Reviewerly/exHarmony huggingface dataset:
Analyzing action games

Design support tools benefit from rich information about games' emergent behavior. Inventing successful AI players for particular games can help producing some of this information, but this is both labor intensive and limited in that it can generally only reveal that a solution exists and not say that no solution exists or that certain classes of solution exist. We show a generic method for posing and answering feasible-path, optimal-path, and reachable-space queries in action games, and we devise a measure of game level difficulty. We accomplish all this by encoding action videogame characters as hybrid dynamical systems, using Flappy Bird and Super Mario as case studies.
Research on Design of the Network Multimedia Courseware based on Wireless Sensor Networks for Art Design Teaching

Network teaching plays an important role in modern teaching with development of multimedia information technology. Therefore, how to design the network multimedia courseware for art design teaching in order to improve the teaching quality is a key problem in the network teaching environment, which can meet requirements of art design innovation and provide an important approach to the art design teaching.
Compressing Sparse Ternary Weight Convolutional Neural Networks for Efficient Hardware Acceleration

Reducing the bit-width of weights is an attractive solution for decreasing the large size of CNN models embedded in IoT devices. In the most extreme case, the bit per weight can be reduced to 1-bit in binary weight CNNs. However, this network cannot be compressed further due to the lack of sparsity because the weight distribution of the well-trained model is not biased to either +1 or -1. On the other hand, sparse ternary weight CNNs can be compressed to less than 1-bit per weight, maintaining higher accuracy than binary weight CNNs. Therefore, we propose the following for a weight compression methodology in sparse ternary weight CNNs to minimize the model size: (1) an encoding scheme exploiting high sparsity, (2) two elaborate compression techniques based on encoding direction exploration and layer-wise optimization. To verify the efficiency of hardware acceleration, we design an accelerator that fully exploits our compression scheme. Moreover, a layer rearrangement technique is presented to address a load imbalance problem that occurs during hardware acceleration. As a result, we reduce the effective bit per weight to 0.67-0.80 bit and achieve 4.52-7.70x and 1.52-2.21x improvement of performance and energy efficiency respectively, with higher accuracy compared to previous binary weight CNN work.
Reconstruction of Surfaces with Ordinary Singularities from Their Silhouettes

We present algorithms for reconstructing, up to unavoidable projective automorphisms, surfaces with ordinary singularities in three dimensional space starting from their silhouette, or "apparent contour" - namely the branching locus of a projection on the plane - and the projection of their singular locus.
Swap Kernel Regression

Recent developments in the field of artificial intelligence have increased the demand for high performance computation devices. An edge device is highly restricted not only in terms of its computational power but also memory capacity. This study proposes a method that enables both inference and learning on an edge device. The proposed method involves a kernel machine that works in restricted environments by collaborating with its secondary storage system. The kernel parameters, which are not essential for calculating the output values for the upcoming inputs, are stored in the secondary storage to make space in the main memory. The essential kernel parameters stored in the secondary storage are loaded into the main memory when required. With the use of this strategy, the system can realize the recognition/regression tasks without reducing its generalization capability.
Algorithms for Optimization and Machine Learning over Cloud

Considering avenues and application areas of automation there is a significant push towards research advance in data mining and machine learning areas. Many relevant problems in these areas are computationally hard - generally modeled as optimization problems. These problems can be solved using exact algorithms or with the help of meta-heuristics - designed and inspired by natural computing. In this chapter, we consider two optimization problems - considered to be most central to machine learning and data mining algorithms design - the PCA and LDA computation. We discuss solving these problems in exact way over the cloud environment. We also present these computations when the data is available in streaming fashion. Finally, we present related probabilistic generative models (PPCA and PLDA) and show comparative study between these algorithms with implemented experiments and related results.
Static Enforcement of Security in Runtime Systems

Underneath every modern programming language is a runtime environment (RTE) that handles features such as automatic memory management and thread scheduling. In the information-flow control (IFC) literature, the RTE is often part of the trusted computing base (TCB), and there has been little focus on applying IFC to the implementation of the RTE itself. In this paper we address this problem by designing an IFC language, Zee, for implementing secure RTEs, thereby removing the RTE from the TCB. We implement Zee and design and implement secure versions of garbage collectors and thread schedulers using Zee. We also prove that a faithful calculus of Zee satisfies a strong variant of timing-sensitive noninterference.
Developing Appreciative Literature Textbook Based on ThematicIntegrative Through Active Learning

The objectives of this research were: (1) to describe students' and lecturers' needs on poetry and drama appreciative textbook, (2) to describe the development of textbook model (3) to find the effectiveness of textbook model 4) to describe textbook dissemination. The type of research used was research and development. Research was done through 4 stages: (1) exploration, (2) model development, (3) model testing, (4) dissemination. Exploration stage used qualitative descriptive approach. Data was collected through in-depth interviews, observation, documentation, and questionnaires. Data analysis technique was interactive analysis model. Model testing was done through experimental research. The results of this research were: (1) the exploration stage showed that the poetry and drama learning textbook used by Elementary Teacher Education Program (ETEP) students in Surakarta did not meet the students' and lecturers' need, (2) model development stage produced appreciative poetry and drama textbook through preliminary field testing; (3) model testing phase was to test effectiveness of the textbook used t-test non-independent technique. It was obtained tobtain of 23 and ttable(90; 0.05) of 1.67. Thus, tobtained (23) > ttable (1.67) which meant that the hypothesis was accepted (Ho was rejected). In conclusion, the Apprec iative Poetry and Drama textbook model was effectively improve students' poetry and drama appreciation skills. Dissemination was done through national seminars, article writing in international journals, as well as the publishing of an Integrative-thematic Textbook of Poetry and Drama Learning with ISBN.
Saliency Object Detection Based on Regions Merging and Its Application in Image Retrieval

In image retrieval, due to ignore the fact that different regions of the image have different levels of attraction to the human visual system in the traditional, so features are extracted of the entire image, result in the time is increased of the features matching. So we apply saliency object detection to image retrieval. However, extracting the saliency objects with complex background in the image is still a challenging problem. Thus we propose a region merging strategy to solve this problem. First, boundary super-pixels are clustered to generate the initial saliency map. Next, adjacent regions are merged by sorting the multiple feature values of each region. Finally, we get the final saliency map by merging regions by means of the distance from regions to the image center and the length of the boundary. After we apply the final saliency map to image retrieval. The experiments demonstrate that our method performs favorably on three datasets than state-of-art.
Evaluating and Optimizing Chaotically Behaving Mobile Robots with a Deterministic Simulation

Testing and comparing prototypes is essential during the development of mobile autonomous robots. These robots often behave chaotically, i.e. their behavior is sensitive to small perturbations and their performance is unpredictable. An evaluation and comparison of solutions cannot be based on a few test runs. We present an approach that uses a digital simulation to acquire the data necessary for statistical comparison. The simulation can be sped up and automated and thus allows the developers to compare solutions more quickly than physical tests can. While it is not possible to replicate a test setup in the physical world with sufficient accuracy, the simulation is deterministic and can be used to investigate the dependency of a specific perturbation. We illustrate this approach using three solutions to an example task where the robot needs to remove objects from an area in the shortest time possible. When executing each solution from 99 randomly chosen starting conditions an average performance difference of as little as 2% was distinguished with statistical significance. Differences in performance under an environmental perturbation such as a slight misaligned of the robot’s starting orientation or a hardware perturbation of imprecisely controlled motor speeds are also found to be significant for some solutions showing how the approach can be used to test robustness of solutions to perturbations. Robot performance is dependent on the interplay between the algorithm that controls the robot, the robot hardware and its environment. By testing different algorithms and subjecting them to perturbations of the environment and the robot’s hardware, it is possible to optimize the algorithm-hardware combination for the least restrictive hardware requirements necessary while still ensuring the desired performance. In this paper we offer a quantitative approach for iterative development of the algorithm-hardware combination of mobile autonomous robot solutions.
Improved Metric-Based Recommender by Historical Interactions

A remarkable success in recommendations has been achieved by using methods based on metric learning, especially in digital marketing. However, the existing methods do not consider the relative preferences among items that users like. To overcome this issue, we propose an improved recommender model. First, the model analyses the user-item bipartite graph from historical interactions, and collects user-item similarities based on the topological features from this graph. Then, similar to other metric-based methods, both users and items are embedded as latent positions in a low-dimensional space, where users’ preferences on items are modelled as distances. Thus, we propose an improved metric-based recommender, i.e. the Graph Embedded Metric Factorisation recommender, under the assumptions that (1) the distance between a target user and an interacted-with item is determined by their topological similarity, and (2) for a target user, non-interacted items are located farther away than interacted-with ones. Comprehensive experiments on three practical datasets were implemented. Empirical results indicate that our improved recommender outperforms current state-of-the-art methods when making personalised recommendations based on users’ implicit feedback.
Ledger of Things - Future Technology for Value Creation

The world of Internet of Things (IoT)with smart gadgets, houses, countries, is a certain future. However, the transition of information, on current infrastructure, of smart gadgets so that they can communicate and interact is still a problem. This is because of the limitation of current bandwidth and database, inhibiting connection when opening to new connections. On the other hand, how to monitor everything, ensuring trust and calculate the contribution of IoT is also a difficult problem. In order to solve these problems, new platform must be established, and IoT need a centralized Ledger of Things. This article evaluates the potential of blockchain technology in pushing the development IoT in the future.
Performance Comparison of Association Rule Algorithms with SPMF on Automotive Industry Data

By the recent developments about the information technologies, companies can store their data faster and easier with lower costs. All transactions (sales, current card, invoicing, etc.) performed in companies during the day combine at the end of the day to form big datasets. It is possible to extract valuable information through these datasets with data mining. And this has become more important for companies in terms of today's conditions where the competition in the market is high. In this study, a dataset of a company selling car maintenance and repair products in Turkey is used. Association Rules are applied on this dataset for determining the items which are bought together by the customers. These rules, which are calculated specifically for the company, can be used to redefine the sales and marketing strategies, to revise the storage areas efficiently, and to create sales campaigns suitable for the customers and regions. These algorithms are also called Frequent Itemset Mining Algorithms. The most recent 11 algorithms from these are applied to this dataset in order to compare the performances according to metrics like memory usage and execution times against varying support values and varying record numbers by using SPMF platform. Three different datasets are created by using the whole dataset like 6-months, 12-months and 22-months. According to the experiments, it can be said that executon times generally increases inversely with the support values as nearly all algorithms have higher execution time values for the lowest support value of 0.1. dEclat_bitset algorithm has the most efficient performance for 6-months and 12-months dataset. But Eclat algorithm can be said to be the most efficient algorithm for 0.7 and 0.3 support values; on the other hand dEclat_bitset is the most efficient algorithm for 0.3 and 0.1 support values on 22-months dataset.
Discovering architectural rules in practice

Architecture conformance checking is an important technique to verify whether a software system's implementation adheres to predefined architectural rules. The adherence to architectural rules is crucial, since architectural rules are intended to ensure that fundamental properties are correctly implemented in the source code. A lot of tools have been proposed and developed for automatic conformance checking. They are also successfully applied in industry. However, those tools mainly focus on the validation of static dependencies between modules or on ensuring the adherence to the layered architecture. In this poster, we present an industrial case study which discovers architectural rules from three industrial projects. This study shows that software architects also define other types of architectural rules that are not yet covered by state-of-the-art conformance checking tools. The architectural rules found in the projects are categorized into architectural rule categories. We use four representative tools as examples and show that the tools are able to formalize only a specific extent of the discovered rules. The results of the study motivate for improving existing tools and to collect further examples of architectural rules that can potentially be reused in projects with similar characteristics.
Experiments with the tablet in informal education

Informal education is an important part of the development of a child’s personality. In informal education a child is not aware of learning, it is more a game and fun. A camp is one of the forms of informal education. A camp oriented on physics enables children to experience the feeling of discovering and exploring. Through activities it is possible to boost children’s constructive and creative thinking and develop their communicative skills. Information and communication technologies is a means that allows children to explore and investigate while having fun. In this contribution we show several activities using tablets in which children learn in an entertaining way. The goal is to demonstrate how tablets can be employed to measure and process data.
One-dimensional local binary pattern based color descriptor to classify stress values from photoelasticity videos

Evaluating the stress distribution in structures under temporal loads is being carry out by many of the engineering applications such as: impacts, cracks, bending, thermal-transient and other. In those cases, conventional photoelasticity techniques are more complex to evaluate the stress field because of their complicated and expensive experiments, quantity of computational procedures, and their time by time analysis. However, dynamic photoelasticity experiments produce temporal information, such as color variations, which could be analyzed, described, and classified in order to perform a whole stress field evaluation. In this paper, the one-dimensional local binary patterns (1D-LBP) are used to describe such color variations and use them to identify the stress values they belong. For different experimental configurations, this proposal achieved an accuracy of 98% when evaluating the stress field of cases with similar light sources than with a reference experiment, and 92% for experiments with other light conditions. These results make this descriptor able to determine categorical stress maps from a photoelasticity video itself, which significantly opens new opportunities to simplify the experimental and computational operations that limit the stress evaluation process in line with the dynamic experiment.
User Tracking Accuracy during Smooth Pursuit of a Target with and without a Crosshair

Gaze interface is a human-computer interaction method that offers numerous benefits to users. Therefore, researchers and practitioners are investigating key design issues in gaze interfaces. In this investigation, we compared user tracking accuracy during smooth pursuit of a target with and without a crosshair. Our comparison was based on data from an experiment in which participants tracked a moving target with a crosshair that they controlled with their eyes and on data from a similar experiment in which users tracked the same target without a crosshair. Maneuvering types and velocity of the target alternated between the experimental blocks. We found that mean tracking accuracy was lower when the target was moving faster and when participants used a crosshair. Our findings suggest that the usage of pointers (e.g., crosshairs, cursors) during smooth pursuit of targets might impair tracking accuracy. These findings resonate with previous concerns about the shifting of user attention from the target to the pointer.
Probabilistic Consensus of the Blockchain Protocol

We introduce a temporal epistemic logic with probabilities as an extension of temporal epistemic logic. This extension enables us to reason about properties that characterize the uncertain nature of knowledge, like “agent a will with high probability know after time s same fact”. To define semantics for the logic we enrich temporal epistemic Kripke models with probability functions defined on sets of possible worlds. We use this framework to model and reason about probabilistic properties of the blockchain protocol, which is in essence probabilistic since ledgers are immutable with high probabilities. We prove the probabilistic convergence for reaching the consensus of the protocol.
Maximising goals achievement through abstract argumentation frameworks: An optimal approach

Argumentation is a prominent AI research area, focused on approaches and techniques for performing common-sense reasoning, that is of paramount importance in a wide range of real-world applications, such as decision support and recommender systems. In this work we introduce an approach for updating an abstract Argumentation Framework (AF) so that achievement with respect to a given set of goals is maximised. The set of goals identifies arguments for which a specific acceptability status (a labelling) will be pursued, distinguishing between “in” and “out” goals. Given an AF, a set of goals and a set of available actions allowing to add or remove arguments and attacks from the AF, our approach will select the strategy (set of actions) that should be applied in order to obtain a new AF where the goals achievement is maximised. Moreover, the selected strategy will be optimal with respect to the number of actions to be applied. In the context of argumentation-based expert and intelligent systems, our approach will provide tools allowing the user to interact with the argumentative reasoning process carried out by the system, learning how the strategy she undertakes will affect the recommendations she receives. For that, we propose an encoding of the AF, the available actions and goals as weighted Boolean formulas, and rely on MaxSAT techniques for selecting the optimal strategy. We provide an experimental analysis of our approach, and formally show that the results we obtain correspond to the optimal strategy.
A Method of Cylindrical Panoramic Video Stitching

Abstract In this paper, we propose a cylindrical stitching method which based on template frame image to realize 360° panoramic video stitching. Firstly, we selected template frame to determine the camera parameters and distortion coefficient. And, removing the image distortion. After that, extracting the template frame feature and making rough matching with the feature points, and then eliminating the feature points of mismatching by the improved feature point screening method. Then, feature points are projected on the cylinder in advance for subsequent video image fusion. Finally, image fusion is carried out after the cylinder projection of the video image. The speed was greatly improved in the subsequent video splicing by using camera parameters, distortion coefficient and transformation matrix of template frame image. In addition, the CUDA platform was adopted for GPU parallel processing in order to further improve the speed to meet the real-time requirements. Finally, we realize real-time video stitching with 30 frames per second.