Miracle-dz/UniFAR-FTUs huggingface dataset:
Theoretical Framework Design for Measuring Student's Preference towards Smart Learning Class

The rapid development of technology creates a modern learning environment, that is more social, interactive, flexible, and user-centered. Smart class is one form of this learning environment, that recently gained massive attention in the education field. The application of technology has increased the learning interest of the student and the quality of education. Since smart learning class is a new term, therefore, in order to have a maximum output, there is a need for a study to decrease the probability of system rejection, and also to accelerate the acceptance process of the system, so the system will be well adopted. The objective of this research is to develop a theoretical framework that representing influential factors towards smart class adoption, using the combination of preference instrument of smart class learning environment (PI-SCLE) and theory of planned behavior (TPB) questionnaire. The result of the questionnaire was analyzed with partial least square (PLS) method, to know the influential factor regarding smart class adoption. As a result, 9 hypotheses are accepted, and 2 hypotheses are rejected. It is expected that the output of this research will be used for evaluating and developing strategies towards smart class adoption to improve the learning quality, to decrease the probability of system rejection and also to accelerate the system acceptance process.
HCCN: a method based on hash center for image retrieval

Due to its computation efficiency and retrieval quality, hashing has been widely applied to approximate nearest neighbor search for large scale image retrieval, while deep hashing further improves the retrieval quality by end-to-end representation learning and hash coding. However, subject to the ill-posed gradient difficulty in the optimization with sign activations, existing pairwise similarity learning based hashing methods need to first learn continuous representations and then generate binary hash codes in a separated binarization step, which cannot learn similarity relationships adequately to generate sufficient good hash codes and suffer from substantial loss of retrieval quality. To overcome this limitation, this work presents a continuous learning method based on hash (HCCN). The main idea is to improve the recognizability of the hash code through central similarity learning. At the same time, continuous learning is used to optimize the deep network with symbolic function activation to solve the ill-posed gradient problem and reduce the feature loss of the data. Comprehensive experiments demonstrate that HCCN can generate cohesive hash codes and achieve noticeable boost in retrieval performance on three datasets, NUS-WIDE, CIFAR-10, and MS-COCO.
Spatio-temporal event discovery in the big social data era

Social networks have been transforming the way people express opinions, post and react to events, and share ideas. Over the last decade, several studies on event detection from social media have been proposed, with the aim of extracting specific types of events, such as, social gatherings, natural disasters, and emergency situations, among others. However, these works do not consider the continuous processing of events over the social data streams, and therefore, cannot determine the spatial and temporal evolution of such events. This paper introduces a big data platform for event discovery, while tracking their evolution over space and time. We propose a scalable and efficient architecture that can manage and mine a huge data flow of unstructured streams, in order to detect geo-social events. The extracted clusters of events are indexed by a spatio-temporal index structure. We conduct experiments over twitter datasets to measure the effectiveness and efficiency of our system with respect to the existing major event detection techniques. An initial demonstration of our platform highlights its major advantage for detecting and tracking events spatially and temporally, thus allowing for great opportunities from application perspectives.
SGFA: A Two-Factor Smartphone Authentication Mechanism Using Touch Behavioral Biometrics

In this paper we propose a user authentication method on smartphone devices based on the popular pattern lock mechanism, called the Simple Game For Authentication (SGFA). The SGFA mechanism resembles a simple game involving connecting points on the touch screen that is relatively easy to perform. To provide increased security, the mechanism utilizes both the user's knowledge and behavioral biometrics based on touch screen interaction an active layer of defence against unauthorized access. Based on an initial experiment, we determine the minimum number of strokes forming a password to reach a satisfying level of success rate. We discuss possible problems and attacks that can potentially break the process and evaluate the impact of over-the-shoulder attacks on the security of the password-matching layer. We further evaluate the biometric layer in terms of user authentication error rates. In an experiment involving 33 participants, the biometric layer achieved the false acceptance rate (FAR) and false rejection rate (FRR) of approx. 1.4% and 2%, respectively. Combined with the password-matching layer, the SGFA mechanism provides a more secure approach than pattern locks.
Decriminalized, Not Legalized: A Pilot Study of South African University Students’ Views on the Use, Impact, Legalization and Decriminalization of Marijuana

Given the 2018 decriminalization of marijuana in South Africa for personal use and medicinal consumption, the aim of this research was to explore the perceptions of university students regarding the use, impact, legalization, and decriminalization of marijuana. Key findings revealed that a fair number of participants acknowledged using marijuana. When asked about the potential impact of marijuana on users, their families and significant others, they seemed to have a good understanding of the effects of the drug. However, while some participants were aware of the addictive nature of marijuana, others were not aware of the risk of addiction from smoking or ingesting this substance. However, they were able to weigh up the risks as well as the benefits of legalization/decriminalization of marijuana. This study has implications for health care professionals working with youth who use illicit substances such as marijuana, and in developing prevention and intervention programs aimed at the youth.
The Development of Digital Marketing Strategies and Distribution Channels for Community Products Using E-Commerce in The Lower Northern Region of Thailand

The objective of this research is to study the digital marketing strategies of community product businesses and to develop the distribution channels for these products with the utilization of e-Commerce in the lower-northern region of Thailand. The effectiveness of the implementation of e-Commerce systems for the digital marketing of community products draws on the digital marketing mix strategy (8P). The results of this research indicate a high level of development in the distribution channels for community products and the efficiency of the use of community product distribution channels in the form of e-Commerce, as evaluated by distributors and customers, as at a very good level (x = 4.55).
Estimation of Urban Travel Time with Sparse Traffic Surveillance Data

The estimation of vehicle travel time in urban area is very important for route planning, commercial site selection, urban management and other issues. Most of the existing methods are based on vehicles' intensive GPS records, but due to privacy, business competition and other reasons, GPS data of all vehicles cannot be obtained, and the use of devices to upload GPS information every other period of time will cause unnecessary energy consumption. There are few methods to estimate the travel time of vehicles using the existing traffic surveillance system in urban areas. In this paper, we propose a new method to estimate vehicles' travel time based on sparse traffic surveillance records at camera-equipped intersections. In order to solve the problem that we only know the total time difference between the two cameras, while the travel time allocated on each road segments and the driving path cannot be obtained, we take Expectation-Maximization (EM) algorithm as the basic framework to update the last two variables mentioned above iteratively. In order to overcome the problem that iterative updating cannot be carried out due to the sparsity of data, we construct a generative adversarial network based on spatiotemporal characteristics (ST-GAN), which uses long short term memory networks (LSTM) to extract the time dynamic characteristics of travel time in the road network, graph convolutional networks (GCN) to extract the spatial characteristics, and then uses semi-supervised learning to complete the data of the whole road network. Finally we test on the real data of Suzhou Industrial Park (SIP), and find that the estimated travel time of our model is better than that of multiple baselines in evaluation indicators.
Location Optimization of The Fighter Squadron For Indonesian Air Defense

Determining the location of a facility is one of the critical factors in a business as well as the military. Indonesia, as a vast and large country, has a challenge in determining the location of the fighter squadron to guard the national defense. The problem gets more complicated since the defense budget is limited. This research integrates the Analytic Hierarchy Process (AHP) and mathematical programming to deal with the problem. Our research shows that the Indonesian Air Force needs three more air force bases for the operational fighter squadron. It is hoped that this research will be used for the development of an optimal national air defense system.
Curvature Estimation on Point Cloud Using an Indicator Function

Curvature estimation is essential for many computational techniques on point cloud, which can be obtained, for example, by scanning real-world objects by a 3D scanner. We propose a novel technique to directly estimate mean curvature and Gaussian curvature on point cloud. We view the object surface as the indicator function of the scanned object and then smooth the function. Curvatures can be computed from the gradient and Hessian of the smoothed indicator function explicitly. We use an integral formula to approximate the gradient and Hessian on point cloud. The surface approximation and smoothing filter are implicit inside the integral formulas. And the experiments show our method is the fastest method that outputs mean curvature and Gaussian curvature with accuracy comparable to other established methods.
At What Point Do Wages Make Ends Meet? Investigating the Relationship Between Wages and Material Hardship in New York City

Several states and local areas have increased the minimum wage in recent years, and we anticipate that many policymakers and advocates will be interested in evaluating the impacts that these increases have had on material hardships such as not having enough money for rent, food, and utilities. The relationship between wages and material hardship absent policy changes has not been thoroughly documented in the literature, however. An understanding of how material hardships relate to wages is critical when trying to determine whether minimum wage policy changes have impacted material hardship. This article asks, is there a linear relationship between wages and material hardship at the bottom of the wage distribution, or do wages have to hit a certain point before we observe significant declines in material hardship? Results come from the New York City Longitudinal Study of Wellbeing (NYC-LSW), also referred to as the “Poverty Tracker,” and are specific to New York City in 2016. The results show that among workers in New York City in 2016, rates of material hardship are relatively constant at the bottom of the wage distribution, if not increasing. However, rates of hardship begin to decline more rapidly in the wage distribution around US$15 per hour.
A Culturally Modified Application of Objectification Theory to Asian and Asian-American Women

In the current study we sought to find support for a culturally modified application of objectification theory to Asian and Asian American women. The positive association between socialization experiences (i.e., racial teasing and appearance-focused social pressure) and the outcome variable (i.e., consideration of cosmetic surgery) was hypothesized to be mediated by self-objectification processes (i.e., internalization of beauty standards, body surveillance, and lower body esteem). Results using path analysis showed that culturally dominant (United States) and culturally specific (Asian) internalization of beauty standards, culturally dominant (United States) and culturally specific (Asian) body surveillance, and lower body esteem were significant mediators between socialization experiences and consideration of cosmetic surgery. A multiple-group analysis demonstrated equivalence of the model for Asian international and Asian American female students, showing that both dominant and culturally specific beauty standards may contribute to self-objectification processes for this population.
QTL Analysis for Plant Height and Leaf Angle by Using Different Populations of Maize

Quantitative trait loci(QTL) analysis for some important agronomic traits(plant height,ear height and leaf angle) were carried out,using SSR markers and three F2:3 populations(H21×Mo17,Zi330×K36 and B73×L050) in order to understand the genetic and molecular mechanisms of these traits.Eighteen QTL were detected for plant height,twelve QTL for ear height and nine QTL for leaf angle.Some of these QTL were located within the same chromosome regions,and the peaks of some QTL regions overlaid.In addition,it was found that some QTL were located in the same regions with known qualitative genetic loci affecting these traits.
Empowering big data analytics with polystore and strongly typed functional queries

Polystores are of primary importance to tackle the diversity and the volume of Big Data, as they propose to store data according to specific use cases. Nevertheless, analytics frameworks often lack a uniform interface allowing to fully access and take advantage of the various models offered by the polystore. It also should be ensured that the typing of the algebraic expressions built with data manipulation operators can be checked and that schema can be inferred before starting to execute the operators (type-safe). Tensors are good candidates for supporting a pivot data model. They are powerful abstract mathematical objects which can embed complex relationships between entities and that are used in major analytics frameworks. However, they are far away from data models, and lack high level operators to manipulate their content, resulting in bad coding habits and less maintainability, and sometimes poor performances. With TDM (Tensor Data Model), we propose to join the best of both worlds, to take advantage of modeling capabilities of tensors by adding schema and data manipulation operators to them. We developed an implementation in Scala using Spark, providing users with a type-safe and schema inference mechanism that guarantees the technical and functional correctness of composed expressions on tensors at compile time. We show that this extension does not induce overhead and allows to outperform Spark query optimizer using bind join.
Exploring the Influencing Factors of User Stickiness in a Chinese Mobile News Application

The mobile news application is a brand-new form of expression of new media. It is the latest form of media carrier in the development of mobile network news reading. With its unique advantages, it provides users with continuous, timely and overall news information, and brings great commercial value to businesses. Based on two management models and the new media characteristics of mobile news app this paper constructs the user stickiness of the news apps influence factors model from different perspectives of product factors and user factors; uses the questionnaire survey method, understands the mobile news apps user experience satisfaction aspects and the mobile news apps present existence questions, takes the existing users of mobile Tencent News app as the research object and conducts empirical research. Through measurement model analysis and structural equation model analysis, it explores the influencing factors of user stickiness.
An infrastructure for faithful execution of remote attestation protocols

Experience shows that even with a well-intentioned user at the keyboard, a motivated attacker can compromise a computer system at a layer below or adjacent to the shallow forms of authentication that are now accepted as commonplace[3]. Therefore, rather than asking "Can we trust the person behind the keyboard", a still better question might be: "Can we trust the computer system underneath? ". An emerging technology for gaining trust in a remote computing system is remote attestation. Remote attestation is the activity of making a claim about properties of a target by supplying evidence to an appraiser over a network[2]. Although many existing approaches to remote attestation wisely adopt a layered architecture-where the bottom layers measure layers above-the dependencies between components remain static and measurement orderings fixed. For modern computing environments with diverse topologies, we can no longer fix a target architecture any more than we can fix a protocol to measure that architecture. Copland [1] is a domain-specific language and formal framework that provides a vocabulary for specifying the goals of layered attestation protocols. It also provides a reference semantics that characterizes system measurement events and evidence handling; a foundation for comparing protocol alternatives. The aim of this work is to refine the Copland semantics to a more fine-grained notion of attestation manager execution-a high-privilege thread of control responsible for invoking attestation services and bundling evidence results. This refinement consists of two cooperating components called the Copland Compiler and the Attestation Virtual Machine (AVM). The Copland Compiler translates a Copland protocol description into a sequence of primitive attestation instructions to be executed in the AVM. When considered in combination with advances in virtualization, trusted hardware, and high-assurance system software components-like compilers, file-systems, and OS kernels-a formally verified remote attestation infrastructure creates exciting opportunities for building system-level security arguments.
Measuring the Cognitive Load of Learning to Program: A Replication Study

Cognitive load (CL) on a learner’s working memory has emerged as an influential concept in computing education and beyond. CL is commonly divided in at least two components, intrinsic load (IL) and extraneous load (EL). We seek progress on two questions: (1) How can CL components be measured in the programming domain? (2) How should CL measurement deal with the “third component” of germane load (GL)? We replicate two studies: Morrison and colleagues’ [49] evaluation of a questionnaire for self-assessing CL in programming, which is an adaptation of a generic instrument; and Jiang and Kalyuga’s [24] study, which found support for a two-component measure of CL in language learning, with GL redundant. We crowd-sourced CL data using Morrison’s questions at the end of a video tutorial on programming for beginners. A confirmatory factor analysis found strong support for a three-factor model, with factors matching the items intended to capture IL, EL, and GL, respectively. A two-factor model with IL-targeting and GL-targeting items combined gave a poorer fit. Our findings strengthen the claims of discriminant validity and internal reliability for Morrison’s CL questionnaire for programming; construct validity for GL remains open, however. We affirm the need for further research on the two-component theory of CL and the sensitivity of CL self-assessments to contextual factors.
Localization of diacylglycerol kinase ζ in rat pancreatic islet cells under normal and streptozotocin-induced stress conditions

Summary. The pancreas comprises exocrine and endocrine portions, the latter of which is a glucose-responsive tissue that secretes hormones in response to serum glucose levels. One pathway implicated in the regulatory mechanism of this gland is the phosphoinositide (PI) cycle, which generates second messengers. Diacylglycerol (DG), the major second messenger in the PI signaling cascade, is catalyzed by the diacylglycerol kinase (DGK) family. We previously described characteristic expression and localization patterns of DGKs in various organs under pathophysiological conditions. Nevertheless, little is known about the characteristics and morphological aspects of this enzyme family in the pancreas. This study was conducted to investigate the pancreas, specifically the expression and localization of the DGK family. RT-PCR analysis reveals that DGKζ is the major isozyme in the pancreas. Additionally, we show that DGKζ is expressed in pancreatic islet cells, but not in the exocrine cells. It localizes predominantly to the nuclei of α-, β-, and δ-cells. We found further that DGKζ translocates from the nucleus to the cytoplasm in β-cells in response to a β-cell-selective toxin streptozotocin (STZ) and that it disappears over time. These findings will substantiate and extend our understanding of the functional roles of DGKζ in pancreatic islet cells.
Multimodal Evaluation of Neurovascular Functionality in Early Parkinson's Disease

Parkinson's disease (PD) is a multisystem neurological condition affecting different neurotransmitter pathways characterized by aberrant functional connectivity (FC) and perfusion alteration. Since the FC, measuring neuronal activity, and cerebral blood flow (CBF) are closely related through the neurovascular coupling (NVC) mechanism, we aim to assess whether FC changes found in PD mirror perfusion ones. A multimodal MRI study was implemented by acquiring resting state functional MRI (rsfMRI) and arterial spin labeling (ASL) datasets on a group of 26 early PD (66.8 ± 8 years, 22 males, median [interquartile range] Hoehn and Yahr = 1.5 [1]) and 18 age- and sex-matched healthy controls (HCs). In addition, a T1-weighted MPRAGE was also acquired in the same scan session. After a standard preprocessing, resting state networks (RSNs) and CBF maps were extracted from rsfMRI and ASL dataset, respectively. Then, by means of a dual regression algorithm performed on RSNs, a cluster of FC differences between groups was obtained and used to mask CBF maps in the subsequent voxel-wise group comparison. Furthermore, a gray matter (GM) volumetric assessment was performed within the FC cluster in order to exclude tissue atrophy as a source of functional changes. Reduced FC for a PD patient with respect to HC group was found within a sensory-motor network (SMN, pFWE = 0.01) and visual networks (VNs, primary pFWE = 0.022 and lateral pFWE = 0.01). The latter was accompanied by a decreased CBF (primary pFWE = 0.037, lateral pFWE = 0.014 VNs), while no GM atrophy was detected instead. The FC alteration found in the SMN of PD might be likely due to a dopaminergic denervation of the striatal pathways causing a functional disconnection. On the other hand, the changes in connectivity depicted in VNs might be related to an altered non-dopaminergic system, since perfusion was also reduced, revealing a compromised NVC. Finally, the absence of GM volume loss might imply that functional changes may potentially anticipate neurodegeneration. In this framework, FC and CBF might be proposed as early functional biomarkers providing meaningful insights in evaluating both disease progression and therapeutic/rehabilitation treatment outcome.