Miracle-dz/UniFAR-FTUs huggingface dataset:
Forecast Evaluation for Multivariate Time-Series Models: The U.S. Cattle Market

A set of rigorous diagnostic techniques is used to evaluate the forecasting performance of five multivariate time-series models for the U.S. cattle sector. The root-mean-squared-error criterion along with an evaluation of the rankings of forecast errors reveals that the Bayesian vector autoregression (BVAR) and the unrestricted VAR (UVAR) models generate forecasts which are superior to both a restricted VAR (RVAR) and a vector autoregressive moving-average (VARMA) model. Two methods for calculating a test evaluating the ability to forecast directional changes are implemented. The BVAR models and the UVAR model unambiguously outperform the VARMA model in the forecasting directional change
GPS-Based Wireless Collision Detection of Construction Equipment

This paper reports on research related to avoiding collisions in construction sites using differential GPS technology. In this project, the researchers developed and implemented a system where GPS technology was used in tracking a single vehicle and relaying its information to a central server. Using another simulated vehicle, the server evaluated collision scenarios and sent cautionary messages to the roving vehicle if a collision is impending. The paper concludes with a summary of the application, along with a discussion of the limitations of GPS technology and the required augmentation by other technologies.
Effects of sad mood on time-based prospective memory

Very little is known about the influence of emotional factors on prospective memory (PM) performance. We used a mood induction (neutral or sad) to examine the effects of sad mood on time-based PM performance. Based on Ellis and Ashbrook's (1988) resource allocation model, we hypothesised an adverse effect of sad mood on PM performance. Results revealed that participants who responded to the sad mood induction procedure showed reduced PM performance that mainly resulted from a decreased timeliness of PM responses, but only in the first half of the task. Mood effects on PM could be explained in terms of reduced and less accurate monitoring. Implications for concepts of PM and the assessment of emotional after-effects are discussed.
A Distributed Algorithm for Constructing a Generalization of de Bruijn Graphs

De Bruijn graphs possess many characteristics that make them a suitable choice for the topology of an overlay network. These include constant degree at each node, logarithmic diameter and a highly-regular topology that permits nodes to make strong assumptions about the global structure of the network. We propose a distributed protocol that constructs an approximation of a de Bruijn graph in the presence of an arbitrary number of nodes. We show that the degree of each node is constant and that the diameter of the network is no worse than 2logN , where N is the number of nodes. The cost of the join and the departure procedures are O(logN) in the worst case. To the best of our knowledge, this is the first distributed protocol that provides such deterministic
Towards formal verification of UML diagrams based on graph transformation

A meta-level and highly automated technique that could formally transform UML diagrams for verification is presented. Firstly, the meta-model hierarchical structure of UML is reviewed and the relationships among different UML diagrams are analyzed from different views. An approach for transforming and verifying UML statechart diagrams is developed based on graph transformation. This approach can be used to transform UML diagrams to Petri-nets while preserving dynamic consistency properties. Finally, the approach is validated through a case study
Towards Truthful Mechanisms for Mobile Crowdsourcing with Dynamic Smartphones

Stimulating participation from smartphone users is of paramount importance to mobile crowd sourcing systems and applications. A few incentive mechanisms have been proposed, but most of them have made the impractical assumption that smartphones remain static in the system and sensing tasks are known in advance. The existing mechanisms fail when being applied to the realistic scenario where smartphones dynamically arrive to the system and sensing tasks are submitted at random. It is particularly challenging to design an incentive mechanism for such a mobile crowd sourcing system, given dynamic smartphones, uncertain arrivals of tasks, strategic behaviors, and private information of smartphones. We propose two truthful auction mechanisms for two different cases of mobile crowd sourcing with dynamic smartphones. For the offline case, we design an optimal truthful mechanism with an optimal task allocation algorithm of polynomial-time computation complexity of O (n+γ)3, where n is the number of smartphones and γ is the number of sensing tasks. For the online case, we design a near-optimal truthful mechanism with an online task allocation algorithm that achieves a constant competitive ratio of 1:2. Rigorous theoretical analysis and extensive simulations have been performed, and the results demonstrate the proposed auction mechanisms achieve truthfulness, individual rationality, computational efficiency, and low overpayment.
How to Train good Word Embeddings for Biomedical NLP

The quality of word embeddings depends on the input corpora, model architectures, and hyper-parameter settings. Using the state-of-the-art neural embedding tool word2vec and both intrinsic and extrinsic evaluations, we present a comprehensive study of how the quality of embeddings changes according to these features. Apart from identifying the most inﬂuential hyper-parameters, we also observe one that creates contradictory re-sults between intrinsic and extrinsic evaluations. Furthermore, we ﬁnd that bigger corpora do not necessarily produce better biomedical domain word embeddings. We make our evaluation tools and resources as well as the created state-of-the-art word embeddings available under open licenses from https://github.com/ cambridgeltl/BioNLP-2016 .
Mesh saliency and human eye fixations

Mesh saliency has been proposed as a computational model of perceptual importance for meshes, and it has been used in graphics for abstraction, simplification, segmentation, illumination, rendering, and illustration. Even though this technique is inspired by models of low-level human vision, it has not yet been validated with respect to human performance. Here, we present a user study that compares the previous mesh saliency approaches with human eye movements. To quantify the correlation between mesh saliency and fixation locations for 3D rendered images, we introduce the normalized chance-adjusted saliency by improving the previous chance-adjusted saliency measure. Our results show that the current computational model of mesh saliency can model human eye movements significantly better than a purely random model or a curvature-based model.
An improved algorithm of PAST and Root-MUSIC for signal frequency tracking

For multi-signal separation and frequency tracking problems, this paper conducted a study and simulation of PAST and Root-MUSIC algorithm, and then pointed out that: due to the signal noise and subspace approach method of PAST algorithm, the solutions may contain many disturbance items. The original Root-MUSIC algorithm can not effectively eliminate the disturbance. This paper presents an improved method based on the tolerance and sorting. It divides all possible solutions into several neighborhoods, then puts every neighborhood for shrinkage and contraction to the local optimal solution, finally obtains the original signal frequencies by weighted and sorted processing. The results of various sources demonstrate the accuracy and the effectiveness of the improvement.
Fuzzy modeling of high-dimensional systems: complexity reduction and interpretability improvement

Fuzzy modeling of high-dimensional systems is a challenging topic. This paper proposes an effective approach to data-based fuzzy modeling of high-dimensional systems. An initial fuzzy rule system is generated based on the conclusion that optimal fuzzy rules cover extrema. Redundant rules are removed based on a fuzzy similarity measure. Then, the structure and parameters of the fuzzy system are optimized using a genetic algorithm and the gradient method. During optimization, rules that have a very low firing strength are deleted. Finally, interpretability of the fuzzy system is improved by fine training the fuzzy rules with regularization. The resulting fuzzy system generated by this method has the following distinct features: (1) the fuzzy system is quite simplified; (2) the fuzzy system is interpretable; and (3) the dependencies between the inputs and the output are clearly shown. This method has successfully been applied to a system that has 11 inputs and one output with 20 000 training data and 80 000 test data.
Measuring Student Engagement in Technology-Mediated Learning Environments.

Measuring Student Engagement in Technology-Mediated Learning Environments Curtis R. Henrie Department of Instructional Psychology and Technology, BYU Doctor of Philosophy This is a multiple-article format dissertation that explores methods for measuring student engagement in technology-mediated learning experiences. Student engagement is the committed, focused, and energetic involvement of students in learning. Student engagement is correlated with academic performance, student satisfaction, and persistence in learning, making it a valuable predictor of important learning outcomes. In order to identify which students need help or to evaluate how well an instructional interaction promotes student engagement, we need effective measures of student engagement. These measures should be scalable, cost effective, and minimally disruptive to learning. This dissertation examines different approaches to measure student engagement in technology-mediated learning environments that meet the identified measurement criteria. The first article is an extended literature review that examines how engagement has been measured in technology-mediated learning experiences. The second article is an instrument evaluation of an activity-level self-report measure of student engagement. The third article explores the relationships between learning management system user-activity data (log data) and results of the activity-level self-report measure of student engagement.
Consumer protection in licensed online gambling markets in France: the role of responsible gambling tools

Abstract Background: French online gambling markets were opened to licenced providers in May 2010. Under the new legislation and overseen by a state authority, licenced operators must comply to consumer protection measures. This paper presents and empirical analysis on the conformity of French operators to the requirements and the potential effect of these measures based on existing research. Method: Currently, 18 gambling websites operate in the French market. The data were collected by creating a player account on these sites, and taking note of the responsible gambling tools they proposed. The data were analysed thematically. Results: Results show that all licenced websites conform to the legal obligations that include verifying players’ identity, displaying a warning banner on the risks of gambling, informing about a possibility to self-exclude, providing a limit-setting tool and providing information on players’ account history. Most sites also provide additional tools, such as information on problem gambling or responsible gambling software such as Playscan. However, the proposed tools also have some shortcomings related to difficulties in using or in finding them, ineffectiveness because they are not mandatory, and their emphasis on player responsibility. Conclusions: The French law promotes consumer protection in online environments, but it leaves room for improvement. Governmentally imposed restrictions are systematically implemented, but levels of problem gambling have not declined. The paper suggests improving the implemented responsible gambling tools by increasing provider responsibility, making all tools mandatory and binding, active promotion of available responsible gambling features, and removing scientifically inaccurate information on problem gambling.
Probing the Functional Impact of Sequence Variation on p53-DNA Interactions Using a Novel Microsphere Assay for Protein-DNA Binding with Human Cell Extracts

The p53 tumor suppressor regulates its target genes through sequence-specific binding to DNA response elements (REs). Although numerous p53 REs are established, the thousands more identified by bioinformatics are not easily subjected to comparative functional evaluation. To examine the relationship between RE sequence variation—including polymorphisms—and p53 binding, we have developed a multiplex format microsphere assay of protein-DNA binding (MAPD) for p53 in nuclear extracts. Using MAPD we measured sequence-specific p53 binding of doxorubicin-activated or transiently expressed p53 to REs from established p53 target genes and p53 consensus REs. To assess the sensitivity and scalability of the assay, we tested 16 variants of the p21 target sequence and a 62-multiplex set of single nucleotide (nt) variants of the p53 consensus sequence and found many changes in p53 binding that are not captured by current computational binding models. A group of eight single nucleotide polymorphisms (SNPs) was examined and binding profiles closely matched transactivation capability tested in luciferase constructs. The in vitro binding characteristics of p53 in nuclear extracts recapitulated the cellular in vivo transactivation capabilities for eight well-established human REs measured by luciferase assay. Using a set of 26 bona fide REs, we observed distinct binding patterns characteristic of transiently expressed wild type and mutant p53s. This microsphere assay system utilizes biologically meaningful cell extracts in a multiplexed, quantitative, in vitro format that provides a powerful experimental tool for elucidating the functional impact of sequence polymorphism and protein variation on protein/DNA binding in transcriptional networks.
Ontology-Based Personalized Information Services for Mining Right Trade

Personalized information services have been born under the background of "information explosion" and will be the main mode of information services in the future. This paper describes the conceptual and formal models of ontology-based personalized information services for mining right trade. And it realizes precisely matching between supply and demand of information services for mining right trade by formalizing providers' services and requestors' demands in mining right trade and calculating the similarity between them. Ontology-based personalized information services for mining right trade can satisfy personalized request of mining right trade requestors.
Physicochemical Properties of flour and isolated starch from Jackfruit seeds (Artocarpus Heterophyllus lam)

A B S T R A C T Jackfruit (Artocarpus heterophyllus Lam.) is one the most significant evergreen trees grown in Asia. The objective of this study was to compare physicochemical properties of jackfruit seed flour and starch, starch was isolated from jackfruit seeds and purified. The jackfruit seed flour and starch were subjected to checkout the parameters like organoleptic characteristics, chemical analysis, limit test physico-chemical analysis and micromeritic properties. The chemical composition of seed flour was carbohydrates, polysaccharides, proteins, steroids and amyloses and content of jackfruit seed starch was carbohydrates, polysaccharides and amyloses. Limit test for chlorides, iron and sulphates was passing. The moisture content of starch was more than that of flours. The pH of starch was shows 6.51 and pH of flour was 6.78. The starch was characterized by using scanning electron microscopy method. The shape of the starch was found to be smooth and o spherical shaped and size of starch was 6.2mm. Gelatinization and pasting characteristics of starch shows at 65-70 C. Swelling index was 5.96 and 8.03 (g/g) for seed flour and purified starch respectively. Microbial growth was absent in seeds flour and isolated jackfruit starch respectively. Micromeritic properties of seeds flour and isolated jackfruit starch showed good.
Distinctive Effects of Cytochalasin B in Chick Primary Myoblasts and Fibroblasts

Actin-based structures play fundamental roles in cellular functions. However it remains controversial how cells cope with the absence of F-actin structures. This report focuses on short- and long-term effects of cytochalasin B (CB) on actin-complexes in fibroblasts and myoblasts. Thirty min of CB treatment dispersed subplasma actin cortices, lamellipodia, ruffled membranes, stress fibers and adhesion plaques into actin patches in fibroblasts and muscle cells. In contrast, 72 hrs CB treatment showed distinct morphological effects. Fibroblasts became giant multinucleated-finger shaped with 5 to 10 protrusions, 3–8 μm in width, and >200 μm in length. They lacked cortical actin, stress fibers, adhesion plaques and ruffled membranes but contained immense lamelliopodia with abnormal adhesion plaque protein complexes. Muscle cells transformed into multinucleated globular-shaped but contained normal I-Z-I and A-bands, indicating that CB did not interfere with the assembly of myofibrils. Within 30 min after CB removal, finger-shaped fibroblasts returned to their original shape and actin-containing structures rapidly reappeared, whereas muscle cells respond slowly to form elongated myotubes following CB washout. The capacity to grow, complete several nuclear cycles, assemble intermediate filaments and microtubules without a morphologically recognizable actin cytoskeleton raises interesting issues related to the role of the actin compartments in eukaryotic cells.
iSNO-AAPair: incorporating amino acid pairwise coupling into PseAAC for predicting cysteine S-nitrosylation sites in proteins

As one of the most important and universal posttranslational modifications (PTMs) of proteins, S-nitrosylation (SNO) plays crucial roles in a variety of biological processes, including the regulation of cellular dynamics and many signaling events. Knowledge of SNO sites in proteins is very useful for drug development and basic research as well. Unfortunately, it is both time-consuming and costly to determine the SNO sites purely based on biological experiments. Facing the explosive protein sequence data generated in the post-genomic era, we are challenged to develop automated vehicles for timely and effectively determining the SNO sites for uncharacterized proteins. To address the challenge, a new predictor called iSNO-AAPair was developed by taking into account the coupling effects for all the pairs formed by the nearest residues and the pairs by the next nearest residues along protein chains. The cross-validation results on a state-of-the-art benchmark have shown that the new predictor outperformed the existing predictors. The same was true when tested by the independent proteins whose experimental SNO sites were known. A user-friendly web-server for iSNO-AAPair was established at http://app.aporc. org/iSNO-AAPair/, by which users can easily obtain their desired results without the need to follow the mathematical equations involved during its development.
Clinician competencies: Strengths and limitations for work with transgender and gender non-conforming (TGNC) clients

ABSTRACT Individuals who identify as transgender or gender nonconforming (TGNC) face a number of health disparities compared to individuals who identify as cisgender (those who self-identify with the sex they were assigned at birth). For example, TGNC individuals experience heightened rates of clinical depression, anxiety, general psychological distress, suicidal ideation, and suicide attempts. Despite these troubling disparities, many TGNC individuals report hesitance to seek mental health services due to concerns regarding culturally insensitive or even overtly discriminatory services from providers. In addition to decreasing service utilization among TGNC populations, discriminatory services impair intervention effectiveness even when TGNC individuals persist in seeking mental health services. The American Psychological Association (APA) and the World Professional Association for Transgender Health (WPATH) provide guidelines for culturally competent work with TGNC clients; however, research indicates a profound lack of TGNC-specific training and resources among mental health care providers. To address this gap, the present investigation utilized a mixed-method design to assess training experiences, understanding of terminology, and TGNC competence among mental health care providers at various training levels. Participants were current mental health clinicians across the United States. Implications for improving reported and demonstrated weaknesses are discussed.
Entropy based informative content density approach for efficient web content extraction

Web content extraction is a popular technique for extracting the main content from web pages and discards the irrelevant content. Extracting only the relevant content is a challenging task since it is difficult to determine which part of the web page is relevant and which part is not. Among the existing web content extraction methods, density based content extraction is one popular method. However density based methods, suffer from poor efficiency, especially when the pages containing less information and long noise. We propose a web content extraction technique build on Entropy based Informative Content Density algorithm (EICD). The proposed EICD algorithm initially analyses higher text density content. Further, the entropy-based analysis is performed for selected features. The key idea of EICD is to utilize the information entropy for representing the knowledge that correlates to the amount of informative content in a page. The proposed method is validated through simulation and the results are promising.
Electron microscopic studies on developing cartilage. II. Demonstration and distribution of glycogen in chick and mouse epiphyseal cartilage.

A procedure for the study of glycogen in the same cell, under both light and electron microscopes is described. The synthesis and accumulation of glycogen is a feature of chondrogenesis in the mouse epiphyseal cartilage and the amount of glycogen increases with the progress of differentiation. In contrast, no glycogen was detected in differentiating epiphyseal cartilage of the chick at any stage; except for a small amount of glycogen in the chick cartilage cells of the diaphysis. Because of this difference it is suggested that presence of glycogen is not essential for cartilage differentiation.
Parallel Variable Neighborhood Searches

Variable Neighborhood Search (VNS) is a recent and effective metaheuristic for solving combinatorial and global optimization problems. It is capable of escaping from the local optima by systematic changes of the neighborhood structures within the search. In this paper several parallelization strategies for VNS have been proposed and compared on the large instances of the p-median problem. ∗The research of this author has been partially supported by the Spanish Ministry of Science and Technology through the project TIC2002-04242-C03-01; 70% of which are FEDER founds.
A 3D perception-based robotic manipulation system for automated truck unloading

This paper presents an autonomous robotic manipulation system that uses 3D vision processing algorithms to locate and unload cardboard boxes from floor-loaded shipping containers or semi-truck trailers. A key novel aspect of this system is the proposed 3D box detection algorithm as well as multiple box manipulation (or gripping) strategies that can be intelligently determined on the fly right after the box has been detected. With the approach, the robotic manipulator can unload product with no-prior knowledge of a loading pattern and can avoid collisions even in loads with piled-up boxes and no clear geometric structure of the loading environment.