Miracle-dz/UniFAR-FTUs huggingface dataset:
A learning analytics approach to investigating pre-service teachers’ change of concept of engagement in the flipped classroom

ABSTRACT To determine the impact of cognitive style on change of concept of engagement in the flipped classroom, a sequential analysis from the perspective of Bloom’s Taxonomy was conducted to establish if significant differences existed between the learning achievements and engagement of students with different cognitive styles. The participants were pre-service teachers who had registered for a school-based curriculum development methodology course in China, with a total of 53 students from two classes, who performed a total of 1,599 behaviors in the discussion sections. The results show that the concepts of “evaluate” (31.52%) and “analyze” (27.77%) were the two most frequent behaviors in the Bloom’s Taxonomy. The “remember” and “evaluate” were the significant starting behaviors for all of the students; “remember” and “evaluate” were the significant starting behaviors for the intuitive-style students, while “understand” was a significant starting behavior for the analytical-style students. This confirms that the students with different cognitive styles implemented multiple strategies for learning. It is also found that the ﬂipped learning promoted the students’ achievement. The conclusions suggest that it is important to match the cognitive styles of students as well as the instructional methods in order to improve students' learning.
Control of Z-Axis MEMS Gyroscope Using Adaptive Fractional Order Dynamic Sliding Mode Approach

This paper proposes a states feedback control method for Z-axis MEMS gyroscopes using fractional calculus and adaptive dynamic sliding mode control method. A new sliding mode control method is proposed to achieve trajectory tracking by adding a fractional order term in the conventional sliding manifold. The new proposed sliding surface contains integer order terms as well as fractional order terms and thus can provide an extra degree of freedom. Besides, in the presence of unknown system parameters, some adaptive laws containing the new designed sliding manifold are proposed to online tune controller parameters. All adaptive laws are derived in the stability framework and the stability of the control system is also guaranteed according to the Lyapunov stability theory and. Simulations results on a Z-axis vibrating gyroscope are provided to illustrate the effectiveness of the control method.
Alcohol intake and methylenetetrahydrofolate reductase polymorphism modify the relation of folate intake to plasma homocysteine

BACKGROUND Folate intake increases plasma folate and reduces total homocysteine (tHcy) concentrations, which may lower coronary artery disease (CAD) and cancer risks. Folate metabolism may be altered by alcohol intake and 2 common polymorphisms in the methylenetetrahydrofolate reductase (MTHFR) gene, 677C-->T and 1298A-->C. OBJECTIVE We examined whether the associations between folate intake and plasma folate and tHcy concentrations were modified by alcohol intake or variations in the MTHFR gene. DESIGN We conducted a cross-sectional analysis among 988 women by using multivariate linear regression models to estimate mean plasma tHcy and folate concentrations. Folate intake was the sum of food and supplemental sources. RESULTS We observed an inverse association between folate intake and tHcy, which was modified by alcohol intake (P for interaction = 0.04) and MTHFR677 genotype (P for interaction = 0.05) but not by MTHFR1298 genotype (P for interaction = 0.97). In the lowest quintile of folate intake, moderate drinkers (>/=15 g alcohol/d) had significantly higher tHcy concentrations (15.2 +/- 2.9 nmol/mL) than did light drinkers (11.3 +/- 0.7 nmol/mL) and nondrinkers (11.0 +/- 0.8 nmol/mL). However, the reduction in tHcy between the highest and lowest quintiles of folate intake was significantly greater in moderate drinkers (-6.6 nmol/mL) than in light drinkers (-2.3 nmol/mL) and nondrinkers (-2.1 nmol/mL). The elevated tHcy in women with low folate intake who also consumed moderate amounts of alcohol was even higher (22.4 +/- 4.8 nmol/mL) in the presence of the variant MTHFR677 allele. The positive association between folate intake and plasma folate was somewhat modified by alcohol intake (P for interaction = 0.08) but not by either MTHFR genotype. CONCLUSIONS Moderate alcohol intake and low MTHFR activity have adverse effects on tHcy, but those effects may be overcome by sufficient folate intake.
Context-Aware Natural Integration of Advertisement Object

Product placement, also called advertisement embedding, is to place some specific products in an image or a video, which may attract consumers to buy their products. However, adding advertisement objects in images is difficult, because where to add the product and how to fuse the background must be concerned. In this paper, to overcome this issue, we present a novel hierarchical framework with conditional generative adversarial network to add advertisement object in all kinds of scene images. The key point of our framework is leaning the relation between surrounding and products . To generate the products more realistic and to make the detail information such characters and logo more clear, we propose local-global discriminator. Our experiment demonstrates the effectiveness of our proposed method on different scene images. Detail codes and results are available in GitHub. https://github.com/happyyuwei/ProductPlacement
SEAGLE: A Platform for Comparative Evaluation of Semantic Encoders for Information Retrieval

We introduce Seagle, a platform for comparative evaluation of semantic text encoding models on information retrieval (IR) tasks. Seagle implements (1) word embedding aggregators, which represent texts as algebraic aggregations of pretrained word embeddings and (2) pretrained semantic encoders, and allows for their comparative evaluation on arbitrary (monolingual and cross-lingual) IR collections. We benchmark Seagle’s models on monolingual document retrieval and cross-lingual sentence retrieval. Seagle functionality can be exploited via an easy-to-use web interface and its modular backend (micro-service architecture) can easily be extended with additional semantic search models.
fMRI encoding and decoding of natural sounds in the aged human auditory cortex

Current models of auditory cortical processing describe the neural response to natural sounds as a function of complex acoustic features. Here, we ask whether and how cortical responses to those acoustic features change over the life span. We acquired 3T-fMRI data while young and older adults listened to a story presented against a competing stream of resynthesized natural sounds. We modelled the fMRI responses in auditory cortex as a function of the spectro-temporal modulations contained in the sound mixture, and derived singleand multi-voxel modulation transfer functions (MTFs; fMRI encoding and decoding). We found that topographical best feature maps were preserved in the aged auditory cortex: Tonotopic maps showed the typical mirror-symmetric frequency gradients along Heschl’s gyrus in both age groups. Decoding yielded highest accuracies at low frequencies of 230–580 Hz, coarse scales of 0.25 cyc/oct, and slow rates of 4–8 Hz, irrespective of age. However, we observed age differences in tuning to temporal rate, with signatures of broadened rate tuning in older participants. These results indicate that the specificity of temporal representation declines in the aged auditory cortex.
Research on the Method and Application of Internet-assisted Translation

This paper Currently, Internet-assisted translation has become a new topic in the field of translation studies. From the practical point of assisted translation, this paper elaborates three major functions of the Internet: resource sharing, search queries and information exchange. Through several examples, this paper discusses how to make clever use of the English-Chinese, ChineseEnglish translation process these features to improve the translation quality and efficiency.
Comparing and Developing Tools to Measure the Readability of Domain-Specific Texts

The readability of a digital text can influence people’s ability to learn new things about a range topics from digital resources (e.g., Wikipedia, WebMD). Readability also impacts search rankings, and is used to evaluate the performance of NLP systems. Despite this, we lack a thorough understanding of how to validly measure readability at scale, especially for domain-specific texts. In this work, we present a comparison of the validity of well-known readability measures and introduce a novel approach, Smart Cloze, which is designed to address shortcomings of existing measures. We compare these approaches across four different corpora: crowdworker-generated stories, Wikipedia articles, security and privacy advice, and health information. On these corpora, we evaluate the convergent and content validity of each measure, and detail tradeoffs in score precision, domain-specificity, and participant burden. These results provide a foundation for more accurate readability measurements and better evaluation of new natural-language-processing systems and tools.
Point Cloud Attribute Inpainting in Graph Spectral Domain

With the prevalence of depth sensors and 3D scanning devices, point clouds have attracted increasing attention as a format for 3D object representation, with applications in various fields such as tele-presence, navigation for autonomous driving and heritage reconstruction. However, point clouds usually exhibit holes of missing data, mainly due to the limitation of acquisition techniques and complicated structure. Hence, we propose an efficient inpainting method for the attribute (e.g., color) of point clouds, exploiting non-local self-similarity in graph spectral domain. Specifically, we represent irregular point clouds naturally on graphs, and split a point cloud into fixed-sized cubes as the processing unit. We then globally search for the most similar cubes to the target cube with holes inside, and compute the graph Fourier transform (GFT) basis from the similar cubes, which will be leveraged for the GFT representation of the target patch. We then formulate attribute inpainting as a sparse coding problem, imposing sparsity on the GFT representation of the attribute for hole filling. Experimental results demonstrate the superiority of our method.
Research on the Application of Item-based Collaborative Filtering Algorithms in MOOC

In the learning process, the recommendation system can recommend learning resources that are in line with the learning situation of the learning objects and help the learning objects to learn more easily and naturally. By analyzing the characteristics of item-based collaborative filtering algorithm, this paper applies it to the recommendation system of mooc learning resources to avoid the possible defects of this algorithm, such as low performance and offline processing in other scenarios, and gives full play to its good real-time performance and high recommendation efficiency. By analyzing the log files of imooc, this paper obtains the learned learning resources of the learning objects in imooc, takes the first 80% data of historical records, obtains the recommended list of learning resources through recommendation algorithm, and then compares the data of the last 20% of historical records. Finally, the algorithm is optimized according to the accuracy of the recommendation algorithm in the experiment and the actual learning scene.
Task Planning and Execution for Human Robot Team Performing a Shared Task in a Shared Workspace

Task Planning and Execution for Human Robot Team Performing a Shared Task in a Shared Workspace Tuly Hazbar Supervising Professor: Dr. Ferat Sahin A cyber-physical system is developed to enable a human-robot team perform a shared task in a shared workspace. The system setup is suitable for the implementation of a tabletop manipulation task, a common human-robot collaboration scenario. The system integrates elements that exist in the physical (real) and virtual world. In this work, we report the insights we gathered throughout our exploration in understanding and implementing task planning and execution for human-robot team.
On The Security of Wide Area Measurement System and Phasor Data Collection

Smart grid is a typical cyber-physical system that presents the dependence of power system operations on cyber infrastructure for control, monitoring, and protection purposes. The rapid deployment of phasor measurements in smart grid transmission system has opened opportunities to utilize new applications and enhance the grid operations. Thus, the smart grid has become more dependent on communication and information technologies such as Wide Area Measurement Systems (WAMS). WAMS are used to collect real-time measurements from different sensors such as Phasor Measurement Units (PMUs) installed across widely dispersed areas. Such system will improve real-time monitoring and control; however, recent studies have pointed out that the use of WAMS introduces significant vulnerabilities to cyber-attacks that can be leveraged by attackers. Therefore, preventing or reducing the damage of cyber attacks onWAMS is critical to the security of the smart grid. In this thesis, we focus our attention on the relation between WAMS security and the IP routing protocol, which is an essential aspect to the collection of sensors measurements. Synchrophasor measurements from different PMUs are transferred through a data network and collected at one or multiple data concentrators. The timely collection of phasors from PMU dispersed across the grid allows to maintain system observability and take corrective actions when needed. This collection is made possible through Phasor Data Concentrators (PDCs) that time-align and aggregate phasor measurements, and forward the resulting stream to be used by monitoring and control applications. WAMS applications relying on these measurements have strict and stringent delay requirements, e.g., end-to-end delay as well as delay variation between measurements from different PMUs. Measurements arriving past a predetermined time period at a data concentrator will be dropped, causing incompleteness of data and affecting WAMS applications and hence the system’s operations. It has been shown that non-functional properties, such as data delay and packet drops, have a negative impact on the system functionality. We show that simply forwarding measurements from PMUs through shortest routes to phasor data collectors may result in data being dropped at their destinations. We believe therefore that there is a strong interplay between the routing paths (delays along the paths) for gathering the measurements and the value of timeout period. This is particularly troubling when a malicious attacker deliberately causes delays on some communication links along the shortest routes. Therefore, we present a mathematical model for constructing forwarding trees for PMUs’ measurements which satisfy the end to end delay as well as the delay variation requirements of WAMS applications at data concentrators. We show that a simple shortest path routing will result in larger fraction of data drop and that our method will find a suitable solution. Then, we study the relation between cyber-attack propagation and IP multicast routing. …
The Orthographic Projection Model for Pose Calibration of Long Focal Images

Most stereovision and Structure from Motion (SfM) methods rely on the pinhole camera model based on perspective projection. From this hypothesis the fundamental matrix and the epipolar constraints are derived, which are the milestones of pose estimation. In this article we present a method based on the matrix factorization due to Tomasi and Kanade that relies on a simpler camera model, resulting in orthographic projection. This method can be used for the pose estimation of perspective cameras in conﬁgurations where other methods fail, in particular, when using cameras with long focal length lenses. We show this projection is an approximation of the pinhole camera model when the camera is far away from the scene. The performance of our implementation of this pose estimation method is compared to that given by the perspective-based methods for several conﬁgurations using both synthetic and real data. We show through some examples and experiments that the accuracy achieved and the robustness of this method make it worth considering in any SfM procedure.
Secure Communication between Network Applications and Controller in Software Defined Network

Network applications in SDN environment operate without any threat prevention mechanism or access control to checkmate what functions or operations they can execute within the network. This gives room for malicious applications to implement an offensive attack against the network or install exploits that can compromise the confidentiality, integrity or availability of network resources. In order to address this problem, this paper proposes a threat mitigation model based on trust that introduces a token-based authentication method that enables the controller to verify and validate every network application that makes changes in the network. The paper contributes in providing an authorisation method Boolean Access Matrix that constrains what operation, functions or privileges every network application can execute within the network. To assess the trustworthiness of network applications, a trust evaluation method based on Subjective Logic Reasoning which is a belief learning model is proposed. Results from tests and experiments show how scalable and efficient the proposed trust framework is.
Achieving Efficient Information Retrieval Query over Encrypted Cloud Data

: Cloud computing is an emerging technology which reshapes the advance in information technology. Cloud computing faces two type of issues i.e. privacy efficiency based on the aggregation and distribution layer we provide an extension to the Ostrovsky scheme by OIRQ i.e. (optimized information retrieval query) OIRQ is the technology that provides optimized information query through matrix construct. To further reduce querying cost incurred on the cloud. Query are classified into multiple ranked where higher ranked query can retrieve the higher percentage of matched files.
An Energy-Efficient Deep Convolutional Neural Network Inference Processor With Enhanced Output Stationary Dataflow in 65-nm CMOS

We propose a deep convolutional neural network (CNN) inference processor based on a novel enhanced output stationary (EOS) dataflow. Based on the observation that some activations are commonly used in two successive convolutions, the EOS dataflow employs dedicated register files (RFs) for storing such reused activation data to eliminate redundant memory accesses for highly energy-consuming SRAM banks. In addition, processing elements (PEs) are split into multiple small groups such that each group covers a tile of input activation map to increase the usability of activation RFs (ARFs). The processor has two different voltage/frequency domains. The computation domain with 512 PEs operates at near-threshold voltage (NTV) (0.4 V) and 60-MHz frequency to increase energy efficiency, while the rest of the processors including 848-KB SRAMs run at 0.7 V and 120-MHz frequency to increase both on-chip and off-chip memory bandwidths. The measurement results show that our processor is capable of running AlexNet at 831 GOPS/W, VGG-16 at 1151 GOPS/W, ResNet-18 at 1004 GOPS/W, and MobileNet at 948 GOPS/W energy efficiency.
Forum Size and Content Contribution per Person: A Field Experiment

Promoting contribution of content is a key challenge for platforms that support the collective creation or transfer of knowledge. We use a field experiment on a massive open online course to study the role of forum size (number of people in a forum) in the contribution of content per person. We find that larger forums elicit more contributions per person. The number of questions and other help-seeking threads posted per person was unchanged by forum size, but replies and other more conversational posts increased sharply. Most of the positive effect of size was in a subset of socially responsive subjects. The implication of social responsiveness driving our results is that the unequal distribution of contribution in online platforms is unlikely to be easily changed: if more contributions are elicited from infrequent contributors, the greatest contributors would contribute even more because there would be more to respond to.This paper was accepted by Chris Forman, information systems.
Integrating Importance-Performance Analysis into E-S-QUAL and E-RecS-QUAL scales for Assessing Electronic Service Quality

In this globalization era, service sector has been arguably affected and influenced by the internet for doing several activities, especially in doing business. As a result, service sector nowadays has transformed from the conventional way into the electronic service (e-service). This paper tried to integrate the importance-performance analysis (IPA) model to E-S-QUAL and E-RecS-QUAL scales to assess the e-service quality. It implies that not only the performance of e-service quality is assessed, but also its importance. It is according to the fact that every service provider is believed to have limited resources. Therefore, the resources have to be best deployed based on the priorities (i.e., importance) to achieve customer satisfaction. A case study was performed in one of the largest online fashion shops in Indonesia to show the applicability of the methods. It is believed that this research could offer the service providers with valued understanding of the service attributes that manifest point of views of the customers.
X-CGRA: An Energy-Efficient Approximate Coarse-Grained Reconfigurable Architecture

In this article, we present an energy-efficient approximate CGRA (X-CGRA). Instead of conventional exact arithmetic units, it employs configurable approximate adders and multipliers in the so-called quality-scalable processing elements (QSPEs). Furthermore, the structure and functionality of the other architectural components, like context memory, are modified based on the quality-scalable operating modes of the QSPEs. The quality reconfigurability of the X-CGRA makes it amenable for both error-resilient and nonresilient applications. To map the applications on the X-CGRA, a mapping technique is proposed that efficiently utilizes the QSPEs and selects appropriate approximation modes in order to lower the energy consumption while satisfying a user-defined quality constraint. We evaluate the efficacy of our X-CGRA for several benchmark applications from different domains, including image/video processing, signal processing, and scientific computations. Different sizes of X-CGRA are synthesized using a 15-nm FinFET technology. For these benchmarks, the results indicate energy consumption reduction of up to $3.21\times $ compared to those of a typical exact CGRA, at the cost of 4% quality loss.