Reviewerly/exHarmony huggingface dataset:
Dynamic Expansion of Behaviour Trees

Artificial intelligence in games is typically used for creating player's opponents. Manual edition of intelligent behaviors for Non-Player Characters (NPCs) of games is a cumbersome task that needs experienced designers. Our research aims to assist designers in this task. Behaviours typically use recurring patterns, so that experience and reuse are crucial aspects for behavior design. The use of hierarchical state machines allows working on different abstraction levels, sharing transitions and reusing pieces from the more detailed levels. However, the static nature of the design process does not release the designer from the burden to completely specify each behaviour. Our approach applies Case-Based Reasoning (CBR) techniques to retrieve and reuse stored behaviors represented as hierarchical state machines (actually, behaviour trees). In this paper we focus on dynamic retrieval of behaviours taking into account the world state and the underlying goals to select the most appropriate state machine to guide the NPC behaviour. The global behaviour of the NPC is dynamically built in run time querying the CBR system. We exemplify our approach through a serious game, developed by our research group, with gameplay elements from First-Person Shooter (FPS) games.
Online Kernel Selection: Algorithms and Evaluations

Kernel methods have been successfully applied to many machine learning problems. Nevertheless, since the performance of kernel methods depends heavily on the type of kernels being used, identifying good kernels among a set of given kernels is important to the success of kernel methods. A straightforward approach to address this problem is cross-validation by training a separate classifier for each kernel and choosing the best kernel classifier out of them. Another approach is Multiple Kernel Learning (MKL), which aims to learn a single kernel classifier from an optimal combination of multiple kernels. However, both approaches suffer from a high computational cost in computing the full kernel matrices and in training, especially when the number of kernels or the number of training examples is very large. In this paper, we tackle this problem by proposing an efficient online kernel selection algorithm. It incrementally learns a weight for each kernel classifier. The weight for each kernel classifier can help us to select a good kernel among a set of given kernels. The proposed approach is efficient in that (i) it is an online approach and therefore avoids computing all the full kernel matrices before training; (ii) it only updates a single kernel classifier each time by a sampling technique and therefore saves time on updating kernel classifiers with poor performance; (iii) it has a theoretically guaranteed performance compared to the best kernel predictor. Empirical studies on image classification tasks demonstrate the effectiveness of the proposed approach for selecting a good kernel among a set of kernels.
Predicting rank for scientific research papers using supervised learning

Automatic data processing represents the future for the development of any system, especially in scientific research. In this paper, we describe one of the automatic classification methods applied to scientific research as a supervised learning task. Throughout the process, we identify the main features that are used as keys to play a significant role in terms of predicting the new rank under the supervised learning setup. First, we propose an overview of the work that has been realized in ranking scientific research papers. Second, we evaluate and compare some of state-of-the-art for the classification by supervised learning, semi-supervised learning and non-supervised learning. During the preliminary tests, we have obtained good results for performance on realistic corpus then we have compared performance metrics, such as NDCG, MAP, GMAP, F-Measure, Precision and Recall in order to define the influential features in our work.
Predicting Action Tubes

In this work, we present a method to predict an entire ‘action tube’ (a set of temporally linked bounding boxes) in a trimmed video just by observing a smaller subset of it. Predicting where an action is going to take place in the near future is essential to many computer vision based applications such as autonomous driving or surgical robotics. Importantly, it has to be done in real-time and in an online fashion. We propose a Tube Prediction network (TPnet) which jointly predicts the past, present and future bounding boxes along with their action classification scores. At test time TPnet is used in a (temporal) sliding window setting, and its predictions are put into a tube estimation framework to construct/predict the video long action tubes not only for the observed part of the video but also for the unobserved part. Additionally, the proposed action tube predictor helps in completing action tubes for unobserved segments of the video. We quantitatively demonstrate the latter ability, and the fact that TPnet improves state-of-the-art detection performance, on one of the standard action detection benchmarks - J-HMDB-21 dataset.
A process framework for information security management

Securing sensitive organizational data has become increasingly vital to organizations. An Information Security Management System (ISMS) is a systematic approach for establishing, implementing, operating, monitoring, reviewing, maintaining and improving an organization's information security. Key elements of the operation of an ISMS are ISMS processes. However, and in spite of its importance, an ISMS process framework with a description of ISMS processes and their interaction as well as the interaction with other management processes is not available in the literature. Cost benefit analysis of information security investments regarding single measures protecting information and ISMS processes are not in the focus of current research, mostly focused on economics. This article aims to fill this research gap by proposing such an ISMS process framework as the main contribution. It is based on a set of agreed upon ISMS processes in existing standards like ISO 27000 series, COBIT and ITIL. Within the framework, identified processes are described and their interaction and interfaces are specified. This framework helps to focus on the operation of the ISMS, instead of focusing on measures and controls. By this, as a main finding, the systemic character of the ISMS consisting of processes and the perception of relevant roles of the ISMS is strengthened.
Face Recognition Through Symbolic Modeling of Face Graphs and Texture

Face recognition helps in authentication of the user using remotely acquired facial information. The dynamic nature of face images like pose, illumination, expression, occlusion, aging, etc. degrades the performance of the face recognition system. In this paper, a new face recognition system using facial images with illumination variation, pose variation and partial occlusion is presented. The facial image is described as a collection of three complete connected graphs and these graphs are represented as symbolic objects. The structural characteristics, i.e. graph spectral properties, energy of graph, are extracted and embedded in a symbolic object. The texture features from the cheeks portions are extracted using center symmetric local binary pattern (CS-LBP) descriptor. The global features of the face image, i.e. length and width, are also extracted. Further symbolic data structure is constructed using the above features, namely, the graph spectral properties, energy of graph, global features and texture features. User authentication is performed using a new symbolic similarity metric. The performance is investigated by conducting the experiments with AR face database and VTU-BEC-DB multimodal database. The experimental results demonstrate an identification rate of 95.97% and 97.20% for the two databases.
Laplacian Edge Detection Algorithm for Road Signal Images and FPGA Implementation

The applications of image processing for road safety, detecting panels and roadway have attracted considerable attention in literature and research, especially in the field of information processing on embedded systems.However, the demanding nature of image processing algorithms conveys a substantial burden for any conventional real-time implementation.Meanwhile, the emergence of reconfigurable architectures, especially FPGA chips, which have been given many facilities for rapid prototyping, where an image processing algorithm, can be designed, tested, and synthesized in a relatively short period of time compared to conventional or traditional approaches.This paper studies a hardware and software combination to obtain an optimal solution for the edge detection, this step is considered essential for the detection of panels and roadways, the dedicated algorithm is based on the Laplacian calculation for Edges detecting and implemented in a Xilinx Spartan 6 FPGA, and the results are displayed by standing in a VGA monitor, with a sync and display controller.
Artificial intelligence: the future for cardiology

Artificial intelligence (AI) is a rapidly evolving field in medicine, especially cardiology. AI is defined as the ‘theory and development of computer systems able to perform tasks normally requiring human intelligence’.1 By creating algorithms using a variety of neural networks, we enable dynamic deep machine learning.2 3 Deep machine learning mimics the human brain by using large high-quality data sets to create layers of neural networks to generate automated predictions, interpret image data and develop pattern recognition.3–6 AI aims to aid cardiologists in making better decisions, improve workflow, productivity, and cost-effectiveness and ultimately patient outcomes.2 4 7 AI has been used in automated predictions of cardiovascular disease risk scores and heart failure diagnosis. Using recurrent neural networks, deep machine learning has …
Handwritten Farsi Word Recognition Using NN-Based Fusion of HMM Classifiers with Different Types of Features

In this paper, an off-line method, based on hidden Markov model, HMM, is used for holistic recognition of handwritten words of a limited vocabulary. Three feature sets based on image gradient, black–white transition and contour chain code are used. For each feature set an HMM is trained for each word. In the recognition step, the outputs of these classifiers are combined through a multilayer perceptron, MLP. High number of connections in this network causes a computational complexity in the training. To avoid this problem, a new method is proposed. In the experiments on 16000 images of 200 names of Iranian cities, from “Iranshahr 3” dataset, the results of the proposed method are presented and compared with some similar methods. An error analysis on these results is also provided.
Design Principles for Learning with Mobile Devices

The chapter highlights some environmental and societal changes that have occurred since the authors contributed chapters on this topic to previous editions of this book. The role of design in relation to learning technology developments is considered, with a focus on design for learning that is personalized, situated, authentic and informal. Design of content, activities, communities, and communication is discussed. The ten design principles proposed by the authors take account of the characteristics of mobile technologies and consider the social context into which they are appropriated. The design principles recognize the centrality of learners with their personal technologies and their preferences, experiences and expectations, alongside the unique nature and added value of mobile learning, and the idea that mobile learning is synonymous with unpredictability and constant change. The principles are likely to have wider applicability beyond mobile learning as personalized, situated, authentic and informal learning becomes the norm.
Model Unit Exploration for Sequence-to-Sequence Speech Recognition.

In conventional speech recognition, phoneme-based models outperform grapheme-based models for non-phonetic languages such as English. The performance gap between the two typically reduces as the amount of training data is increased. In this work, we examine the impact of the choice of modeling unit for attention-based encoder-decoder models. We conduct experiments on the LibriSpeech 100hr, 460hr, and 960hr tasks, using various target units (phoneme, grapheme, and word-piece); across all tasks, we find that grapheme or word-piece models consistently outperform phoneme-based models, even though they are evaluated without a lexicon or an external language model. We also investigate model complementarity: we find that we can improve WERs by up to 9% relative by rescoring N-best lists generated from a strong word-piece based baseline with either the phoneme or the grapheme model. Rescoring an N-best list generated by the phonemic system, however, provides limited improvements. Further analysis shows that the word-piece-based models produce more diverse N-best hypotheses, and thus lower oracle WERs, than phonemic models.
Performance evaluation of various desmogging techniques for single smoggy images

The worsening air pollution is producing serious smog problems in the world. The high concentration of aerosol, that attenuates the scene radiance and adds undesired scattering illumination into the actual illumination values, has a serious effect on the visibility of the images. Therefore, images captured under smoggy environments suffer from poor visibility. Because, the smog particles attenuate the illumination reflected by the targets and add undesired scattering light. Therefore, imaging under smoggy environments affect the performance of many machine vision systems such as intelligent transportation system, remote sensing imaging, aerial imaging, etc. From the literature, it has been observed that majority of existing researchers have either focused on foggy or hazy images only.
LSTM Based Paraphrase Identification Using Combined Word Embedding Features

Paraphrase identification is the process of analyzing two text entities (sentences) and Aravinda Reddy,D. determining whether the two entities represent the similar sense or not. This is a task of Natural Language Processing (NLP) in which we need to identify the sentences whether it is a paraphrase or not. Here, theAnand Kumar,M. chosen approach for this task isSoman,K. P. a deep Learning model that is Recurrent Neural Network-LSTM with word embedding features. Word embedding is an approach, from where we can extract the semantics of the word in dense vector representation. The word embedding models that are used for the feature extraction in Telugu are Word2Vec, Glove and Fasttext. These extracted feature models are added in the embedding layer of Long Short-Term Memory algorithm in order to classify the Telugu sentence pairs Soman,K. P. whether they are Paraphrase or not. The corpus for Telugu is generated manually from various Telugu newspapers. The sentences for word embedding model is also gathered from Telugu newspapers. This is the first attempt for paraphrase identification in Telugu using deep learning approach.
Structure-Aware Window Optimization for Texture Filtering

Texture filtering seeks to smooth out textured details in order to present structures prominently. To filter out multi-scale textured details while preserving structures, some methods propose to adjust the size of the filtering windows by handling the pixels near structures with small windows and the other pixels with large windows. Unfortunately, their adjustment measures are not very effective in treating complex situations. They may handle the pixels inside small structures with large windows, which overly smooths them, or they may be incapable of smoothing out large-scale textures. With regard to this, in this paper, we present a novel method that adjusts the window size for a pixel by checking similar pixels in its neighborhood. In general, pixels nearer structures have fewer similar pixels in their neighborhoods than the pixels that are farther away from structures. Thus, our adjustment can adaptively adjust window sizes by the distances from pixels to structures, which improves the potential to treat complex situations. The experimental results show that we can well filter out very large-scale textures while preserving small structures with high quality in a manner superior to the state-of-the-art methods. In addition, our method is very simple and efficient, and can be used easily in applications.
RETRACTED ARTICLE: Analysis of sports image detection technology based on machine learning

Abstract Current sports competitions are mostly broadcast in the form of live video or video files, and information detection for athletes and sports economic processes can also be carried out through image detection technology. However, from the current situation, we can see that sports image detection technology is still immature. Therefore, this study uses sports video as a material to analyze the application of sports image detection technology. In this study, image detection technology edge detection, grayscale processing, object capture, target recognition, etc. are combined with the actual needs of sports video to achieve a variety of needs for sports image detection. Simultaneously, this study has realized the recognition of athletes, motion recognition, sports behavior judgment, etc. and built a test platform to verify the effectiveness of this research method. The results show that the research method has certain practicality and can provide a theoretical reference for subsequent related research.
Dynamic Service Selection Based on Adaptive Global QoS Constraints Decomposition

As there are more and more available Web services with the same or similar functionalities but different Quality of Service (QoS), the challenge of QoS-aware service composition is to efficiently select appropriate component services to achieve maximum utility and meet the global QoS constraints with low time cost. In this paper, we propose a dynamic service selection approach based on adaptive global QoS constraints decomposition. Fuzzy logic technology and Cultural Genetic Algorithm are used to adaptively decompose global QoS constraints into near-optimal local constraints. According to the near-optimal local constraints, the optimal service is selected for each service class during the running time efficiently. Experimental results show that the proposed approach not only achieves the near-optimal solution, but also significantly reduces the computation time, and has good adaptability and scalability.
Energy Efficient Camera Solution for Video Surveillance

Video surveillance is growing rapidly, new problems and issues are also coming into view which needs serious and urgent attention. Video surveillance system requires a beneficial energy efficient camera solution. In this paper, a single overhead camera solution is introduced which overcomes the problems ex-isting in various frontal and overhead based surveillance systems. This will increases the efficiency and accuracy of surveillance system i.e. frontal and overhead. In this paper, two energy efficient overhead camera models are presented. The first model consists of a single overhead camera with a wide angle lens which covers a wide field of view addresses problems present in the traditional surveillance system. The second model, presents a single smart centralized overhead camera which controls various frontal based cameras. Several factors associated with camera models such as field of view, focal length and distortion are also discussed. Impact of the surveillance cameras are finally discussed which shows that a single energy efficient overhead camera surveillance system can solves many problems present in traditional surveillance system like power consumption, storage, time, human resource and installation cost and small coverage area.
A Data-Aware Latent Factor Model for Web Service QoS Prediction

Accurately predicting unknown quality-of-service (QoS) data based on historical QoS records is vital in web service recommendation or selection. Recently, latent factor (LF) model has been widely and successfully applied to QoS prediction because it is accurate and scalable under many circumstances. Hence, state-of-the-art methods in QoS prediction are primarily based on LF. They improve the basic LF-based models by identifying the neighborhoods of QoS data based on some additional geographical information. However, the additional geographical information may be difficult to collect in considering information security, identity privacy, and commercial interests in real-world applications. Besides, they ignore the reliability of QoS data while unreliable ones are often mixed in. To address these issues, this paper proposes a data-aware latent factor (DALF) model to achieve highly accurate QoS prediction, where ‘data-aware’ means DALF can easily implement the predictions according to the characteristics of QoS data. The main idea is to incorporate a density peaks based clustering method into an LF model to discover the neighborhoods and unreliable ones of QoS data. Experimental results on two benchmark real-world web service QoS datasets demonstrate that DALF has better performance than the state-of-the-art models.
Accurate surface profilometry using differential optical sectioning microscopy with structured illumination

A differential optical sectioning microscopy with structured-illumination (DOSM-SI) with enhanced axial precision is explored in this paper for three-dimensional (3D) measurement. As the segment of data on the linear region of the contrast depth response curve (CDR) is very sensitive to variation of the height information, the DOSM-SI introduces a new CDR2 with an axial shift to intersect the linear region of the CDR1, which is achieved by using two charge-coupled detectors (CCDs) in the optical path. The CCD1 is located on the imaging plane and the CCD2 is displaced from the imaging plane. The difference between the CDR1 and CDR2 for each pixel is defined as the differential depth response curve (DCDR). Further, the zero-crossing point of the DCDR for each pixel is accurately extracted using the line-fitting technique, and finally, the sample surface can be reconstructed with a high resolution and precision. Since the slope around the zero-crossing point of the DCDR is apparently larger than that of near the focal position, an enhanced resolution and precision can be realized in DOSM-SI. The experiments and theoretical analysis are elaborated to demonstrate the feasibility of DOSM-SI.
Pattern Matching for DNA Sequencing Data Using Multiple Bloom Filters

Storing and processing of large DNA sequences has always been a major problem due to increasing volume of DNA sequence data. However, a number of solutions have been proposed but they require significant computation and memory. Therefore, an efficient storage and pattern matching solution is required for DNA sequencing data. Bloom filters (BFs) represent an efficient data structure, which is mostly used in the domain of bioinformatics for classification of DNA sequences. In this paper, we explore more dimensions where BFs can be used other than classification. A proposed solution is based on Multiple Bloom Filters (MBFs) that finds all the locations and number of repetitions of the specified pattern inside a DNA sequence. Both of these factors are extremely important in determining the type and intensity of any disease. This paper serves as a first effort towards optimizing the search for location and frequency of substrings in DNA sequences using MBFs. We expect that further optimizations in the proposed solution can bring remarkable results as this paper presents a proof of concept implementation for a given set of data using proposed MBFs technique. Performance evaluation shows improved accuracy and time efficiency of the proposed approach.