Reviewerly/exHarmony huggingface dataset:
Kernel Fuzzy Clustering with Output Layer Self-Connection Recurrent Neural Networks for Software Cost Estimation

In the current world, the software cost estimation problem has been resolved using various newly developed methods. Significantly, the software cost estimation problems can be dealt with effectively with the recently grown recurrent neural network (RNN) than the other newly developed methods. In this paper, an improved approach is proposed to software cost estimation using Output layer self-connection recurrent neural networks (OLSRNN) with kernel fuzzy c-means clustering (KFCM). The proposed OLSRNN method follows the basics of traditional RNN models for integrating self-connections to the output layer; thereby, the output temporal dependencies are better captured. Also, the performance of neural networks is improved using the kernel fuzzy clustering algorithm to enhance software estimation results. Ultimately, five publicly available software cost estimation datasets are adapted to verify the efficacy of the proposed KFCM-OLSRNN method using the validation metrics such as MdMRE, PRED (0.25) and MMRE. The experimental results proved the efficiency of the proposed method for solving the software cost estimation problem.
CUPS: Secure opportunistic cloud of things framework based on attribute‐based encryption scheme supporting access policy update

Abstract The ever‐growing number of internet connected devices, coupled with the new computing trends, namely within emerging opportunistic networks, engenders several security concerns. Most of the exchanged data between the internet of things (IoT) devices are not adequately secured due to resource constraints on IoT devices. Attribute‐based encryption is a promising cryptographic mechanism suitable for distributed environments, providing flexible access control to encrypted data contents. However, it imposes high decryption costs, and does not support access policy update, for highly dynamic environments. This paper presents CUPS, an ABE‐based framework for opportunistic cloud of things applications, that securely outsources data decryption process to edge nodes in order to reduce the computation overhead on the user side. CUPS allows end‐users to offload most of the decryption overhead to an edge node and verify the correctness of the received partially decrypted data from the edge node. Moreover, CUPS provides the access policy update feature with neither involving a proxy‐server, nor re‐encrypting the enciphered data contents and re‐distributing the users' secret keys. The access policy update feature in CUPS does not affect the size of the message received by the end‐user, which reduces the bandwidth and the storage usage. Our comprehensive theoretical analysis proves that CUPS outperforms existing schemes in terms of functionality, communication and computation overheads.
Towards Visualizing Large Scale Evolving Clones

Software systems in this big data era are growing larger and becoming more intricate. Tracking and managing code clones in such evolving software systems are challenging tasks. To understand how clone fragments are evolving, the programmers often analyze the co-evolution of clone fragments manually to decide about refactoring, tracking, and bug removal. Such manual analysis is infeasible for a large number of clones with clones evolving over hundreds of software revisions. We propose a visual analytics framework, that leverages big data visualization techniques to manage code clones in large software systems. Our framework combines multiple information-linked zoomable views, where users can explore and analyze clones through interactive exploration in real time. We discuss several scenarios where our framework may assist developers in real-life software development and clone maintenance. Experts' reviews reveal many future potentials of our framework.
Characterizing Duplicate Code Snippets between Stack Overflow and Tutorials

Developers are usually unaware of the quality and lineage of information available on popular Web resources, leading to potential maintenance problems and license violations when reusing code snippets from these resources. In this paper, we study the duplication of code snippets between two popular sources of software development information: the Stack Overflow Q a significant number (31%) of answers that contained a duplicate code block were chosen as the accepted answer. Qualitative analysis reveals that developers commonly use Stack Overflow to ask clarifying questions about code they reused from tutorials, and copy code snippets from tutorials to provide answers to questions.
Mathematical Representations Mapping of High School Students after using Multimedia Learning Modules Assisted by an Android Smartphone

Abstract Physics is one subject that has various applications in everyday life. However, many students assume that physics is a difficult lesson because physics is difficult to understand. One key to understanding physics is a mathematical representation. The purpose of this study is to map the mathematical representation of students after using a multimedia learning module assisted by an Android smartphone. The most prominent errors of mathematical representation ability of students learning after using a multimedia learning modules assisted by an android smartphone are solving problem involving arithmetic operation, changing the CGS unit to SI units and the proper use of units in writing the final results.
Modeling Behavior Patterns with an Unfamiliar Voice User Interface

Voice User Interfaces (VUIs) are becoming increasingly popular. However, how VUIs can adapt to user differences remains insufficiently understood. We analyze usage data from a user study (n=50) where participants interacted with an unfamiliar VUI. Through automated clustering and statistical analysis, we present user models of their behavior patterns. We found user behavior can be grouped into three clusters: people who become proficient with the system and typically stay proficient while completing different tasks, people who exhibit an exploratory approach to completing tasks, and people who struggled to complete tasks. We discuss design implications based on these behavior clusters.
Anomaly Detection with Machine Learning Technique to Support Smart Logistics

Accurate planning and cost-effective management on product delivery are among key factors leading to the success of most manufacturing sectors in the current era of the fourth industrial revolution, or Industry 4.0. In this work, we focus our study on the development of a model based on machine learning technique to support smart logistics by detecting anomaly events on the big stream of electronic orders obtained from ubiquitous customers. We use the data-driven approach to build the order-anomaly model and present the built model as the classification and regression tree. Our model in a tree formalism is to be used as an automatic detector for unusual events inherent in the customers’ order stream. The anomaly events should invoke special care in the smart logistics environment that delivery tasks are performed in an automatic manner. Early detection of anomaly ordering events is expected to improve accuracy on delivery planning.
Using Boosted k-Nearest Neighbour Algorithm for Numerical Forecasting of Dangerous Convective Phenomena

The authors propose a boosted k-nearest neighbour algorithm for numerical forecasting of dangerous convective phenomena. The algorithm is applied for processing the output data of the numerical cloud model. The results show that boosted algorithm is able to predict the very fact of convective phenomena occurrence with the high accuracy, but it is not so good in distinguishing the specific type of convective phenomena. Comparison with the k-NN algorithm without boosting shows that boosting resulted in better accuracy of forecasting.
Speech Pause Patterns in Collaborative Dialogs

This chapter discusses the multimodal analysisKoutsombogera, Maria of human behavioral data from the big data perspective. Though multimodal big data bring tremendous opportunities for related applications, we present current challenges in the domain ofVogel, Carl multimodal and behavioral analytics. We argue that in the case of analysing human behavior in interaction, we need to shift to the analysis of samples, smaller datasets, before scaling up to large data collections. We describe a dataset developed to study group collaborative interaction from measurable behavioral variables. As a case study, we investigate speech pauses and their patterns in the data, as well as their relationship to the topics of the dialog and the turn-taking mechanism, and we discuss their role in understanding the structure of collaborative interactions as well as in interpreting the behavior of the dialog participants.
Text Retrieval Priors for Bayesian Logistic Regression

Discriminative learning algorithms such as logistic regression excel when training data are plentiful, but falter when it is meager. An extreme case is text retrieval (zero training data), where discriminative learning is impossible and heuristics such as BM25, which combine domain knowledge (a topical keyword query) with generative learning (Naive Bayes), are dominant. Building on past work, we show that BM25-inspired Gaussian priors for Bayesian logistic regression based on topical keywords provide better effectiveness than the usual L2 (zero mode, uniform variance) Gaussian prior. On two high recall retrieval datasets, the resulting models transition smoothly from BM25 level effectiveness to discriminative effectiveness as training data volume increases, dominating L2 regularization even when substantial training data is available.
Semantic Similarity Computation in Knowledge Graphs: Comparisons and Improvements

Computing semantic similarity between concepts is a fundamental task in natural language processing and has a large variety of applications. In this paper, first of all, we will review and analyze existing semantic similarity computation methods in knowledge graphs. Through the analysis of these methods, we find that existing works mainly focus on the context features of concepts which indicate the position or the frequency of the concepts in the knowledge graphs, such as the depth of terms, information content of the terms, or the distance between terms, while a fundamental part to describe the meaning of the concept, the synsets of concepts, are neglected for a long term. Thus, in this paper, we propose a new method to compute the similarity of concepts based on their extended synsets. Moreover, we propose a general hybrid framework, which can combine our new similarity measure based on extended synsets with any of existing context feature based semantic similarities to evaluate the concepts more accurately. We conducted experiments on five well-known datasets for semantic similarity evaluation, and the experimental results show that our general framework can improve most of existing methods significantly.
Cracking Randomized Coalescing Techniques with An Efficient Profiling-Based Side-Channel Attack to GPU

GPUs have been increasingly used to accelerate a wide range of general purpose applications, including the encryption/decryption algorithms. However, recent research has shown that GPUs can be vulnerable to side-channel attacks (SCAs). Aiming at protecting GPUs against these SCAs, the Randomized Coalescing (RCoal) techniques are proposed with proven effectiveness on security improvement. In this paper, we propose to leverage a Profiling-based Side-Channel Attack (pSCA) to suppress the effectiveness of the RCoal techniques on security improvement or force the most secure choice which inevitably results in substantial performance degradation. Without the detailed information of the RCoal configuration, the pSCA is still able to rebuild the secure key of a GPU-supported AES algorithm under the RCoal protection in a reasonable duration.
How much do cross-modal related semantics benefit image captioning by weighting attributes and re-ranking sentences?

Although image description generated from attributes achieves big progress, there are still two main problems to be solved further: (1) How to obtain more accurate attributes? (2) How to mitigate the difference between sentence generation and evaluation. To address these issues, we propose a new method to incorporate the cross-modal related semantics into the encoder-decoder structure for image captioning. In the encoding stage, we utilize the salient words derived from cross-modal retrieval to improve the accuracy of attributes. In the decoding stage, we explore two ways to re-rank the sentences generated through beam search with the guidance of semantics acquired through a modified cross-modal retrieval method. The evaluation results on the benchmark dataset MS-COCO in both offline and online prove the benefits of cross-modal related semantics on image captioning.
Towards compiling graph queries in relational engines

The increasing demand for graph query processing has prompted the addition of support for graph workloads on top of standard relational database management systems (RDBMS). Although this appears like a good idea --- after all, graphs are just relations --- performance is typically suboptimal since graph workloads are naturally iterative and rely extensively on efficient traversal of adjacency structures that are not typically implemented in an RDBMS. Adding such specialized adjacency structures is not at all straightforward due to the complexity of typical RDBMS implementations. The iterative nature of graph queries also practically requires a form of runtime compilation and native code generation which adds another dimension of complexity to the RDBMS implementation and any potential extensions.
Blockchain Combined with Smart Contract to Keep Safety Energy Trading for Autonomous Vehicles

With the development of the autonomous hybrid plug-in electric vehicles (AHEVs), majority of AHEVs are equipped with bidirectional chargers. However, during the energy trading process, the centralized schemes are exposed to many flaws like the high energy transmission loss and cannot resist the single-point failure. Therefore, it is more important to use the decentralized manner to keep the safety energy trading between AHEVs. In this paper, we propose a decentralized energy trading architecture in which AHEVs can broadcast their requirements to neighbors and negotiate with other AHEVs on the transmission parameters without a transaction server. The smart contract (SC) is deployed among RSUs which can be automatically executed after the negotiation process. To promote AHEVs to behave honestly on the road, we associate the trust value of AHEVs with the charging price. We use proof-of-concept to theoretically demonstrate the feasibility of the development of the SC and the security properties of the proposed scheme. The simulation results demonstrate the proposed negotiation scheme outperforms the non-negotiation scheme in the aspect of efficiency.
On the role of non-effective code in linear genetic programming

In linear variants of Genetic Programming (GP) like linear genetic programming (LGP), structural introns can emerge, which are nodes that are not connected to the final output and do not contribute to the output of a program. There are claims that such non-effective code is beneficial for search, as it can store relevant and important evolved information that can be reactivated in later search phases. Furthermore, introns can increase diversity, which leads to higher GP performance. This paper studies the role of non-effective code by comparing the performance of LGP variants that deal differently with non-effective code for standard symbolic regression problems. As we find no decrease in performance when removing or randomizing structural introns in each generation of a LGP run, we have to reject the hypothesis that structural introns increase LGP performance by preserving meaningful sub-structures. Our results indicate that there is no important information stored in structural introns. In contrast, we find evidence that the increase of diversity due to structural introns positively affects LGP performance.
Investigation into User-Centric QoE and Network-Centric Parameters for YouTube Service on Mobile Networks

The number of users on mobile networks has continuously increased due to many attractive multimedia services. According to this reason, the service operators have to regularly improve their networks in order to retain the need of existing users and also attract the expectation of new users. Nowadays, the operators concern Quality of Experience (QoE) metric as the key performance index to evaluate the networks rather than Quality of Service (QoS) metric. However, the QoE metric is based on the viewpoints of domain's experiences which the experiences from users and networks might not be correlated. Therefore, in this paper, the demonstration of user-centric and network-centric viewpoints based on QoS measurement via Drive Tests on mobile networks is presented. The difference viewpoints of QoE derived from user-centric and QoS parameters achieved by network-centric have been investigated through mobile networks of top three operators in Thailand. The results indicate that only some QoS parameters can provide the same trend as QoE. It implies that the operators should set some parameters as the higher priority for improving the network quality if they want to satisfy the experience of users.
Spline Truncated Estimator in Multiresponse Semiparametric Regression Model for Computer based National Exam in West Nusa Tenggara

Abstract Multiresponse semiparametric regression model is a combination of parametric regression model and nonparametric regression model with response variables more than one and correlate. The estimate used in estimate the parameters is spline truncated. Excess spline truncated is a model that has excellent statistical and visual interpretation and can model data with changing patterns on certain sub-intervals, because spline is a kind of polynomial pieces. The data used in this study is the value of Computer Based National Examination (CBNE) Vocational High School (VHS) in the province of West Nusa Tenggara (NTB) in 2017, each subject tested on CBNE serve as response variables. Based on the significant correlation test results obtained p-value &lt;0.05 so it can be concluded that there is correlation between the responses. The result of the multiresponse semiparametric regression model estimation is obtained by the best model with the value of MSE of 49,608; R 2 of 0.84 and minimum GCV value of 0.00000323 so it can be concluded that the value of CBNE VHS in NTB province satisfies goodness of fit criterions.
Implementation and Analysis of Homomorphic Facial Image Encryption and Manipulation

Homomorphic cryptography allows for encrypted data to be operated on securely without requiring decryption. Current homomorphic cryptosystems are either limited in their permitted operations or are significantly more time-intensive than standard non-homomorphic cryptosystems. Regardless, homomorphic cryptography has been targeted for use in secure image processing and facial recognition, due to their ability to maintain data privacy. In this paper, we assessed the viability of the Paillier, Damgård--Geisler--Krøigaard (DGK), and Brakerski--Gentry--Vaikuntanathan (BGV) cryptosystems for facial image processing applications, by implementing a software library equipped with these cryptosystems, and comparing their time efficiency and accuracy. As an extension to previous research, we attempt to support non-linear image processing operations. Preliminary results have shown that the Paillier and DGK cryptosystems are comparable in accuracy and may be used for image negation, but only the Paillier cryptosystem is consistent enough to produce reasonable to accurate results.
Exact memory–constrained UPGMA for large scale speaker clustering

This work focuses on clustering large sets of utterances collected from an unknown number of speakers. Since the number of speakers is unknown, we focus on exact hierarchical agglomerative clustering, followed by automatic selection of the number of clusters. Exact hierarchical clustering of a large number of vectors, however, is a challenging task due to memory constraints, which make it ineffective or unfeasible for large datasets. We propose an exact memory–constrained and parallel implementation of average linkage clustering for large scale datasets, showing that its computational complexity is approximately O(N2), but is much faster (up to 40 times in our experiments), than the Reciprocal Nearest Neighbor chain algorithm, which has O(N2) complexity. We also propose a very fast silhouette computation procedure that, in linear time, determines the set of clusters. The computational efficiency of our approach is demonstrated on datasets including up to 4 million speaker vectors.
Product Prediction and Recommendation in E-Commerce Using Collaborative Filtering and Artificial Neural Networks: A Hybrid Approach

In modern society, online purchasing using popular website has become a new trend and the reason beyond it is E-commerce business which has grown rapidly. These E-commerce systems cannot provide one to one recommendation, due to this reason customers are not able to decide about products, and they may purchase. The main concern of this work is to increase the product sales, by keeping in mind that at least our system may satisfy the needs of regular customers. This paper presents an innovative approach using collaborative filtering (CF) and artificial neural networks (ANN) to generate predictions which may help students to use these predictions for their future requirements. In this work, buying pattern of the students who are going to face campus interviews has been taken into consideration. In addition to this, buying patterns of the alumni for luxurious items was also considered. This recommendation has been done for the products, and the results generated by our approach are quite interesting.
IA-graph based inter-app conflicts detection in open IoT systems

This paper tackles the problem of detecting potential conflicts among independently developed apps that are to be installed into an open Internet of Things (IoT) environment. It provides a new set of definitions and categorizations of the conflicts to more precisely characterize the nature of the problem, and employs a graph representation (named IA Graph) for formally representing IoT controls and inter-app interplays. It provides an efficient conflicts detection algorithm implemented on a SmartThings compiler and shows significantly improved efficacy over prior solutions.
A Flexible and Lightweight Agent Deployment Architecture

While the multi-agent system (MAS) paradigm is popular for many application domains, different frameworks exist for different applications. These frameworks are not interoperable and many times force the developer into a specific model for agents and for the system as a whole. This paper presents a framework featuring a lightweight, open model for the entities in a MAS, together with a means of deployment that brings both flexibility and versatility. This architecture is compared to the most popular frameworks in the MAS domain.