Reviewerly/exHarmony huggingface dataset:
Toward Kurdish language processing: Experiments in collecting and processing the AsoSoft text corpus

In this article, we introduce the first Kurdish text corpus for Central Kurdish (Sorani) branch, called AsoSoft text corpus. Kurdish language, which is spoken by more than 30 million people, has various dialects. As one of the two main branches of Kurdish, Central Kurdish is the formal dialect of Kurdish literature. AsoSoft text corpus is of size 188 million tokens and has been collected mostly from Web sites, published books, and magazines. The corpus has been normalized and converted into Text Encoding Initiative XML format. In both collecting and processing the text, we have faced several challenges and have proposed solutions to them. About 22% of the corpus is topic annotated with six topic tags, and a topic identification task has been done to evaluate the correctness of annotation. The computational experiments of the Central Kurdish text processing are also presented with the support of related supplementary statistics. For the first time, the validity of Zipf’s law for Central Kurdish is presented and also perplexity of this language is calculated using standard N-gram language models. The perplexity of Central Kurdish is 276 for a tri-gram language model.
$LiveForen$ : Ensuring Live Forensic Integrity in the Cloud

To expedite the forensic investigation process in the cloud, excessive and yet volatile data need to be acquired, transmitted, and analyzed in a timely manner. A common assumption for most existing forensic systems is that credible data can always be collected from a cloud infrastructure, which might be susceptible to various exploits. In this paper, we present the design, implementation, and evaluation of LiveForen, a system that enforces a trustworthy forensic data acquisition and transmission process in the cloud, whose computer platforms' integrity has been verified. To fulfill this objective, we propose two secure protocols that verify the fingerprints of the computer platforms, as well as the attributes of the human agents, by taking advantage of the trusted platform module and the attribute-based encryption. To transmit forensic data as a data stream and verify its integrity at the same time, a unique fragile watermark is embedded into the data stream without altering the data itself. The watermark allows not only the data integrity to be verified but also any malicious data manipulation to be localized, with minimum communication overhead. The experimental results demonstrate that LiveForen achieves good scalability and limited performance overhead for authentication, data transmission, and integrity verification in an Infrastructure-as-a-Service cloud environment.
Frame-synchronized blind speech watermarking via improved adaptive mean modulation and perceptual-based additive modulation in DWT domain

This paper presents a blind speech watermarking algorithm that adopts different strategies to embed synchronization codes and information bits into separate DWT subbands. Speech frames for data hiding are chosen based on intensity thresholding. Under the guidance of the source-filter theory, a bipolar synchronization code sequence is substituted for the noisy part of the inverse filtered excitation in the 2nd level detail subband. Such a design allows the increase of embedding strength without adversely disturbing the pitch harmonics of voiced speech. Experiment results show that a synchronization code of size 640 is sufficient to render reliable detection. The PESQ metrics indicate that the quality degradation due to the synchronization code embedding is almost negligible. As for the binary embedding in the 2nd level approximation subband, we improve the adaptive mean modulation scheme to secure the retrieval of watermark bits on a frame basis. Experiment results confirm that, with the payload capacity set at 200 bit per second (bps), the proposed scheme demonstrates better robustness than three other DWT-based methods in the presence of commonly encountered signal processing attacks. Furthermore, reducing the capacity down to 100 bps alters the low-frequency spectral distribution, which contributes noteworthy improvements in robustness and imperceptibility.
Functional Requirement Management Automation and the Impact on Software Projects: Case Study in Ecuador

This study intends to show the importance to improve and automate the functional requirement management through the implementation of a system and measure out the impact of it in software projects. The goal in some cases is to release the product in less time possible reducing the schedule in the main phase like the functional requirement elicitation. There is a blended approach quantitative and qualitative, exploratory, documentary work as well as field work. In this study some performance indicators were applied to measure the impact of the established system had, and it was based on the equations suggested by authors Uwizeyemungu & Raymond about how important is to apply software management of functional requirements. The system's impact measurement outcome was 4.10 over a scale of 5, a moderately significant outcome interpreted by 82% of user's acceptability percent value.
Quality evaluation of Chinese red wine based on cloud model

Determining the quality of red wine is based on many qualitative and quantitative factors. Compared with other evaluation methods, the cloud model has an uncertainty transformation between a qualitative concept and its corresponding quantitative value, and the uncertainty transformation included fuzziness and randomness, which is suitable for solving the complexity of red wine evaluation. This study introduced the cloud model into quality evaluation of red wine for the first time, and a novel algorithm of comprehensive cloud model was proposed based on an addition algorithm of two cloud models. Furthermore, to validate the cloud model adopted in our red wine evaluation system, we used the gray relational analysis and fuzzy evaluation method. The evaluation result for the red wine sample was Good, and the result confirmed that our cloud model can be used to evaluate the quality of red wine. PRACTICAL APPLICATIONS: In 2013, China surpassed France to become the largest country of red wine consumption in the world. Red wine is made by a natural fermentation process. There are several components that make up red wine, but the most abundant is grape juice. Ethyl alcohol is the second most abundant element and it is made naturally by the fermentation of the sugar in grape. There are more than 1,000 remaining components in the recipe for red wine, where 300 are comparatively important. Although the proportion of these components is not high, they are important factors in determining the quality of red wine. Sensory evaluation is the most common method used to determine the quality of red wine. This work has identified a cloud model that can be used, based on sensory evaluation, to determine the quality of red wine.
A Mobile Solution for Augmenting a Manufacturing Environment with User-Generated Annotations

The ever-increasing complexity in manufacturing environments has caused delay and uncertainty for on-site personnel when retrieving critical information. Currently, information associated with manufacturing environments is created and stored in centralized terminals and even paper-based logbooks. Any delays in accessing this information can hinder critical decision-making processes that are essential to ensure maximum efficiency and productivity. To increase the competitiveness of manufacturing companies, the authors present a user-oriented markerless augmented reality authoring solution for the manufacturing environment utilizing the capability of a modern smartphone or head-mounted display. With a one-time setup and calibration, the end user is able to augment a physical workspace with a digital layer and annotate any stationary object with application data including maintenance records, service tickets, and usage logs. By directly mapping the data onto a relevant physical object, it allows more efficient reuse and better contextualization of manufacturing information. The backend expandable data structure also enhances extensibility so that the users can modify the digital layer with ease when the physical environment changes. This annotation tool defines a novel way of managing manufacturing information to help companies work more efficiently and effectively.
Using Feature Selection Techniques to Improve the Accuracy of Breast Cancer Classification

Classification is a data mining process that aims to divide data into classes to facilitate decision-making; it is therefore an important task in medical field. In this paper we will try to improve the accuracy of the classification of six machines learning algorithms: Bayes Network (BN), Support Vector Machine (SVM), k-nearest neighbors algorithm (Knn), Artificial Neural Network (ANN), Decision Tree (C4.5) and Logistic Regression using feature selection techniques, for breast cancer classification and diagnosis. We examined those methods of classification and techniques of feature selection in WEKA Tool (The Waikato Environment for Knowledge Analysis) using two databases, Wisconsin breast cancer datasets original (WBC) and diagnostic (WBCD) available in UCI machine learning repository.
A Bank of Decentralized Extended Information Filters for Target Tracking in Event-Triggered WSNs

This paper presents a hierarchical estimation method for maneuvering target tracking in event-triggered wireless sensor networks. First, several process noise covariances are chosen to characterize the dynamic characteristic of the target in the presence of maneuvers, and a bank of decentralized extended information filters (DEIFs) are used to generate state estimates of the target. Second, the estimates from the DEIFs are combined by covariance intersection (CI) to obtain an improved state estimate while still maintaining a consistent estimate. Thus, the DEIF and the CI methods form complementary advantages by satisfying the requirement of the consistency in the hierarchical estimation framework. Finally, both simulations and experiments of a target tracking example demonstrate that the proposed method is more suitable for applications to the maneuvering target tracking and it achieves a more satisfactory performance than the conventional DEIF method.
Assessing source code vulnerabilities in a cloud‐based system for health systems: OpenNCP

Healthcare systems have been improved in order to provide support to cross-border situations where one citizen from one country travels to another country and requires the use of their health records. Several initiatives have been carried out to tackle this problem. This is the case for the OpenNCP which is supported by the European Commission by providing a common network and an infrastructure to connect different national healthcare systems which most of the times are cloud-based systems. The OpenNCP plays a key role in communicating health records among European Union's member states, and therefore it manages sensitive information. Therefore, this study provides a security analysis of this platform and a prototype is developed for identifying secure patterns in source code.
Translating privacy: developer cultures in the global world of practice

This paper makes the case for considering the role of cross-cultural encounters in shaping developers’ notions of information privacy. Recent studies on privacy by design shed light on developer practices yet tend to regard these workers as a generic category. The paper draws on two interviews with workers in a late-stage Israeli startup as a step toward localizing developers and the global products they design. The analysis identifies four narratives that juxtapose the local, the global and the commodification of users’ personal information: (1) the origin myth of the company; (2) workers’ personal and professional biographies; (3) reports on external regulations; and (4) accounts of work practices, rituals and communication formats. The analysis suggests how the globalization of the startup was implicated in changing ideas and practices relating to users’ information. The paper concludes by discussing some of the challenges facing a culturally-sensitive study of developers as mediators of privacy.
Foundations of ontology-based data access under bag semantics

Ontology-based data access (OBDA) is a popular approach for integrating and querying multiple data sources by means of a shared ontology. The ontology is linked to the sources using mappings, which assign to ontology predicates views over the data. The conventional semantics of OBDA is set-based—that is, the extension of the views defined by the mappings does not contain duplicate tuples. This treatment is, however, in disagreement with the standard semantics of database views and database management systems in general, which is based on bags and where duplicate tuples are retained by default. The distinction between set and bag semantics in databases is very significant in practice, and it influences the evaluation of aggregate queries. In this article, we propose and study a bag semantics for OBDA which provides a solid foundation for the future study of aggregate and analytic queries. Our semantics is compatible with both the bag semantics of database views and the set-based conventional semantics of OBDA. Furthermore, it is compatible with existing bag-based semantics for data exchange recently proposed in the literature. We show that adopting a bag semantics makes conjunctive query answering in OBDA coNP-hard in data complexity. To regain tractability of query answering, we consider suitable restrictions along three dimensions, namely, the query language, the ontology language, and the adoption of the unique name assumption. Our investigation shows a complete picture of the computational properties of query answering under bag semantics over ontologies in the DL-Lite family.
Heterogeneous multi-unmanned aerial vehicle task planning: Simultaneous attacks on targets using the Pythagorean hodograph curve

The coupled task allocation and path planning problem for heterogeneous multiple unmanned aerial vehicles performing a search and attack mission involving obstacles and no-fly zones are addressed. The importance of the target is measured using a time-dependent value. A task allocation algorithm is proposed to obtain the maximum system utility. In the system utility function, the reward of the target, path lengths of unmanned aerial vehicles, and number of unmanned aerial vehicles to perform a simultaneous attack are considered. The path length of the unmanned aerial vehicles based on the Pythagorean hodograph curve is calculated, and it serves as the input for the task allocation problem. A resource management method for unmanned aerial vehicles is used, so that the resource consumption of the unmanned aerial vehicles can be balanced. To satisfy the requirement of simultaneous attacks and the unmanned aerial vehicle kinematic constraints in an environment involving obstacles and no-fly zones, a distributed cooperative particle swarm optimization algorithm is developed to generate flyable and safe Pythagorean hodograph curve trajectories for unmanned aerial vehicles to achieve simultaneous arrival. Monte Carlo simulations are conducted to demonstrate the performance of the proposed task allocation and path planning method.
A Novel Approach of Load Balancing and Task Scheduling Using Ant Colony Optimization Algorithm

Cloud computing is a model for conveying data innovation benefits in which assets are recovered from the web through online devices and applications, instead of an immediate association with a server. Clients can set up and boot the required assets and they need to pay just for the required assets. Subsequently, later on giving a component to a productive asset administration and the task will be a vital target of Cloud computing. Load balancing is one of the major concerns in cloud computing, and the main purpose of it is to satisfy the requirements of users by distributing the load evenly among all servers in the cloud to maximize the utilization of resources, to increase throughput, provide good response time and to reduce energy consumption. To optimize resource allocation and ensure the quality of service, this article proposes a novel approach for load-balancing based on the enhanced ant colony optimization.
A Stereo-Vision System for Measuring the Ram Speed of Steam Hammers in an Environment with a Large Field of View and Strong Vibrations

The ram speed of a steam hammer is an important parameter that directly affects the forming performance of forgers. This parameter must be monitored regularly in practical applications in industry. Because of the complex and dangerous industrial environment of forging equipment, non-contact measurement methods, such as stereo vision, might be optimal. However, in actual application, the field of view (FOV) required to measure the steam hammer is extremely large, with a value of 2⁻3 m, and heavy steam hammer, at high-speed, usually causes a strong vibration. These two factors combine to sacrifice the accuracy of measurements, and can even cause the failure of measurements. To solve these issues, a bundle-adjustment-principle-based system calibration method is proposed to realize high-accuracy calibration for a large FOV, which can obtain accurate calibration results when the calibration target is not precisely manufactured. To decrease the influence of strong vibration, a stationary world coordinate system was built, and the external parameters were recalibrated during the entire measurement process. The accuracy and effectiveness of the proposed technique were verified by an experiment to measure the ram speed of a counterblow steam hammer in a die forging device.
What is (un)acceptable?

column Share on What is (un)acceptable?: thoughts on social acceptability in HCI research Authors: Marion Koelle University of Oldenburg University of OldenburgView Profile , Thomas Olsson Tampere University Tampere UniversityView Profile , Robb Mitchell University of Southern Denmark University of Southern DenmarkView Profile , Julie Williamson University of Glasgow University of GlasgowView Profile , Susanne Boll University of Oldenburg University of OldenburgView Profile Authors Info & Claims InteractionsVolume 26Issue 3May - June 2019 pp 36–40https://doi.org/10.1145/3319073Published:23 April 2019Publication History 5citation2,197DownloadsMetricsTotal Citations5Total Downloads2,197Last 12 Months498Last 6 weeks39 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
A Learning Platform for SQL Injection

We present a web application system where users can learn about and practice SQL injection attacks. Our system is designed for students in a university level database or computer security class, and is aimed towards students familiar with SQL but with little experience in web security. Our platform currently contains 12 levels, each of which demonstrates a SQL vulnerability that the user must exploit. For each level, we explain the goal of the challenge, and also provide detailed solutions. Our system provides advantages over other methods of teaching SQL injection because it is hands-on, the challenges provide a greater scope of vulnerability coverage, and is easily extensible, allowing instructors to add their own SQL injection problems for their students.
SwitchAgg

Many distributed applications adopt a partition/aggregation pattern to achieve high performance and scalability. The aggregation process, which usually takes a large portion of the overall execution time, incurs large amount of network traffic and bottlenecks the system performance. To reduce network traffic, some researches take advantage of network devices to commit innetwork aggregation. However, these approaches use either special topology or middle-boxes, which cannot be easily deployed in current datacenters. The emerging programmable RMT switch brings us new opportunities to implement in-network computation task. However, we argue that the architecture of RMT switch is not suitable for in-network aggregation since it is designed primarily for implementing traditional network functions. In this paper, we first give a detailed analysis of in-network aggregation, and point out the key factor that affects the data reduction ratio. We then propose SwitchAgg, which is an innetwork aggregation system that is compatible with current datacenter infrastructures. We also evaluate the performance improvement we have gained from SwitchAgg. Our results show that, SwitchAgg can process data aggregation tasks at line rate and gives a high data reduction rate, which helps us to cut down network traffic and alleviate pressure on server CPU. In the system performance test, the job-completion-time can be reduced as much as 50%.
The Propagated Skeleton: A Robust Detail-Preserving Approach

A skeleton is a centered geometric representation of a shape that describes the shape in a simple and intuitive way, typically reducing the dimension by at least one. Skeletons are useful in shape analysis and recognition since they provide a framework for part decomposition, are stable under topology preserving deformation, and supply information about the topology and connectivity of the shape. The main drawback to skeletonization algorithms is their sensitivity to small boundary perturbations: noise on a shape boundary, such as pixelation, will produce many spurious branches within a skeleton. As a result, skeletonizations often require a second pruning step. In this article, we propose a new 2D skeletonization algorithm that directly produces a clean skeleton for a shape, avoiding the creation of noisy branches. The approach propagates a circle inside the shape, maintaining neighborhood-based contact with the boundary and bypassing boundary oscillations below a chosen threshold. By explicitly modeling the scale of noise via two parameters that are shape-independent, the algorithm is robust to noise while preserving important shape details. Neither preprocessing of the shape boundary nor pruning of the skeleton is required. Our method produces skeletons with fewer spurious branches than other state-of-the-art methods, while outperforming them visually and according to error measures such as Hausdorff distance and symmetric difference, as evaluated on the MPEG-7 database (1033 images).
Automated Enemy Avoidance of Unmanned Aerial Vehicles Based on Reinforcement Learning

This paper focuses on one of the collision avoidance scenarios for unmanned aerial vehicles (UAVs), where the UAV needs to avoid collision with the enemy UAV during its flying path to the goal point. Such a type of problem is defined as the enemy avoidance problem in this paper. To deal with this problem, a learning based framework is proposed. Under this framework, the enemy avoidance problem is formulated as a Markov Decision Process (MDP), and the maneuver policies for the UAV are learned based on a temporal-difference reinforcement learning method called Sarsa. To handle the enemy avoidance problem in continuous state space, the Cerebellar Model Arithmetic Computer (CMAC) function approximation technique is embodied in the proposed framework. Furthermore, a hardware-in-the-loop (HITL) simulation environment is established. Simulation results show that the UAV agent can learn a satisfying policy under the proposed framework. Comparing with the random policy and the fixed-rule policy, the learned policy can achieve a far higher possibility in reaching the goal point without colliding with the enemy UAV.