Miracle-dz/UniFAR-FTUs huggingface dataset:
Inductive learning for engineering design optimization

We are working on using machine learning to make the numerical optimization of complex engineering designs faster and more reliable. We envision a system that learns from previous design sessions knowledge that enables it to assist the engineer in setting up and carrying out a new design optimization. We have performed initial experiments for two aspects of setting up an optimization: selecting a prototype to serve as a starting point for the optimization and selecting a reformulation of the search space. Both choices can dramatically affect the speed and the reliability of design optimization.
Power-line communication-based network architecture for LVDC distribution system

In smart grids, a communication network with bidirectional data transmission between network components is required for functionalities, such as grid monitoring, control and protection. These functionalities set different requirements for communication. This paper focuses on the use of power-line communication (PLC) as a data transmission method for monitoring, control and protection functions in a proposed novel smart grid concept - low-voltage direct current (LVDC) distribution system. Hence, a PLC-based communication architecture for the LVDC system is proposed, its feasibility analyzed theoretically and verified by measurements. Channel capacity and practical data transmission tests are performed in a laboratory environment. Finally, the suitability of the proposed network architecture for the LVDC system and its compliance with the set requirements are analyzed.
Tag correlation and user social relation based microblog recommendation

A microblog recommendation method based on tag correlation and user social relation is proposed via analyzing microblog features and the deficiencies of existing microblog recommendation algorithm. Specifically, a tag retrieval strategy is established to add tags for unlabeled users and users with few tags, and the user-tag matrix is then built and user-tag weights are then obtained. In order to solve the problem of sparsity of the matrix, the correlation between the tags is investigated to update the user-tag matrix. Considering the significance of user social relation for microblog recommendation, a user-user social relation similarity matrix is constructed and a mechanism is designed to iteratively obtain user interest. Experimental results show that the algorithm is effective for microblog recommendation.
Adaptive probabilistic flooding for Information Hovering in VANETs

Information Hovering applies in many applications in Vehicular Ad Hoc Networks, where useful information needs to be made available to all vehicles within a confined geographical area for a specific time interval. A straightforward approach is to have all vehicles within the hovering area exchange messages with each other. However, this method does not guarantee that all vehicles within the hovering area will receive the message due to potential partitioning of the network in areas with low traffic density and/or low market penetration rate. To alleviate this problem in this work we propose a scheme which is based on the application of epidemic routing within the hovering area and probabilistic flooding outside the hovering area. Informed vehicles outside the area can serve as information bridges towards partitioned uninformed areas thus leading to high reachability. A unique feature of the proposed protocol is that it is adaptive in the sense that the rebroadcast probability outside the hovering area is adaptively regulated based on estimates of the vehicle density within the hovering area. We evaluate the performance of the proposed scheme using VISSIM. The reference model used in all simulation experiments represents a section of the road network in the cities of Bellevue and Redmond in Washington. The obtained simulation results indicate that the proposed protocol is successful in satisfying its design objectives and that it outperforms other candidate hovering protocols.
Reduced Runoff Due to Anthropogenic Intervention in the Loess Plateau, China

To maintain the sustainable utilization of water resources and reduce soil erosion in the Loess Plateau, the Chinese government has adopted a number of environmental restoration strategies since 1999, including the Grain for Green Project (GFGP) and the Natural Forest Conservation Program; these large projects greatly alter the regional water cycle. Detecting runoff changes and quantitatively assessing the contribution of anthropogenic activities (including land use/cover change (LUCC) and water diversion) and climate change (including potential evaporation and precipitation) are imperative for implementing sustainable management strategies. Using observed records from 15 hydrological stations and 85 national meteorological stations from 1980 to 2013, the decomposition method, based on the Budyko hypothesis, is used to quantify the impact of climate variation and anthropogenic interference on annual runoff for the 12 catchments in the Loess Plateau. The results show the following: (1) the observed annual runoff exhibited a negative trend in all 12 catchments (significant in eight catchments) with a range of −1.94 to −0.16 mm·year−1 and exhibited a substantial difference before and after 1999; (2) the sensitivity of runoff to vegetation change, precipitation, and potential evapotranspiration increased in most catchments after 1999, indicating that great challenges and uncertainties might be introduced to regional water resource availability; and (3) the anthropogenic interference, particularly LUCC caused by forest strategies, has become the main contribution to runoff change. We suggest that more attention should be given to water resource availability and that the hydrologic consequences of revegetation should be taken into account in future management.
Two-Level Metadata Management for Data Deduplication System

Data deduplication is an essential solution to reduce storage space requirement. Especially chunking based data deduplication is very effective for backup workloads which tend to be files that evolve slowly, mainly through small changes and additions. In this paper, we introduce a novel data deduplication scheme which can be efficiently used with low bandwidth network in a rapid time. The key points are using tree map searching and classifying data as global and metadata. These are the main aspects to influencing fast performance of the data deduplication
Toxoplasma Gondii Bradyzoites Form Spontaneously during Sporozoite-Initiated Development

ABSTRACT Tachyzoites (VEG strain) that emerge from host cells infected withToxoplasma gondii sporozoites proliferate relatively fast and double their number every 6 h. This rate of growth is intrinsic, as neither the number of host cells invaded nor host cell type appears to influence emergent tachyzoite replication. Fast tachyzoite growth was not persistent, and following ∼20 divisions, the population uniformly shifted to slower growth. Parasites 10 days post-sporozoite infection doubled only once every 15 h and, unlike emergent tachyzoites, they grew at this slower rate over several months of continuous cell culture. The spontaneous change in tachyzoite growth rate preceded the expression of the bradyzoite-specific marker,BAG1. Within 24 h of the growth shift, 2% of the population expressed BAG1, and by 15 days post-sporozoite infection, 50% of the parasites were positive for this marker. Spontaneous BAG1 expression was not observed in sporozoites or in tachyzoites during fast growth (through day 6 post-sporozoite inoculation), although these tachyzoites could be induced to expressBAG1 earlier by culturing sporozoite-infected cells at pH 8.3. However, alkaline treatment also reduced the replication of emergent tachyzoites to the rate of growth-shifted parasites, supporting a link between reduced parasite growth and bradyzoite differentiation. The shift to slower growth was closely correlated with virulence in mice, as the initially fast-growing emergent tachyzoites were avirulent (100% lethal dose, >104 parasites), while a mutant VEG strain (MS-J) that is unable to growth shift caused 100% mortality in mice inoculated with 10 parasites. Parasites recovered from gamma interferon knockout mice inoculated with emergent tachyzoites grew at a slow rate and expressed BAG1, confirming that the replication switch occurs in animals and in the absence of a protective immune response.
Quo vadis water distribution model calibration?

Hydraulic water distribution system (WDS) simulation models are widely used by planners, water utility personnel, consultants and many others involved in analysis, design, operation or maintenance of water distribution systems. Similarly, water quality models that work in concert with hydraulic models are now commonly used to simulate the dispersion of and changes in water quality in a water distribution network. As with all mathematical models, WDS model parameters (hydraulic and water quality) require calibration before useful results may be obtained. Nowadays, automated calibration procedures are widely available for hydraulic models, but less so for water quality models. Even when they are available as commercial software, the use of these advanced calibration tools in engineering practice has been limited. This paper provides a review of model calibration approaches and considers future challenges and directions.
The Organization of Computations for Uniform Recurrence Equations

A set equations in the quantities <italic>a<subscrpt>i</subscrpt></italic>(<italic>p</italic>), where <italic>i</italic> = 1, 2, · · ·, <italic>m</italic> and <italic>p</italic> ranges over a set <italic>R</italic> of lattice points in <italic>n</italic>-space, is called a <italic>system of uniform recurrence equations</italic> if the following property holds: If <italic>p</italic> and <italic>q</italic> are in <italic>R</italic> and <italic>w</italic> is an integer <italic>n</italic>-vector, then <italic>a<subscrpt>i</subscrpt></italic>(<italic>p</italic>) depends directly on <italic>a<subscrpt>j</subscrpt></italic>(<italic>p</italic> - <italic>w</italic>) if and only if <italic>a<subscrpt>i</subscrpt></italic>(<italic>q</italic>) depends directly on <italic>a<subscrpt>j</subscrpt></italic>(<italic>q</italic> - <italic>w</italic>). Finite-difference approximations to systems of partial differential equations typically lead to such recurrence equations. The structure of such a system is specified by a <italic>dependence graph G</italic> having <italic>m</italic> vertices, in which the directed edges are labeled with integer <italic>n</italic>-vectors. For certain choices of the set <italic>R</italic>, necessary and sufficient conditions on <italic>G</italic> are given for the existence of a schedule to compute all the quantities <italic>a<subscrpt>i</subscrpt></italic>(<italic>p</italic>) explicitly from their defining equations. Properties of such schedules, such as the degree to which computation can proceed “in parallel,” are characterized. These characterizations depend on a certain iterative decomposition of a dependence graph into subgraphs. Analogous results concerning implicit schedules are also given.
Face Recognition Using Robust Rule Based Local Binary Patter

A novel efficient and robust methodology for quick face recognition by using Rule based Local Binary Pattern (RLBP) has been presented. The face image is said to be divided into a number of 3x3 regions, called as micro patterns, indicating the structure of the gray level pixels within a neighborhood to describe the spatial context of represented as rule number to evaluate the membership degree of the central pixel to the others within a neighborhood. A Local face distributor for each of the 3x3 neighborhood, called LBP descriptor, is obtained by applying Rules from which the RLBP feature distributions are extracted. Use of contributes to more than a single bin in the distribution of LBP values in the feature vector. The recognition is performed using a nearest neighbor classifier in the computed feature space. Experiments clearly show that the use of RLBP leads to improved reliable face recognition than to the original methods, LBP and Rule Based LBP.
Interacting with Boids in an Incompressible Fluid Environment

With increasing power of the computing hardware, computer simulations are being realized in real-time. It is now possible to create real-life phenomena using computers. In this study, we present a crowd and fluid interaction environment, where fish in the environment depict flocking behaviour, affected by the flow of an incompressible fluid that can be manipulated by a Kinect controller. This system can be used in virtual reality applications and video games to provide immersive underwater scenarios. We developed our application on a commercially available game engine that provides real-time experience for the users.
Erroneous gambling-related beliefs emerge from broader beliefs during problem-solving: a critical review and classification scheme

Abstract Erroneous gambling-related beliefs (EGRBs) can be defined as beliefs that imply a failure to recognise how commercial gambling activities are designed to generate a guaranteed loss to players. In theorising about how EGRBs develop, previous reviews have proposed that EGRBs are extensions of decision-making heuristics and associated biases. We propose an alternative generative mechanism: one in which gambling games make substantial wins seem possible through problem-solving and eventual correct strategic action. EGRBs are then beliefs in the possibility of correct strategic action (illusions of control) that develop as players trial candidate strategies—strategies selected based on various broader beliefs. We further propose that EGRBs can be classified based on what is theorised in cognitive science about categories of general human beliefs about the world. For example, it has been theorised that human beliefs about supernatural forces and randomness have certain similarities across cultures, and so we propose that there exists a category of supernatural EGRBs, as well as a category of EGRBs based on broader beliefs about the nature of randomness. We review evidence for this classification scheme and discuss how it can be applied in researching and treating gambling disorder.
Brief report: Attentional Control Moderates the Relationship Between Attentional Bias and Anxiety in Children

Abstract This current study tested whether attentional control moderates the relationship between attentional bias to threat and anxiety in children. Children completed a self-report measure of attentional control and anxiety and an attentional bias task. Attentional control was examined as a moderator in the relationship between attentional bias and different dimensions of anxiety. The interaction between attentional control and attentional bias was significant for total and generalized anxiety. Although attentional control significantly moderated the relationship between attentional bias and anxiety, the direction of the interaction was not consistent with existing literature. Study implications and explanations for the study results are discussed.
Active SANs : Hardware Support for Integrating Computation and Communication

This paper explores the view that the SAN network infrastructure can be an active computational entity capable of supporting certain classes of data intensive computations effectively during communication. The performance is achieved via the use of Field Programmable Gate Arrays (FPGAs) in the network interfaces (NIs). This paper describes the programming model and the design of a prototype hardware/software implementation using commercial FPGA devices coupled with Myrinet. An active messages style of programming is used to support applicationtransparent, dynamic reconfiguration of the FPGA hardware to accommodate different computations over time. Performance evaluation of this implementation quantifies the overheads and sources of performance improvement.
Resource-Conscious Optimization of Cryptographic Algorithms on an Embedded Architecture

Cryptographic algorithms are widely used in embedded systems. Applications of cryptographic algorithms include credentials establishment (e.g., authentication), securing contents (e.g., encryption/decryption of communication data or rights-managed objects), IP protection, and tamper-resistance checking. Resource constrained embedded systems can benefit greatly from employing cryptographic algorithms that are tuned to consume as little system resources as possible, while at the same time providing reasonable performance. In this paper, we study some of the most commonly used cryptographic algorithms. We analyze their performance on an embedded system targeting the Blackfin DSP. We attempt to reduce encryption/decryption time, as well as reduce the energy consumed, by exploiting the architectural features available on the Blackfin. Our results show that by applying a set of optimization steps we can obtain 4x improvement in execution time and as much as 90% reduction in energy consumption.
Infrared face recognition based on adaptively local directional pattern

Extracting robust and discriminatory features from images is a crucial task for infrared face recognition. For this reason, we have developed an infrared face recognition algorithm based on improved local features, which applies adaptive threshold quantization to encode the local directional energy. The conventional LBP-based feature as represented by the fix threshold encoding has limited distinguishing ability. The adaptive quantization measure of local directional responses can reduce the quantization loss and thus preserve more local structure information in infrared face images. The experimental results under variable ambient temperatures show the recognition rates of proposed infrared face recognition method outperform the state-of-the-art methods based on traditional local features.
Can we use the universal instance assumption without using nulls?

We claim that the representative instance of [Ho1, Va3] is a correct representation of the data stored in a database even when the relations of the database are not the projections of a single universal instance. If no constraint (other than functional and join dependencies) is imposed on the data, then projections of the representative instance cannot always be computed by lossless joins. We show that if the database satisfies a modified foreign-key constraint, then projections of the representative instance can be computed by performing the union of several lossless joins. A class of relation schemes for which no constraint is necessary is characterized, and we show how to compute projections of the representative instance for databases that belong to this class.
Locality-sensitive Hashing Using Stable Distributions 4.1 the Lsh Scheme Based on S-stable Distributions

In this chapter, we introduce and analyze a novel locality-sensitive hashing family. The family is defined for the case where the distances are measured according to the l s norm, for any s ∈ [0, 2]. The hash functions are particularly simple for the case s = 2, i.e., the Euclidean norm. The new family provides an efficient solution to (approximate or exact) randomized near neighbor problem. Part of this work appeared earlier in [DIIM04]. 4.1. 1 s-stable distributions Stable distributions [Zol86] are defined as limits of normalized sums of independent identically distributed variables (an alternate definition follows). The most well-known example of a stable distribution is Gaussian (or normal) distribution. However, the class is much wider; for example, it includes heavy-tailed distributions.
Data partitioning for multiprocessors with memory heterogeneity and memory constraints

The paper presents a performance model that can be used to optimally distribute computations over heterogeneous computers. This model is application-centric representing the speed of each computer by a function of the problem size. This way it takes into account the processor heterogeneity, the heterogeneity of memory structure, and the memory limitations at each level of memory hierarchy. A problem of optimal partitioning of an n-element set over p heterogeneous processors using this performance model is formulated, and its efficient solution of the complexity O(p^{3}× log_{2} n) is given.
Defining Occupational Therapy Interventions for Children with Fine Motor and Handwriting Difficulties

ABSTRACT Fine motor deficits are often correlated with poor handwriting which can impact a variety of academic-related tasks. Occupational therapists (OTs) frequently provide interventions for the remediation of handwriting difficulties. Evidence exists to provide information as to the most effective forms of interventions used by occupational therapists. However, what OTs do within a treatment session has yet to be defined and studied for effectiveness. This study provides an initial feasibility study to examine the method of text mining to explore and define the interventions used by occupational therapists in the treatment of fine motor skills as related to handwriting difficulties. Researchers performed a retrospective review of 157 participants’ narrative treatment notes using text mining. Data were categorized into treatment interventions based on five previously identified treatment categories. Pre and posttest fine motor scores were examined to determine change. Specific handwriting outcomes were not gathered at this time. Results revealed three primary treatment interventions for the treatment of handwriting difficulties multisensory and motor approaches. A significant change in pretest and posttest scores was indicated. Overall, therapists in this setting utilized evidence-based interventions that demonstrated significant changes in fine motor skills. The study exhibited limitations in the single site sample and size of the sample and no direct assessment of handwriting outcomes. Future research would expand the study to additional sites and further explore relationships between interventions and outcomes.
Social Appearance Anxiety and Rosenberg Self-esteem Scores in Young Physical Disabled Athletes

Introduction: The social appearance anxiety is defined as tension and anxiety experienced by people when they are assessed by others in terms of their physical appearance and self-esteem is defined as feelings of one's personal self-worth, reflecting person's evaluation of his or her own worth. Aim: This study was conducted to investigate the differences in social appearance anxiety and self-esteem scores in physical disabled athletes and non-disabled athletes. Materials and Methods: One hundred and forty-eight physical disabled athletes and 60 non-disabled athletes participated in the study. The Turkish versions of the Social Appearance Anxiety Scale and Rosenberg Self-Esteem scale was used to assess the points associated with social appearance anxiety and self-esteem. Results: Disabled athletes had higher self-esteem and social appearance anxiety scores compared to non-disabled athletes. Also, disabled athletes having medal had decreased self-esteem and social appearance anxiety scores compared to disabled athletes not having medal. These results suggest that physical activity can improve mental health in especially successful athletes. Also, the self-esteem scores had positive correlation with the social appearance anxiety score in total sample, as well as in disabled athletes and non-disabled athletes. Conclusion: The higher self-esteem and social appearance anxiety scores in disabled athletes may be related to the higher depression, anxiety and psychological stress in athletes compared to non-athletes. These results suggest that physical activity can improve mental health in only top successful athletes.