Landmark Detection

The topic of fashion landmark detection has become popular recently, especially with the development of convolutional neural networks. However, there are still some challenges that need to be addressed, including the detection of hard keypoints such as those which are occluded or invisible. To tackle this problem, we propose in this paper a novel Multi-Depth Dilated (MDD) block which is composed of different numbers of dilated convolutions in parallel, so that the MDD can effectively extract large-scale context information at different levels and hence obtain relative positional relationship information between keypoints, information which is necessary for the inference of hard keypoints. Furthermore, we use an Online Hard Keypoints Mining method for training to further boost the effectiveness of hard keypoints detection. Through the experiment, we demonstrate that by stacking the MDD blocks, we construct a Multi-Depth Dilated Network (MDDNet) that achieves state-of-the-art results on fashion benchmark datasets.
Security Analysis of Social Networking Based Authentication in Infrastructureless PAC Networks

IEEE 802.15.8 standard defines specifications and characteristics of infrastructureless peer-aware communications (PACs) with fully coordination in an ad hoc environment.The PAC aims at supporting social connectivity for broad types of PAC devices, especially lightweight Internet of things.Since no coordinator exists in the PAC, security is considered as one of the main challenges for a successful communication between PAC devices.In this status quo, social networking based authentication (SNAuth) protocol proved its advances by supporting multi-security levels for diverse PAC devices.In this paper, we conduct a comprehensive security analysis of SNAuth protocol in order to provide a convenient reference for PAC users to select appropriate SNAuth configuration based on their demands.
[DC] Case-studies of Contemporary Presence Theory: Towards More Objective and Reliable Measures of Presence

A large body of literature is concerned with models of presence-the sensory illusion of being part of a virtual scene-but there is still no general agreement on how to measure it in an objective and reliable way. When it comes to virtual reality, presence is often considered as one of the main factors contributing to quality of experience, yet existing methods either rely on subjective assessments of users or on specifics of the virtual environment they are applied in, making it difficult for experimental procedures to be generalized. This paper presents ideas for research into promising measures of presence, based on first experiments with novel behavioral measures inside a rich environment which users can feel present in more naturally.
Cyclotomic Trace Codes

A generalization of Ding’s construction is proposed that employs as a defining set the collection of the sth powers ( s ≥ 2 ) of all nonzero elements in G F ( p m ) , where p ≥ 2 is prime. Some of the resulting codes are optimal or near-optimal and include projective codes over G F ( 4 ) that give rise to optimal or near optimal quantum codes. In addition, the codes yield interesting combinatorial structures, such as strongly regular graphs and block designs.
Evaluating state-of-the-art classifiers for human activity recognition using smartphones

Human activity recognition using smartphones and wearables is a field gathering a lot of attention. Although a plethora of systems have been proposed in the literature, comparing their results is not an easy task. As a universal evaluation framework is absent, direct comparison is not feasible. This paper compares state-of-the-art classifiers already used on mobile human activity recognition, under the same conditions. In addition, an Android application was developed and the method yielding the best results was evaluated in real world in a semi-supervised environment. Results shown that deep learning techniques have better performance and could be transferred to a phone without many modifications.
ERAC-MAC efficient revocable access control for multi-authority cloud storage system

In the current scenario, there is an appreciable escalation in the utilisation of cloud computing by critical industrial applications due to its cost-effective storage and computing. However, due to an untrusted server in a cloud, the access control turns out to be the challenging issue to ensure the confidentiality of sensitive data. The ciphertext policy attribute-based encryption (CP-ABE) is deliberated to be an apt technique to enforce the access control for encrypted cloud outsourced data. But, due to the computation complexity of decryption, user revocation and complexity of key management for achieving granularity, prevailing CP-ABE schemes when applied directly to multi authority attribute system, incurs more computational costs in the order of NP. In this paper, an efficient CP-ABE based multi authority attribute system is put forth that supports decryption and user revocation by CSP with the advent of a blind encryption/decryption technique and a novel colouring scheme for predicting user behaviour analysis. Security and performance of ERAC-MAC was analysed and found to be much better than the other prevailing schemes. The implementation was done using the paring based cryptography library of the Stanford University in Ubuntu environment.
Design of Multimedia Engineering Teaching System Based On Internet of Things Technology

In order to improve the multimedia control performance of teaching, a multimedia engineering teaching system design model based on internet of things technology is proposed. The overall structure model of the real estate engineering multimedia teaching system design is constructed based on the embedded ARM core. The system integration control is implemented through the decentralised control method, and the overall design framework of the real estate engineering teaching system is carried out. The internet of things networking technology is used for system network design, the VIX bus technology is used to carry out real estate engineering teaching data communication and information processing, and the software design of the multimedia engineering teaching system is realised in embedded environment. The test results show that adopting this method to design the multimedia engineering teaching system can provide better robustness, stronger human-computer interaction, and can realise multimedia integrated control of teaching system.
Complexity estimation by using multiparadigm approach: a proposed metrics and algorithms

The factors upsetting directness of a software code were calculated manually using traditional approach which is not suitable in these days. There are numerous software tools are available in the market but they are not reliable due to vast number of metrics are available and each and every metrics are not specific definitions. This paper investigated factors which are affecting for understanding the procedural and object oriented code and found that software metrics can be calculated by using the web-engineering technology especially multiparadigm language. The most important parts of this paper are propped algorithms for estimating an object-oriented metrics. The planned metrics were empirically authenticated in diverse paradigms. The exigent task of this paper is all about the evaluation strategy of an object-oriented metrics by using multiparadigm language (JavaScript). The proposed model is developed to analyse the CK metric suite. This model extracts the number of classes available in the Java Script programs. Basically this model works in two different phases. In first phase the parser is able to find and generate the full abstract syntax tree and second stage the identification of the classes and generated the metrics itself.
Modeling and Analysis of Innovative Path of English Teaching mode under the Background of Big Data

In order to improve the efficiency of the best way to choose innovative English teaching mode, this paper proposes an innovative path model of English teaching mode based on ant colony algorithm. The innovation strategy of English teaching mode is analysed, then the influence index of English teaching mode is analysed, the weight of index is calculated, and the objective function is constructed according to the influence index of English teaching mode. On this basis, the ant colony algorithm is used to find the best path of English teaching mode innovation and the modelling and analysis of English teaching mode innovation path is completed under the background of big data. The experimental results show that the proposed method has higher efficiency in selecting the best path, and the time for searching for the optimal path is shorter, which has a good convergence speed.
An Improved FastIMM Algorithm Based on Fading Memory Filter Model

In this study, a novel target tracking algorithm is proposed with a better tracking performance for the case where the measurement noise and target arrange are high and target maneuvers are intense. The proposed FastIMM-FM algorithm has been shown to have a better position estimation than the FastIMM algorithm and the FastIMM-imp algorithm. Also, FastIMM-FM algorithm has less computational cost than the other two algorithms.
A Bayesian Nonparametric Model for Unsupervised Joint Segmentation of a Collection of Images

Jointly segmenting a collection of images with shared classes is expected to yield better results than single-image based methods, due to the use of the shared statistical information across different images. This paper proposes a Bayesian approach for tackling this problem. As a first contribution, the proposed method relies on a new prior distribution for the class labels, which combines a hierarchical Dirichlet process (HDP) with a Potts model. The latter classically favors a spatial dependency, whereas the HDP is a Bayesian nonparametric model that allows the number of classes to be inferred automatically. The HDP also explicitly induces a sharing of classes between the images. The resulting posterior distribution of the labels is not analytically tractable and can be explored using a standard Gibbs sampler. However, such a sampling strategy is known to have poor mixing properties for high-dimensional data. To alleviate this issue, the second contribution reported in this paper consists of an adapted generalized Swendsen-Wang algorithm which is a sampling technique that improves the exploration of the posterior distribution. Finally, since the inferred segmentation depends on the values of the hyperparameters, the third contribution aims at adjusting them while sampling the posterior label distribution by resorting to an original combination of two sequential Monte Carlo samplers. The proposed methods are validated on both simulated and natural images from databases.
Active Learning with Bayesian Nonnegative Matrix Factorization for Recommender Systems

Active learning is a method of analyzing the observed data such that choosing the next observation will give the most information about the variable to be predicted. However, when observations are costly, one needs strategies to obtain informative data to arrive at accurate predictions with less data. In this study, we compare various observation sequence selection strategies on the matrix completion problem. We used Gibbs Sampling and Variational Bayes as inference mechanisms on the MovieLens dataset. Our results suggest that the Gibbs sampler coupled with the selection of the element with minimal observations on a row and column is the superior approach for the Bayesian Nonnegative Matrix Factorization (NMF).