Reviewerly/exHarmony huggingface dataset:
Effects of Button Width, Height, and Location on a Soft Keyboard: Task Completion Time, Error Rate, and Satisfaction in Two-Thumb Text Entry on Smartphone

This paper examined touch performance and satisfaction as a function of button width, height, and location in two-thumb interactions on a soft keyboard. Thirty-two college students with an experience of smartphone-use were recruited for testing. Task completion time (s), error rate, and subjective satisfaction were measured for different combinations of button widths (4.0, 5.3, 6.7, and 8.0 mm), heights (4.0, 5.3, 6.7, and 8.0 mm), and locations (LT: left-top, LC: left-center, LB: left-bottom, RT: right-top, RC: right-center, and RB: right-bottom). Task completion times (0.83 s) at LC and RC were significantly shorter than the peripheral button locations (0.91 s). Satisfaction scores (1: highly dissatisfied ~ 7: highly satisfied) at LB and RB (4.1) were significantly worse than the other buttons (4.8). The linear regression analyses with button widths and heights for button locations revealed that increasing button height had a relatively larger impact in improving task completion times and error rates at LT and RT than increasing button width. However, increasing button height or width rate at the other button locations had similar impacts on one another in improving the task completion times and errors.
Input Estimation Over Frequency Region in Presence of Disturbances

A new formulation of the unknown input estimation problem in presence of unknown disturbances over a frequency region is presented. Necessary and sufficient conditions for existence of a solution are given and three estimation filters are given. The minimal attenuation is equal to the norm of a rational matrix over the frequency region. As a result, the minimum can be computed by solving linear matrix inequalities. An example is given, and the estimation with the proposed filters is illustrated by a numerical simulation and is compared with some state-of-the-art estimation filters.
Enemy At the Gateways: Censorship-Resilient Proxy Distribution Using Game Theory

A core technique used by popular proxy-based circumvention systems like Tor is to privately and selectively distribute the IP addresses of circumvention proxies among censored clients to keep them unknown to the censors.In Tor, for instance, such privately shared proxies are known as bridges.A key challenge to this mechanism is the insider attack problem: censoring agents can impersonate benign censored clients in order to learn (and then block) the privately shared circumvention proxies.To minimize the risks of the insider attack threat, in-thewild circumvention systems like Tor use various proxy assignment mechanisms in order to minimize the risk of proxy enumeration by the censors, while providing access to a large fraction of censored clients.Unfortunately, existing proxy assignment mechanisms (like the one used by Tor) are based on ad hoc heuristics that offer no theoretical guarantees and are easily evaded in practice.In this paper, we take a systematic approach to the problem of proxy distribution in circumvention systems by establishing a gametheoretic framework.We model the proxy assignment problem as a game between circumvention system operators and the censors, and use game theory to derive the optimal strategies of each of the parties.Using our framework, we derive the best (optimal) proxy assignment mechanism of a circumvention system like Tor in the presence of the strongest censorship adversary who takes her best censorship actions.We perform extensive simulations to evaluate our optimal proxy assignment algorithm under various adversarial and network settings.We show that the algorithm has superior performance compared to the state of the art, i.e., provides stronger resistance to censorship even against the strongest censorship adversary.Our study establishes a generic framework for optimal proxy assignment that can be applied to various types of circumvention systems and under various threat models.We conclude with lessons and recommendations for the design of proxy-based circumvention systems.
DAMPAK PERUBAHAN GUNA LAHAN TERHADAP KEMAMPUAN RESAPAN AIR (KASUS: KOTA SAMARINDA)

As the capital of the province of East Kalimantan, Samarinda City developments has a rapid progress from year to year. Samarinda City Development has a tendency oriented towards infrastructure development without regard to the existence of the quality of the existing environment. Imbalance of development in Samarinda city is to start decreasing the water catchment area, so its make increasing intensity of flood in the Samarinda City. The purpose of this study was to analyze the impact of changes in land use in the Samarinda city on the ability of the water catchment area. This research method using descriptive approach, the data collection system of primary and secondary. Intensity flood in the Samarinda city is increasing from year to year, this condition happened as a problem that always occurs during the rainy season. Current development trends, always take an area that should be an infiltration area for Samarinda City. Culture and inadequate infrastructure conditions such as lack of system of drainage and polder, was another factor causing the high intensity of flood in Samarinda City. Therefore, the relevant regulations development guidelines for Samarinda City must consider all aspects of planning, in this case especially the important of a balance of cultivated land and protected areas or zones.Keywords: Changes in land use; intensity of puddles; Samarinda
NEED ANALYSIS ON THE DEVELOPMENT OF WRITING COMPETENCY TEST FOR FOREIGN UNIVERSITY STUDENTS

Purpose of Study: This study aims to explore the needs of BIPA learners and BIPA program managers. The research strategy applied in this research is descriptive explanatory. The subjects of this study are foreign students of Darmasiswa and KNB programs and the managers of BIPA programs at Muhammadiyah Universities that manage BIPA programs.&#x0D; Methodology: Data collection is done by document (document analysis), participant observation, in-depth interviewing, and questionnaire. The data validity test is done by data triangulation technique, investigative triangulation, and source triangulation.&#x0D; Results: Based on the results of the analysis, it is known that there is a difference of need between BIPA learner and BIPA manager in developing writing competence test for foreign students.&#x0D; Implications/Applications: This has an impact on the importance of needs considerations as a basis for developing the competency test of foreign students writing.
Investigation of the Kolmogorov–Wiener filter for treatment of fractal processes on the basis of the Chebyshev polynomials of the second kind

We consider the Kolmorogov-Wiener filter for continuous fractal processes with a power-law structure function.The corresponding filter is used for data forecast; the noiseless case is considered.The aim of the paper is to obtain the weight function for the corresponding filter based on the integral Wiener-Hopf equation.The problem under consideration is important, for example, for traffic forecast in telecommunication systems and for the forecast of the chemical composition of cast iron.An exact analytical solution for the corresponding equation meets difficulties, so an approximate solution is sought in the form of a truncated Chebyshev polynomial expansion.The Chebyshev polynomials of the second kind are used.The behavior of the polynomial solutions for different numbers of polynomials is investigated.The results are compared with the corresponding results of our previous paper where another polynomial set is used.It is found that the corresponding behavior is almost identical for different polynomial sets.
Augmented Reality for Real-Time Navigation Assistance to Wheelchair Users with Obstacles’ Management

Despite a rapid technological evolution in the field of technical assistance for people with motor disabilities, their ability to move independently in a wheelchair is still limited. New information and communication technologies (NICT) such as augmented reality (AR) are a real opportunity to integrate people with disabilities into their everyday life and work. AR can afford real-time information about buildings and locations’ accessibility through mobile applications that allow the user to have a clear view of the building details. By interacting with augmented environments that appear in the real world using a smart device, users with disabilities have more control of their environment. In this paper, we propose a decision support system using AR for motor disabled people navigation assistance. We describe a real-time wheelchair navigation system equipped with geological mapping that indicates access path to a desired location, the shortest route towards it and identifies obstacles to avoid. The prototyped wheelchair navigation system was developed for use within the University of Lille campus.
Automatic Rule Generator via FP-Growth for Eye Diseases Diagnosis

The conventional approach in developing a rule-based expert system usually applies a tedious, lengthy and costly knowledge acquisition process. The acquisition process is known as the bottleneck in developing an expert system. Furthermore, manual knowledge acquisition can eventually lead to erroneous in decision-making and function ineffective when designing any expert system. Another dilemma among knowledge engineers are handing conflict of interest or high variance of inter and intrapersonal decisions among domain experts during knowledge elicitation stage. The aim of this research is to improve the acquisition of knowledge level using a data mining technique. This paper investigates the effectiveness of an association rule mining technique in generating new rules for an expert system. In this paper, FP-Growth is the machine learning technique that was used in acquiring rules from the eye disease diagnosis records collected from Sumatera Eye Center (SMEC) Hospital in Pekanbaru, Riau, Indonesia. The developed systems are tested with 17 cases. The ophthalmologists inspected the results from automatic rule generator for eye diseases diagnosis. We found that the introduction of FP-Growth association rules into the eye disease knowledge-based systems, able to produce acceptable and promising eye diagnosing results approximately 88% of average accuracy rate. Based on the test results, we can conclude that Conjunctivitis and Presbyopia disease are the most dominant suffering in Indonesia. In conclusion, FP-growth association rules are very potential and capable of becoming an adequate automatic rules generator, but still has plenty of room for improvement in the context of eye disease diagnosing.
Improving Randomized Learning of Feedforward Neural Networks by Appropriate Generation of Random Parameters

In this work, a method of random parameters generation for randomized learning of a single-hidden-layer feedforward neural network is proposed. The method firstly, randomly selects the slope angles of the hidden neurons activation functions from an interval adjusted to the target function, then randomly rotates the activation functions, and finally distributes them across the input space. For complex target functions the proposed method gives better results than the approach commonly used in practice, where the random parameters are selected from the fixed interval. This is because it introduces the steepest fragments of the activation functions into the input hypercube, avoiding their saturation fragments.
Integrating Semantic Run-Time Modelsfor Adaptive Software Systems

Software-intensive systems work in ever-changing environments requiring expensive technical efforts to manage their evolution. In order to mitigate their risks and costs they should dynamically self-adapt to any modification of their environment. MAPE-K (Monitor,Analyze, Plan, Execute – Knowledge) is the basic architectural pattern for building software-intensive self-adaptable systems. In this paper we propose an approach in which all the information about a system and its environment is unified by using Semantic Web technologies into a set of semantic run-time models which enhance the Knowledge in MAPE-K. Ontologies are used to manage the interaction and integration of these models with disparate data sources. The resulting knowledge base is then used to drive adaptation activities exploiting well known languages and notations. We discuss how MAPE-K can be exploited in order to take advantage of ontological representations, along with Semantic Web languages and tools, by studying a real-word case study: a legacy system that was not designed to perform automatic adaptation. We discuss merits and limits of our approach based on semantic runtime models both in the context of this specific case study and in a broader scope.
Analisis Keamanan Sistem Informasi Berdasarkan Framework COBIT 5 Menggunakan Capability Maturity Model Integration (CMMI)

Information technology is a very important part of a company or institution. The information system itself is expected to provide better benefits for companies or institutions. However, along with technological developments are often misused by some irresponsible parties that can lead to threats of the use of technology. Information system security is very important for institutions to maintain information optimally and safely. The existence of a security problem triggers a procedure to control access rights in an information system. A good information system is an information system that can be assessed at a security level, so that it can provide comfort for users. COBIT 5 as an information technology security control standard. Whereas to achieve the standard level of achievement CMMI is needed in information technology security. The combination of the two standards in the information system is able to provide a level of achievement of information technology. The results obtained from the maturity value are 4,458 which means the institutions are at the Managed and Measurable level. This level, institutions are increasingly made aware of technological developments. Institutions have implemented the quantification concept in each process, and are always monitored and controlled for performance. Information system security at this level is good, it's just that it still needs innovation and development to be ready, fast and right in handling security threats.
Registration of vascular structures using a hybrid mixture model

Morphological changes to anatomy resulting from invasive surgical procedures or pathology, typically alter the surrounding vasculature. This makes it useful as a descriptor for feature-driven image registration in various clinical applications. However, registration of vasculature remains challenging, as vessels often differ in size and shape, and may even miss branches, due to surgical interventions or pathological changes. Furthermore, existing vessel registration methods are typically designed for a specific application. To address this limitation, we propose a generic vessel registration approach useful for a variety of clinical applications, involving different anatomical regions. A probabilistic registration framework based on a hybrid mixture model, with a refinement mechanism to identify missing branches (denoted as HdMM+) during vasculature matching, is introduced. Vascular structures are represented as 6-dimensional hybrid point sets comprising spatial positions and centerline orientations, using Student’s t-distributions to model the former and Watson distributions for the latter. The proposed framework is evaluated for intraoperative brain shift compensation, and monitoring changes in pulmonary vasculature resulting from chronic lung disease. Registration accuracy is validated using both synthetic and patient data. Our results demonstrate, HdMM+ is able to reduce more than $$85\%$$ of the initial error for both applications, and outperforms the state-of-the-art point-based registration methods such as coherent point drift and Student’s t-distribution mixture model, in terms of mean surface distance, modified Hausdorff distance, Dice and Jaccard scores. The proposed registration framework models complex vascular structures using a hybrid representation of vessel centerlines, and accommodates intricate variations in vascular morphology. Furthermore, it is generic and flexible in its design, enabling its use in a variety of clinical applications.
Application of ANCOVA and MANCOVA in language assessment research

This chapter introduces the analysis of covariance (ANCOVA) and the multivariate analysis of covariance (MANCOVA) as statistical techniques for comparing group means in language assessment research. The chapter starts with a brief description of the basics and assumptions of ANCOVA and/or MANCOVA and provides suggestions for reporting the results. Research articles that have used these techniques are reviewed from four major language assessment journals. Key issues in the application of ANCOVA and MANCOVA, such as assumption check and effect size reporting, are highlighted. A sample dataset derived from the 2009 Programme for International Student Assessment (PISA) with variables related to students’ reading performance is analyzed to demonstrate how ANCOVA and MANCOVA can be conducted using SPSS. Data analysis procedures, including model building and assumption checks, are described in the companion tutorial. The results of the demonstration study are then summarized and briefly discussed, providing an accessible overview of the application of ANCOVA and MANOVA in language assessment research.
Lattice structure based metric for feature data fusion

For stably reducing data redundancy which has negative impact on data processing, this paper integrates the ideas from lattice structure and develops a lattice structure based metric for feature fusion. The metric distance between two lattice nodes, which is a key factor for the lattice structure based metric, is determined by the shortest path length from these two nodes to their nearest descendant node in lattice structure. Then, based on the metrics between lattice nodes, the source feature dataset is divided into different subsets following the principles of minimum metric precedence and gradual expansion. The duplicate feature data in a subset is fused into an object data unit by using the median operation. The experimental results show that the proposed feature fusion method can better improve the completeness and conciseness of the existing feature data stably and has wide range of applications in the related fields on data processing.
Sentiment analysis on educational datasets: a comparative evaluation of commercial tools

Sentiment analysis systems have been gaining increasing popularity for extracting information regarding students' affective state. Developing such systems from scratch is a challenging task, thus, many studies employ commercial, general-purpose tools that are not domain-specific. The aim of the present work is to comparatively evaluate the performance of five well-known commercial/academic sentiment analysis tools on two educational datasets and contrast it with the performance of educational domain-specific tools, at document and sentence level. Findings suggest that: a) different tools work better for specific datasets and analysis levels, b) depending on the dataset, a general-purpose tool might be a viable solution, and c) any method, domain-specific or general-purpose one, should be evaluated before employed.
AUTOMATIC DETECTION AND LABELLING OF PHOTOGRAMMETRIC CONTROL POINTS IN A CALIBRATION TEST FIELD

Abstract. In this work, a new method is developed for the automatic and accurate detection and labelling of signalized, un-coded circular targets for the purpose of automated camera calibration in a test field. The only requirements of this method are the approximate height of the camera, an approximate range of orientations of the camera, and the object-space coordinates of the targets. In each image, circular targets are detected using adaptive thresholding and robust ellipse fitting. Labelling of those targets is performed next. First, the exterior orientation parameters of the image are estimated using a one-point pose-estimation approach, where a list of possible orientation and target labels are used, along with height, to calculate the camera position. The estimated position and orientation of the camera combined with the interior orientation parameters (IOPs) are then used to back-project the known object-space coordinates of the targets into the image space. These targets are then matched against the targets detected in the image, and the list entry with the best fit is chosen as the solution. This resolves both the detection and labelling of the targets, without the need for any coded targets or their associated software packages, and each image is solved independently allowing for parallel processing. This process accurately labels 92–97% of images, with average accuracy rates of 97% or better, and average completeness rates of 70–95% in imagery from the three cameras tested. The cameras were calibrated using observations from the detection and labelling process, which resulted in sub-pixel root mean square (RMS) values determined for the pixel space residuals.
Cryptanalysis of an Identity-Based Encryption Scheme With Equality Test and Improvement

Privacy-presenting for cloud computing has been concerned for several years. Public key encryption with equality test as a variant of keyword searchable encryption is one of the important concepts in this area. In order to simplify the management of certificate and optimize the scheme, an identity-based encryption with equality test was proposed by Wu et al. In this paper, we analyze that some binary operations of the encryption in the scheme are not clear so that the scheme is not more efficient than the existing schemes or the test of the scheme is not available when we analyze the most possible definitions of the binary operations in the scheme. Finally, we improve the scheme and furthermore, our improved scheme is fine-grained.
Convex-Split and Hypothesis Testing Approach to One-Shot Quantum Measurement Compression and Randomness Extraction

This paper concerns the problem of quantum measurement compression with side information in the one-shot setting with shared-randomness. In this problem, Alice shares a pure quantum state with Bob and the reference system. She performs a measurement on her registers and wishes to communicate the outcome to Bob using shared-randomness and classical communication. The outcome that Bob receives must be correctly correlated with the reference system and his own registers. Our goal is to concurrently minimize the classical communication and shared-randomness cost. The suggested protocol presented in this paper is based on convex-split and position based decoding. The communication is upper bounded in terms of smooth max and hypothesis testing relative entropies. A second protocol addresses the task of strong randomness extraction in the presence of quantum side information. The protocol provides an error guarantee in terms of relative entropy (as opposed to trace distance) and extracts close to the optimal number of uniform bits. As an application, we provide a new achievability result for the task of quantum measurement compression without feedback, in which Alice does not need to know the outcome of the measurement. The result achieves the optimal number of bits communicated and the required number of bits of shared-randomness, for the same task in the asymptotic and i.i.d. setting.