Miracle-dz/UniFAR-FTUs huggingface dataset:
Face detection under varying lighting conditions in video streams

Auto human face detection from video streams is the base of studies for human face recognition and tracking. This paper proposes an efficient and robust method to detect face in video streams based on image enhancement, Gabor wavelet transform and adaboost algorithm. The key step and the main contribution of this work is to use the technique of image enhancement to alleviate the impact of human face detection caused by variation illumination such as local shadow and highlight. The approach uses a cascade of classifiers to adopt a coarse-to-fine strategy for achieving higher detection rates with lower false positives. The experimental results demonstrate that our proposed approach can improve the accuracy of face detection significantly even under varying lighting conditions.
Human T cells cannot act as autonomous antigen-presenting cells, but induce tolerance in antigen-specific and alloreactive responder cells

The ability of two HLA-DR-expressing human T cell clones to function as antigen-presenting cells (APC) was investigated using highly purified T cells. The results demonstrated that these T cell clones are unable to act as autonomous APC, and that recognition of nominal or alloantigens on the surface of T cells leads to a state of nonresponsiveness. The first observation was that a T cell clone with specificity for the 306- 324 peptide of influenza hemagglutinin (HA), and raised from a DR1 responder, exhibited apparent degeneracy of major histocompatibility complex restriction when cultured with peptide in the presence of peripheral blood mononuclear cells (PBMC) expressing a wide variety of structurally unrelated DR types. However, when the PBMC were pulsed with peptide and washed before coculture with the clone, peptide was exclusively recognized with DR1Dw1. This implied that in the presence of soluble peptide the T cells were displaying ligand to each other, and that the third-party APC were providing costimulatory signals. To test the ability of T cells to act as autonomous APC, accessory cell- free preparations of two DR1-restricted clones were cultured with peptide in the presence or the absence of added B cell APC. T cell purity was established by the absence of proliferation in response to the mitogen phytohemagglutinin (PHA). PHA-nonresponsive T cells were completely unable to proliferate in response to peptide alone; furthermore, preculture of the HA-specific clone, in the complete absence of accessory cells, with the same concentration of peptide (1 microgram/ml) that induced optimal proliferation when presented by conventional APC, led to profound nonresponsiveness. The same phenomenon was also observed when two of three anti-DR1 alloreactive T cell clones were precultured with a DR1-expressing T cell clone. The ability of the DR1-expressing clone to induce nonresponsiveness in anti- DR1 clones correlated with recognition of the DR1 alloantigen on the DR1-expressing clone.
A Framework for Systematic Evaluation of Process Improvement Priorities

Each process improvement project must prioritize and select the areas or aspects to focus on. Moreover, each such selection needs to be communicated, motivated, and justified to all stakeholders (management as well as participating or affected parties). For both challenges, a conceptual framework and tool set is presented in this paper which is based on a quantitative model of the relations between improvement efforts and outcomes. These relations are represented as weighted dependency graph with extensions for time dynamics as well as uncertainties. The graph can be used for a goal-driven search of strategies as well as for simulation analysis of specific scenarios. The development of the graph basically requires a systematic documentation of the expectations or experiences in the respective context. The framework is outlined at the example of agile process fragments based on a knowledge base of evidential data.
Ideology Its Resurgence in Social , Personality , and Political Psychology

We trace the rise, fall, and resurgence of political ideology as a topic of research in social, personality, and political psychology. For over 200 years, political belief systems have been classified usefully according to a single left–right (or liberal–conservative) dimension that, we believe, possesses two core aspects: (a) advocating versus resisting social change and (b) rejecting versus accepting inequality. There have been many skeptics of the notion that most people are ideologically inclined, but recent psychological evidence suggests that left–right differences are pronounced in many life domains. Implicit as well as explicit preferences for tradition, conformity, order, stability, traditional values, and hierarchy—versus those for progress, rebelliousness, chaos, flexibility, feminism, and equality—are associated with conservatism and liberalism, respectively. Conservatives score consistently higher than liberals on measures of system justification. Furthermore, there are personality and lifestyle differences between liberals and conservatives as well as situational variables that induce either liberal or conservative shifts in political opinions. Our thesis is that ideological belief systems may be structured according to a left–right dimension for largely psychological reasons linked to variability in the needs to reduce uncertainty and threat. CONSERVATIVE, n. A statesman who is enamored of existing evils, as distinguished from the Liberal, who wishes to replace
The Role of Perceived Consumer Effectiveness in Motivating Environmentally Conscious Behaviors

While perceived consumer effectiveness (PCE) has consistently been linked to socially conscious attitudes, the concept appears to have been confounded with other related constructs in the empirical studies measuring its effects on behavior. In addition, the few studies which have considered the effects of PCE on reported behavior have done so only in aggregate. This research demonstrates that PCE is distinct from environmental concern and contributes uniquely to the prediction of certain pro-ecological behaviors. In addition, differences in PCE are shown to be associated with differences in demographics and political affiliation. The results suggest that motivating consumers to express their concern through actual behavior is to some extent a function of increasing their perception that individual actions do make a difference.
Chapter 4 Constrained Compound MRF Model with Bi-Level Line Field for Color Image Segmentation

Image segmentation is a basic early vision problem which serves as precursor to many high level vision problems. Color image segmentation provides more information while solving high level vision problems such as, object recognition, shape analysis etc. Therefore, the problem of color image segmentation has been addressed more vigorously for more than one decade. Different color models such as RGB, HSV, YIQ, Ohta (I1, I2, I3), CIE(XYZ, Luv, Lab) are used to represent different colors [5]. From the reported study, HSV and (I1, I2, I3) have been extensively used for color image segmentation. Ohta color space is a very good approximation of the Karhunen-Loeve transformation of the RGB, and is very suitable for many image processing applications [1]. Image Modeling plays a crucial role in image anal‐ ysis. Stochastic models, particularly MRF models, have been successfully used as the image model for image restoration and segmentation [2], [3], [4]. MRF model has also been success‐ fully used as the image model while addressing the problem of color image segmentation both in supervised and unsupervised framework. Kato et al [6] have proposed a MRF model based unsupervised scheme for color image segmentation. In Kato 's method, the model pa‐ rameters have been estimated using Maximum Likelihood criterion and the only parameter identified by the user is the number of class. This algorithm could be validated using differ‐ ent color textures and real images. Another color texture unsupervised segmentation algo‐ rithm has been proposed by Deng et al [7] and the method has been retermed as JSEG method. Recently, an unsupervised image segmentation algorithm has been proposed by Guo et al [8] where K-means has been used to initialize the classification in the classification of numbers. Very recently Scarpa et al. [13] have proposed a multiscale texture model and a related algorithm for the unsupervised segmentation of color images. In this scheme, the feature vectors have been collected and based on the feature vector the textures are then re‐
UNIVERSITY-TO-INDUSTRY KNOWLEDGE TRANSFER: FRAMEWORK OF EXISTING LITERATURE AND FUTURE QUESTIONS

2 Introduction This essay was prepared as a 'framework paper' for the Center for Knowledge-Based Enterprises at Queen's University. The objective of the Center is to promote research in the general area of knowledge management, which includes defining specific research programs within this broad mandate. One such program concerns knowledge transfer from universities to industry. This paper provides a review of and commentary on the literature related to this topic. The economic importance of this issue provides significant motivation for deepening our understanding of the knowledge transfer system. It is well understood that the creation and application of new knowledge is the primary factor that drives economic growth. It is also commonly accepted that universities are an important source of new knowledge, especially in the areas of science and technology. Thus, it is important to build as clear a picture as possible of the mechanisms by which university science moves into the economy.
Separating a walkable environment into layers

A multi-layered environment (MLE) [van Toll et al. 2011] is a representation of the walkable environment (WE) in a 3D virtual environment that comprises a set of two-dimensional layers together with the locations where the different layers touch, which are called connections. This representation can be used for crowd simulations, e.g. to determine evacuation times in complex buildings, or for finding the shortest routes. The running times of these algorithms depend on the number of connections. Finding an environment with the smallest number of connections, is an NP-Hard problem [Hillebrand et al. 2016]. Our first algorithm tackles this problem by using an integer linear program which is capable of finding the best possible solution, but naturally takes a long time. Hence, we provide two heuristics that search for MLEs with a low number of connections. One algorithm uses local search to gradually improve the found solution. The other one, called the height heuristic, is very fast and gives good solutions in practical environments.
Identifying symptoms of recurrent faults in log files of distributed information systems

The manual process to identifying causes of failure in distributed information systems is difficult and time-consuming. The underlying reason is the large size and complexity of these systems, and the vast amount of monitoring data they generate. Despite its high cost, this manual process is necessary in order to avoid the detrimental consequences of system downtime. Several studies and operator practice suggest that a large fraction of the failures in these systems are caused by recurrent faults. Therefore, significant efficiency gains can be achieved by automating the identification of these faults. In this work we present methods, which draw from the areas of information retrieval as well as machine learning, to automate the task of infering symptoms pertinent to failures caused by specific faults. In particular, we present a method to infer message types from plain-text log messages, and we leverage these types to train classifiers and extract rules to identify symptoms of recurrent faults automatically.
Evaluation of Diagnostic Criteria for von Willebrand Disease in Sudan

Von Willebrand disease (VWD) is the most common inherited disorder of primary haemostasis. It is very heterogeneous, both in its phenotype and genotype VWD comprises a heterogeneous group of patients in whom the clinical diagnosis is often difficult because of a considerable intra -individual phenotypic variation and limited laboratory data, bleeding and family history. Even internationally there is no consensus about diagnostic criteria for VWD
Term Weighting Vs. Logistic Regression Performance on E-Commerce Data

Text categorization can become a very difficult problem to solve in many cases. However many text categorization algorithms have been developed in the history of computer science, they are not always as accurate as we expect. Some of them are highly accurate in special cases while others perform well in different cases. In this work, we are comparing two famous methods in text categorization; the first one is the well-known term weighting algorithm and the second one is the logistic regression algorithm. All the dataset is got from our previous start-up named “Ume Market Network” which was an online peer-to-peer e-commerce system, and was synchronized with Facebook sales groups. Every offer in this dataset should be categorized as a sale/purchase offer; therefore, the problem is a classical binary categorization on a text dataset of formal as well as colloquial expressions in English, Italian, and German languages. After overcoming all the ambiguities the logistic regression algorithm outperformed the term weighting algorithm by around 25% in acuracy.
Unbroken Wholeness in Nonlinear Processes.

In order to understand the complex behaviors in megaloscopic systems, it is necessary to analyse the detailed mechanisms of non-separability in nonlinear processes. The essential roles of nonlinearity are pursued in the local-global linkage from deterministic points of view carrying out with nonlinear dynamical models; onset of subliminal threshold , self-organization of soft interface, and scaling relation in barrier-free cascade. It is emphasized that the non-separability and uctuations in soft interfaces are closely associated with emergent behaviors of megaloscopic systems and that the law of unbroken wholeness is described by a relation of relations.
Impacts of FTAs and BITs on the Locational Choice of Foreign Direct Investment: The case of Japanese firms

This paper examines the impacts of Japan's free trade agreements (FTAs) and bilateral investment treaties (BITs) on the locational choice of Japanese firms' foreign direct investment (FDI). Japan's FTAs have comprehensive coverage, as they cover not only trade liberalization in the form of tariff elimination/reduction but also FDI liberalization and facilitation in the forms of granting foreign firms national treatment, non-application of performance requirements, etc. In light of the inclusion of provisions concerning FDI liberalization/facilitation in Japan's FTAs, the paper analyzes whether Japan's FTAs have expected positive impacts on Japanese firms' decision of the location of FDI by applying the conditional logit model. The paper also examines the impacts of BITs. The analysis finds that both FTAs and BITs have positive impacts on Japan's FDI.
Stimulant and Atypical Antipsychotic Medications For Children Placed in Foster Homes

Objectives The purpose of this study is to examine the use of prescribed psychoactive medications in a prospective cohort of children shortly after they entered foster homes; and to identify demographics, maltreatment history, psychiatric diagnoses including ADHD comorbidity, and level of aggression that contribute to prescribed use of stimulant and atypical antipsychotic medication over time. Methods The sample included N = 252 children (nested in 95 sibling groups) followed for three years up to 4 yearly waves. Results Nearly all (89%) met criteria for at least one of eight psychiatric diagnoses and 31% (75/252) used one or more prescribed psychoactive medications. Over half (55%) were diagnosed with Attention Deficit Hyperactivity Disorder (ADHD); of these 38% used stimulants and 36% used atypical antipsychotics. Of the 75 medicated children, 19% received ≥3 different classes of drugs over the course of the study. Stimulants (69%) and atypical antipsychotics (65%) were the most frequently used drugs among medicated children. Adjusted odds ratios (AOR) showed that male gender (AOR = 3.2; 95% CI = 1.5–9.3), African American vs Latino ethnicity (AOR = 5.4; 95% CI = 2.1–14.2), ADHD regardless of Oppositional Defiant (ODD) or Conduct (CD) comorbidity (AOR = 6.0, 95% CI = 1.3–27.5), ODD or CD (AOR = 11.1, 95% CI = 2.1–58.6), and Separation Anxiety (AOR = 2.0, 95% CI = 1.0–4.0) psychiatric disorders were associated with the use of prescribed stimulants; while male gender (AOR = 3.8, 95% CI = 1.5–9.3), African American vs Latino (AOR = 5.1, 95% CI = 1.2–9.2) or Mixed/Other ethnicity (AOR = 3.3, 95% CI = 1.9–13.7), ADHD regardless of ODD or CD comorbidity (AOR = 5.8, 95% CI = 1.2–28.7), ODD or CD (AOR = 13.9, 95% CI = 3.3–58.5), Major Depression/Dysthymia (AOR = 2.8, 95% CI = 1.1–6.7) psychiatric disorders, and history of sexual abuse (AOR = 4.6, 95% CI = 1.3–18.4) were associated with the use of prescribed atypical antipsychotics. Conclusion The aggressive use of atypical antipsychotics, which has unknown metabolic risks, suggests that the efficacy and safety of such treatment strategies for psychiatrically ill children in foster care should be monitored.
Towards Automatic Vandalism Detection in OpenStreetMap

The OpenStreetMap (OSM) project, a well-known source of freely available worldwide geodata collected by volunteers, has experienced a consistent increase in popularity in recent years. One of the main caveats that is closely related to this popularity increase is different types of vandalism that occur in the projects database. Since the applicability and reliability of crowd-sourced geodata, as well as the success of the whole community, are heavily affected by such cases of vandalism, it is essential to counteract those occurrences. The question, however, is: How can the OSM project protect itself against data vandalism? To be able to give a sophisticated answer to this question, different cases of vandalism in the OSM project have been analyzed in detail. Furthermore, the current OSM database and its contributions have been investigated by applying a variety of tests based on other Web 2.0 vandalism detection tools. The results gathered from these prior steps were used to develop a rule-based system for the automated detection of vandalism in OSM. The developed prototype provides useful information about the vandalism types and their impact on the OSM project data.
Chip Layout Optimization Using Critical Path Weighting

A chip layout procedure for optimizing the performance of critical timing paths in a synchronous digital circuit is presented. The procedure uses the path analysis data produced by a static timing analysis program to generate weights for critical nets on clock and data paths. These weights are then used to bias automatic placement and routing in the layout program. This approach is shown to bring the performance of the chip significantly closer to that of an ideal layout which is assumed to have no delay due to routing between cells.
Role of a Zn‐independent DksA in Zn homeostasis and stringent response

DksA is a global transcriptional regulator that directly interacts with RNA polymerase (RNAP) and, in conjunction with an alarmone ppGpp, alters transcription initiation at target promoters. DksA proteins studied to date contain a canonical Cys‐4 Zn‐finger motif thought to be essential for their proper folding and thus activity. In addition to the canonical DksA protein, the Pseudomonas aeruginosa genome encodes a closely related paralogue DksA2 that lacks the Zn‐finger motif. Here, we report that DksA2 can functionally substitute for the canonical DksA in vivo in Escherichia coli and P. aeruginosa. We also demonstrate that DksA2 affects transcription by the E. coli RNAP in vitro similarly to DksA. The dksA2 gene is positioned downstream of a putative Zur binding site. Accordingly, we show that dksA2 expression is repressed by the presence of exogenous Zn, deletion of Zur results in constitutive expression of dksA2, and Zur binds specifically to the promoter region of dksA2. We also found that deletion of dksA2 confers a growth defect in the absence of Zn. Our data suggest that DksA2 plays a role in Zn homeostasis and serves as a back‐up copy of the canonical Zn‐dependent DksA in Zn‐poor environments.
A Look Into the World of Reddit with Neural Networks

Creating, placing, and characterizing social media comments and reactions is a challenging problem. This is particularly true for reddit.com, a highly trafficked social media website with thousands of posts per day. Each post has an associated comment thread, and users of Reddit can vote the comments up or down, generating a net score, or ”Karma,” for each comment. Users aspire to collect this ”Karma,” and these comments build the community on Reddit. When reading the content of the comment thread, however, it is often unclear why some comments succeed and receive high Karma while other comments lose Karma. This project explores using Recursive Neural Networks to give better insight into Reddit communities through the sentiment analysis of Reddit comments and Recurrent Neural Networks on Reddit comments to characterize the voting patterns of Reddit users and determine the Karma strength of Reddit comments through identifying comments with positive and negative Karma.
What does it mean that an agent is performing a typical procedure?: a formal definition in the situation calculus

We briefly recall the basic notions of the Situation Calculus and of the programing language GOLOG. Then, it is shown that typical procedures, like procedures performed by pilots in the field of aeronautics, do not have the same status as programs. In particular they may specify that some actions should not be performed. The GOLOG language is extended to fit these differences, and the semantics of the extended language is defined in the Situation Calculus. In the following we introduce the predicate Doing(α,s,s') which characterises the fact that the execution of the procedure α has started and not ended between the observed situations s and s'. That gives a logical answer to the question posed in the title.We present an implementation in PROLOG that recognises procedures that satisfy the Doing predicate. Also, an interface allows to simulate the actions performed by a pilot and shows the procedures that have been recognised.The presented results can be used to define a method to select among the recognised procedures, the procedure that can be assigned to pilot's intention. The results are not specific to the application domain and can be applied to the interactions among any kinds of agents.
A New Rapid Sensor Deployment Approach for First Responders

This paper introduces a new approach for selecting the location of Radio Frequency (RF) sensors used for detecting objects in a building. It is assumed that a building, its map and its main building materials are given. A number of moving or static objects, carrying RF tags are to be detected in this building. RF sensors are used to detect the tags. The time interval between the moment that the building information (map, etc.) become available and the moment that the detection must start is very short, perhaps less than 15 minutes. In this short duration of time, two problems are of interest and must be solved. First, the number and the deploy ment locations of sensors must be computed. Second, the detection procedure that uses the data from the deployed sensors must be developed. We present two algorithms to solve the stated problems. Our results from real world tests are satisfactory and show that the developed methods have the potential to be used by first responders such as police and firemen in detecting their indoor crews in the event of fire and other threats.