Reviewerly/exHarmony huggingface dataset:
The Delta Maintainability Model: Measuring Maintainability of Fine-Grained Code Changes

Existing maintainability models are used to identify technical debt of software systems. Targeting entire codebases, such models lack the ability to determine shortcomings of smaller, fine-grained changes. This paper proposes a new maintainability model - the Delta Maintainability Model (DMM) - to measure fine-grained code changes, such as commits, by adapting and extending the SIG Maintainability Model. DMM categorizes changed lines of code into low and high risk, and then uses the proportion of low risk change to calculate a delta score. The goal of the DMM is twofold: first, producing meaningful and actionable scores; second, compare and rank the maintainability of fine-grained modifications. We report on an initial study of the model, with the goal of understanding if the adapted measurements from the SIG Maintainability Model suit the fine-grained scope of the DMM. In a manual inspection process for 100 commits, 67 cases matched the expert judgment. Furthermore, we report an exploratory empirical study on a data set of DMM scores on 3,017 issue-fixing commits of four open source and four closed source systems. Results show that the scores of DMM can be used to compare and rank commits, providing developers with a means to do root cause analysis on activities that impacted maintainability and, thus, address technical debt at a finer granularity.
Recommender system using item based collaborative filtering (CF) and K-means

The heightening in the available information in the form of digital data and the number of users on the Internet have engendered a challenge of overburden of data which obstructs access to interested item on the Internet timely. There are many information retrieval systems which try to solve the pr oblem of information overloading but in their cases prioritization and personalization of information were absent. The main aim is to develop a recommender system using item based collaborative filtering technique and K-means. The most popular algorithm in the recommender system’s field is the collaborative filtering technique. Recommender systems are the filtering systems for information that concerned with the problem of information overburden by filtering essential information fragment out of enormous dynamically promoted information according to person’s attentiveness, taste and distinguished behavior about them. We are considering m users, n items (in numbers) and presenting a model to fabricate a recommendation for the mobile user by a new approach.
Optimising infrastructure as a service provider revenue through customer satisfaction and efficient resource provisioning in cloud computing

With limited resources, it is quite challenging for cloud providers to meet dynamic and massive customers' demands. Higher utilisation or refusing any service level agreement (SLA) may lead to penalties which play a crucial role in the cloud business. Overutilisation of resources, instead of maximizing the revenue, may lead to a decrease in revenue due to the SLA violations. Various studies have been conducted to investigate these issues; however, there is still room for improvement. In this study, the authors proposed a model to address the resource scalability and SLA violation issues by hiring external resources at low prices. However, in contrast to a federated cloud, the proposed model allows a provider to hire resources from any external provider with flexible terms and price. They designed algorithms to optimise providers' revenue by taking into account different parameters, including resource utilization, customer satisfaction, SLA violation, and prices. Simulation result shows that the proposed model is efficient in handling massive demands, and improves revenue generation and customer satisfaction. Offering joint pricing on customers' choice and outsourcing the extra workload to external resources leads to revenue maximization. Hiring external resources earns external revenue as well as it maximizes the total revenue.
Generative Memory for Lifelong Learning

Lifelong learning is a crucial issue in advanced artificial intelligence. It requires the learning system to learn and accumulate knowledge from sequential tasks. The learning system needs to deal with increasingly more domains and tasks. We consider that the key to an effective and efficient lifelong learning system is the ability to memorize and recall the learned knowledge using neural networks. Following this idea, we propose Generative Memory (GM) as a novel memory module, and the resulting lifelong learning system is referred to as the GM Net (GMNet). To make the GMNet feasible, we propose a novel learning mechanism, referred to as P -invariant learning method. It replaces the memory of the real data by a memory of the data distribution, which makes it possible for the learning system to accurately and continuously accumulate the learned experiences. We demonstrate that GMNet achieves the state-of-the-art performance on lifelong learning tasks.
On Importance of Non-functional Requirements in Agile Software Projects—A Survey

Context. Among many Agile software development practices, over 30 concern Requirements Engineering (RE). However, none of them mentions explicitly non-functional requirements (NFRs). The question arises – how important are NFRs in Agile software projects? Method. We conducted a survey asking Agile software development practitioners how they perceive the importance of having NFRs definedKopczyńska, Sylwia in their projects. Then, we juxtaposed the answers with their opinions on the perceived importance of 31 Agile RE practices. Results. The opinions of 118 respondents from a wide range of countries around the globe allowed us to determine how important it is to define NFRs. Moreover, we showed their importance from the perspective of the rankingOchodek, Mirosław of Agile RE practices. We also identified some relationships between the demographic data such as experience and the perceived importance of requirements concerning quality. Conclusions. We found that over 77% of respondents perceive having NFRs defined in Agile software project as at least important, and for 30% it is critical. Also, the perceived importance of NFRs increasesNawrocki, Jerzy with the increase of respondents’ experience.
Deep Learning Based Real-time Daily Human Activity Recognition and Its Implementation in a Smartphone

A novel method for classifying and identifying human activities in real-time is needed in various human-machine interaction fields. In this paper, a multi-channel motion data collected from a smartphone is structured in a new way and converted to a virtual image. An iOS application software was developed to record and stream motion data and to recognize real-time activities. The time series data of an accelerometer and gyroscope motion sensors are structured into 14x60 virtual image. Similarly, their respective amplitudes of 1 dimensional DFT (Discrete Fourier Transformation) are organized into 14x60 image format. The resultant data was given to the designed CNN (Convolutional Neural Network) for classification. Both data structuring methods were analyzed and compared yet the time series data structuring showed a better result and attained an accuracy of 99.5%. Additionally, the model was tested for real-time activity recognition in a computer and smartphone and achieved an excellent result.
Sustainable automatic data clustering using hybrid PSO algorithm with mutation

Widespread use of various mobiles, social networks and IOT devices results into continuous generation of the data, often leading to the formation of the big data. Sustainable grouping of such data into various clusters is an open research problem, which aims to provide solutions which are computationally efficient and maintainable over dynamic data. This paper proposes a new sustainable clustering algorithm HPSOM by hybridization of PSO with mutation operator for clustering of the data generated from different networks. The data generated by such networks is usually dynamic and heterogeneous in nature and the number of clusters is not fixed/ known in advance. Hence the proposed algorithm is further extended as AHPSOM for generating and re-adjusting the clusters automatically over the mobile network devices, and it facilitates the generation of sustainable clusters. Firstly, the performance of basic HPSOM is evaluated on six real life data sets and is also compared with some known evolutionary clustering techniques in terms of SWCD and Convergence speed. Then the performance of AHPSOM is evaluated using some synthetic and real life datasets using some validity metrics (cluster numbers, intra cluster distance, inter cluster distance, ARI and F-measure) and is also compared with some prevalent state-of-art automatic clustering techniques. The results show that the proposed algorithm is very efficient in terms of creating well separated, compact and sustainable clusters.
Deep Natural Language Processing for Search and Recommender Systems

Search and recommender systems share many fundamental components including language understanding, retrieval and ranking, and language generation. Building powerful search and recommender systems requires processing natural language effectively and efficiently. Recent rapid growth of deep learning technologies has presented both opportunities and challenges in this area. This tutorial offers an overview of deep learning based natural language processing (NLP) for search and recommender systems from an industry perspective. It first introduces deep learning based NLP technologies, including language understanding and language generation. Then it details how those technologies can be applied to common tasks in search and recommender systems, including query and document understanding, retrieval and ranking, and language generation. Applications in LinkedIn production systems are presented. The tutorial concludes with discussion of future trend.
Object-Level Trajectories Based Fine-Grained Action Recognition in Visual IoT Applications

The emerging computer vision and deep learning technologies are being applied to the intelligent analysis of sports training videos. In this paper, a deep learning based fine-grained action recognition (FGAR) method is proposed to analyze soccer training videos. The proposed method was applied to indoor training equipment for evaluating whether a player has stopped a soccer ball successfully or not. First, the problem of FGAR is modeled as human-object (player-ball) interactions. The object-level trajectories are proposed as a new descriptor to identify fine-grained sports videos. The proposed descriptor can take advantage of high-level semantic and human-object interaction motions. Second, a cascaded scheme of deep networks based on the object-level trajectories is proposed to realize FGAR. The cascaded network is constructed by concatenating a detector network with a classifier network (a long-short-term-memory (LSTM)-based network). The cascaded scheme takes the advantage of the high efficiency of the detector on object detection and the outstanding performance of the LSTM-based network on processing time series. The experimental results show that the proposed method can achieve an accuracy of 93.24%.
Matching μ-Logic

Matching logic is a logic for specifying and reasoning about structure by means of patterns and pattern matching. This paper makes two contributions. First, it proposes a sound and complete proof system for matching logic in its full generality. Previously, sound and complete deduction for matching logic was known only for particular theories providing equality and membership. Second, it proposes matching μ -Iogic, an extension of matching logic with a least fixpoint μ -binder, It is shown that matching μ -Iogic captures as special instances many important logics in mathematics and computer science, including first-order logic with least fixpoints, modal μ -Iogic as well as dynamic logic and various temporal logics such as infinite/finite-trace linear temporal logic and computation tree logic, and notably reachability logic, the underlying logic of the \mathbbk framework for programming language semantics and formal analysis. Matching μ -logic therefore serves as a unifying foundation for specifying and reasoning about fixpoints and induction, programming languages and program specification and verification.
Image-Based Visual Servoing of a Quadrotor with Improved Visibility Using Model Predictive Control

In this paper, an observer-based model predictive control scheme is proposed for the image-based visual servoing (IBVS) of a quadrotor. The control objective is to regulate the relative pose of the quadrotor to a planar ground target using a minimal sensor set, i.e., a monocular camera and an inertia measurement unit (IMU). Image features are deliberately selected to decouple the roll/pitch motion from the image kinematics, such that the overall system dynamics possesses a cascaded structure. This structural property enables the simplification of the controller design by adopting a dual-loop control structure. During visual servoing, the target object could potentially leave the field of view (FOV) of the camera, which causes failure of control input generation. To improve the visibility, a model predictive control (MPC) controller is developed to explicitly bound the roll and pitch angles to alleviate the feature loss due to large rotation. In addition, a high-gain observer is designed to facilitate the estimation of the linear velocity, which is unavailable due to the absence of the global positioning system (GPS). In the end, simulation results are presented to verify the effectiveness of the proposed control scheme.
A dimension-based information security culture model and its relationship with employees’ security behavior: A case study in Malaysian higher educational institutions

Despite strong recommendations by scholars to establish Information Security Culture (ISC), the lack of ISC guidelines persists, particularly in aspects that could effectively improve employees' security behavior in an organization. This study proposes an ISC model based on seven new formulated dimensions to examine its influence on employees' Information Security Policy (ISP) compliance behavior. The dimensions represent specific aspects of ISC and were formulated based on widely accepted concepts of Organizational Culture and ISC. The model was tested at 19 out of 21 public universities in Malaysia and validated using Partial Least Square Structural Equation Modelling (PLS-SEM). Findings revealed all seven dimensions are significant in contributing to the underlying concept of ISC, with Information Security Knowledge being the most important dimension. This ISC concept was also found to be significant in influencing ISP compliance behavior. This study contributes to ISC literature in terms of conceptualization and operationalization of an ISC concept based on the new comprehensive dimensions in relation to ISP compliance behavior. The model could be employed by practitioners in assessing, improving and cultivating a positive ISC that would effectively influence employees' security behavior in higher educational institutions.
Dark web forum correlation analysis research

With the rapid development of the Internet, the dark network has also been widely used in the Internet [1]. Due to the anonymity of the dark network, many illegal elements have committed illegal crimes on the dark. It is difficult for law enforcement officials to track the identity of these cyber criminals using traditional network survey techniques based on IP addresses [2]. The threat information is mainly from the dark web forum and the dark web market. In this paper, we introduce the current mainstream dark network communication system TOR and develop a visual dark web forum post association analysis system to graphically display the relationship between various forum messages and posters, and help law enforcement officers to explore deep levels. Clues to analyze crimes in the dark network.
An Adaptive Global and Local Tone Mapping Algorithm Implemented on FPGA

We present a fast global and locally adaptive tone mapping algorithm and its field-programmable gate array (FPGA) implementation. The specially designed tone mapping function, which is based on local histogram equalization, controls global, and local characteristics individually. In contrast to other tonemap operators, our algorithm manages light/dark halos separately and by using local tonemap function alone, it can effectively suppress noise. We validated the effectiveness of our algorithms using subjective and objective assessment. Using an average of the bins, we achieve fast smoothed local histogram estimation with fewer bins while maintaining high accuracy. Our new implementation method requires minimal data access and reduced memory as it operates with a downscaled frame size of 240 × 135 pixels. Relative local area size is 248 × 248 @Full-HD resolution (1920 × 1080). For low-latency pixel output, the system performs the tone mapping using pixel information from the previous frame. When we implemented the system on FPGA (TB-7K-325TIMG and Xilinx Kintex-7), we achieved lightweight hardware as the total usage rate is about 25% of the available FPGA resource. Using an online 1080p video we demonstrate, a real-time video processing using our hardware tone mapping system.
A Critical Data Gap in Earthquake Physics

Editorial| July 31, 2019 A Critical Data Gap in Earthquake Physics Yehuda Ben‐Zion Yehuda Ben‐Zion Corresponding Author aDepartment of Earth Sciences and Southern California Earthquake Center, University of Southern California, Los Angeles, California 90089 U.S.A., benzion@usc.edu Search for other works by this author on: GSW Google Scholar Seismological Research Letters (2019) 90 (5): 1721–1722. https://doi.org/10.1785/0220190167 Article history first online: 31 Jul 2019 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Yehuda Ben‐Zion; A Critical Data Gap in Earthquake Physics. Seismological Research Letters 2019;; 90 (5): 1721–1722. doi: https://doi.org/10.1785/0220190167 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietySeismological Research Letters Search Advanced Search Earthquakes are a prime example of a complex natural process that is not well understood, yet has immense societal relevance. Mechanical energy stored in crustal rocks over centuries and millennia is released during earthquake ruptures within seconds, producing at times catastrophic destruction and loss of life. Seismic hazard and risks continue to increase rapidly, due to urbanization and development of high‐risk utility plants (chemical and nuclear), as well as critical distributed infrastructure (water, energy, and communications) below and above the surface (e.g., Bilham, 2009; Porter et al., 2011; Detweiler and Wein, 2017). Historical earthquakes led to... You do not have access to this content, please speak to your institutional administrator if you feel you should have access.
A Refined Bilateral Filtering Algorithm Based on Adaptively-Trimmed-Statistics for Speckle Reduction in SAR Imagery

This paper proposes a refined bilateral filtering algorithm based on adaptively trimmed-statistics (ATS-RBF) for speckle reduction in SAR imagery.The new de-speckling method is based on the bilateral filtering method, where the similarities of gray levels and the spatial location of the neighboring pixels are exploited.However, the traditional bilateral filter is not effective to reduce the strong speckle, which is often presented as impulse noise.The ATS-RBF designs an adaptive sample trimming method to properly select the samples in the local reference window and the trimming depth used for sample trimming is automatically derived according to the homogeneity of the local reference window.Furthermore, an alterable window size-based scheme is proposed to enhance the speckle noise smoothing strength in homogeneous backgrounds.Finally, bilateral filtering is applied using the adaptively trimmed samples.The ATS-RBF has an excellent speckle noise smoothing performance while preserving the edges and the texture information of the SAR images.The experiments validate the effectiveness of the proposed method using TerraSAR-X images.
PANet: A Context Based Predicate Association Network for Scene Graph Generation

Scene graph generation is widely studied in recent years, which tries to understand the interactions of different objects as a whole. The earlier researches only recognize a few relationships or model contexts among different relationships, neglecting the associations of predicates for each object pair. In this paper, we propose a two-stage framework named predicate association network (PANet) to properly extract contexts and model predicate association. In the first stage, instance-level and scene-level context are extracted for object classification and further used for predicate classification in the next stage. With a recurrent neural network, alignment technique and attention mechanism are combined to collect the associations of predicates in the second stage. The experiments on the Visual Genome dataset show that our method is effective and outperforms the state-of-the-art methods.
Comments on “Dehydration of hot oceanic slab at depth 30–50 km: Key to formation of Irankuh-Emarat Pb-Zn MVT belt, Central Iran” by Mohammad Hassan Karimpour and Martiya Sadeghi

The Malayer-Esfahan Metallogenic belt (MEMB), in the southwestern Iran, contains numerous different types of the sediment-hosted Zn-Pb (±Ba±Ag), volcanic-sediment hosted Zn-Pb ± Ba, sideritic Fe-Mn-Pb (±Ba±Cu), and barite mineralizations. These deposits are hosted mostly in Jurassic shales and sandstones and in Early to Late Cretaceous carbonates and siltstones with minor volcanic rocks. In contrast to the orogenic-related Mississippi Valley type (MVT) deposits, the MEMB deposits formed in an extensional back-arc environment and are characterized by their stratabound and stratiform orebodies. In these deposits, silicification and dolomitization (± sericitization) are the main wall-rock alteration styles. The presence of primary laminated sulfides, fine-grained disseminated sphalerite and galena in association with framboidal pyrite, sedimentary structures in sulfide laminae and bands, and the association of some tuffaceous and volcanic rocks with sulfide mineralizations, along with replacement ore textures in the MEMB deposits are not compatible with orogenic-related MVT model for these mineralizations. These characteristics in the Cretaceous MEMB deposits are more compatible with a sub-marine hydrothermal system with sub-seafloor replacement mineralization (e.g., Irish type). Some deposits also share characteristics between Irish type and volcanogenic massive sulfide (VMS) deposits, called VSHMS in this paper. The main argument against the MVT model of Karimpour and Sadeghi (2018) is that this model is not acceptable for the MEMB deposits and could not explain metallogenic aspects of the Zn-Pb (±Ba±Ag) and other mineralizations in this belt.