Reviewerly/exHarmony huggingface dataset:
Semantics-driven extraction of timed automata from Java programs

The automatic verification of time properties of models extracted from programs is challenging, mainly because modern programming languages, such as Java, represent time without a proper semantics. Current approaches to extract time models from source code either represent time only as a tree-like sequence of events or require developers to manually provide a formal model of the time behavior. This makes it difficult for software developers to verify various aspects of their systems, such as timeouts, delays and periodicity of the execution. In this paper, we introduce a formal definition of the time semantics for the Java programming language. Based on the semantics, we present an approach to automatically extract timed automata and their time constraints from Java programs at method level. First, our approach detects the Java statements that involve time, from which it then extracts the timed automata. Our extracted automata are directly amenable to the verification of time properties of the corresponding Java methods. We evaluated the accuracy of our approach on twenty open source Java projects that implement time behavior in their source code. The results show that our approach achieves 100% precision and recall in identifying time related information. They also show that 95% of the timed automata extracted from source code correctly model the time behavior of the method. Finally, we show the applicability of our timed automata to identify eight real errors in four open source Apache systems.
Inferring target locations from gaze data

Although smartphones are widely used in everyday life, studies of viewing behavior mainly employ desktop computers. This study examines whether closely spaced target locations on a smartphone can be decoded from gaze. Subjects wore a head-mounted eye tracker and fixated a target that successively appeared at 30 positions spaced by 10.0 × 9.0 mm. A "hand-held" (phone in subject's hand) and a "mounted" (phone on surface) condition were conducted. Linear-mixed-models were fitted to examine whether gaze differed between targets. T-tests on root-mean-squared errors were calculated to evaluate the deviation between gaze and targets. To decode target positions from gaze data we trained a classifier and assessed its performance for every subject/condition. While gaze positions differed between targets (main effect "target"), gaze deviated from the real positions. The classifier's performance for the 30 locations ranged considerably between subjects ("mounted": 30 to 93 % accuracy; "hand-held": 8 to 100 % accuracy).
Assessing the Usage of Ubiquitous Learning

High success is the main objective of any education system. Thus, in any educational organization, the education offered must be efficient and effective. It is evident that some factors are critical for such achievements. Ubiquitous learning systems and interactive video courses incorporate features which can be measured using learning analytics. In this paper, we adopt nine dimensions of u-learning. According to the learners’ interactions, we suggest indexes and metrics for the assessment of ubiquitous and interactive video courses. These indexes and metrics are associated with the presented u-learning dimensions. The proposed metrics are calculated for a case study in a higher education institute, and the results are explained and associated with the introduced u-learning dimensions.
Effect of Wavelet Decomposition on Database Size Reduction for Face Recognition Rate

This paper presents the effect of the discrete wavelet decomposition on the database reduction, which is used in biometric face recognition. The Olivetti and Oracle Research Laboratory is own face databases for training and testing. The first level wavelet decomposition was applied to the training and testing faces made the size of database decreasing by 75% out of original size. Both databases are treated by principal component analysis algorithm to get the effect of that on face recognition rate. Principal component analysis algorithm is biometric face recognition. The experiment results appeared the reduction in face recognition rate between original and reduced database is 2%. This approach is more efficient for reduced the database, and computational load with a maintaining an acceptable proportion of recognition rate.
Introduction to Linked Data

This chapter presents Linked Data, a new form of distributed data on the web which is especially suitable to be manipulated by machines and to share knowledge. By adopting the linked data publication paradigm, anybody can publish data on the web, relate it to data resources published by others and run artificial intelligence algorithms in a smooth manner. Open linked data resources may democratize the future access to knowledge by the mass of internet users, either directly or mediated through algorithms. Governments have enthusiastically adopted these ideas, which is in harmony with the broader open data movement.
Towards a low cost and high speed mobile eye tracker

Despite recent developments in eye tracking technology, mobile eye trackers (ET) are still expensive devices limited to a few hundred samples per second. High speed ETs (closer to 1 KHz) can provide improved flexibility for data filtering and more reliable event detection. To address these challenges, we present the Stroboscopic Catadioptric Eye Tracking (SCET) system, a novel approach for mobile ET based on rolling shutter cameras and stroboscopic structured infrared lighting. SCET proposes a geometric model where the cornea acts as a spherical mirror in a catadioptric system, changing the projection as it moves. Calibration methods for the geometry of the system and for the gaze estimation are presented. Instead of tracking common eye features, such as the pupil center, we track multiple glints on the cornea. By carefully adjusting the camera exposure and the lighting period, we show how one image frame can be divided into several bands to increase the temporal resolution of the gaze estimates. We assess the model in a simulated environment and also describe a prototype implementation that demonstrates the feasibility of SCET, which we envision as a step further in the direction of a mobile, robust, affordable, and high-speed eye tracker.
Time-Universal Data Compression

Nowadays, a variety of data-compressors (or archivers) is available, each of which has its merits, and it is impossible to single out the best ones. Thus, one faces the problem of choosing the best method to compress a given file, and this problem is more important the larger is the file. It seems natural to try all the compressors and then choose the one that gives the shortest compressed file, then transfer (or store) the index number of the best compressor (it requires log m bits, if m is the number of compressors available) and the compressed file. The only problem is the time, which essentially increases due to the need to compress the file m times (in order to find the best compressor). We suggest a method of data compression whose performance is close to optimal, but for which the extra time needed is relatively small: the ratio of this extra time and the total time of calculation can be limited, in an asymptotic manner, by an arbitrary positive constant. In short, the main idea of the suggested approach is as follows: in order to find the best, try all the data compressors, but, when doing so, use for compression only a small part of the file. Then apply the best data compressors to the whole file. Note that there are many situations where it may be necessary to find the best data compressor out of a given set. In such a case, it is often done by comparing compressors empirically. One of the goals of this work is to turn such a selection process into a part of the data compression method, automating and optimizing it.
Safety precautions for swimmers - A frequent whirlpool appearance at Pattaya Bay

Water current flow characteristics represent crucial information required for the management of coastal tourism destinations. Strong currents or strange flow patterns can pose risks to beach visitors. This research examined the currents at Pattaya Bay, one of the most famous beaches in the world. Numerical simulations were undertaken. Sophisticated simulation techniques were applied so that the calibrated simulation results were reliable. A whirlpool at the southern part of the bay was discovered, occurring every day during flood tides. The vortex rotated clockwise, with a magnitude between 0.34 and 0.41 m/s, and its radius could sometimes reach 500 m. The swirl was a natural phenomenon since it was created by the oblique orientation of the southern headland. The whirlpool was not deadly since its strength was normal for open coastal waters. However, care should not be neglected. This research raised awareness about the whirlpool's existence, so that proper management can be exercised.
Cross-Platform Enterprise Application Development Framework for Large Screen Surfaces

In recent years there has been an increasing interest in the development of applications on large surfaces that allow interaction with the user through gestures and multitouch events, this is how born the idea of Large Screen Surfaces Devices. Due to the current task of developing applications that rely on privative frameworks and having to rewrite the code to port the project to multiple platforms, we have seen as an opportunity the implementation of a cross-platform framework for the development of enterprise applications designed to support this technology. This paper presents the procedures for the creation of the framework and the implementation of two applications that validate the worked functionalities. Additionally, this Framework presents a low cost implementation compared to actual devices in the market.
A modified algorithm for improving the coding of Arabic characters by the Freeman chain code

This work proposes a modified algorithm to give the Freeman code for the Arabic character (manuscript or printed, after making it thin) in a manner that takes into account all the forms on which the Arabic character can be, and with the lowest possible code length, without losing any information about the details of the character. So as to facilitate its later normalization and exploit it for the purpose of Arabic character recognition or for another purpose. We tested the code that our modified algorithm gives with a group of Arabic characters in its various forms and compared it with the code given by the algorithm used in previous related works. The average of reduction of our algorithm for the length of the code compared to the previous works algorithm is around 45%. In addition, the code we obtained represents the Arabic character accurately in all its forms, even those not represented by the previous works algorithm accurately.
Adaptable Concept for Projects in Computer Science Degree Programs – An Experience Report

Nowadays many praxis-oriented courses like projects are included in program curricula. They prepare the students to work in real-world projects. The setup of project courses shall offer an environment similar to real-world projects, such that the students can gain experience. This paper presents a concept consisting of several variable aspects in the setup of project courses in computer science education. Different implementation alternatives for the aspects are shown. Their benefits and drawbacks are discussed in detail as well as their dependencies. The aspects can be adapted to the requirements for the project course like the students' work load. Proven adaptations for setting up project courses of high practical relevance are outlined from the author's perspective.
Learning by Arguing in Argument-Based Machine Learning Framework

We propose an approach for the development of argument-based intelligent tutoring systems in which a domain that can be successfully addressed by supervised machine learning is taught in an interactive learning environment. The system is able to automatically select relevant examples and counter-examples to be explained by the students. The students learn by explaining specific examples, and the system provides automated feedback on students' arguments, including generating hints. The role of an argument-based intelligent tutoring system is then to train the students to find the most relevant arguments. The students learn about the high-level domain concepts and then use them to argue about automatically selected examples. We demonstrate our approach in an online application that allows students to learn through arguments with the goal of improving their understanding of financial statements.
Promoting and Implementing Digital STEM Education at Secondary Schools in Africa

This paper discusses an ongoing initiative aimed at promoting and implementing digital STEM education at secondary schools in Africa. This initiative, coined Go-Lab Goes Africa (GO-GA), is an innovation action supported by the European Commission through its H2020 Framework Programme for Research and Technological Development in Information and Communication Technologies (ICT). The general vision and the implementation strategy are outlined in detail, as well as the challenges faced and results achieved during its first year.
Probabilistic reasoning for real-time UAV decision and control

Developing onboard abilities to control and task unmanned vehicles (UxVs) and swarms of UxVs is key to both wider use of systems. Herein, the authors develop and apply a probabilistic reasoning framework for UxVs. The reasoning system considers tasks, in this case search and rescue, based on both prior knowledge and sensor feedback. The approach considered is an imperative program to generate situation de-scriptions and decision problems as probabilistic, declarative programs. This operation replaces human tasking of UxVs. Results indicate a significant decrease in swarm fuel usage when compared to manned tasking of assets for the same task..
Chemostratigraphic constraints on the nature and origin of felsic schist units hosting stratabound and orogenic vein gold on Lone Star Ridge, Klondike Gold District, Yukon

Felsic schist units in the Lone Star Ridge area of the northern Klondike Gold District, Yukon, Canada, host both locally gold-bearing orogenic quartz veins and much more extensive zones of disseminated gold mineralization that is stratabound within specific schist units and is interpreted to be syngenetic in origin. Scarcity of outcrop and the difficulty in distinguishing between subtly different schist units has hampered correlation between surface exposures and drill core, and production of a detailed geological map of the area. Lithogeochemistry offers an alternative, unbiased way to map and interpret the various subunits of the felsic schist package that host disseminated gold mineralization and gold-bearing veins in parts of the Klondike, and that can potentially form the basis for correlations between various drill holes, and between drill holes and surface exposures. A detailed investigation of 4590 multi-element geochemical analyses (by 4-acid digestion) from a set of 27 diamond drill holes in the vicinity of the Boulder Lode open cut (Lone Star gold occurrence) on Lone Star Ridge has revealed several lithogeochemical signatures that correlate closely with gold content and to some extent with lithological units distinguished during initial logging of the drill core. Research quality geochemical analyses for a suite of representative rock units selected from the 2005 drill holes have been used to verify the multi-element geochemical analyses, and to determine the petrochemical character of some of the individual rock units. Results of this study demonstrate that it is possible to construct a chemostratigraphic framework for the felsic schist units in the vicinity of the Boulder Lode. Distinctive rock units can be broken out that have very consistent geochemical signatures, and elevated gold values related to the presence of disseminated gold appear to be consistently associated with specific rock units. These chemostratigraphic marker units have been utilized to constrain the geology and structure of the immediate study area, and in particular to define the geometry of the schist units that contain disseminated gold. The same approach has also been applied to a very extensive set of geochemical data (comprising nearly 34,000 individual samples) from chip samples, diamond drill core, reverse circulation drill samples, and conventional soil samples obtained during other historical and more recent exploration programs, to better resolve the geological interpretation of entire Lone Star Ridge area.
Motion Camouflage for Point-Mass Robots Using a Lyapunov-based Control Scheme

This article presents a new solution to the motion camouflage problem for point-mass robots using a Lyapunov-based control scheme. We consider a workspace with one shadower and one shadowee. We design our control inputs for shadower and the shadowee such that at each moment the shadower must follow the camouflage constraint line (the line that joins the instantaneous position of the shadowee with a fixed focal point). This strategy allows the shadower to appear stationary to the shadowee and will help the shadower to effectively catch the shadowee without spending much of its energy. The convergence of the two robots to a neighborhood of a pre-defined target is guaranteed by the Lyapunov's direct method. Computer simulations validate the control inputs and the effectiveness of Lyapunov-based control scheme.
Scientific Developers v/s Static Analysis Tools: Vision and Position Paper

Usability and the use of automated static analysis tools in the software development process have been an evolving subject of research in the last decades. Several studies shed light on issues like high false positive rates and low comprehensibility, which hinder tool adoption for even software engineers. Yet, the tools' perceived usefulness and ease of use play a much larger role when it comes to untrained software developers, as is usually the case in scientific software development. In this paper, we outline a multi-stage interview study to learn more about how scientists come to accept and use static analysis tools.
Library adoption in public software repositories

We study the the spread and adoption of libraries within Python projects hosted in public software repositories on GitHub. By modelling the use of Git pull, merge, commit, and other actions as deliberate cognitive activities, we are able to better understand the dynamics of what happens when users adopt new and cognitively demanding information. For this task we introduce a large corpus containing all commits, diffs, messages, and source code from 259,690 Python repositories (about 13% of all Python projects on Github), including all Git activity data from 89,311 contributing users. In this initial work we ask two primary questions: (1) What kind of behavior change occurs near an adoption event? (2) Can we model future adoption activity of a user? Using a fine-grained analysis of user behavior, we show that library adoptions are followed by higher than normal activity within the first 6 h, implying that a higher than normal cognitive effort is involved with an adoption. Further study is needed to understand the specific types of events that surround the adoption of new information, and the cause of these dynamics. We also show that a simple linear model is capable of classifying future commits as being an adoption or not, based on the commit contents and the preceding history of the user and repository. Additional work in this vein may be able to predict the content of future commits, or suggest new libraries to users.
Deep fusion of contextual and object-based representations for delineation of multiple nuclear phenotypes

Nuclear delineation and phenotypic profiling are important steps in the automated analysis of histology sections. However, these are challenging problems due to (i) technical variations (e.g. fixation, staining) that originate as a result of sample preparation; (ii) biological heterogeneity (e.g. vesicular versus high chromatin phenotypes, nuclear atypia) and (iii) overlapping nuclei. This Application-Note couples contextual information about the cellular organization with the individual signature of nuclei to improve performance. As a result, routine delineation of nuclei in H&E stained histology sections is enabled for either computer-aided pathology or integration with genome-wide molecular data.The method has been evaluated on two independent datasets. One dataset originates from our lab and includes H&E stained sections of brain and breast samples. The second dataset is publicly available through IEEE with a focus on gland-based tissue architecture. We report an approximate AJI of 0.592 and an F1-score 0.93 on both datasets.The code-base, modified dataset and results are publicly available.Supplementary data are available at Bioinformatics online.
Analyzing the Usage of the Classical ITS Software Architecture and Refining It

The classical ITS software architecture with four components is an old, popular architecture. We analyzed 93 ITS software architectures found in papers with regard to the classical ITS architecture to review the usage of its components, its naming of the components and the addition of new components. In the analysis we detected 13 recurring problems in these architectures. To fix this we propose two refinements to the classical ITS software architecture that should reduce the occurrences of these problems.
Scale adaptive part-based tracking method using multiple correlation filters

Visual tracking plays a significant role in computer vision. Although numerous tracking algorithms have shown promising results, target tracking remains a challenging task due to appearance changes caused by deformation, scale variation, and partial occlusion. Part-based methods have great potential in addressing the deformation and partial occlusion issues. Owing to the addition of multiple part trackers, most of these part-based trackers cannot run in real time. Correlation filters have been used in target tracking owing to their high efficiency. However, the correlation filter-based trackers face great problems dealing with occlusion, deformation, and scale variation. To better address the above-mentioned issues, we present a scale adaptive part-based tracking method using multiple correlation filters. Our proposed method utilizes the scale-adaptive tracker for both root and parts. The target location is determined by the responses of root tracker and part trackers collaboratively. To estimate the target scale more precisely, the root scale and each part scale are predicted with the sequential Monte Carlo framework. An adaptive weight joint confidence map is acquired by assigning proper weights to independent confidence maps. Experimental results on the publicly available OTB100 dataset demonstrate that our approach outperforms other state-of-the-art trackers.
Research on Hybrid Quantum Genetic Algorithm Based on Cross-Docking Delivery Vehicle Scheduling

Quantum genetic algorithm (QGA) is a new algorithm to solve the optimal problem by applying classical quantum theory to genetic algorithm and introducing quantum states into the traditional bit model. Cross-docking delivery vehicle scheduling is a classical combinatorial optimization problem. Based on QGA, in order to improve the speed and efficiency of logistics distribution process, this paper studies a hybrid QGA framework, proposes a new idea to solve the distribution optimization scheme in traditional logistics scheduling, and studies new strategies of quantum updating and probability adjustment, so as to make the method more suitable for the actual problems of logistics distribution.