Reviewerly/exHarmony huggingface dataset:
A new hybrid approach to improve the efficiency of homomorphic matching for graph rules

Conceptual graphs (CGs) have logical backgrounds and support visual reasoning. They are widely studied and used with the development of artificial intelligence. Homomorphic matching is the basic operation for the logical deduction based on CGs, which is a NP-complete problem. When the scale of the CG database is large, how to perform efficient homomorphic matching is a key factor affecting the usage of graph rules. In this paper, the CGs are transformed into labeled undirected graphs which have no multiple edges, then a new hybrid approach is proposed to improve the efficiency of homomorphic matching for graph rules. The hybrid approach is based on the traditional backtrack algorithm with a standard filter, and there are three improvements. Firstly, in the graph data sets, which are generated and enriched by using graph rules, there are frequent patterns occurred. Therefore, optimal homomorphic matching orders (OHMOs) of graph rules can be extracted from the historical graph data. OHMOs can reduce the number of comparisons during searching homomorphisms. Secondly, label type checking orders of nodes in the hypotheses of graph rules are optimized with the frequency statistics method. Using the two-stage optimized label checking orders (OLCOs) can save the calculation time of obtaining the candidate nodes and reduce the comparison times during searching homomorphisms. Thirdly, signatures of nodes in CGs are defined, and we use them to further filter the candidate nodes, then the search space of homomorphic matching is reduced. OHMOs and OLCOs are obtained off-line and signatures are calculated incrementally, so the on-line calculation for searching homomorphisms is seldom affected. At last, a study case is conducted, and the experiment results illustrate that the proposed approach is reasonable and effective.
Penerapan Pathfinding Menggunakan Algoritma A* Pada Non Player Character (NPC) Di Game

In this Game , we implement a character designer at the enemy by applying the algorithm A * pathfinding Algorithm. Furthermore, A * is as the algorithm for searching solutions with the use of additional information (heuristics) in which it is an optimal solution. The purpose of developing this Adventure Game is A * Algorithm implementation for Pathfinding using Unity 3D. Pathfinding is the fastest path of searching process from point of origin to point of destination by avoiding the various barriers along the path traveled without crashing the existing barrier along the way. Its design and development applies Unity 3D. The test result of algorithm in the Game shows that the total of passed node result is 50 nodes. It is similar with the previous manual count result. Furthermore, the Game route shows that its passed path is similar with manual count.
Quantum Energy of a Particle in a Finite-potential Well Based Upon Golden Metric Tensor

In our previous work titled “Riemannian Quantum Theory of a Particle in a Finite-Potential Well", we constructed the Riemannian Laplacian operator and used it to obtain the Riemannian Schrodinger equation for a particle in a finite-potential well. In this work, we solved the golden Riemannian Schrodinger equation analytically to obtain the particle energy. The solution resulted in two expressions for the energy of a particle in a finite-potential well. One of the expressions is for the odd energy levels while the other is for the even energy levels.
A MapReduce based Algorithm for Data Migration in a Private Cloud Environment

When a resource in a data center reaches its end-of-life, instead of investing in upgrading, it is possibly the time to decommission such a resource and migrate workloads to other resources in the data center.Data migration between different cloud servers is risky due to the possibility of data loss.The current studies in the literature do not optimize the data before migration, which could avoid data loss.MapReduce is a software framework for distributed processing of large data sets with reduced overhead of migrating data.For this study, we design a MapReduce based algorithm and introduce a few metrics to test and evaluate our proposed framework.We deploy an architecture for creating an Apache Hadoop environment for our experiments.We show that our algorithm for data migration works efficiently for text, image, audio and video files with minimum data loss and scale well for large files as well.
Automatic Grammar Augmentation for Robust Voice Command Recognition

This paper proposes a novel pipeline for automatic grammar augmentation that provides a significant improvement in the voice command recognition accuracy for systems with small footprint acoustic model (AM). The improvement is achieved by augmenting the user-defined voice command set, also called grammar set, with alternate grammar expressions. For a given grammar set, a set of potential grammar expressions (candidate set) for augmentation is constructed from an AM-specific statistical pronunciation dictionary that captures the consistent patterns and errors in the decoding of AM induced by variations in pronunciation, pitch, tempo, accent, ambiguous spellings, and noise conditions. Using this candidate set, greedy optimization based and cross-entropy-method (CEM) based algorithms are considered to search for an augmented grammar set with improved recognition accuracy utilizing a command-specific dataset. Our experiments show that the proposed pipeline along with algorithms considered in this paper significantly reduce the mis-detection and mis-classification rate without increasing the false-alarm rate. Experiments also demonstrate the consistent superior performance of CEM method over greedy-based algorithms.
On Monte-Carlo tree search for deterministic games with alternate moves and complete information

We consider a deterministic game with alternate moves and complete information, of which the issue is always the victory of one of the two opponents. We assume that this game is the realization of a random model enjoying some independence properties. We consider algorithms in the spirit of Monte-Carlo Tree Search, to estimate at best the minimax value of a given position: it consists in simulating, successively, n well-chosen matches, starting from this position. We build an algorithm, which is optimal, step by step, in some sense: once the n first matches are simulated, the algorithm decides from the statistics furnished by the n first matches (and the a priori we have on the game) how to simulate the ( n + 1)th match in such a way that the increase of information concerning the minimax value of the position under study is maximal. This algorithm is remarkably quick. We prove that our step by step optimal algorithm is not globally optimal and that it always converges in a finite number of steps, even if the a priori we have on the game is completely irrelevant. We finally test our algorithm, against MCTS, on Pearl’s game [Pearl, Artif. Intell. 14 (1980) 113–138] and, with a very simple and universal a priori , on the game Connect Four and some variants. The numerical results are rather disappointing. We however exhibit some situations in which our algorithm seems efficient.
Relaxing and Restraining Queries for OBDA

We advocate the use of ontologies for relaxing and restraining queries, so that they retrieve either more or less answers, enabling the exploration of a given dataset. We propose a set of rewriting rules to relax and restrain conjunctive queries (CQs) over datasets mediated by an ontology written in a dialect of DL-Lite with complex role inclusions (CRIs). The addition of CRI enables the representation of knowledge about data involving ordered hierarchies of categories, in the style of multi-dimensional data models. Although CRIs in general destroy the first-order rewritability of CQs, we identify settings in which CQs remain rewritable.
Survey on Image Data Hiding Based on Block Histogram Shifting Encryption Algorithm

Describes the thought of Reversible Information Hiding (RDH) methodology is based on steganography.Recently a lot of attention is paid to RDH in encrypted image.Security should be provided for the transmission of confidential and sensitive information over the network and insecure channel, loss in information happens.To secure the information, encode the wrap information and introduce is secret information into cover media.so the protection of image and embedded information is maintained, Since RDH manages the outstanding secured property, and therefore the original image are often recovered with none loss, Reversible Information Hiding (RDH).Within the planned rule, Histogram Shifting (HS) is employed for RDH, and therefore the BHSEA methodology is used for encoding.The BHSEA methodology performs four processes for encoding; Reversible information hiding could be a form of information hiding techniques whereby the host image is often recovered specifically.Being lossless makes this method appropriate for medical, military applications and cloud storage.In cloud storage, a content owner will encode a picture to preserve his/her privacy, and transfer the encrypted knowledge onto cloud.Reversible knowledge activity in encrypted image, which might recover the first image with none distortion from the marked image when the hidden information, are extracted.Existing rule consists of various hierarchal steps for the information embedding and therefore the encoding.Therefore, the planned rule are often appropriate for the hierarchical access system, wherever the permission is granted consistent with the assorted access rights and planned algorithmic rule up strength and authentication of image information hiding.
Web 2.0 Technologies in Business: Why not?

The objective of the study was to explore the barriers or the reasons behind the insufficient use of Web 2.0 technology in companies.The empirical research was conducted among managers in companies in Bosnia and Herzegovina during 2015.The results indicate that managers see information technology infrastructure (department size, technology implementation costs) and computer illiteracy as the main barriers to intensified use of Web 2.0 technology in business.The least relevant obstacles are related to the lack of support from, and the inability to protect the data privacy and integrity in an adequate manner.The results indicate that significant efforts to promote the benefits that Web 2.0 technology brings to the business are needed, which would, in turn, significantly affect the perception of their disadvantages.
THE EFFECTS OF JOB CHARACTERISTICS ON EMPLOYEE’S ORGANIZATIONAL CITIZENSHIP BEHAVIOR AMONG BANKING SECTOR EMPLOYEES IN PESHAWAR CITY

The objective of the study was to measure the relationship between job characteristics and organizational citizenship behavior among banking sector employees. The study was conducted in the banking sector in the city of Peshawar. The research methodology used is quantitative approach and survey for data collection. Through sampling, we generated a sample of 60 respondents belonged to the six different banks. Our results indicate that task significance and autonomy has significant influence on staff OCB; while, results for skills variety, task identity, and feedback turned out to be insignificant. The five dimensions of job characteristic model together explained 27.8% change in the dependent variable of employee OCB. Our results support the previous studies finding that OCB is influenced by job design.
Sharpness of the Satisfiability Threshold for Non-Uniform Random k-SAT

We study a more general model to generate random instances of Propositional Satisfiability (SAT) with n Boolean variables, m clauses, and exactly k variables per clause. Additionally, our model is given an arbitrary probability distribution (p_1, ..., p_n) on the variable occurrences. Therefore, we call it non-uniform random k-SAT. The number m of randomly drawn clauses at which random formulas go from asymptotically almost surely (a.a.s.) satisfiable to a.a.s. unsatisfiable is called the satisfiability threshold. Such a threshold is called sharp if it approaches a step function as n increases. We identify conditions on the variable probability distribution (p_1, ..., p_n) under which the satisfiability threshold is sharp if its position is already known asymptotically. This result generalizes Friedgut’s sharpness result from uniform to non-uniform random k -SAT and implies sharpness for thresholds of a wide range of random k -SAT models with heterogeneous probability distributions, for example such models where the variable probabilities follow a power-law.
Towards Task Understanding in Visual Settings

We consider the problem of understanding real world tasks depicted in visual images. While most existing image captioning methods excel in producing natural language descriptions of visual scenes involving human tasks, there is often the need for an understanding of the exact task being undertaken rather than a literal description of the scene. We leverage insights from real world task understanding systems, and propose a framework composed of convolutional neural networks, and an external hierarchical task ontology to produce task descriptions from input images. Detailed experiments highlight the efficacy of the extracted descriptions, which could potentially find their way in many applications, including image alt text generation.
Smart monitoring information system based on RF 433 Mhz (SMIS)

Many research works are devoted to the design of remote control systems of objects. Remote monitoring is among the technologies of data acquisition on a remote object and its characteristics without physical contact with it. These data can be collected by acoustic waves, the distribution of forces and electromagnetic energy to process them to locate the object and its characteristics. This paper presents a smart monitoring information system based on RF 433MHz, Arduinonano and arduinouno named SMIS.
A Combination of Hill CIPHER-LSB in RGB Image Encryption

The progress of the development of digital technology today, many people communicate by sending and receiving messages. However, along with extensive technological developments, many crimes were committed. In avoiding these crimes, data security needs to be done. Form of data security in the form of cryptography and steganography. One of the cryptographic techniques is the hill cipher algorithm. Hill ciphers include classic cryptographic algorithms that are very difficult to solve. While the most popular steganography technique is Least Significant Bit (LSB). Least Significant Bit (LSB) is a spatial domain steganography technique using substitution methods. This study discusses the merging of message security with hill cipher and LSB. The message used is 24-bit color image for steganography and text with 32, 64 and 128 characters for cryptography. The measuring instruments used in this study are MSE, PSNR, Entropy and travel time (CPU time). Test results prove an increase in security without too damaging the image. This is evidenced by the results of the MSE trial which has a value far below the value 1, the PSNR is&gt; 64 db, the entropy value ranges from 5 to 7 and the results of travel time &lt;1 second.
Long-Term Real-Time Correlation Filter Tracker for Mobile Robot

Computer vision has received a significant attention in recent years, which is one of the important parts for robots to apperceive external environment. Discriminative Correlation Filter (DCF) based trackers gained more popularity due to their efficiency, however, most of the-state-of-the-art trackers are effective for short-term tracking, not yet successfully addressed in long-term scene. In this work, we tackle the problems by introducing Long-term Real-time Correlation Filter (LRCF) tracker. First, fused features only including HOG and Color Names are employed to boost the tracking efficiency. Second, we used the standard principal component analysis (PCA) to reduction scheme in the translation and scale estimation phase for accelerating. Third, we learned a long-term correlation filter to keep the long-term memory ability. Finally, we update the filter with interval updates. The extensive experiments on popular Object Tracking Benchmark OTB-2013 datasets have demonstrated that the proposed tracker outperforms the state-of-the-art trackers significantly achieves a high real-time (33FPS) performance in our mobile robot hardware. The experimental results show that the novel tracker performance is better than the-state-of-the-art trackers.
A Novel Image Encryption Scheme with Huffman Encoding and Steganography Technique

In today's day and age when everything is done with the aid of computing technology, the need for confidential communication has drastically increased.Not only the sensitive data such as top intelligent secrets of our nation but personal information of common people needs to be secure.Several combinations of cryptography and steganography techniques in different ways are used by researchers over the past to protect the data being transmitted.Cryptography uses mathematical algorithms to convert the data into an incomprehensible form and Steganography, on the other hand hides the data in a carrier such as image, data, audio or video.Cryptography provides necessary mechanisms for providing accountability, accuracy and confidentiality in public communication mediums such as the Internet and steganography is used in other fields such as copyright, preventing e-document forging etc.We are of the opinion that this security mechanism can further be increased by incorporating the use of Huffman coding in order to reduce the data length.This paper is an effort in the direction to hide, secure and compress the data.It explains the executed procedure by applying various encryption techniques one by one and our aim is to get the best security out of the existing ones.The proposed technique is implemented in MATLAB2016a and the results shown in this paper that our technique is better approach then the conventional techniques.
Aplikasi Belajar Interaktif Algoritma Sorting Berbasis Desktop

Making software requires a lot of algorithms that are implemented into the making, one of the algorithms used is the sorting algorithm. Algorithms are logical sequences of steps to solve problems that are arranged systematically. Sorting algorithms (sequencing) are generally defined as the process of rearranging a series of objects in a certain order. The purpose of the sequencing process is to facilitate the search process for a beginner who wants to learn to make software, of course they have to go through stages in learning algorithms and in the learning process there will usually be obstacles faced such as not fully understanding how the algorithm works, especially the sorting algorithm or the media used as learning materials have not been able to maximize the knowledge of sorting algorithms, therefore it requires an application of learning aids media that can help in learning and understanding the material of the sorting algorithm more optimally. In making assistive media applications learn how to work the steps of the sorting algorithm, the author uses the luther-sutopo method. The stages are concept, design, collecting materials, assembly, testing, and distribution. In the application there is general material about sorting and video algorithms that can help maximize understanding of the sorting algorithm material. The results achieved by an auxiliary media application are learning desktopbased sorting algorithms that are used for learning specific sorting algorithms in theory
Mining Symptom and Disease Web Data with NLP and Open Linked Data

Machine Learning (ML) technologies in recent years are widely applied in various areas to assist knowledge gaining and decision-making on healthcare.However, there is no reliable dataset that contains semantic structured knowledge on symptom and disease enable to apply advanced machine learning algorithms such clustering or prediction.In this paper, we propose a framework that can extract data from web with apply Natural Language Processing (NLP) process and semantic annotation to create Open Linked Data (OLD) based knowledge graph.At the end, the knowledge graph can be used for ML algorithms and graph oriented Deep Learning techniques.
Visualization of Multimodal Brain Connectivity for Neurosurgical Planning Using Handheld Device Augmented Reality

In neurosurgical procedures, precise preoperative planning requires extensive knowledge of the patients' anatomy as well as critical structures for brain functionality.Recently, there has been an increase in the use of minimally invasive approaches, owing in part to advancements in multimodal medical imaging techniques such as structural (SC), and functional-based brain mapping (FC), which have been shown to be useful metrics for surgical trajectory planning.The main challenges associated with their use is the lack of intuitive visualization and interactive methods available to neurosurgeons and trainees.AR systems represent a pivotal advancement towards augmenting the training of trainees as well as providing a platform for senior surgeons to maintain their skills in a low-risk training environment.Advanced image processing was performed on multimodal neuroimaging data (T1-weighted image, diffusion weighted image, resting-state functional magnetic resonance imaging) to characterize the SC and FC of the brain.An AR application, NeuroAR, was designed to take these as inputs and allow the user to visualize and interact with the neuroanatomy in the context of its associated SC and FC.The performances of 10 users on 24 targets were evaluated using an extension of Fitts' methodology.The users were able to use an interactive tool to select and visualize brain regions and their associated fibers.The fibers could be visualized based on their FC scores.As expected, the data showed that task difficulty increased as the volume of the fibers decreased.Movement time also increased as task difficulty increased.We introduced a new mobile device AR application based on data derived from advanced image processing of neuroimaging data.Evaluation of the 3D pointing tasks showed consistency in user performance indicating its utility.
Productivity and Constraint on Multi-Verb Constructions in Old Japanese

Old Japanese is a dead language from the Asuka and Nara periods (7th - 8th century AD). Its writing system, case system and word order make it distinct from Modern Japanese in many respects. This study presents a quantitative linguistic analysis to the patterns of multiple verb combining in Old Japanese. To this end, two databases were built: multi-verb construction in the Early Nara Period written in variant Chinese (AD. 712) and purely classical Chinese (AD. 720), and multi-verb construction in the Late Nara Period written by man’yōgana (AD. 759).The findings reveal that, in the Nara period, the formation of multi-verb constructions is an issue of verb serialisingand is facilitated at a syntactic level. Grammaticalisation of unaccusative change-of-state verbs and motion verbs results in tighter integrity of multiple verbs, which, in turn, inspires the device of verb compounding.The entropy ofthe Vfinal unaccusative reveals that the formation via verb serialising is more productive than the formation via verb compounding.
Extended Abstract: Searching with Consistent Prioritization for Multi-Agent Path Finding

We study prioritized planning for Multi-Agent Path Finding (MAPF). Existing prioritized MAPF algorithms depend on rule-of-thumb heuristics and random assignment to determine a fixed total priority ordering of all agents a priori. We instead explore the space of all possible partial priority orderings as part of a novel systematic and conflict-driven combinatorial search framework. In a variety of empirical comparisons, we demonstrate state-of-the-art solution qualities and success rates, often with similar runtimes to existing algorithms. We also develop new theoretical results that explore the limitations of prioritized planning, in terms of completeness and optimality, for the first time. This paper was published at AAAI 2019.