Miracle-dz/UniFAR-FTUs huggingface dataset:
How Social Anxiety and Reduced Self-Efficacy Induce Smartphone Addiction in Materialistic People

Recent research has suggested that materialism influences consumers’ usage of technological communication devices, especially smartphones. The current study contributes to this evolving research stream by examining more closely the dynamics of mechanisms that might potentially cause smartphone addiction. We propose self-efficacy and social anxiety as two underlying mechanisms and further test whether their mediating effects are consistent across people with differing levels of power distance belief (PDB). We also examine the moderating role of PDB at the cultural level (Study 1: China vs. the United States) and at the individual level (Study 2: Taiwan). The empirical data are analyzed using the parallel multiple mediator model proposed by Hayes. The results confirm that these two mediators explain the relationship between materialism and smartphone addiction. The mediating effects are stronger for people with a high level of PDB than for their counterparts with low PDB. Theoretical and practical implications are discussed.
Method and apparatus for detecting a suspicious activity by using video analysis

The system determines the transaction result by obtains the video data relating to the transaction area, and acquires at least one video transaction parameter related Transactions area by analyzing the video data. Transaction parameter is a video count of items showing video data, the video count is detected by the automatic item detection algorithm applied to the video data. System, such as the scan count such items scanned at the POS terminal, acquiring at least one of the predicted transaction parameters relating to prediction transaction occurring transaction area. The system automatically compares the video transaction parameter (s) predicted transaction parameters (s), confirms suggesting transaction results misbehaving like "cozy Transactions" in a retail store environment.
Instability monitoring in boost converters by resonant perturbation and control

Introduces a method for monitoring power electronic converters to detect impending instability. The method involves using a perturbation signal that is near-resonant with respect to a possible period doubling instability. A system output variable is monitored to detect any significant amplification of the probe signal. The method can be applied either to the circuit itself or to a model run in parallel to the circuit. It is found that, in addition to providing a warning of impending instability, the perturbing signal increases the system stability margin.
A VLSI implementation of a cryptographic processor

Current CPU's and DSP's are not well equipped to deal with encryption for communication purposes. The fastest server CPUs cannot handle large numbers of secure connections while low power devices cannot meet the high throughput that new 3G networks are offering without severely sacrificing battery life. To combat these problems, a specialized crypto-CPU was designed and simulated. The design utilizes new processor and low power design methodologies to create a general purpose processor optimized for cryptographic algorithms. Specialized encryption units and their corresponding instructions were added so that common cryptographic operations could be done quickly and efficiently. The design methodologies, efficiency, cost trade-offs, and simulated performance are presented in this paper.
Model-Based Network Analysis and Optimization

Emerging technologies like cloud computing have changed the usage of data centers in the last years. While a few decades ago, often dedicated use-case specific hardware has been deployed, todays data centers are faced with dynamic allocation of shared resources. Beside the availability of the resources, a data center provider has to ensure Quality of Service (QoS) criteria like bandwidth or response time, contracted with customers in Service Level Agreements (SLAs). The dynamic, high frequent resource allocation and redistribution requires continuous monitoring and observation of the agreed objectives. A proactive approach predicts SLA violations before bottlenecks occur, in a reactive scenario the current system state is used to assess the SLA fulfillments. As violations are forecasted or occur, the resources in the data center have to be reconfigured, to prevent or solve a broken agreement. Self-aware systems adapt themselves automatically, based on the current workload and specified degrees of freedom, without any human interaction. Existing approaches for dynamic resource management often focus on computing resources like auto-scaling or placement of Virtual Machines (VMs). The network part is commonly abstracted or modeled in a simplified manner. Other existing works deal with network performance analysis, usually via a simulative approach, but these analyses do not consider the adaptation of networks. Especially modeling the degrees of freedom is not supported by such simulation frameworks. This thesis focuses on the model-based adaptation and optimization of networks. Based on observed SLAs, adaptations for the network are suggested, if a SLA violation is detected. The model-based approach introduces two benefits. Firstly, the suggested solution can be evaluated through an analysis to ensure that the violation is solved and especially no other violations are generated through reconfiguration. At second, the model-based approach allows executions of what-if analyses on workloads, predicted by forecasters, or planned reconfigurations. One contribution of this work is the development of an adaptation points model as extension to the Descartes Network Infrastructures Modeling (DNI) language. Additionally, other models for the adaptation process are developed. A second contribution is a target-oriented, runtime-optimized adaptation approach to discover the cost-optimal solutions for network adaptations, which solve SLA violations. The third contribution is the implementation of the developed adaptation models and adaptation process in an adaptation framework for DNI. The target-oriented adaptation process has to fulfill several requirements, related to costoptimality and runtime-optimization. These requirements are assessed in a qualitative and quantitative evaluation. It is shown that the size of the network has no direct impact on the runtime of the adaptation process, which makes the approach suitable for large networks. In comparison to a brute force approach, the developed adaptation process discovers the cost-optimal solutions up to 250 times faster.
Integrated multi-objective process planning and flexible job shop scheduling considering precedence constraints

Abstract Process planning and scheduling decisions are independently performed in a manufacturing system in traditional approaches. Integration of these two decisions provides significant benefits such as enhancing the productivity of manufacturing resources, lead time reduction, and decreasing total production costs. An approximately 10% improvement has been created in the small problems by this integration. In this research, a mathematical model is proposed to simultaneously make these decisions. Flexible job shop, as a prevalent configuration in production systems, is the supposed configuration in the study in which the feasible process plans are recognized based on the precedence relations between operations. Due to the NP-Hardness of the problem, a solving procedure based on the genetic algorithm is devised so that all the alternative process plans could be considered implicitly. Makespan, critical machine workload, and machines total workload are considered as objective function. These has been combined in a weighted-sum to form an objective function and solved in pareto space. A comparison of the exact solutions with the proposed algorithms (WGA & NSGA II) results confirms the efficiency and the effectiveness of the proposed algorithms in obtaining the final solutions.
Communication delay in circuit-switched interconnection networks

Interconnection network design plays a central role in the design of parallel systems. The paper presents an analytical model to predict communication delay in circuit-switched k-ary n-cube interconnection networks augmented with virtual channel support. The main feature of the proposed model is the use of Markov chains to compute the path set-up time and to capture the effects of using virtual channels to reduce message blocking in the network. The mean waiting time that a message experiences at a source node before entering the network is calculated using an M/G/1 queueing system. The model is validated through flit-level simulation experiments.
China Healthcare Reform Simulation and Evaluate the Dynamic Performance

A novel dynamic healthcare insurance system is presented to meet the China’s healthcare coverage extension demand and to alleviate the patient cost burden. This new healthcare system is built up by the two parts in the old system: urban health insurance and new rural cooperative medical that specially exist in China. The research on the progress of the dynamic combination is discussed in detail. The altruistic style of government is also analyzed. We use multi-agent based dynamic agent number simulation to analyze the healthcare insurance coverage, service and treatment cost. The social welfare and service satisfaction of the medical healthcare reform are discussed. Research results indicate that our new designed healthcare insurance system formed by merging the two branches can improve the medical service satisfaction and reduce the patient cost.
Regenerating-Codes-Based Efficient Remote Data Checking and Repairing in Cloud Storage

The dramatic development of cloud storage services has led growing companies and individuals to outsource their data to cloud. However, users still concern about the availability and integrity of the data stored in cloud. To relieve these concerns, data redundancy is introduced into cloud storage systems, and data integrity verification schemes are used to check whether data is corrupted. Once data corruption is detected, the repair operations should be executed. However, most of the existing schemes based on erasure codes or network coding techniques either introduce high computation cost or cannot efficiently support remote data repairing. In this paper, we propose an efficient Remote Data Checking and Repairing (RDCR) scheme based on the minimum bandwidth regenerating codes. Our scheme reduces data owners' burden of checking data integrity by enabling a third party to perform the public integrity verification. In addition, unlike previous schemes, our scheme supports exact repair of corrupted data so that the computation cost is further reduced. We implement our scheme and the experiment results show that, compared with the existing schemes, RDCR has lower computational overhead and communication cost.
CATEGORY-LEVEL VISUAL OBJECT RECOGNITION USING NOVEL MACHINE LEARNING TECHNIQUES

Automatic visual recognition of generic objects is a highly relevant area of study. Nonetheless, intra-class and pose variations, as well as, background clutter and partial occlusion, are some of the main difficulties to achieve this goal. As an application case, robots with a robust visual system can achieve a high level of autonomy and a semantic understanding of their environments. Current state-of-the-art approaches to visual category-level object recognition are generally based on two main steps: generation of visual descriptors and training of visual classifiers using these descriptors and labeled images. Furthermore, these steps are usually complemented using techniques oriented to include contextual information in the models. In this thesis, we contribute to the area of visual recognition by proposing three techniques oriented to improve each of the previous steps, respectively. First, we introduce a technique for building visual descriptors based on a Bag-of-Words (BoWs) representation. In contrast to current approaches based on unsupervised clustering techniques, our proposal combines unsupervised and supervised information leading to more discriminative BoWs representations. Afterwards, we present a technique to improve the performance of current visual classifiers using a divide-and-conquer strategy based on a Mixture of Expert (MoE) approach. We innovate with respect to current MoE techniques by incorporating an embedded local feature selection scheme within each visual classifier. Finally, we propose an approach that exploits contextual information to improve the performance of object recognition techniques. We innovate with respect to state-of-theart techniques by considering scene dependent contextual relations among object classes. We test the performance of all these techniques by applying them to common benchmark datasets. Our results validate our main hypotheses indicating improvements with respect to alternative state-of-the-art methods. This also shows that the ideas presented in this thesis represent a relevant contribution to the state-of-the-art of category-level object recognition.
Application of Text Rank Algorithm for Context Based Indexing in Text Summarization

Today immeasurable information present on an internet. It often contains high quality information in the form of web pages. But it is not easy job to manually find out significant information and to pick the topmost collection of information for a particular information need. This problem can be resolved by Text Summarization. Huge amount of work has been done on similarity measure based text summarization task. Existing extraction based text summarization systems shows that the similarity values are calculated using indexing weights of the terms in the given input document. Due to this context independent indexing weights calculated similarity values remains independent on the context of a document. Very small work has been done for the problem of this type of indexing in text summarization task. This research work proposes the combine approach of Lexical association (semantic association) and context based document term indexing with NLP. In the particular we have also used novel concept of Lexical association to calculate the contextual similarity between sentences using computed indexing Weights. Then graph based ranking algorithms has been used with computed similarity values to obtain summary of document. Keywords-term indexing, text summarization, text rank, cosine similarity, sentence vector, lexical association.
A 300 MIPS/W RISC core processor with variable supply-voltage scheme in variable threshold-voltage CMOS

A 300 MIPS/W RISC core processor with variable supply-voltage (VS) scheme in variable threshold-voltage CMOS (VTCMOS) is presented. Performance in MIPS/W can be improved by a factor of more than two with no modification in the RISC core except for substrate contacts for the VTCMOS. From a 3.3 V external power supply the VS scheme automatically generates minimum internal supply voltages which meet the demand on its operation frequency.
Fine Circular Pupil Localization

In this paper a simple, fa st, and accurate method is proposed to localize the pupil. After analyzing eye images and their histograms, it has been found that the pupil area takes about 10% of the eye image area and mainly it is the darkest part. A threshold value was computed from t he histogram of the image, the computed threshold is used to convert the eye image to binary image, the seed fill algorithm was used as region growing method to segment the binary image and locate the pupil as the maximum segment, then fill it with black c olor to remove any specular spot reflection. A circle fitting algorithm is used to locate the best pupil circle. The conducted test on the proposed system using 10 eye images, downloaded from CASIA -IRISV3-Interval database, indicated that the pupil area av erage error ratio is 0.6%, the pupil center average position error is 0.5%, and the pupil radius average error ratio is 2.1%.
Design and simulation of leg exoskeleton cycling-actuated wheelchair

This article presents a design of novel wheelchair with a leg exoskeleton for locomotion that can be powered by user legs through a cycling action. In addition, the control system is designed with leg-motion assistance for lower limb muscles to perform exercise during wheelchair motion, targeting elderly persons and users with partial hemiplegia. The simulation results characterize the dynamic operation in three possible modes, fully active, fully passive, and user passive–active action.
A competitive genetic algorithm for resource-constrained project scheduling

In this paper we consider the resource-constrained project scheduling problem (RCPSP) with makespan minimization as objective. We propose a new genetic algorithm approach to solve this problem. Subsequently, we compare it to two genetic algorithm concepts from the literature. While our approach makes use of a permutation based genetic encoding that contains problem-specific knowledge, the other two procedures employ a priority value based and a priority rule based representation, respectively. Then we present the results of our thorough computational study for which standard sets of project instances have been used. The outcome reveals that our procedure is the most promising genetic algorithm to solve the RCPSP. Finally, we show that our genetic algorithm yields better results than several heuristic procedures presented in the literature. © 1998 John Wiley & Sons, Inc. Naval Research Logistics 45: 733–750, 1998
A Novel Routing Technique For Mobile Ad Hoc Networks (Manet)

Actual network size depends on the application and the protocols developed for the routing for this kind of networks should be scalable and efficient. Each routing protocol should support small as well as large scale networks very efficiently. As the number of node increase, it increases the management functionality of the network. Graph theoretic approach traditionally was applied to networks where nodes are static or fixed. In this paper, we have applied the graph theoretic routing to MANET where nodes are mobile. Here, we designed all identical nodes in the cluster except the cluster head and this criterion reduces the management burden on the network. Each cluster supports a few nodes with a cluster head. The intracluster connectivity amongst the nodes within the cluster is supported by multi-hop connectivity to ensure handling mobility in such a way that no service disruption can occur. The inter-cluster connectivity is also achieved by multi-hop connectivity. However, for inter-cluster communications, only cluster heads are connected. This paper demonstrates graph theoretic approach produces an optimum multi-hop connectivity path based on cumulative minimum degree that minimizes the contention and scheduling delay end-toend. It is applied to both intra-cluster communications as well as inter-cluster communications. The performance shows that having a multi-hop connectivity for intra-cluster communications is more power efficient compared to broadcast of information with maximum power coverage. We also showed the total number of required intermediate nodes in the transmission from source to destination. However, dynamic behavior of the nodes requires greater understanding of the node degree and mobility at each instance of time in order to maintain end-to-end QoS for multi-service provisioning. Our simulation results show that the proposed graph theoretic routing approach will reduce the overall delay and improves the physical layer data frame transmission.
Wavelet denoising for P300 single-trial detection

Template-based analysis techniques are good candidates to robustly detect transient temporal graphic elements (e.g. event-related potential, k-complex, sleep spindles, vertex waves, spikes) in noisy and multi-sources electro-encephalographic signals. More specifically, we present the impact on a large dataset of a wavelet denoising to detect evoked potentials in a single-trial P300 speller. Using coiflets as a denoising process allows to obtain more stable accurracies for all subjects.
Comparative Study of Three Different Topologies of Five-Level Inverter with SPWM Modulation Technique

Multilevel Inverter (MLI) has attracted a great attention by different researchers and industries due to its capability in handling high power application and minimizing the harmonics contents in the output. This study propose three different topologies of MLI (5-level) which are Cascaded H-Bridge Multilevel Inverter (CHMLI) ,Diode Clamped Multilevel Inverter and Flying Capacitor Multilevel Inverter (FCMLI) . These three topologies have been modelled in MATLAB/SIMULINK and compared in terms of THD and number of components used. Sinusoidal Pulse Width Modulation is utilized to control both of the topologies with same DC source. The results showed that ,CHMLI is superior compared to DCMLI and FCMLI in which the CHMLI produce 26.29% THD while DCMLI and FCMLI produce 29.14% and 33.53% respectively . Moreover less components and switching losses is obtained when using the CHMLI.
Enhanced Tame-Based Key Predistribution Scheme for Sensor Networks

The pairwise key establishment in sensor networks is a fundamental and challenge task because public key cryptography and key distribution center (KDC) are not desirable to use on strictly resource-limited sensor nodes. In order to improve the performance, some of the existing key management schemes proposed to utilize the location information of sensor nodes to do the key distribution. However, in some applications, the location information of sensor nodes may not be available. In this paper, we first take advantage of heterogeneous sensor nodes to enhance the tame-based key predistribution scheme. The analysis and simulation demonstrate that the enhanced scheme not only provides deterministic authentication service, but also has better performance in terms of the initialization time, memory overhead, communication overhead, probability to establish a pairwise key between two neighboring nodes, and resilience against node compromises. We then extend the enhanced scheme to a cluster-based key predistribution scheme. The analysis and experiment indicate that in addition to preserving the advantages of the enhanced scheme, the cluster-based scheme has more efficient key predistribution process, much less energy consumption for the pairwise key establishment, and perfect resilience against node compromises. Finally, we apply the clusterbased scheme in mobile sensor networks to support the pairwise key establishment between mobile sensor nodes.
Using IBE Key Distribution Strategies to Development of DDoS Attack Detection and Prevention

System based on public key of the certificate biggest problem is the management of the certificate, the certificate inside bind the user ID and the user's public key. To solve this problem, some researchers have proposed the identity-based IBE public key system. The DDoS attacks performer, can be any type of network node. Based on the user's private key distribution scheme identity-based cryptogram system, will give an escrow agent system private key escrow, users have at least a proxy information fragments collected can calculate their private key. The paper proposes using IBE key distribution strategies to development of DDoS attack detection and prevention.