Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting Abnormal Social Robot Behavior through Emotion Recognition

DETECTING ABNORMAL SOCIAL ROBOT BEHAVIOR THROUGH EMOTION RECOGNITION Rajapaksha Pathiranage, Subhash Marquette University, 2019 Sharing characteristics with both the Internet of Things and the Cyber Physical Systems categories, a new type of device has arrived to claim a third category and raise its very own privacy concerns. Social robots are in the market asking consumers to become part of their daily routine and interactions. Ranging in the level and method of communication with the users, all social robots are able to collect, share and analyze a great variety and large volume of personal data. In this thesis, we focus the community’s attention to this emerging area of interest for privacy and security research. We discuss the likely privacy issues, comment on current defense mechanisms that are applicable to this new category of devices, outline new forms of attack that are made possible through social robots, highlight paths that research on consumer perceptions could follow, and propose a system for detecting abnormal social robot behavior based on emotion detection.
An Evolutionary Approach to Bombay Stock Exchange Prediction with Deep Learning Technique

The prediction and analysis of stock market data have gotten avitalrole in today’s economy. The financial prediction or in particular stock market prediction is one of the hottest fields of research lately due to its commercial applications owing to high stakes and the kinds of temptations gain that it has to offer. In this paper, we are using two types of models and compared those using historical data from the Bombay Stock Exchange. The models used were based on firstly, the Deep learning parameters updated through particle swarm optimization and secondly, the Deep learning parameters updated through the least mean square and data taken from Bombay Stock Exchange (BSE). The Deep learning algorithms vary extensively in the preference of network structure, activation function, and other model parameters, and their performance is known to depend heavily on the procedure of data representation. Its capability to extract features from a huge set of raw data without believingonearlier knowledge of predictors makes deep learning potentially tempting forBombay stock market prediction at high frequencies. The predicting the Bombay stock price of a company based on the historical value day to day open, high, low, close and volume of the related index. The outcome of the experiment has been depicted with the assistance of appropriate curves where a comparative analysis of the two types of models is done on the basis ofseveralparameters. This prediction model is used with some general indicators to maximize the return and reduce the risk of the stock market.From the outcome obtained, it is understandable that the deep neural network models are competent of recognizingthe patterns existing in the Bombay stock markets.
The Effects of Labor Market Characteristics on Women’s Poverty in Korea

Background: Poverty in Korea is not gender neutral. Both male headed and female headed households experience poverty in distinct ways. This research discusses poverty and how it has evolved in Korea from a gender perspective. Methods: It describes the characteristics of poverty among the working population based on gender and other household attributes. It measures poverty relative to the mean and median incomes of the population in three ways: headcount, poverty gap, and poverty severity. The study uses the probit model to estimate the incidence of poverty and the Heckman sample selection model to analyze poverty’s gap and severity. Our empirical results are based on an unbalanced household level panel covering the period 2006–2016. Results: Our results indicate that multiple factors including issues related to the labor market and demographic characteristics contribute to women’s poverty. Within the working population, women are less likely to be poor than men because they share their partners’ incomes. However, single female workers with children are the poorest demographic group. Conclusion: “Part-time jobs” are a critical factor in determining women’s poverty status, while “work years” and “the quality of occupation” have a crucial impact on the incidence and severity of poverty.
Multi-Graph Convolutional Neural Networks for Representation Learning in Recommendation

Personalized recommendation plays an important role in many online services. Substantial research has been dedicated to learning vector representations of users and items with Graph Convolutional Networks (GCNs), however, we argue that existing methods do not make full use of the information that is available from user-item interaction data and the similarities between user pairs and item pairs. In this work, we develop a graph convolution-based recommendation framework, named Multi-Graph Convolution Collaborative Filtering (Multi-GCCF). MultiGCCF not only expressively models the high-order information via a bipartite user-item interaction graph, but integrates the proximal information by building and processing user-user and item-item graphs. We conduct extensive experiments on four publicly accessible benchmarks, showing significant improvements relative to several state-of-the-art collaborative filtering and graph neural network-based recommendation models.
Double-Edged Lipid Nanoparticles Combining Liposome-Bound TRAIL and Encapsulated Doxorubicin Showing an Extraordinary Synergistic Pro-Apoptotic Potential

Although TRAIL (TNF-related apoptosis-inducing ligand, also known as Apo2L) was described as capable of inducing apoptosis in transformed cells while sparing normal cells, limited results obtained in clinical trials has limited its use as an anti-tumor agent. Consequently, novel TRAIL formulations with enhanced bioactivity are necessary for overcoming resistance to conventional soluble TRAIL (sTRAIL) exhibited by many primary tumors. Our group has generated artificial liposomes with sTRAIL anchored on their surface (large unilamellar vesicle (LUV)-TRAIL), which have shown a greater cytotoxic activity both in vitro and in vivo when compared to sTRAIL against distinct hematologic and epithelial carcinoma cells. In this study, we have improved LUV-TRAIL by loading doxorubicin (DOX) in its liposomal lumen (LUVDOX-TRAIL) in order to improve their cytotoxic potential. LUVDOX-TRAIL killed not only to a higher extent, but also with a much faster kinetic than LUV-TRAIL. In addition, the concerted action of the liposomal DOX and TRAIL was specific of the liposomal DOX and was not observed when with soluble DOX. The cytotoxicity induced by LUVDOX-TRAIL was proven to rely on two processes due to different molecular mechanisms: a dynamin-mediated internalization of the doxorubicin-loaded particle, and the strong activation of caspase-8 exerted by the liposomal TRAIL. Finally, greater cytotoxic activity of LUVDOX-TRAIL was also observed in vivo in a tumor xenograft model. Therefore, we developed a novel double-edged nanoparticle combining the cytotoxic potential of DOX and TRAIL, showing an exceptional and remarkable synergistic effect between both agents.
Effects of alpha-lipoic acid and its combined use with vitamin C on periodontal tissues and markers of oxidative stress in rats with experimental periodontitis -

Objective: The potential impacts of antioxidants and their combinations upon inflammatory periodontal diseases are a popular research area. This study aimed to investigate the therapeutic effects of alpha-lipoic acid (ALA) and its combined use with Vitamin C (VitC) on periodontal tissues in terms of oxidative stress and alveolar bone loss in rats with experimental periodontitis. Methods: Thirty-six male rats were divided into four groups: control (C), experimental periodontitis (PED), periodontitis treated with ALA (P-ALA), and periodontitis treated with ALA and VitC (P-ALA/VitC). For periodontitis induction, rats’ first mandibular molars were ligatured submarginally for 5 weeks. After ligature removal, intragastric treatments with ALA of VitC (50 mg/kg each) were given for 15 consecutive days. Following euthanizing of the animals, mandibular tissues were obtained for biochemical and histologic analysis. Results: Malondialdehyde (MDA) and 8-hydroxydeoxyguanosine (8-OHdG) levels were lower in both P-ALA and P-ALA/VitC groups compared to the PED group. While 8-OHdG levels were statistically higher in the P-ALA/VitC group compared to the ALA group, MDA and reduced glutathione (GSH) levels were statistically similar between these groups. Also, GSH levels were statistically higher in the P-ALA and P-ALA/VitC groups compared to the PED group. Additionally, histologic analysis showed that treatment with ALA and ALA/VitC combination inhibited periodontal tissue destruction. Conclusions: This study revealed that ALA regulated oxidant and antioxidant parameters in periodontally diseased tissues and restricted periodontal tissue destruction. However, VitC did not provide any additional supportive therapeutic effect to ALA.
Feeding practices among the childcaregivers of 5 year-old children attended to for acute malnutrition at Nyangabwe Hospital, Botswana

Infant and young child feeding practices have substantial consequences on the growth, development, and health of infants and children. Adequate nutrition depends on the proper feeding practices by mothers or caregivers. However, factors such as unemployment, household food insecurity, and cultural beliefs are some of the challenges in feeding young children. This study aimed to describe the feeding practices of caregivers of less than five-year olds. A descriptive quantitative survey was undertaken among caregivers of children less than five years attended to for malnutrition at Nyangabgwe Hospital, Botswana. Structured questionnaires were used to collect data on feeding practices and challenges faced by caregivers. Descriptive statistics were used to summarize data and determine the frequencies of events. Pearson Chi-square test was used to determine association between feeding practices caregivers and child characteristics. A total of 197 caregivers were recruited and almost all (189) were single females, 80.2% (158) were in the 21-40 year age group, about 59.4% (117) were rural dwellers; and 74.6% (147) reported not having enough to eat sometimes. Caregivers’ education significantly played a role in starting complementary feeding. A significant association (p<0.05) was observed between food security and the source of income, educational status, and the number of children in the household. This study has shown that food security in association with caregiver unemployment and education were the main challenges faced in feeding children less than five years at Nyangabgwe Hospital. This requires a home-based nutrition education for caregivers in order to improve child-feeding practices.
Duration of untreated psychosis

The duration of untreated psychosis (DUP) is generally determined as the time from the onset of psychotic symptoms to initiation of treatment or first clinical presentation, when diagnosis of a first episode of psychosis (FEP) may be made. FEP is distinct from diagnosis of a first episode of schizophrenia, which requires particular symptoms to be present for a defined period, whereas FEP can refer to a number of different psychotic disorders. It has been proposed that untreated psychosis may have an inherently “toxic” effect, contributing to psychological deterioration and possibly adverse neural processes with longer DUP being associated with poorer prognosis. DUP is also thought to be a predictor of the likelihood and extent of recovery in first-episode schizophrenia. Consequently, understanding the effects of DUP is particularly important because it is potentially modifiable, and thereby altering prognosis. Topics included in this table include how DUP is measured and reported and how the length of DUP differs across regions. See the table DUP and relationship with outcomes for the impact of DUP on clinical and social outcomes.
HOW DOES DIVERSITY IMPACT ACCOUNTABILITY IN THE NGO SECTOR?

Defining the sector comprising nongovernmental organizations (NGOs) is difficult, due to the diversity of organizations within it, funding and operating alongside it. Yet, despite the sector‟s heterogeneous nature, regulators, funders and donors often treat NGOs as homogeneous when demanding accountability. Does this lead to an accountability expectation gap if there are misunderstandings as to NGO‟s main stakeholders, „for what‟ it is accountable and which mechanisms it may best use to discharge accountability? This paper utilizes a six-cluster NGO categorisation based on revenue streams to encourage a more informed discussion of accountability schemas for these organizations. The literature review found case studies covering: advocacy NGOs, classic charities, infrastructure providers, member-based NGOs, philanthropists and service providers. While this research suggests that accountability across NGO clusters diverges, an objective of this research was to „test‟ the generalisability of the case studies‟ findings to the six NGO clusters. This research therefore seeks to inform our understanding of accountability within these clusters.
A Review on Measuring Affect with Practical Sensors to Monitor Driver Behavior

Using sensors to monitor signals produced by drivers is a way to help better understand how emotions contribute to unsafe driving habits. The need for intuitive machines that can interpret intentional and unintentional signals is imperative for our modern world. However, in complex human–machine work environments, many sensors will not work due to compatibility issues, noise, or practical constraints. This review focuses on practical sensors that have the potential to provide reliable monitoring and meaningful feedback to vehicle operators—such as drivers, train operators, pilots, astronauts—as well as being feasible for implementation and integration with existing work infrastructure. Such an affect-sensitive intelligent vehicle might sound an alarm if signals indicate the driver has become angry or stressed, take control of the vehicle if needed, and collaborate with other vehicles to build a stress map that improves roadway safety. Toward such vehicles, this paper provides a review of emerging sensor technologies for driver monitoring. In our research, we look at sensors used in affect detection. This insight is especially helpful for anyone challenged with accurately understanding affective information, like the autistic population. This paper also includes material on sensors and feedback for drivers from populations that may have special needs.
Network Intrusion Detection with a Hashing Based Apriori Algorithm Using Hadoop MapReduce

Ubiquitous nature of Internet services across the globe has undoubtedly expanded the strategies and operational mode being used by cybercriminals to perpetrate their unlawful activities through intrusion on various networks. Network intrusion has led to many global financial loses and privacy problems for Internet users across the globe. In order to safeguard the network and to prevent Internet users from being the regular victims of cyber-criminal activities, new solutions are needed. This research proposes solution for intrusion detection by using the improved hashing-based Apriori algorithm implemented on Hadoop MapReduce framework; capable of using association rules in mining algorithm for identifying and detecting network intrusions. We used the KDD dataset to evaluate the effectiveness and reliability of the solution. Our results obtained show that this approach provides a reliable and effective means of detecting network intrusion.
Associations Between Weight Change and Meal Frequency, Breakfast Consumption, and Alcohol Intake in College Students

Objective: To determine if weight gain in college subjects is associated with meal frequency, breakfast consumption, breakfast type, and alcohol intake over one year of college. Secondary aims were to determine whether BMI and body fat percent are associated with breakfast consumption and type as well as meal frequency. Design: A longitudinal observational study. Health history, work schedule and sleep patterns were recorded. Subjects fasted overnight and height, weight, skin fold measurements, waist, and hip measurements were taken. Two seven day food and activity records were self recorded in September 2005 and in April of 2006. Subjects: One-thousand fifty college students enrolled in freshman level foods and nutrition or personal health courses in fall 2005 were invited to participate in the study; 507 agreed to participate in the study. Main Outcome Measures: Weight change, meal frequency, number of breakfasts per week, breakfast type, and amount of alcohol consumed were analyzed. Weight change by breakfast category was also analyzed. Statistical analyses: Diet records were entered into Nutritionist ProTM for nutrient analysis. Associations between weight change and meal frequency, breakfast
Detection of Moving Objects in Colour based and Graph’s axis Change method

This paper describes two algorithms for detecting moving objects in video surveillance with a VGA camera. Based on the grey-level change characteristic between two successive video frames, grey information is broadly used for the detection of moving objects. However, the method will lose a lot of useful colour information. In this paper First algorithm detects the moving objects in colour domain. This method depends upon intensity of light or brightness of colours and does work only, when the object and background have different colour. On other hand, Second algorithm is implemented for removing the disadvantages of background subtraction method (do not work with varying background), and Colour method (movement of objects is not detected when the background and object have same colour). This new method (Graph’s axis change method) does not depend upon intensity of light and background model. Graph’s axis change method works with the movement of pixel according to x and y axis. After implementation, First method is compared with Graph’s axis change method. Second method takes less time and successfully extracts moving edges from dynamic images sequences. KeywordsMotion detection, Space vector difference method, Graph’s axis change method, Background and foreground model.
EXPLOITING DISTRIBUTIONAL SIMILARITY FOR LEXICAL ACQUISITION

Lexical acquisition has been dubbed the bottleneck of large scale robust natural language processing applications for at least two decades. There is now a substantial body of research dedicated to this important subfi eld of computational linguistics. Since the 1990s, researchers have turned to corpora for automatic lexical acquisition, rather than rely on extraction from existing online lexical resources. This allows for coverage of new domains, genres and languages without existing resources and where available resources do not provide suffi cient coverage or require tailoring to the specifi c text type. A large body of lexical acquisition from corpora uses distributional similarity whereby the similarity between two words is calculated from the extent that the words have similar contexts of occurrence. Distributional similarity approaches are used for smoothing unseen events using data from seen events. They are also used as an approximation of semantic similarity since there is a strong tendency for words that exhibit similar distributional behaviour to share in their underlying semantics. This paper provides a summary of research that I, along with various collaborators, have conducted using distributional similarity to automatically acquire sense frequency information, selectional preferences and estimates of semantic non-compositionality of putative multiwords.
Therapeutic effects of nerve leachate-treated adipose-derived mesenchymal stem cells on rat sciatic nerve injury

Peripheral nerve injury (PNI) is a common condition, often resulting from physical nerve injury and trauma. Successful repair of the peripheral nerve is dependent on the regenerative activity of Schwann cells (SCs). Application of SC-like adipose-derived mesenchymal stem cells (ADSCs) may be a suitable cell-based therapy for PNI. In the present study, nerve leachate derived from the rat sciatic nerve was used to induce the differentiation of ADSCs. These cells were placed in an acellular biological scaffold, which was then grafted to a rat sciatic nerve to bridge a 1-cm gap. Sprague-Dawley rats were divided into four groups: Scaffold only, untreated ADSCs + scaffold, nerve leachate-treated ADSCs + scaffold and autograft. Two-months post-transplant, the structure and function of the regenerated nerves and the recovery of the innervated muscles was analyzed. After transplant, there was a significant increase in the average area (15.86%; P<0.05), density (23.13%; P<0.05) and thickness (43.24%; P<0.05) of regenerated nerve fibers in the nerve leachate-treated ADSCs + scaffold group compared with the untreated ADSCs + scaffold group. The nerve conduction velocity in the nerve leachate-treated ADSCs + scaffold and autograft groups was superior to that in the other groups. In the nerve leachate-treated ADSCs + scaffold group, the cross-sectional area of the gastrocnemius increased by 39.28% (P<0.05) and the cross-sectional area of collagen fibers decreased by 29.87% (P<0.05) compared with the ADSCs + scaffold group. Moreover, the therapeutic effect of nerve leachate-treated ADSCs + scaffold on PNI was similar to that of an autograft. These results suggest that nerve leachate-treated ADSCs may promote the repair of PNI.
R-Impact : Reliability-Based Citation Impact Factor R

RELIABILITY-BASED citation impact (R-impact) factor considers the factors of both the citation impact, and longlasting impact of published journals as measured by the cited half-life. The R-impact is simple to implement, yet a meaningful measure, which is defined as the cited half-life multiplied by the citation impact factor. This reliability-based citation impact factor (R-impact) gives an index of the life span of the published journals, which can properly measure the effectiveness instead of just the short-term performance of the journals. The R-impact measures the life of published articles, as well as the marketability of the published articles as measured by the widely used short-term (two-year) citation impact factors.