Miracle-dz/UniFAR-FTUs huggingface dataset:
"I don't see why I would ever want to use it": Analyzing the Usability of Popular Smartphone Password Managers

Passwords are an often unavoidable authentication mechanism, despite the availability of additional alternative means. In the case of smartphones, usability problems are aggravated because interaction happens through small screens and multilayer keyboards. While password managers (PMs) can improve this situation and contribute to hardening security, their adoption is far from widespread. To understand the underlying reasons, we conducted the first empirical usability study of mobile PMs, covering both quantitative and qualitative evaluations. Our findings show that popular PMs are barely acceptable according to the standard System Usability Scale, and that there are three key areas for improvement: integration with external applications, security, and user guidance and interaction. We build on the collected evidence to suggest recommendations that can fill this gap.
Attention-Based Word Vector Prediction with LSTMs and its Application to the OOV Problem in ASR

We propose three architectures for a word vector prediction system (WVPS) built with LSTMs that consider both past and future contexts of a word for predicting a vector in an embedded space where its surrounding area is semantically related to the considered word. We introduce an attention mechanism in one of the architectures so the system is able to assess the spe-ciﬁc contribution of each context word to the prediction. All the architectures are trained under the same conditions and the same training material, following a curricular-learning fashion in the presentation of the data. For the inputs, we employ pretrained word embeddings. We evaluate the systems after the same number of training steps, over two different corpora composed of ground-truth speech transcriptions in Spanish language from TCSTAR and TV recordings used in the Search on Speech Challenge of IberSPEECH 2018. The results show that we are able to reach signiﬁcant differences between the architectures, consistently across both corpora. The attention-based architecture achieves the best results, suggesting its adequacy for the task. Also, we illustrate the usefulness of the systems for re-solving out-of-vocabulary (OOV) regions marked by an ASR system capable of detecting OOV occurrences.
PointAtrousGraph: Deep Hierarchical Encoder-Decoder with Point Atrous Convolution for Unorganized 3D Points

Motivated by the success of encoding multi-scale contextual information for image analysis, we propose our PointAtrousGraph (PAG) - a deep permutation-invariant hierarchical encoder-decoder for efficiently exploiting multi-scale edge features in point clouds. Our PAG is constructed by several novel modules, such as Point Atrous Convolution (PAC), Edgepreserved Pooling (EP) and Edge-preserved Unpooling (EU). Similar with atrous convolution, our PAC can effectively enlarge receptive fields of filters and thus densely learn multi-scale point features. Following the idea of non-overlapping maxpooling operations, we propose our EP to preserve critical edge features during subsampling. Correspondingly, our EU modules gradually recover spatial information for edge features. In addition, we introduce chained skip subsampling/upsampling modules that directly propagate edge features to the final stage. Particularly, our proposed auxiliary loss functions can further improve our performance. Experimental results show that our PAG outperform previous state-of-the-art methods on various 3D semantic perception applications.
ANALISIS VOLATILITAS HARGA DAGING SAPI POTONG DAN DAGING AYAM BROILER DI INDONESIA

Beef consumption needs of Indonesian population tends to increase with the increasing number of the population and public awareness of the importance of animal protein. Meanwhile the price of beef and broiler meat are fluctuative. The value of volatility describes how much the level of risk that will be faced in the future. Fluctuations in the price of beef and broiler meat can be caused by the unequilibrium of supply and demand. The objectives of this research are (1) to identify the price volatility forecasting models of beef and broiler meat in Indonesia, (2) to identify factors that affect the price volatility of beef and broiler meat in Indonesia, and (3) to identify alternative strategies related to the price volatility of beef and broiler meat in Indonesia. This research used secondary data with the time series form in the period of February 2003 to February 2013. In this research ARCH-GARCH was used to analyze the price volatility of beef and broiler meat in Indonesia. This analysis showed that the price volatility of beef and broiler meat in Indonesia would be smaller. Factors that affect the price volatility of beef in Indonesia are the price volatility of previous period and the price variance of the previous period. While the factors that affects the price volatility of broiler meat in Indonesia is the amount of price volatility in the previous period.
Struggles over property in the ‘post-political’ era: Notes on the political from Rome and Dublin

Geographical analyses on protests against austerity politics using the framework of post-politics have proliferated in recent years, mostly building on the work of Jacques Rancière and his conceptualisation of the political and the police order. The paper continues this tradition but seeks to move beyond those analyses reducing the political gesture to a ‘rare’ and ‘heroic’ act. It does so by bridging the work of Rancière with the work of Jean-Luc Nancy, developing two main arguments. The first one concerns the local and situated dimension of the political moment; the second concerns the dialectical relation between the police order and its disruption, while at the same time viewing insurgent acts as part of a chain of perpetual acts that destabilise the police order, which moreover are the inevitable outcome of its excess. These theoretical arguments are developed in relation to the analysis of the trajectory of disruptive politics around vacant property in Dublin and Rome. In both cities, several contentious political initiatives around property emerged as a response to the crisis and austerity politics, but they were unable to translate into bigger movements. To account for this, the paper identifies two main factors: the limited violence of the crisis in terms of evictions and foreclosures, and the instrumental use of ‘legality’ and ‘rules’ by the police order. Nevertheless, we argue, activist engagements with vacant property can be considered as examples of ‘world forming’ that create the possibilities for further disruptive politics.
Improved strategy for optimal control of Civil Engineering structures

Inside the Industrial Revolution 4.0, the concepts as Smart Cities and Smart Society are important parts in protection of humans against natural disasters, as strong earthquakes. Older or newer protection strategies of Civil Engineering structures (bridges, buildings) must be updated with elements of the actual technological revolution as sensors, drones, robots, nanotechnologies, AI, big data, cloud computing and so on. This paper is focused on a optimal structural active control of buildings started by many researchers during the last decades. Now, the target is to improve an optimal strategy developed by authors by proposing a more efficient use of computation. The success of this strategy leads to a faster response of computers that direct the control devices. This provides the ground for a larger range of control devices based on a larger number of sensors and, therefore, a better energy use together with a more precise anti-seismic structural protection
Family Changes in Iranian Kurdistan: A Mixed Methods Study of Mangor and Gawerk Tribes

Over the last few decades, the Iranian Kurdish society, including family and kinship systems, has experienced enormous changes as a result of government implemented modernization efforts. This paper reports the results of a quantitative/ qualitative mixed methods study aimed at exploring (a) the nature of change in family and kinship systems and (b) how people understand and interpret these changes. The sample for this study was drawn from the Mangor and Gawerk tribes residing in the Mahabad Township located in the West Azerbaijan Province of Iran. Using standardized questionnaires, 586 people were sampled as part of the quantitative portion of the study. For the qualitative portion, data was collected on 20 people using both in-depth interviews and participant observations. The quantitative data was analyzed by SPSS software and the qualitative data was interpreted using grounded theory procedures. The quantitative findings showed that the urbanization, modern education, and mass media have all contributed to the emergence of a new form of family and kinship life. In addition, while supporting quantitative findings, the qualitative results revealed that participants were aware of and sensitive to sources, processes, and effects of modernization on their family and kinship life.
Mathematical and statistical modelling of CD4 T-cell reconstitution in HIV-infected children starting antiretroviral therapy

Children's immune systems develop over the first 20 years of life and are fundamentally different to adults' in terms of lymphocyte numbers, turnover rates and population structure. Children perinatally infected with the human immunodeficiency virus (HIV) can expect their infection and any antiretroviral therapy (ART) administered to affect the developing population dynamics of the immune system and long-term immunological health in adulthood. Describing, understanding and predicting these effects is important for guiding individualised treatment regimes, public health policy and future research. The work described here develops an empirical model for CD4 T-cell reconstitution in HIV-infected children starting ART based on a monophasic, asymptotic recovery function and applied to data in a nonlinear mixed-effects framework. The model quantifies effects of age on both pre-ART and long-term CD4 count, and an association between poor pre-ART and poor long-term immune status. It allows prediction, by extrapolation, of CD4 counts in adulthood given ART initiation at different ages and CD4 levels. These predictions suggest that current ART initiation guidelines preserve potential for good long-term CD4 numbers in young children, but that this potential is progressively damaged as age increases. The model also identifies groups of children with qualitatively different CD4 trajectories and illustrates fundamental differences between recovery in children and adults probably due to children’s more active thymuses. Next, a mechanistic model of T-cell homeostasis is described, aiming to develop a biological understanding of some of the effects identified using the empirical model. A model of homeostasis in HIV proves a valid, useful tool for understanding pathogenesis and the reasons for incomplete recovery on ART, and illustrates the power of mixed-effects modelling for gleaning information about differences between children’s T-cell dynamics. In the future, more complex and complete models building on this foundation will help understanding of the interactions between development, HIV and ART.
The Intrusiveness of Sensor-Suit Components on the Postures Associated with Performing Repeated Whole-Body Manual Lifting Tasks

The Intrusiveness of Sensor-Suit Components on the Postures Associated with Performing Repeated Whole-Body Manual Lifting Tasks Lauren Amanda Visnansky Ergonomics can be defined as the responsibility to ensure that the demands of a job do not exceed the capabilities of a worker (Garg, Chaffin, and Herrin, 1978). Evaluating physiological demands on workers, particularly individuals performing highly varied tasks or monitoring their work in the field, can be a complex problem. Using on-person sensors to record kinematic and physiological measurements throughout extended is one proposed method by which to collect data necessary to evaluate the demands placed on the workers. In order to assess the efficacy of the data that would be collected, it is critical to evaluate the intrusive effects of the onperson sensors on the manner in which the work is performed. For the purpose of this study, various outfit ensembles, consisting of combinations of Clo, Mass, and Banding, were analyzed in order to determine whether or not they affect the posture while individuals perform a repetitive lifting task. Thirty-two paid volunteers participated in this study. Each subject was randomly assigned one of eight experimental ensembles. VICON MX-13 near-infrared cameras were used to capture whole-body kinematics. Subjects were asked to perform a cyclic work protocol that consisted of six twenty-minute cycles, including five-minutes each of 30 groundto-waist lifts and lowers, 30 standing arm lifts and lowers, continuous walking on a treadmill, and a rest period. A 2 factorial, between-subjects design was used, producing 8 experimental ensemble conditions for combinations of Clo, Mass, and Banding. Statistical analyses were performed with a stepwise regression and general linear model (GLM). The bilateral angles for the hips, knees, and ankles were the dependent variables; independent variables were Banding, Clo, Mass, Part, Phase, and Cycle. From the stepwise regression (α=0.10 to remove), Part and Phase were removed from the model. From the GLM, the adjusted R values indicated that a good fit existed between the variables in the model. ANOVA results indicated that the main effects and all interactions effects of the ensemble conditions were significant, but the significance varied across lower body joints. Results indicate that Banding and Clo have significant effects on posture, but their effects are less than the nominally fatiguing aspect of the tasks performed. As expected, adding mass to the subjects caused significant changes to their posture over time, suggesting elevated levels of fatigue. Future studies should include other populations, fitness and experience constraints, and tasks with a lower physiological burden.
Facial Emotion Recognition Using Eigenface and Feature Optimization

There is lot of communications between individuals using emotions as it plays important role in information transferring. Broad studies are carried out to explore the dealings between human sentiments and machine interactions. In this paper an efficient approach using feature extraction and optimization is proposed for facial emotion acknowledgment in the form of Eigenfaces. It is an adjusted strategy which begins with the human vision as a standard reference point – by making utilization of the standard database and processes the look contained by the picture of a test confront. It is a novel approach which specifically orders a test picture as having a place with one of the five standard expressions - anger, fear, happy, sad or surprise in a precise manner. In this paper, exploratory verification was given in terms of accuracy, false acceptance rate, false rejection error rates and exchange on further approaches to enhance it.
Scale Pyramid Attention for Single Shot MultiBox Detector

Feature pyramid mechanism has improved the performance of object detectors by a large margin, especially for the objects with small scale. As one of the first attempts to use pyramidal feature hierarchy, Single Shot MultiBox Detector (SSD) has largely accelerated the pipeline of detection with the competitive performance. Even if the feature pyramid mechanism is used for better detecting objects with small scale, the complex background information still misleads the network to focus on invalid areas. Image areas where small objects exist are more easily to be misjudged which influences the performance of lower-level layer in feature pyramid. In addition, a great deal of false positives may also be introduced into each pyramid layer because of the complex background. In this paper, we propose a novel method named scale pyramid attention hierarchy to better assist the SSD. The general feature pyramid detector can be guided to focus on the valid image areas as well as neglect the influence of complex background with the assist of this proposed module. Furthermore, the end-to-end training can highlight the foreground information which reduces the pressure of detectors, especially for detecting small objects. Flexible implementation makes it easily embedded in the feature pyramid mechanism. Experimental results on PASCAL VOC 2007, VOC 2012 and MS COCO confirm the effectiveness of the proposed module.
Study of Detection of Various types of Cancers by using Deep Learning: A Survey

In this survey paper we have concentrated on the detection of the cancer by using the applications of Machine Learning / deep learning. There are more than 100 types of cancer in the human body but, for study, we have considered the most common and most dangerous cancer diseases like breast cancer, lung cancer, brain cancer and Skin cancer. This paper also gives a basic introduction of machine learning and deep learning and also gives information about various deep learning algorithms. We have considered only latest references for this survey and in the last section of this paper, we have provided the summary, comments on the applications of various algorithms of deep learning.
Deep Learning Technique for Cloud Detection using Satellite Data

— Cloud detection is a crucial task and has varied ranges of implications in retrieving important parameters using satellite data. Identifying clouds from clear sky hold great importance in many satellite Imagery applications. Many approaches are used for performing cloud detection on satellite data products. Some of the well-known approaches are a threshold-based approach, a machine learning approach, and a few others, but these approaches lack robustness as these approaches require a profuse amount of time in performing feature-selection. Most of the algorithms fail in taking advantage of spatial arrangement and are time intensive. In tasks like image recognition and object detection, deep learning has outperformed compared to other approaches. In this paper, a deep learning model was proposed for performing cloud detection using INSAT 3D satellite data product which overcomes all the above-mentioned limitations. The proposed model architecture consists of encoder and decoder modules, which will perform sampling, feature extraction, and up-sampling. The proposed model takes five features consisting of SWIR, VIS, TIR1, TIR2, and MIR spectral band’s/channel’s data as input and generates cloud mask as output. Generated cloud mask performs better distinction between cloudy and non-cloudy pixels under different surface conditions, mostly over ice and snow. The proposed model will generate a day-time cloud mask as SWIR and VIS spectral bands data are available only during the day-time.
Prediction of a Movie’s Success From Plot Summaries Using Deep Learning Models

As the size of investment for movie production grows bigger, the need for predicting a movie’s success in early stages has increased. To address this need, various approaches have been proposed, mostly relying on movie reviews, trailer movie clips, and SNS postings. However, all of these are available only after a movie is produced and released. To enable a more earlier prediction of a movie’s performance, we propose a deep-learning based approach to predict the success of a movie using only its plot summary text. This paper reports the results evaluating the efficacy of the proposed method and concludes with discussions and future work.
Preliminary findings from the evaluation of Project ESCAPE 25-Alive

Preliminary Findings from the Evaluation of Project ESCAPE 25-Alive. (December 2006) Christopher Michael Ledingham, B.S., New Mexico State University M.P.H. , New Mexico State University Chair of Advisory Committee: Dr. B.E. Pruitt This study was conducted as part of the evaluation process of a federally funded physical activity initiative undertaken by a large urban school district. The purpose of this study was two-fold: (1) to provide evidence of effectiveness of Project ESCAPE 25Alive, an innovative physical activity promotion initiative; and (2) to examine the relationships among a number of factors related to observed changes in the health promoting physical activity levels of high school students. The sample for the evaluation process consisted of 26 high school physical education teachers and their respective students enrolled in physical education class. Each teacher was observed three times while teaching physical education. Observations resulted in objective measures of the level and duration of physical activity in the classroom. Each teacher also was asked to complete a survey designed to measure his or her intent to adopt the Project ESCAPE 25-Alive program into their class curriculum. Finally, teachers were asked to complete a survey designed to measure self-efficacy related to teaching physical education.
Cognitive profiles, hormonal replacement therapy and related factors in Thai menopausal women.

Background: Assessment of cognitive performance is helpful to identify women at risk of Alzheimer’s diseases. There are very few studies regarding cognitive functions of menopausal women in Thailand. Objective: To examine the characteristics of cognitive functions and related factors among menopausal women. Method: Two hundred ten subjects were recruited at King Chulalongkorn Memorial Hospital, and systematically randomized. The instruments were composed of self-reported questionnaires assessment for demographic data and related factors. The Thai mini-mental state examination (TMSE) and neuropsychological battery (Thai version), including digit span (DST), and visual memory span test (VMST), verbal paired association I test (VPT), and similarity test (ST), were used to evaluate attention/concentration, verbal memory/learning, and concept formation. The data were statistically analyzed using SPSS for Chi-square test, t-test, one-way ANOVA, Pearson correlation, and multiple regression analysis. Results: Ninety nine percent of subjects had a normal condition of TMSE score. Ninety six percent of subjects had TMSE score more than 25 for possible cognitive impairment according to Petersen criteria. DST, VMST, VPT, and ST had normal distributions. The univariate analyses showed that subjects with TMSE score more than 26 were related with history of aerobic exercise. Subjects with TMSE score of possible cognitive impairment had lower ST scores. Supplementary food and hormone replacement therapy were not directly related with impairment of cognitive functions. Other factors, including age, educational level, monthly income, and occupation, were related with most cognitive scores. Multiple regression analyses revealed that low educational level could predict low TMSE score. Low educational level and monthly-income could predict low DST score, while low monthly-income, older age and low educational level could predict low VMST score. Low VPT score and low ST score could be predicted by low monthly-income and educational level below diploma degree, respectively. Conclusion: The cognitive functions of most studied subjects were normal. Factors to predict the cognitive function score were age and educational level. Subjects with low concept formation score and without aerobic exercise were related with low TMSE score. HRT was not significantly related with cognitive deficits.