Miracle-dz/UniFAR-FTUs huggingface dataset:
APPLICATION OF CONSTRAINT LOGIC PROGRAMMING TO JOB SEQUENCING

In this paper, we show an application of constraint logic programming to the operation scheduling on machines in a job shop. Constraint logic programming is a new genre of programming technique combining the declarative aspect of logic programming with the efficiency of constraint manipulation and solving mechanisms. Due to the latter feature, combinatorial search problems like scheduling may be resolved efficiently. In this study, the jobs that consist of a set of related operations are supposed to be constrained by precedence and resource availability. We also explore how the constraint solving mechanisms can be defined over a scheduling domain. Thus the scheduling approach presented here has two benefits: the flexibility that can be expected from an artificial intelligence tool by simplifying greatly the problem; and the efficiency that stems from the capability of constraint logic programming to manipulate constraints to prune the search space in an a priori manner.
Distributed cooperative agents for service management in communications networks

This paper concentrates on issues of resilience and fault tolerance of cooperative intelligent agents (complex software systems), introducing some basic concepts of global multiservice networks, and identifying service provision and restoration as problem areas. The distributed nature of the network of agents gives scope for reducing the total amount of data passed to a central point, and so improving performance. The agent community is self-organising and continues to function with reduced performance after failure of one or more agents. Other advantages are concurrent execution and dynamic use of local knowledge.
Image segmentation based on region merging technique

This paper presents an efficient algorithm to segment Synthetic Aperture Radar (SAR) image. Multi-direction ratio edge detector is used to capture the locally directional variation information of a SAR image and construct ratio edge strength map (RESM) of the SAR image. An efficient hierarchical region merging procedure based on the region adjacency graph (RAG) representation of the image regions is proposed. Experimental results show that the proposed method is outperforming a widely used SAR image segmentation approach.
Impact of Kernel Fisher Analysis Method on Face Recognition

Human Face recognition is a challenging task in computer vision and pattern recognition. Face recognition is difficult because it is a real world problem. The Human Face is complex, natural object that tends not to have easily identified edges and features. Because of this, it is difficult to develop a mathematical model of that face that can be used as prior knowledge when analyzing a particular image.This paper deals with the correspondence presents Color and Frequency Features based face recognition. The CFF method, which applies an Enhanced Fisher Model (EFM), extracts the complementary frequency features in a new hybrid color space for improving face recognition performance. The new color space, the RIQ color space, which combines the component image of the RGB color space and the chromatic components and of the YIQ color space, displays prominent capability for improving face recognition performance due to the complementary characteristics of its component images. The EFM then extracts the complementary features from the real part, the imaginary part, and the magnitude of the image in the frequency domain. The complementary features are then fused by means of concatenation at the feature level to derive similarity scores for classification. The complementary feature extraction and feature level fusion procedure applies to the I and Q component images as well. The hybrid color space improves face recognition performance significantly, and the complementary color and frequency features further improve face recognition performance. In CFF method particular, the Indian database had used for experimental analysis. There are many problems with face recognition such as facial expression, pose, age and occlusion. The Training set contains 200 images that are either controlled or uncontrolled. The Target set has 400 controlled images and the Query set has 100 uncontrolled images. While the faces in the controlled images have good image resolution, the faces in the uncontrolled images have lower image resolution and . These uncontrolled factors pose grand challenges to the face recognition performance. The face images used in our experiments are normalized to 64×64 to extract the facial region, which contains only face and the performance of face recognition is thus not affected by the factors not related to face, such as hair styles. These experimental results show that the combination of the hybrid color and frequency features by the CFF method is able to further improve face recognition performance. In particular the CFF method achieves the face verification rate (corresponding to the TestSet3) of 80.3% at the false accept rate of 0.1%. Future research will be considerd applying kernel methods, such as the multiclass Kernel Fisher Analysis (KFA) method to replace the EFM method for improving face recognition performance. And Note that the KFA method achieves, at 0.1% false accept rate, 84% face verification rate (FVR) respectively. Experimental result shows that the proposed method is efficient and improves the face recognition performance by large margin
Fingertip Interaction Metrics Correlate with Visual and Haptic Perception of Real Surfaces

Both vision and touch contribute to the perception of real surfaces. Although there have been many studies on the individual contributions of each sense, it is still unclear how each modality’s information is processed and integrated. To fill this gap, we investigated the similarity of visual and haptic perceptual spaces, as well as how well they each correlate with fingertip interaction metrics. Twenty participants interacted with ten different real surfaces from the Penn Haptic Texture Toolkit by either looking at or touching them and judged their similarity in pairs. By analyzing the resulting similarity ratings using non-metric multi-dimensional scaling (NMDS), we found that surfaces are similarly organized within the three-dimensional perceptual spaces of both modalities. Also, between-participant correlations were significantly higher in the haptic condition. In a separate experiment, we obtained the contact forces and accelerations acting on one finger interacting with each surface in a controlled way. We analyzed the collected fingertip interaction data in both the time and frequency domains. Our results suggest that the three perceptual dimensions for each modality can be represented by roughness/smoothness, hardness/softness, and friction, and that these dimensions can be estimated by surface vibration power, tap spectral centroid, and kinetic friction coefficient, respectively.
48 Differences Between Good and Bad Video Games: Game Playability Principles (PLAY) For Designing Highly Ranked Video Games

Game developers are beginning to apply formal humancomputer interaction (HCI) principles in game design. To aid in developing game-specific methodologies, one of the authors utilized a set of Heuristics for productivity software and adapted them to games. The resulting set was presented at CHI 2004 as Heuristics to Evaluate Playability (HEP). This recent follow-up study was designed to focus on Principles of Playability (PLAY) that can be applied earlier in the game development process, and reflects on our discussions with game developers from several studios. The goal was to create a framework of Principles that could be used to maximize game usability/playability. Fifty-four gamers rated High and Low-ranked games on 116 potential Principles. The ratings on forty-eight were found to differ significantly between High and Low-ranked games. Implications for how these Principles can help developers improve the quality of their games are discussed.
Fractal-Dimension-Based Method for Quantification of T-Wave Alternans Using Short Time Series

The presence of T-Wave Alternans (TWA) in an electrocardiogram (ECG) has been certified as an important predictor for the risk of sudden cardiac death (SCD). TWA is a beat-to-beat change in the amplitude of a T-wave, but is rarely visible to the naked eye. Thus, automatic detection and quantification of TWA are desirable. While several automatic algorithms, such as the periodogram method or the modified moving average method (MMA), have been developed to detect or quantify TWA, most conventional methods do not effectively measure short-duration TWA (SDTWA) (<16 beats). In this paper, we proposed a fractal dimension based SDTWA quantification method, and evaluated it with simulated ECG signals with SDTWA episodes (<16 beats) based on the European ST-T database. In the evaluation, the proposed method was applied to ECG signals with TWA amplitude of 5, 15, 30, 45, 60 and 75 μV. Sensitivity and positive predictivity of over or closed to 90% were obtained except for the 5 μV SDTWA episodes. Even for 5 μV SDTWA episodes, the sensitivity reached 75%. We believe that proposed s fractal dimension based method is a promising method for SDTWA analysis.
Finding of Weighted Sequential Web Access Patterns for Effective Web Page Recommendations

Recommender systems aim at directing users through this information space, toward the resources that best meet their needs and interests by extracting knowledge from the previous users’ interactions. Currently much research is focus on web page recommendations using sequential pattern mining techniques. Sequential access pattern mining discovers interesting and frequent user access patterns from web logs. Most of the previous studies have adopted Apriori-like sequential pattern mining techniques, which faced the problem on requiring expensive multiple scans of databases. In this paper a traditional sequential pattern mining algorithm called prefixspan is modified by incorporating two measures such as, spending time and recent view. Then, the weighted sequential patterns are utilized to construct the recommendation model using the Patricia trie-based tree structure. Finally, the recommendation of the current users is done with the help of markov model.
Fight Detection in Hockey Videos using Deep Network

Understanding actions in videos is an important task. It helps in finding the anomalies present in videos such as fights. Detection of fights becomes more crucial when it comes to sports. This paper focuses on finding fight scenes in Hockey sport videos using blur & radon transform and convolutional neural networks (CNNs). First, the local motion within the video frames has been extracted using blur information. Next, fast fourier and radon transform have been applied on the local motion. The video frames with fight scene have been identified using transfer learning with the help of pre-trained deep learning model VGG-Net. Finally, a comparison of the methodology has been performed using feed forward neural networks. Accuracies of 56.00% and 75.00% have been achieved using feed forward neural network and VGG16-Net, respectively.
SNMP Based Disovery tool on Raspberry pi 2

In commercial industries, large scale computer networks are deployed. It is not feasible to manage these networks manually. Solution to this issue is Network Management System (NMS). However, the current NMS are too costly, complex to understand and needs to be configured by an operator level person. This paper proposes a Simple Network Management Protocol (SNMP)-based web application that handles automatic device discovery and devices monitoring for local network. This system is implemented on Single Board Computer (SBC). SBC used in this system is Raspberry Pi 2. Key contribution of this system is that it can be deployed at server side without making any major changes in existing server system. Main Purpose of using Raspberry Pi is that it has low investment and maintenance cost, affordable replacement and fulfills single operation requirement. It is Server based system, so it does not need to configure devices manually. Thus any number of hosts can be traced. In short, this web software along with hardware gives effective solution for managing and monitoring local networks.
A forward-inverse optics model of reciprocal connections between visual cortical areas

We propose that the feedforward connection from the lower visual cortical area to the higher visual cortical area provides an approximated inverse model of the imaging process (optics), while the backprojection connection from the higher area to the lower area provides a forward model of the optics. By mathematical analysis and computer simulation, we show that a small number of relaxation computations circulating this forward-inverse optics hierarchy achieves fast and reliable integration of vision modules, and therefore might resolve the following problems. (i) How are parallel visual modules (multiple visual cortical areas) integrated to allow a coherent scene perception? (ii) How can ill-posed vision problems be solved by the brain within several hundreds of milliseconds?
Intelligent Reliable Schedule with Budget Constraints in Grid Computing

This study proposes an intelligent reliable schedule with budget constraints in grid computing to reduce the makespan in grid environment. A good schedule improves the usability of the grid environment, but often faces the challenge of reliability and budget. In our paper we have addressed the reliability and budget constraints in grid environment by augmenting intelligence to the schedule by means of intelligent exploitation of random pushing and random job stealing. The objective of this paper is to schedule the tasks onto the reliable processor based on the user requirement and also provide the opportunity to schedule the tasks onto the reliable processor which don't have high budgets. The experimental outcomes have supported the theoretical analysis by classifying the tasks and scheduling those to the various resources which are again divided as levels of criteria based on rank values enabling us to get a good and efficient schedule.
Space Communications and Navigation (SCaN) Network Simulation Tool Development and Its Use Cases

In this work, we focus on the development of a simulation tool to assist in analysis of current and future (proposed) network architectures for NASA. Specifically, the Space Communications and Navigation (SCaN) Network is being architected as an integrated set of new assets and a federation of upgraded legacy systems. The SCaN architecture for the initial missions for returning humans to the moon and beyond will include the Space Network (SN) and the Near-Earth Network (NEN). In addition to SCaN, the initial mission scenario involves a Crew Exploration Vehicle (CEV), the International Space Station (ISS) and NASA Integrated Services Network (NISN). We call the tool being developed the SCaN Network Integration and Engineering (SCaN NIE (2) to optimize system configurations by testing a larger parameter space than may be feasible in either production networks or an emulated environment; (3) to test solutions in order to find issues/risks before committing more significant resources needed to produce real hardware or flight software systems. We describe two use cases of the tool: (1) standalone simulation of CEV to ISS baseline scenario to determine network performance, (2) participation in Distributed Simulation Integration Laboratory (DSIL) tests to perform function testing and verify interface and interoperability of geographically dispersed simulations/emulations.
CONCEPTUAL FOUNDATIONS OF CONSTRUCTION OF THE MODELS AND PROCEDURES FOR PREDICTION OF THE AVALANCHE-DANGEROUS SITUATIONS INITIATION

The conceptual foundations of the models and procedures for prediction of the avalanche-dangerous situations initiation are considered. The interpretation model for analysis of the avalanche-dangerous situations initiation based on the definition of probabilities of correspondence of studied parameters to the probabilistic distributions of avalanche-dangerous or avalanche non-dangerous situations is offered. The possibility to apply such a model to the real data is considered. The main approaches to the use of multiple representations for the avalanche dangerous situations initiation analysis are generalized.
Data Mining in Cloud Computing

This discussion explains how through cloud computing, the process of data mining is facilitated. Through data mining, information that is potentially useful can be retrieved from raw data. People often face the need for targeted advertising, whereby data mining techniques give businesses greater efficiency, hence helping to lower costs. In the sector of cloud computing, data mining has become of great importance. The facilitation of data mining through cloud computing will enable users to obtain useful information via virtual warehouse of integrated data, helping to lower expenses of storage, technical staff, and purchase of infrastructure. Key terms: Data mining, cloud computing
Combining two methods to enhance band-notch characteristic of ultra wide band antenna

In this paper, we combine two conventional stop-band creating methods to enhance band-notched function of a planar microstrip-fed monopole antenna with truncated ground plane. By combining a parasitic strip under the substrate and an inverted-U slot on the patch, a notch with two very sharp edges around 5.5 GHz WLAN band is obtained. The proposed antenna is fabricated on the substrate FR4 (relative permittivity of 4.7) and has a compact size of 16 × 30 × 1.6 mm. A good impedance matching is obtained in 3.1 –10.6 GHz frequency range, except the band 5.16–5.8 GHz and the radiation pattern is similar to the monopole antenna.
Isolating grammar in phonological data

Rather than forcing linguists to use the statistical conventions followed in other disciplines, the statistics should be designed specifically for linguistic hypotheses (about competence) and linguistic data (usually not reaction times, accuracy, etc). • Linguistic data are usually categorical: attested vs. not, acceptable vs. not. The process should be made as easy as possible by automating it in software and linking it to tools already familiar to linguists (e.g. Optimality Theory tableaux).
High-order Consensus of Heterogeneous

This paper studies the high-order consensus prob- lem for heterogeneous multi-agent systems with unknown com- munication delays. It is shown that high-order consensus may exist in systems with heterogeneous agents. A necessary and sufficient condition is given for the existence of high-order consen- sus solution to heterogeneous multi-agent systems. The obtained condition shows that for systems with diverse communication delays, high-order consensus does not require each self-delay of agent is equal to the corresponding communication delay. A matching condition for self-delays and communication delays is derived. Finally, when communication delays are unknown, an adaptive adjustment mechanism is proposed for on-line adjusting self-delays.
Evolving Technology to Better Support Teaching Introductory Programming Inside Moodle

Nowadays, it is increasing the importance of developing skills related to the computational thinking at earlier stages of education, and the adoption of tools that implement the visual programming paradigm has been well succeeded in presenting introductory notions of programming from kindergarten to college. Such tools allows the user to program while manipulating blocks that represent programming languages instructions. iVProg is among this class of tools and was firstly implemented in Java and could be integrated to Moodle using iAssign. Due to technological evolution, iVProg also evolved to iVProgH and its integration to Moodle is still possible by using the new version of iAssign that allow the integration of HTML stack packages to Moodle. In this paper we describe how this evolution takes place and presents iVProgH functionalities as well as iAssign extension to include HTML packages into Moodle. In addition, we give some teaching support whenever integrated using iVProgH with Moodle using iAssign.
REVIEW: ROBUST APPROACH TO DETECT AND LOCALIZE TEXT FROM NATURAL SCENE IMAGES

In natural scene large amount of information is hidden which can be automatically recognized and processed. To get this automatic segmentation, detection and recognition of visual text entities in natural scene images should be done. For extracting the text from natural scene images, robust approaches like combination of algorithm such as by combining the edge and connected component based are adopted. Efficient outcome is the result of combining. The connected component based algorithm is more robust to scale and lighting condition as compared to edge based. In literature unary component properties and binary contextual component of conditional random field model are preferred to detect nontext components. In region based method, the speed is relatively slow and performance is sensitive to text alignment orientation and for connected component based methods cannot segment text component accurately. Combination of these two models can achieve a far more robust algorithm, which would be invariant to scale, lighting and orientation changes. This robust approach will fail to segment text. The paper emphasizes a strong focus on four methods of detecting and localizing the text already proposed in the literature. A brief review along with comparative analysis and future scope of the algorithm are focused in the paper
Algorithms for Academic Search and Recommendation Systems

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Enhanced Generic Video Summarization using Large Scale Categorization

Over the past few years, there has been a massive increase in amount of video content created. Massive growth in video content poses problem of information overload and management of content. In order to manage the growing videos on the web and also to extract efficient and valid information from the videos, more attention has to be paid towards video and image processing technologies. Video summaries provide condensed and succinct representations of the content of a video stream through a combination of still images, video segments, graphical representations and textual descriptors. Existing video summarization techniques have attempted to solve the problem of condensing the content of a video in domain specific manner. However, such domain specific summarization mechanisms do not generalize well over different genres of videos. To make video summarization scalable enough to cater to the needs of growing massive online video content, it needs to be generic and adaptable for its applicability on any category of video. This work presents a general approach towards video summarization process and proposes a two-step approach towards domain independent generic video summarization incorporating video categorization for enhanced keyframe extraction. This paper also talks about building effective mechanisms for large scale categorization over big hierarchical category tree of videos.