Miracle-dz/UniFAR-FTUs huggingface dataset:
Cooperative Communications with Relay Selection for QoS Provisioning in Wireless Sensor Networks

Cooperative communications have been demonstrated to be effective in combating the multiple fading effects in wireless networks, and improving the network performance in terms of adaptivity, reliability and network throughput. In this paper, we investigate the use of cooperative communications with adaptive relay selection for resource-constrained wireless sensor networks, and propose QoS-RSCC, a QoS-support multi-agent reinforcement learning based relay selection scheme for cooperative communications. In QoS-RSCC, optimal relays, in terms of outage probability and channel efficiency, are selected distributedly from multiple relaying candidates for the intermediate routers along the multi-hop route, without the needs of prior knowledge of the wireless network model and centralized control. We compare the network performance of QoS-RSCC with CRP [1], and investigate the impacts of network traffic load, channel bit error rate, and node's mobility on the network performance. Simulation results show that QoS-RSCC can achieve a near-optimal performance on both diversity gains and channel efficiency, and fits well in dynamic environments.
Identifying and labeling search tasks via query-based hawkes processes

We consider a search task as a set of queries that serve the same user information need. Analyzing search tasks from user query streams plays an important role in building a set of modern tools to improve search engine performance. In this paper, we propose a probabilistic method for identifying and labeling search tasks based on the following intuitive observations: queries that are issued temporally close by users in many sequences of queries are likely to belong to the same search task, meanwhile, different users having the same information needs tend to submit topically coherent search queries. To capture the above intuitions, we directly model query temporal patterns using a special class of point processes called Hawkes processes, and combine topic models with Hawkes processes for simultaneously identifying and labeling search tasks. Essentially, Hawkes processes utilize their self-exciting properties to identify search tasks if influence exists among a sequence of queries for individual users, while the topic model exploits query co-occurrence across different users to discover the latent information needed for labeling search tasks. More importantly, there is mutual reinforcement between Hawkes processes and the topic model in the unified model that enhances the performance of both. We evaluate our method based on both synthetic data and real-world query log data. In addition, we also apply our model to query clustering and search task identification. By comparing with state-of-the-art methods, the results demonstrate that the improvement in our proposed approach is consistent and promising.
Numerical Hedging Of Electricity Contracts Using Dimension Reduction

The basic contracts traded on energy exchanges involve fixed-rate payments for the delivery of electricity over a certain period of time. It has been shown that options on these electricity swaps can be priced efficiently using a Hilbert space-valued time-inhomogeneous jump-diffusion model for the forward curve. We consider the mean-variance hedging problem for European options under this model. The computation of hedging strategies leads to quadratic optimization problems whose parameters depend on the solution of an infinite-dimensional partial integro-differential equation. The main objective of this article is to find an efficient numerical algorithm for this task. Using proper orthogonal decomposition (a dimension reduction method), approximately optimal strategies are computed. We prove convergence of the corresponding hedging error to the minimal achievable error in the electricity market. Numerical experiments are performed to analyze the resulting hedging strategies.
What Mobilizes Information Contribution to Electronic Word-of-Mouth System ? Explanations from a Dual-Process Goal Pursuit Model

Electronic word-of-mouth systems (EWOMS) facilitate electronic commerce tremendously. Although adequate consumption information supply is critical for these systems and to some extent for electronic commerce to succeed, there is a lack of investigations on information provision as current studies related to EWOMS mainly concentrate on the consequences of the deployment of the systems. We do not have a systematic understanding of what may motivate consumers to engage in electronic word-of-mouth activities. This study attempts to address the knowledge gap by constructing a dual-process goal pursuit model to explain consumers’ initial participation in electronic word-of-mouth activities. We conceptualize the consumer’s participation as a joint behavioral product of the consumer’s both conscious and unconscious responses to electronic word-of-mouth systems to pursue some goals that are either embedded in the participation process itself or endowed by the consequences of the participation behavior by devoting multiple resources such as effort, cognitive resources, and time to the activity. The consumer’s habitual dependence on online communication and opinion leadership behavior are identified as intrinsic goals that the consumer pursues automatically without cognitive processing. Additionally, the consumer would also perceive and derive extrinsic goals based on the incentives such as status and economic rewards that are offered by the system and on her past consumption experience. However, these extrinsic goals operate within the consumer’s cognitive context. The consumer’s assessment of the attractiveness and attainment expectance of the goals would moderate the effects of the goals on electronic word-of-mouth participation.
Software bug localization using Pachinko Allocation Model

Bug localization is the process of identifying the elements of source code that require modification to fix the bug. By automating the task of bug localization efficiently, the cost of software can also be reduced. For performing bug localization, many Information Retrieval models have been used in past. In this paper, bug localization has been performed using Pachinko Allocation Model (PAM). PAM is also an IR model, which falls under the category of topic models and has not been used for locating bugs yet. This paper describes the proposed PAM based approach for bug localization at file level. The PAM based approach is compared with LDA based approach and it has been shown that PAM based bug localization performs better as compared to LDA based bug localization. For evaluating the performance of PAM and LDA based approaches, the datasets downloaded from two open source projects, i.e. Rhino and ModeShape, have been used.
New Product Development Performance: the Case of a Small Canadian Audio Company

This paper is based on a study into new product development in the high-end audio industry during which 21 British, Japanese and North American companies were benchmarked. The original research sample contained data on product development performance and practice obtained from 31 new product development projects. Performance was gauged by several measures, including lead times, cost and schedule adherence, internal and external quality and product profitability. In this paper we give a close look at two projects that had achieved performance levels that were remarkably different even if they were conducted within the same small Canadian audio company. Our results suggest that some of the characteristics of small companies may favor new product development performance, providing that the company evinces a certain level of formalism.
TEST BUS ASSIGNMENT OF SYSTEM ON CHIP BY USING CULTURAL PARTICLE SWARM OPTIMIZATION

The system on chip (SoC) must be tested in order to insure the correctness of functions. The test of SoC involves the test of cores and the test of interconnects among cores. The test set of cores must be transported to the inputs of cores through the test access architecture in SoC. Therefore, in order to make the total test time to be minimal, it is necessary to determine an assignment of cores to test access architecture. In this paper, a new method is presented for test bus assignment of system on chip, which is based on cultural particle swarm optimization. In this method, the feasible solutions of test bus assignments are represented by the individuals. The better schemes of test bus assignments are obtained by the evolution of the individuals. The implementation of the method consists of a population space, a belief space, and a communication protocol that describes the exchange mode of knowledge between the population space and belief space. The particle swarm optimization with selection strategy is used for carrying out the evolution of individuals in population space. A lot of experimental results for SoC benchmark circuits demonstrate that the proposed method in this paper can effectively obtain the test bus assignments in shorter time than conventional genetic algorithms.
Relative transfer function identification using speech signals

An important component of a multichannel hands-free communication system is the identification of the relative transfer function between sensors in response to a desired source signal. In this paper, a robust system identification approach adapted to speech signals is proposed. A weighted least-squares optimization criterion is introduced, which considers the uncertainty of the desired signal presence in the observed signals. An asymptotically unbiased estimate for the system's transfer function is derived, and a corresponding recursive online implementation is presented. We show that compared to a competing nonstationarity-based method, a smaller error variance is achieved and generally shorter observation intervals are required. Furthermore, in the case of a time-varying system, faster convergence and higher reliability of the system identification are obtained by using the proposed method than by using the nonstationarity-based method. Evaluation of the proposed system identification approach is performed under various noise conditions, including simulated stationary and nonstationary white Gaussian noise, and car interior noise in real pseudo-stationary and nonstationary environments. The experimental results confirm the advantages of proposed approach.
Cognitive Model - Based Emotion Recognition From Facial Expressions For Live Human Computer Interaction

Human emotions are deeply intertwined with cognition. Emotions direct cognitive processes and processing strategies of humans. The goal of this work is to design a model with the capability of classifying the uncertainty, contradiction and the cognitive nature of the emotions. For achieving this, 3D cognitive model is designed. This model enhances our vision of classification of emotions produced by reinforcing stimuli. In this model the dimensions represent the positive reinforcers, the negative reinforcers and the emotion content present. The positive reinforcer increases the probability of emission of a response on which it is contingent, whereas the negative reinforcer increases the probability of emission of a response that causes the reinforcer to be omitted. This model increases the number of emotions, that can be classified. Presently this model can classify 22 emotions subject to the presence of a facial expression database. It has the flexibility to increase upon the number of emotions. For emotion (pattern) identification, the pose and illumination factor are removed using Gabor wavelet transforms and the size is reduced by finding its principle components (PCA). This component vector is used for training the neural network. The test result shows the recognition accuracy of 85.7% on The Cohn-Kanade Action Unit Coded Facial Expression Database. The real time processing for identification, aids in applying emotions to real time audio player. An environment, that is all pervasive or ubiquitous, that would sense one's mental state and play the appropriate musical track to maintain the positive emotional state or ease from a negative emotional state
Stone Weathering in a Photograph

The appearance of weathering effects on stone is important for creating outdoor scenes in computer graphics. To achieve them, previous research has built upon physical simulation, which, while yielding a degree of realism, is computationally expensive and inapplicable to the situation when the object geometry is unknown. Also, physical simulation requires specific knowledge of the stone properties and environmental processes. In this paper, we present a simple visual simulation pipeline for creating weathering effects on stone within a single image. Two primary effects of stone weathering, i.e., smoothing and roughening, are considered. In addition, erosion on the object silhouette is treated. These challenging effects involve significant geometry changes, which are intractable for previous image‐based editing techniques. The effectiveness of our technique is illustrated on a variety of scenes and types of stone. While it can be fully automatic, it also allows easy user interaction.
Genome matching on modern architectures

Exact string matching algorithms are critical components in computational biology applications regarding the nucleotide or aminoacid sequences. The current paper presents a slightly modified version Boyer-Moore algorithm for string matching (in particular genomes) that has been optimized for a dual Xeon 5860. We highlight several ways to improve execution time and conclude based on our measurements that STTNI hardware instructions alongside core parallelism achieve most of the performance gain.
Antibacterial, Antifungal, Cytotoxic, Phytotoxic, Insecticidal, and Enzyme Inhibitory Activities of Geranium wallichianum

The present study describes the phytochemical investigations of the crude extracts of rhizomes and leaves of Geranium wallichianum. The crude extracts were fractionated to obtain n-hexane, ethyl acetate, and n-butanol fractions, which were subjected to different biological activities and enzyme inhibition assays to explore the therapeutic potential of this medicinally important herb. The results indicated that the crude extracts and different fractions of rhizomes and leaves showed varied degree of antimicrobial activities and enzyme inhibitions in different assays. Overall, the rhizome extract and its different fractions showed comparatively better activities in various assays. Furthermore, the purified constituents from the repeated chromatographic separations were also subjected to enzyme inhibition studies against three different enzymes. The results of these studies showed that lipoxygenase enzyme was significantly inhibited as compared to urease. In case of chemical constituents, the sterols (2–4) showed no inhibition, while ursolic acid (1) and benzoic ester (6) showed significant inhibition of urease enzymes.
Fast and accurate vanishing point detection in complex scenes

Most existing approaches detect the vanishing point by voting the local dominant texture orientation at each pixel. However, when it is hard to distinguish natural road clues (items for finding the vanishing point) from background noises (e.g. stones, grasses) in complex scenes, they may suffer deteriorated accuracy and efficiency. In this paper, we introduce a novel vanishing point detection algorithm with the proposed Weber Orientation Descriptor (WOD). We first employ the differential excitation component of WOD to extract reliable road clue regions from a complex background, and then adopt the orientation component of WOD and our proposed line-voting scheme (LVS) to locate the vanishing point. Experimental results on the benchmark dataset reveal a step forward in detection performance against the state-of-the-art vanishing point detection methods.
AutoGuitarTab: Computer-Aided Composition of Rhythm and Lead Guitar Parts in the Tablature Space

We present AutoGuitarTab, a system for generating realistic guitar tablature given an input symbolic chord and key sequence. Our system consists of two modules: AutoRhythmGuitar and AutoLeadGuitar. The first of these generates rhythm guitar tablatures which outline the input chord sequence in a particular style (using Markov chains to ensure playability) and performs a structural analysis to produce a structurally consistent composition. AutoLeadGuitar generates lead guitar parts in distinct musical phrases, guiding the pitch classes towards chord tones and steering the evolution of the rhythmic and melodic intensity according to user preference. Experimentally, we uncover musician-specific trends in guitar playing style, and demonstrate our system's ability to produce playable, realistic and style-specific tablature using a combination of algorithmic, user-surveyed and expert evaluation techniques.
Conditions for target recovery in spatial compressive sensing for MIMO radar

We study compressive sensing in the spatial domain for target localization in terms of direction of arrival (DOA), using multiple-input multiple-output (MIMO) radar. A sparse localization framework is proposed for a MIMO array in which transmit/receive elements are placed at random. This allows to dramatically reduce the number of elements, while still attaining performance comparable to that of a filled (Nyquist) array. Leveraging properties of a (structured) random measurement matrix, we develop a novel bound on the coherence of the measurement matrix, and we obtain conditions under which the measurement matrix satisfies the so-called isotropy property. The coherence and isotropy concepts are used to establish respectively uniform and non-uniform recovery guarantees for target localization using spatial compressive sensing. In particular, nonuniform recovery is guaranteed if the number of degrees of freedom (the product of the number of transmit and receive elements MN) scales with K(log G)2, where K is the number of targets, and G is proportional to the array aperture and determines the angle resolution. The significance of the logarithmic dependence in G is that the proposed framework enables high resolution with a small number of MIMO radar elements. This is in contrast with a filled virtualMIMO array where the product MN scales linearly with G.
Enabling keyword search on Linked Data repositories: An ontology-based approach

The Web is experiencing a continuous change that is leading to the realization of the Semantic Web. Initiatives such as Linked Data have made a huge amount of structured information publicly available, encouraging the rest of the Internet community to tag their resources with it. Unfortunately, the amount of interlinked domains and information is so big that handling it efficiently has become really difficult for final users. Thus, we have to provide them with tools to search the needed resources in an easy way. In this paper, we propose an approach to provide users with different domain views on a general data repository, enabling them to perform both keyword and refinement searches. Our system exploits the knowledge stored in ontologies to 1 perform efficient keyword searches over a specified domain, and 2 refine the user's domain searches. In this way, we enable the definition of different semantic views on Linked Data datasets without having to change the original semantics. We present a prototype of our approach that focuses on the case of DBpedia, which provides a semantic way to access to Wikipedia.
Motion planning for an autonomous vehicle driving on motorways by using flatness properties

In this paper, a motion planning algorithm based on differential flatness for an autonomous vehicle driving in a structured environment with moving obstacles is presented. A descriptions of the structured environment and definitions of safety and availability of the lane are proposed. In order to ensure that the trajectory is feasible and effective, dynamic longitudinal and lateral acceleration constraints are taken into account. Simulation results are validated by measurements of the outputs of a 14 degrees of freedom vehicle dynamics model with the generated trajectory as inputs.
Study of cascade PID controller parameter optimization based on IMEA for thermal system

The stable constraint conditions of cascade PID controller were obtained by generalized Hermite-Biehler theorem, and an optimal controller was researched. Through mind evolutionary algorithm(MEA), optimal cascade PID controller parameters were found. According to the defects lying in mind evolutionary algorithm, the niche technique is developed to improve MEA in this paper. The swarm is classified and reduced the searching zone, which keeps the swarm diversity and to speed up optimization. Based on parameter optimization and tuning for the main steam temperature cascade control systems in power plants with serious parameter uncertainty, multi-disturbance and large delay, the simulation results show that this method has the advantages of fast optimization and low computational complexity; it is very effective for cascade PID parameter optimization and endues main steam temperature control systems with better control qualities and better system robustness.
Peer Review : Consistency Effectiveness of Argumentation Strategy In Direct Instruction Model to Improve Student Problem Solving Skill

This research aims to find consistency of argumentation strategy in direct instruction model to improve problem solving skill students on thermodynamic course. This study is the stage of implementation of the direct instruction model with argumentation strategy. The implementation is done to 66 physics education students from two classes in FKIP ULM that programmed the thermodynamics module as research subject. To see the consistency of effectiveness of the learning strategy in improving problem solving skills an independent t-test was performed on the normalized gain score analysis of the pre-test post-test scores for both classes. The result of n-gain analysis for both classes is in moderate category with n-gain score for first and second class are 0.69 and 0.60. The independent t-test of the n-gain results shows that the t count of 1.75 and the t critical of 1.99. Since the value of t count is smaller than the t critical value means there is no significant difference between the improvements of students’ problem solving skills taught by the argumentation
Biodistribution of Mesenchymal Stem/Stromal Cells in a Preclinical Setting

Due to their multi/pluripotency and immunosuppressive properties, mesenchymal stem/stromal cells (MSCs) are important tools for treatment of immune disorders and tissue repair. The increasing uses of MSCs lead to the development of production processes that need to be in accordance with good manufacturing practices (GMP). In Europe, MSCs are somatic cell-therapy products, referred to as advanced-therapy medicinal products (ATMPs), and in the United States MSCs must comply with current good tissue practice requirements. The safety and efficacy of MSCs must be ensured, whatever the cell source, and studies of dose and biodistribution are important aspects of safety testing. Preclinical data on biodistribution and pharmacodynamics are mandatory for approval. It is important to demonstrate that MSCs do not have unwanted homing that could drive to inappropriate differentiation in some organ or to support cancer development as suggested in some experiments. All these aspects should be addressed in a risk-based approach according to recently published guidelines by EMA. In the present article, we summarize the main approaches for labeling and tracking of infused MSCs, report on current animal models, and give an overview of available results on biodistribution.