Miracle-dz/UniFAR-FTUs huggingface dataset:
An Efficient Scheme of Deep Convolution Neural Network for Multi View Face Detection

— The aim of this paper is to detect multi-view faces using deep convolutional neural network (DCNN). Multi-view face detection is a challenging issue due to wide changes in appearance under different pose expression and illumination conditions. To address challenges, we designed a deep learning scheme with different network structures to enhance the multi view faces. More specifically, we design cascade architecture on convolutional neural networks (CNNs) which quickly reject non-face regions. Implementation, detection and retrieval of faces will be obtained with the help of direct visual matching technology. Further, a probabilistic calculation of resemblance among the images of face will be conducted on the basis of the Bayesian analysis for achieving detection of various faces. Experiment detects faces with ±90 degree out of plane rotations. Fine-tuned AlexNet is used to detect multi view faces. For this work, we extracted examples of training from AFLW (Annotated Facial Landmarks in the Wild) dataset that involve 21K images with 24K annotations of the face.
Thought-action fusion and anxiety disorders

The present study examined thought–action fusion (TAF) in a large sample of normal adolescents (n=427). Participants completed the Thought–Action Fusion Questionnaire for Adolescents (TAFQ–A) and scales measuring trait anxiety, symptoms of obsessive–compulsive disorder, other anxiety disorders, and depression. Results showed that the TAFQ–A is a reliable instrument assessing two dimensions of TAF, viz. Morality (i.e., the belief that unacceptable thoughts are morally equivalent to overt actions) and Likelihood (i.e., the belief that thinking of an unacceptable or disturbing situation will increase the probability that that situation actually occurs). Furthermore, TAF was not only associated with symptoms of OCD, but also with symptoms of other anxiety disorders and depression. However, when controlling for levels of trait anxiety, most connections between TAF and anxiety disorders symptoms disappeared. Symptoms of OCD and generalised anxiety remained significantly related to TAF. Altogether, the data are supportive of the notion that TAF is involved in a broad range of anxiety disorders and in particular OCD.  2001 Elsevier Science Ltd. All rights reserved.
The Effect of Early Commitment of Financial Aid on Matriculation to Senior High School Among Poor Junior High Students in Rural China

Students from poor, rural areas of China receive less secondary schooling than their urban peers in part because of high direct and opportunity costs. This study uses a randomized controlled trial to estimate the effectiveness of providing early commitment for financial aid (ECFA) in mitigating secondary schooling costs among poor ninth graders (eight months before their graduation). We find that the likelihood of attending senior high school increases by 9.7 percentage points among ECFA recipients. However, ECFA does not measurably increase student academic performance, reduce dropout, or increase self-esteem.
eKNOW 2019

— The visual representation of Information System (IS) artefacts is an important aspect in the practical application of visual representations. However, important and known visual representation principles are often undervalued, which could lead to decreased effectiveness in using a visual representation. Decision Management (DM) is one field of study in which stakeholders must be able to utilize visual notations to model business decisions and underlying business logic, which are executed by machines, thus are IS artefacts. Although many DM notations currently exist, little research actually evaluates visual representation principles to identify the visual notations most suitable for stakeholders. In this paper, the Physics of Notations framework of Moody is operationalized and utilized to evaluate five different DM visual notations. The results show several points of improvement with regards to these visual notations. Furthermore, the results could show the authors of DM visual notations that well-known visual representation principles need to be adequately taken into account when defining or modifying DM visual notations.
Growth Tilt of Chinese Stock Mutual Funds

We show that Chinese actively managed stock mutual funds persistently exhibit a preference for growth stocks over value stocks, despite the fact that value stocks outperform growth stocks on average. Moreover, funds with a growth tilt do not under-perform their value-oriented peer funds. To address this puzzle, we show evidence for funds’ superior skill in picking growth stocks over value stocks. Specifically, mutual fund ownership of growth stocks positively forecast returns, especially around earnings announcements. Additionally, we show corroborating evidence that growth stocks contain more inside-information than value stocks.
EVALUATION OF URBAN HIGHWAY ENVIRONMENTAL NOISE POLLUTION IN HYDERABAD

Traffic noise is a serious concern of the population living along the highway corridors. A number of models have been developed to predict traffic noise along the highway from fundamental variables (noise descriptors). Statistical methods are generally used in model envisaging and compiling. Experimental data are gathered for three sites along the National Highway No. 65 (Old NH 9) (Punjagutta, Abids and Kukatpally of Hyderabad) in the present study. Some of the most imperative noise prediction models are recapped. A methodical comparison of simulated and experimental data is made, in order to test the perfection and behavior of the models in three different studied sites in an Indian road condition. KEYWORDSHighway noise, Prediction models, Noise assessment, Noise variables.
Public Market Players in the Private World: Implications for the Going-Public Process

We investigate the effect of pre-IPO investments by public market institutional investors (institutions) on the exit of venture capitalists (VCs). Results indicate that institutions’ pre-IPO investments reduce IPO underpricing by mitigating VCs’ reliance on all-star analysts to boost market liquidity. We conclude that institutions facilitate VC exits in the secondary market. Supporting this view, our analysis reveals that the presence of institutions allows VCs to exit with a reduced price impact in the secondary market. Consistent with the ease of exit, VCs offer fewer shares at the IPO and are more likely to invest in institutionally backed startups.
A study of the relationships of self-efficacy of self-management of asthma and asthma self-management knowledge

A Study of the Relationships of Self-Efficacy of Self-Management of Asthma and Asthma Self-Management Knowledge. (August 2005) Laura Steed McCorkle, B.S., Texas A&M University; M.Ed., Texas A&M University Co-Chairs of Advisory Committee: Dr. Douglas J. Palmer Dr. Linda H. Parrish The purposes of this study are to examine the relationship of self-efficacy regarding self-management of asthma and student self-management knowledge and also examine the extent to which self-efficacy and self-management knowledge predicts student outcomes such as reading grades, the number of times a student was absent and the number of visits a student made to the school nurse. Students were sampled from one public school district within a suburban city in the southwest portion of the United States. The sample was comprised of 33 males and 12 females ranging in age from six to eleven years of age. Three data collection instruments were developed for this study. Parents of the participants were asked to fill out a demographic survey to provide descriptive data. Participants of the study were administered two face-to-face interview surveys: The Asthma Student Self-Management Knowledge in a School Setting Survey (SMS) and the
Reduction-Based Problem Mapping for Quantum Computing

Having dictated classical computing technology advances for decades, today, Moore’s law has been slowed to a crawl by the limitations of physics. In recent years, a significant amount of time and resources from the industrial and academic communities has been devoted to making quantum computing technologies a reality.13 D-Wave systems are claimed to be the first commercially available quantum computer since 2011.6 They are, however, specifically designed for annealing computations14 as opposed to being general circuit-model quantum computers,9 which would provide general quantum computing capabilities. Multiple current efforts are devoted to building the first commercially available general circuit-model quantum computers,3,13 with IBM making its latest systems publicly available.13 Although there is a wave of quantum computers on the horizon, quantum computing is not experiencing the same growth. Because quantum computing models behave in ways that are so different from their classical computing counterparts, much of our understanding of how computation systems work and how best to design algorithms and software programs is rendered unusable.20 Thus, with a half-century of research efforts on theoretical quantum computing to rely on, there has been a relatively small set of quantum algorithms ever discovered. And from a software point of view, to be able to comprehend a simple Reduction-Based Problem Mapping for Quantum Computing
A Sentiment Lexicon for Urdu

Sentiment analysis is a data mining technique, which measures the inclination of people’s opinions. Recent studies have shown that the sentiment lexicon can be developed using automatic and manual tagging techniques. The seminal works on Urdu lexicon done so far do not actually denote a broad Lickert scale for data tagging and also do not cover all the open word classes. The current study aims to develop a sentiment lexicon and test its validity using manual and automatic methods. The dictionary-based method is used to design this lexicon using three authentic Urdu dictionaries. The data was tagged on a five point lickert scale i.e. -2 to +2 using the formulated guidelines. The lexicon is composed of four-word classes namely nouns, verbs, adjectives and adverbs. Once the lexicon was developed using manual tagging techniques it was tested both manually and automatically. The manual testing yielded an inter annotator agreement of 75% while the automatic testing included the comparing of the sentiments of the developed lexicon with the UCI Corpus. The result yielded a percentage accuracy of 84%. The lexicon was validated for its accuracy by both the results.
The synergic action of amyloid-β peptide and LPS in amyloid plaque formation

Introduction: The current model which assume amyloid-β (Aβ) deposition to be an accidental process resulting from the abnormal behavior of an incidental product of catabolism, is now in contrast with the fact that Aβ, a key player in AD, may have a normal function because belongs to the group of AMPs also called host defense peptide. AMPs such as Aβ, are small molecule peptides which are not only intended to kill pathogens through their antimicrobial activity but they also have a high affinity for bacterial lipopolysaccharide (LPS) or membrane receptors. Once that Aβ is deposited can interact with the LPS which acts as a fibrillogenesis promoter; the positive charge of antibacterial peptides can increase the ability of binding to LPS. The interaction between LPS and Aβ occurs at molecular level, on the basis of their common surfactant properties and considering that detergents and fatty acids are able to form micelles. Material and methods: SH-SY5Y (neuroblastoma cells) were treated with Aβ1–42 in the form of monomers at the starting concentration of 10 μg/ml, with LPS dissolved in order to obtain a starting concentration of1 μg/ml and their combination. Each experiment was performed for 24, 48, and 72 h at 37°C. Western blot analysis for Aβ1-42 monomers, Beclin-1 and Lamp-1; Cytokines (IL-1β) quantification with Elisa; Transmission electron microscopy (TEM) and Statistical analysis were performed. Results: LPS leads to increased production and impaired degradation of Aβ mediated by upregulation of proinflammatory cytokines and inhibition of Aβ degrading enzyme, respectively intracellular accumulation at the endoplasmic reticulum and in lysosomes; the overexpression of IL-1 occurs at the beginning of the inflammatory process. Over-production of Aβ peptides directed against pathogenic neuroinvasion can cause accumulation of Aβ in plaques; Aβ deposits seem to trigger autophagy. Conclusions: In this study authors observed the binding of the Aβ spherical oligomers to LPS micellar particles inside neuroblastoma cells, the incorporation of LPS to micellar particles occurs at an early stage of Aβ aggregation acting as a nucleation factor, these results are in agreement with the possibility that microorganisms could play a role in the formation of senile plaques in AD.
Unleashing the Power of Convolutional Neural Networks for Profiled Side-channel Analysis

Proﬁled side-channel analysis based on deep learning, and more precisely Convolutional Neural Networks, is a paradigm showing signiﬁcant potential. The results, although scarce for now, suggest that such techniques are even able to break cryptographic implementations protected with countermeasures. In this paper, we start by proposing a new Convolutional Neural Network instance able to reach high performance for a number of considered datasets. We compare our neural network with the one designed for a particular dataset with masking countermeasure and we show that both are good designs but also that neither can be considered as a superior to the other one. Next, we address how the addition of artiﬁcial noise to the input signal can be actually beneﬁcial to the performance of the neural network. Such noise addition is equivalent to the regularization term in the objective function. By using this technique, we are able to reduce the number of measurements needed to reveal the secret key by orders of magnitude for both neural networks. Our new convolutional neural network instance with added noise is able to break the implementation protected with the random delay countermeasure by using only 3 traces in the attack phase. To further strengthen our experimental results, we investigate the performance with a varying number of training samples, noise levels, and epochs. Our ﬁndings show that adding noise is beneﬁcial throughout all training set sizes and epochs.
Neighborhood Sorting, Endogeneity, and the Valuation of School Quality

In this paper I estimate the parental valuation of public school quality in a unit of measurement comparable to the tuition charged by private schools: per child, year and grade. I also develop a new method to identify the valuation of neighborhood amenities in the presence of neighborhood sorting. The method controls for unobservable neighborhood amenities, including those affected by sorting, which are likely to bias the valuation estimates. For the state of Minnesota, I found that parents are willing to pay yearly about $2400 per child for a one standard deviation (16%) increase in elementary school quality. The corresponding valuations are $1400 for middle school and $2700 for high school.
Monitoring and Learning by Institutional Investors: Theory and Evidence

This paper revisits information acquisition theory from a corporate governance perspective, provides a novel theoretical motivation for external governance, and enriches the set of feasible empirical tests regarding information acquisition. Institutional investors can enhance firm fundamental values through costly monitoring, while their portfolio decisions are determined by endogenous information acquisition. I provide a simplified model, joining theoretical frameworks of external governance and information acquisition, to understand the intrinsic link between monitoring and learning activities of institutional investors. Intuitively, investors would inevitably acquire some firm-specific information during the process of monitoring a firm. One implication of this “non-separability” assumption, compared to the baseline case, is that investors optimally re-allocate monitoring effort from firms with low value of information acquisition to those with high value of information acquisition. This novel motivation for investors to exert costly monitoring effort, labelled as the “monitoring-for-learning” mechanism, is the major contribution of this paper that can lend theoretical support to recent empirical findings of monitoring and learning activities. Regarding information acquisition, the model predicts that institutional investors tend to acquire more precise information about the firms they can more effectively engage with and about those they initially hold larger stakes in. Consistent with recent research about distracted investor attention, the model suggests that monitoring attention to certain firms be squeezed out when attention for information acquisition is substantially attracted to other firms. Suggestive empirical evidence is provided to support these predictions. JEL classification: G23, G30.
Physicochemical and Functional Properties of Starch from Ackee (Blighia sapida) Seeds

Seeds of the ackee fruit are high in starch content and are a major waste product of the ackee aril canning industry. The objective of this study was to investigate the physicochemical and functional properties of isolated ackee seed starch. De-hulled seeds were dried and milled into ‘flour’ which was defatted by Soxhlet extraction using petroleum ether. Starch extraction was carried out using 0.2% w/v NaOH solution (24°C, 6 h) and the starch residue soaked in aqueous NaOH (0.05% w/v) for 12 h to remove soluble impurities and then subjected to a bleaching treatment (HCl, 0.01 N). Solubility, swelling power, water absorption, oil absorption and extent of syneresis o f the starch were measured and hypoglycin content was determined by reversed phase HPLC. Pasting, thermal properties, crystalline pattern, granule morphology and gel texture were determined, and the gelatinised starch used to prepare retrograded resistant starch. Ackee seed starch comprised small granules which exhibit a C-type diffraction pattern. The starch showed restricted swelling, moderate peak viscosity, and low breakdown compared with commercial corn and potato starches, while the water absorption and oil absorption values were similar to the commercial starches. Ackee starch had a high setback, high syneresis, produced opaque pastes and formed a hard gel texture. Apparent amylose content and the content of retrograded starch were high. Based on the properties, the starch may be suitable in manufacturing of noodles and to produce retrograded resistant starch and may have applications in fat replacers, dusting/face powders and bioplastics.
An Application-oriented Review of Deep Learning in Recommender Systems

— The development in technology has gifted huge set of alternatives. In the modern era, it is difficult to select relevant items and information from the large amount of available data. Recommender systems have been proved helpful in choosing relevant items. Several algorithms for recommender systems have been proposed in previous years. But recommender systems implementing these algorithms suffer from various challenges. Deep learning is proved successful in speech recognition, image processing and object detection. In recent years, deep learning has been also proved effective in handling information overload and recommending items. This paper gives a brief overview of various deep learning techniques and their implementation in recommender systems for various applications. The increasing research in recommender systems using deep learning proves the success of deep learning techniques over traditional methods of recommender systems.
BUSTECH 2018 Proceedings

Today more and more business processes are digitized by implementing them in specialized workflow languages like the Business Process Execution Language (BPEL) or Business Process Model and Notation (BPMN 2.0), which orchestrate services along the process flow. Because these process models are software artefacts of critical importance to the functioning of the organization, high quality and reliability of these processes are mandatory. Testing therefore becomes an important activity in the development process. Test Coverage Metrics have long been used in software development projects to assess test quality and test progress. Current approaches to test coverage calculation for BPEL either relies on instrumentation, which is slow, or is limited to vendor-provided unit test frameworks, in which all dependent services are mocked (unit tests), which limits the applicability of such approaches. Our approach relies on analyzing process event logs that are written during process execution. This approach does not require additional infrastructure and can be used in unit tests, as well as in system and integration tests. We found that our approach for measuring test coverage is not only more flexible but also faster than an instrumentation-based approach. Keywords–Test Coverage; Process Mining; BPEL; Event Log.
A Novel Text Representation Model to Categorize Text Documents using Convolution Neural Network

— This paper presents a novel text representation model called Convolution Term Model (CTM) for effective text categorization. In the process of text categorization, representation plays a very primary role. The proposed CTM is based on Convolution Neural Network (CNN). The main advantage of proposed text representation model is that, it preserves semantic relationship and minimizes the feature extraction burden. In proposed model, initially convolution filter is applied on word embedding matrix. Since, the resultant CTM matrix is higher dimension, feature selection methods are applied to reduce the CTM feature space. Further, selected CTM features are fed into classifier to categorize text document. To discover the effectiveness of the proposed model, extensive experimentations are carried out on four standard benchmark datasets viz., 20-NewsGroups, Reuter-21758, Vehicle Wikipedia and 4 University datasets using five different classifiers. Accuracy is used to assess the performance of classifiers. The proposed model shows impressive results with all classifiers.
Text-based Healthcare Chatbots Supporting Patient and Health Professional Teams: Preliminary Results of a Randomized Controlled Trial on Childhood Obesity

Health professionals have limited resources and are not able to personally monitor and support patients in their everyday life. Against this background and due to the increasing number of self-service channels and digital health interventions, we investigate how text-based healthcare chatbots (THCB) can be designed to effectively support patients and health professionals in therapeutic settings beyond on-site consultations. We present an open source THCB system and how the THCP was designed for a childhood obesity intervention. Preliminary results with 15 patients indicate promising results with respect to intervention adherence (ca. 13.000 conversational turns over the course of 4 months or ca. 8 per day and patient), scalability of the THCB approach (ca. 99.5% of all conversational turns were THCB-driven) and over-average scores on perceived enjoyment and attachment bond between patient and THCB. Future work is discussed.