Reviewerly/exHarmony huggingface dataset:
An Empirical Evaluation of Assorted Risk Management Models and Frameworks in Software Development

Software risk management is one the key factors in software project management with the goal to improve quality as avoid vulnerabilities. The term defect refers to an imperfection that may arise because of reasons including programmers' skills, lack of suitable testing strategies, and many others. When actual results are different from expected result or meeting wrong requirement, it is called defect and it forms the basis of risk escalation in a software project which is obviously not accepted in any type of deployment. Making a reliable software should be risk free from any vulnerability. Along with reliability another issue arises is software quality which is a factor with software risk management. The quality of software is to reduce the occurrence of risks and defects with the objective to produce an effectual value software which is key point of consideration. In this article, is underlined the present assorted risk management strategies proposed and projected by a number of researchers and academicians on the different parameters using benchmark datasets from renowned sources of research.
Analysis of Performance between Kinect v1 and Kinect v2 for Various Facial Part Movements

The aim of this study is to determine the suitable version of Kinect motion sensor for developing facial therapy or exercise throughout the analysis of face tracking performance. A face tracking system is developed in both version of Kinect cameras by referring to the respective version of Kinect SDK. The created face tracking algorithms are then modified to display the detected facial points which are 121 points and 1347 points in total for Kinect v1 and Kinect v2 respectively. A total number of 18 desired facial feature point at similar landmarks will be extracted in the format of 3D coordinates for both Kinect cameras. To investigate the changes in the movement of facial feature points, the points will be paired up for distance ratio calculation between different frames of face image. The action unit of facial points for both Kinect cameras are different and there are some improvements in Kinect v2: asymmetrical facial points, high definition face detection and eye tracking as added action units. However, it shows poor detection in outer eyebrow part compared to Kinect v1. In overall, the Kinect v2 has better performance than Kinect v1 as it provides faster response speed and more detailed facial points movement detection in real-time operation for rehabilitation purposes.
Mathematical competence development of bachelor of engineering

Abstract The authors of this article draw attention to the fact that engineering education requires deep knowledge in mathematics and its methods that are fundamental for many disciplines and contribute to the development of a fundamental system of training students during their university study. Attention is focused on the need to introduce heuristic professional tasks into the studying process that develop mathematical competence among students, which includes motivational, value-based, cognitive, activity-based, reflexive and evaluative aspects. The need for the development of mathematical competence that increases intellectual base of a competitive specialist in modern society is determined in the article.
Fighting terrorism in Africa when existing terrorism levels matter

This study examines policy tools in the fight against terrorism when existing levels of terrorism matter in 53 African countries for the period 1998–2012. The empirical evidence is based on contemporary, non-contemporary and Instrumental Variable Quantile regressions (QR) which enable the investigation throughout the conditional distributions of domestic, transnational and total terrorism dynamics. The following findings are established. First, counterterrorism policy instruments of inclusive human development and military expenditure further fuel terrorim. Second, political stability negatively affects terrorism with a negative threshold effect. Political stability estimates are consistently significant with increasing negative magnitudes throughout the conditional distributions of domestic and total terrorism. Policy implications are discussed.
A Comparison of Tuning Methods for PID-Controllers with Fuzzy and Neural Network Controllers

Conventional approaches to control systems still present a reasonable solution for a variety of different tasks in control engineering problems. Controllers based on the PID approach are used in a wide range of applications due to their easy handling, realization and set up, as well as their modest need of computational resources during the runtime. In order to heuristically find near-optimal parameters for the controller design, different approaches to tuning PID controllers have been developed. The Ziegler–Nichols methods are still commonly used despite that they have long been known, though modern methods, such as the T-Sum method, have also emerged. In this work, a comparison of the tuned PID controllers with a Mamdani-Fuzzy-Logic controller and an adaptive neural network controller is offered. A unified step response is used to classify the performance of controllers. It is shown that a PID control can work just as well as a fuzzy logic or neural network control for simple applications with time-invariant parameters or in applications where the parameters only change slightly and no strict constancy of the plant output is necessary.
CHOpinionMiner: An unsupervised system for Chinese opinion target extraction

Summary Opinion target extraction (OTE) is an important task in fine‐grained sentiment analysis field, which focuses on the identification of the targets of users' opinions or sentiments from online reviews. Existing approaches to OTE are mainly based on unsupervised rule‐based methods or supervised machine learning methods. However, the latter needs a large number of labeled samples to train their models and lack of labeled corpus limits the research progress on OTE of Chinese. In this paper, we proposed a novel unsupervised and domain independent system called CHOpinionMiner. First, noun phrases are extracted as candidate opinion targets based on phrase structure grammar, then the syntactic paths of the candidate opinion targets and the opinion words in one opinionated sentence are extracted. After that, the legal paths, or rather, the opinion targets‐opinion lexicon pairs that are syntactically associated are selected according to the syntactic rules. Finally, not only the formal targets but also the orientations are obtained. Experiments on data sets consisting of microblog topics and product reviews demonstrate that our approach outperforms the existing state‐of‐the‐art methods.
DeepBbox: Accelerating Precise Ground Truth Generation for Autonomous Driving Datasets

Autonomous driving requires various computer vision algorithms, such as object detection and tracking. Precisely-labeled datasets (i.e., objects are fully contained in bounding boxes with only a few extra pixels) are preferred for training such algorithms, so that the algorithms can detect exact locations of the objects. However, it is very time-consuming and hence expensive to generate precise labels for image sequences at scale. In this paper, we propose DeepBbox, an algorithm that "corrects" loose object labels into right bounding boxes to reduce human annotation efforts. We use Cityscapes [1] dataset to show annotation efficiency and accuracy improvement using DeepBbox. Experimental results show that, with DeepBbox, we can increase the number of object edges that are labeled automatically (within 1% error) by 50% to reduce manual annotation time.
Scheduling Content in Pervasive Display Systems

Digital displays are a ubiquitous feature of public spaces; London recently deployed a whole network of new displays in its Underground stations, and the screens on One Time Square (New York) allow for presentation of over 16,000 square feet of digital media. However, despite decades of research into pervasive displays, the problem of scheduling content is under-served and there is little forward momentum in addressing the challenges brought with large-scale and open display networks. This paper presents the first comprehensive architectural model for scheduling in current and anticipated pervasive display systems. In contrast to prior work, our three-stage model separates out the process of high level goal setting from content filtering and selection. Our architecture is motivated by an extensive review of the literature and a detailed consideration of requirements. The architecture is realised with an implementation designed to serve the world's largest and longest-running research testbed of pervasive displays. A mixed-methods evaluation confirms the viability of the architecture from three angles: demonstrating capability to meet the articulated requirements, performance that comfortably fits within the demands of typical display deployments, and evidence of its ability to serve as the day-to-day scheduling platform for the previously described research testbed. Based on our evaluation and a reflection on paper as a whole, we identify ten implications that will shape future research and development in pervasive display scheduling.
Software Requirements Prioritization with the Goal-Oriented Requirement Language

Requirements prioritization is an important activity of the software development process. It is performed to rank information systems (IS) requirements in the order in which they will be implemented to maximize the value delivered to customers. A number of methods and techniques have been proposed to help organizations prioritize IS requirements. However, they all suffer from a number of limitations and their implementation are mostly informal. This work aims to design a novel method that automates the requirements prioritization process. The proposed method uses the Goal-oriented Requirement Language (GRL) that permits the: (i) modeling of requirements and linking them to the business objectives/goals, and (ii) evaluation of the impact of requirement choices on business objectives/goals. The method attaches contribution and preference values to GRL edges (e.g., contribution links) and GRL nodes (e.g., goals and solutions) to compute the score of IS requirements. The attached values can be based on any set of business and IT factors (e.g., value for the customer, cost of implementation, risk). This paper also presents the principles underlying the proposed IS requirements prioritization method and discusses its possible implementation in practice.
Optimal Sensing Slots Determination for UAV under Correlated Log-normal Shadowing

Due to spectrum congestion, cognitive radio technique should be adopted by UAVs to improve their communication capability. Under the constraints of missed detection probability and false alarm probability, the less the sensing slot, the longer the transmission slot and the greater the throughput in a sensing period. In this paper, the sensing performance of UAV under the correlated log-normal shadowing is analyzed, and a method for determining the optimal sensing slots of UAV is presented.
Context Discovery for Personalised Automotive Functions

Context: With the growing prevalence of machine learning applications within the automotive domain, we observe an increase of built-in sensors and generated data. This data includes complex data such as point clouds for the environmental model but also less sophisticated data like temperature and road type. When developing data-driven functions there is often no need for additional sensors since a huge amount of data is already sensed, but often hidden. Aim: We aim to discover vehicle signals that describe a system's or user's behaviour when interacting with vehicle functions. These signals could directly serve as input for data-driven vehicle functions. Method: Based on supervised feature selection algorithms we propose an approach to discover these vehicle signals. This approach can either be deployed in the backend on test fleet data or onboard the vehicle. Results: Based on seven test cases, we evaluated the approach with 17 feature selection algorithms on eight customer vehicle data sets. To evaluate the resulting signal subsets, we trained machine learning models that in turn were able to predict the behaviour of the user. Conclusion: These trained models achieved high accuracy in the prediction, which shows that current vehicles already collect enough data to predict the user's behaviour and the proposed approach was able to discover the appropriate vehicle signals. Considering the huge amount of data and vehicles as well as the highly diverse behaviour of every user, a scalable discovery approach considering every user is inevitable.
Analyzing vocational school students’ error in solving mathematics problems involving higher order thinking skills

Abstract Higher-order thinking skill is conceived as the top end of Bloom’s cognitive taxonomy. Cognitive domain of higher order thinking skill consists of analyzing, evaluating, and creating. The study aimed to describe the type of error made by vocational school students in solving problems involving higher order thinking skills based on Newman, Lai, Subanji &amp; Mulyoto. This study employed a qualitative descriptive approach with a test as data collecting technique. The respondents are 12 students of vocational school. The skill domain of analyzing contributes reading &amp; decoding error, comprehension error, conceptual error, process skills error, and encoding error. The highest percentage of students’ error in this domain is conceptual error (33%). The skill domain of evaluating contributes decoding &amp; reading error, comprehension error, language interpretation error, process skills error, and encoding error. The highest percentage of students’ error in this domain is encoding error (25%). The skill domain of creating contributes conceptual error, comprehension error, and process skill error. The highest percentage of students’ error in this domain is conceptual error (42%). The results suggest mathematics teacher to explore deeply about their students’ skill of analyzing, skill of evaluating, and skill of creating.
Utilising geochemical data for the identification and characterisation of mineral exploration sample media within cover sequence materials

A first pass workflow for placing cover sequence materials into four broad lithological groups using A–CNK–FM (Al2O3–CaO + Na2O + K2O–Fe2O3+MgO) and A–CN–K (Al2O3–CaO + Na2O–K2O) ternary diagrams in conjunction with the SiO2 vs Al2O3, Fe2O3 and CaO plus the Ca versus Sr XY diagrams has been derived from lithological logging and laboratory whole-rock geochemistry of 2346 samples. These lithological groups include siliciclastics (quartz sands and clays), weathered material, carbonate lithologies (marine limestone, pedogenic carbonate and compositions intermediate between the two end members) and ferruginous lithologies (including ferricrete and ferruginous sediments). Depositional, weathering and groundwater processes influence sample geochemistry during or after the formation of the material and will influence where the sample will plot on the geochemical diagrams used in this study. Geochemical data for discrete lithologies targeted during sampling (e.g. carbonate lithologies) clusters on diagrams used in the geochemical workflow, whereas data from targeted stratigraphic horizons (e.g. basement–cover interface) are more variable. The ability to characterise rock types that are texturally defined using the geochemical workflow is limited. For instance, the geochemistry of diamictite/conglomerate samples is dependent on the matrix versus clast content and composition as well as the source rock and catchment area. This workflow has the potential to be applied in a mineral exploration environment for rapid interpretation of real-time geochemical (e.g. portable XRF) data to gain an understanding of background geochemistry and geochemical signatures that may be related to underlying mineralisation.Key pointsA workflow for broad lithogeochemcial characterisation of cover sequence materials has been developed.Uses commonly collected major element plus strontium data acquired by laboratory or portable techniques.Categorises materials into siliciclastics, weathered material, carbonate lithologies and ferruginous lithologies.Applications include a mineral exploration environment for interpretation of large geochemical datasets to understand geology and mineralisation vectors.
Attributed Graph Pattern Set Selection Under a Distance Constraint

Pattern mining exhaustively enumerates patterns that occur in some structured dataset and each satisfy some constraints. To avoid redundancy and reduce the set of patterns resulting from the enumeration, it is necessary to go beyond the individual selection of patterns and select a pattern subset which, as a whole, contains relevant and non redundant information. This is particularly useful when enumerating bi-patterns, which represent pairs of attribute patterns describing for instance subnetworks in two-mode attributed networks. We present and experiment a general greedy algorithm performing pattern set selection on attributed graphs.
Superpixel Correspondence for Non-parametric Scene Parsing of Natural Images

Scene parsing refers to the task of labeling every pixel in an image with the class label it belongs to. In this paper, we propose a novel scalable non-parametric scene parsing system based on superpixels correspondence. The non-parametric approach requires almost no training and can scale up to datasets with thousands of labels. This involves retrieving a set of images similar to the query image, followed by superpixel matching of the query image with the retrieval set. Finally, our system warps the annotation results of superpixel matching, and integrates multiple cues in a Markov Random Field (MRF) to obtain an accurate segmentation of the query image. Our non-parametric scene parsing achieves promising results on the LabelMe Outdoor dataset. The system has limited parameters, and captures contextual information naturally in the retrieval and alignment procedure.
A Digital Hardware Implementation for A new Mixed-Order Nonlinear 3-D Chaotic System

This paper introduces a generic modeling for a 3-D nonlinear chaotic based on fractional-order mathematical rules. Also, a novel modeling for the system using a mixture between integer and fractional-order calculus is proposed. Dynamics of the new realization are illustrated using phase portrait diagrams with complex behavior. Also, a great change in the parameter ranges is investigated using bifurcation diagrams. MATLAB and Xilinx ISE 14.5 are used in system simulations. Furthermore, the digital hardware implementation is done using Xilinx FPGA Virtex −5 kit. The synthesis report shows that the mixed-order design utilizes 2019 slices and 1809 registers. The proposed system proves its possibility to be used in cryptosystems.
A Management Method of Interest-rate in UTXO Model

Cryptocurrencies based on UTXO (Unspent Transaction Output) model keep the deposit's total amount to meet withdraws' one for preventing double spend, in other words, they audit “the conservation of currency value” in every transaction. In order to use the cryptocurrencies in financial business, it is necessary to express the increase or decrease of debt amount by interest, however, it is incompatible with the safety of cryptocurrency operation. We propose a management method which can express interests or repayments with keeping the conservation of currency value. In the proposed method, an issuer simultaneously issues the same amount of positive and negative cryptocurrencies, where the former means the lending amount considering interest and the latter means the debt. Following these issues, the two currencies are offset only by the issuer.
Evaluation of a Rack-Scale Disaggregated Memory Prototype for Cloud Data Centers

Disaggregated data centers propose a modular architecture where memory and compute resources are utilized in a finer granularity, aiming for a better optimized capacity and power consumption. In contrast to a classic compute node in a data center, memory is not required to be co-located with the processor, disaggregation can enhance hardware elasticity, improve virtual machine (VM) migration, and reduce the total cost of ownership (TCO), when compared to current data center solutions.
Implementation of Web Assembly Technology as Visual Learning Media to help High School Students in Human Body System Learning

Abstract The aim of this paper is development of application learning system for the human body system using Web Assembly technology to help high school students to recognize system organs of the human body including general functions and diseases in visual learning. Development of system using APPED model which consist of five stages development. The First stage is analysis and preliminary research to analyse the system needs and characteristic, second stage is design system to describe the system architecture, flowchart, Screen Design and Storyboard. Third stage is Production to develop the system using authoring tool, fourth stage is Evaluation to test functionality of system using blackbox testing and test reaction to students using summative evaluation to measure the level of satisfaction program usage. Last stage is dissemination to publish applications in general through social media or the internet. The media visual learning system built based on for web applications because it can be used by students anywhere and anytime. The Experimental result it show that applications based on the total score obtained from the questionnaire to 35 respondents obtained a total score of 674 which are based on the attitude categories it show positive attitude the application can help high school students understand the material systems of the human body, with material that has 3D objects, and can recognize the organs of the human body system along with their general functions and diseases and by giving 3D objects as a teaching aid.
CameraKeyboard: A Novel Interaction Technique for Text Entry Through Smartphone Cameras

We present CameraKeyboard, a text entry technique that uses smartphone cameras to extract and digitalise text from physical sources such as business cards and identification documents. After taking a picture of the text of interest, the smartphone recognises the text through OCR technologies (specifically, Google Cloud Vision API) and organises it in the same way in which it is displayed in the original source. Next, users can select the required text - which is split into lines, and further split into words - by tapping on it. CameraKeyboard is designed to easily complement the standard keyboard when users need to digitalise text. In fact, the camera is integrated directly in the standard mobile QWERTY keyboard and can be accessed by tapping a button. CameraKeyboard is general-purpose like any other smartphone keyboard: it works with any application (e.g., Gmail, Whatsapp) and is able to extract text from any kind of source. We evaluated CameraKeyboard through a user study with 18 participants who carried out four different text entry tasks, and compared it to a standard smartphone keyboard (Google Keyboard) and a standard (physical) desktop keyboard. Results show that CameraKeyboard is the faster one in most cases.