nils-herrmann/clean_scirepeval huggingface dataset:
Developing Cognitive Diagnostic Assessments System for Mathematics Learning

The aim of this study is to develop a diagnostic system for mathematical concepts. By adopting a Bayesian network for its high recognition rate in artificial intelligence and diagnosis, and combining and applying deduction methods in computerized tests, this system helps students to understand the difficulties they encounter in mathematical learning, and subsequently helps in implementing immediate remedies. The computerized diagnostic tests established in this research module can diagnose the types of mistakes students make; and in addition to helping students realize their erroneous concepts, this system also helps teachers to grasp the types of mistakes students make, and to implement group remedial teaching accordingly. The study result indicates that the mean recognition rates of the computerized diagnostic system developed in this study are 95.72 %, 99.10 %, 98.73 %, 99.02 %, and 98.96 %; this system can effectively and automatically detect the types of mistakes that students make.
A Secure Routing Protocol for Mobile Ad hoc Networks

How to prevent the routes from attacks by malicious nodes is a main topic of secure communications in mobile Ad hoc networks. A secure and efficient routing protocol (SERP) is proposed in this paper. A shared session key, which is set up based on two nodes’ identities, is used to encrypt routing messages between these two nodes. Thus, the malicious nodes cannot get any useful routing information. We compare SERP with other existed secure routing protocols. The analysis results and simulations show that the proposed protocol can effectively restrain the malicious routing behaviors. Meanwhile, SERP can reduce the computational cost and improve the packets delivery fraction (PDF) for routes discovery.
Development of intelligent massage manipulator and reconstruction of massage process path using image processing technique

This paper develops a motor-driven massage artificial electro-mechanical manipulator system with intelligent biomedical sensing-monitoring capabilities and constructs the path of massage process by using CCD image processing technique. In this paper, we integrate a versatile inter-digital electrocardiograph (ECG) into the manipulator system and construct a massage path by using twin CCD image processing technique with inverse engineering method. So, this electro-mechanical manipulator can automatically do the full massage process including pushing, pick-up and kneading actions to get the best therapy effect.
Analysis of Time-Management Pattern of Interactive Behaviors during Online Project-Based Learning

This study aims to explore the time-management patterns of learners' interactive behaviors during online project-based learning (PBL) from an empirical observation without teachers' interception. According to the derived pattern, the study found how learners allocated their time and how these interactive activities influence learners to allocate their time more appropriately. Finally, we also gave some suggestions for teachers' interception and guidance for promoting the quality of students' projects.
A Novel QoS Multicast Routing Algorithm Based on Ant Algorithm

We propose a novel QoS multicast routing algorithm using ant algorithm. It considers multiple QoS parameters of nodes and links, including delay, delay jitter, bandwidth and packet loss. Our approach of constructing a multicast tree is that ants orderly set out from every destination node, search the multicast tree, then pick out an optimal path and put nodes and edges along the path into the tree. When all destination nodes have joined, the multicast tree is generated. Simulation results show that the proposed algorithm is a feasible approach to multicast routing with multiple constraints.
Mind and Body Learn Together: Embodied Cognition and Language Learning

The study aims at investigating how the fitness between learners' learning styles and adopted learning modalities influences the learning of English as a foreign language (EFL) by elementary school students. A quantitative research was adopted. 87 fifth graders participated in this study. They were randomly assigned to three groups of learning modalities: video, gesture-based, and 3D virtual. They learned 18 English phrases related to motions for 3 times, once a week. Both performance test and learning style questionnaire were administered after the treatment completed. The analysis results of the current study show that the EFL learners' performances did not differ among the three learning modalities. However, EFL learners' learning styles also influence their performance. Implications of this study for language education and teaching are discussed.
Josephson 32-bit shift register

A 32-b shift register designed by edge-triggered gates was tested with +or-25% bias margin and +or-81% input margin for the full array. Simulations showed +or-55% bias margin at 3.3 GHz and worked up to a maximum frequency of 30 GHz with a junction current density of 2000 A/cm/sup 2/, although the shift register has only been tested up to 500 MHz, limited by instrumentation. This edge-triggered gate, consisting of a pair of conventional Josephson logic gates in series, has the advantages of wide margins, short reset time, and insensitivity to global parameter variations.
Collision obstacle in dynamic environment based heuristic using sonar and optical flow sensors

A method of real-time collision obstacle for autonomous mobile robots in local dynamic environment using multi-sensor data fusion is proposed. Due to the effectiveness of the optical flow sensors, the robot can acquire more accurate self-motion information. This paper also proposes an approach to complete enhancement based on the earlier vector field histogram algorithm (VFH), which is called VFH#. This paper improved a new heuristic function to the VFH# algorithm in particular. By using this way, robots can select a more appropriate steeling angle in local dynamic environments. The results of experiments with a Pioneer robot demonstrated that robot collision obstacle can be obtained using the proposed method.
Semi-supervised Domain Adaptation with Subspace Learning for visual recognition

In many real-world applications, we are often facing the problem of cross domain learning, i.e., to borrow the labeled data or transfer the already learnt knowledge from a source domain to a target domain. However, simply applying existing source data or knowledge may even hurt the performance, especially when the data distribution in the source and target domain is quite different, or there are very few labeled data available in the target domain. This paper proposes a novel domain adaptation framework, named Semi-supervised Domain Adaptation with Subspace Learning (SDASL), which jointly explores invariant low-dimensional structures across domains to correct data distribution mismatch and leverages available unlabeled target examples to exploit the underlying intrinsic information in the target domain. Specifically, SDASL conducts the learning by simultaneously minimizing the classification error, preserving the structure within and across domains, and restricting similarity defined on unlabeled target examples. Encouraging results are reported for two challenging domain transfer tasks (including image-to-image and image-to-video transfers) on several standard datasets in the context of both image object recognition and video concept detection.
A tree-based routing protocol in wireless sensor networks

In order to efficiently utilize the limited energy resources of sensor nodes to prolong the lifetime of wireless sensor networks, this paper presents a power-aware tree-based routing protocol called TRP. Each sensor node finds a parent node according to the energy dissipation of communication, the distance between neighbor nodes and sink, and residual energy of neighbor nodes. Thus a near optimal minimum spanning tree rooted on sink is constructed. Data gathered by sensor nodes is transmitted along tree and is fused in overlap nodes. Simulation results show that TRP effectively decreases the energy consumption of sensor nodes and obviously prolongs the network lifetime. It performs better than LEACH.
AR-Based Learning and AR Guides as Strategy in Two-Phase Learning Enhancement: A Case Study

Traditional paper-based teaching activities are restricted by physical space. Therefore, one cannot integrate and collaborate with the design of the instructional materials effectively. As a result, it is unlikely that instructors are able to successfully moderate learners' focus on the content, as well as the order of their focus during their reading and learning processes, which forms a blind spot in teaching and learning. This study applied augmented reality (AR) and image positioning technologies to paper textbooks, and proposed using AR technology to develop two-phase learning processes to effectively control learners' progress in learning textbooks and their learning sequences, so that learning activities match the design of instructional materials. Preliminary empirical research demonstrated a good experience by students using the system and a positive learning attitude from the users.
Jointly Modeling Embedding and Translation to Bridge Video and Language

Automatically describing video content with natural language is a fundamental challenge of computer vision. Re-current Neural Networks (RNNs), which models sequence dynamics, has attracted increasing attention on visual interpretation. However, most existing approaches generate a word locally with the given previous words and the visual content, while the relationship between sentence semantics and visual content is not holistically exploited. As a result, the generated sentences may be contextually correct but the semantics (e.g., subjects, verbs or objects) are not true. This paper presents a novel unified framework, named Long Short-Term Memory with visual-semantic Embedding (LSTM-E), which can simultaneously explore the learning of LSTM and visual-semantic embedding. The former aims to locally maximize the probability of generating the next word given previous words and visual content, while the latter is to create a visual-semantic embedding space for enforcing the relationship between the semantics of the entire sentence and visual content. The experiments on YouTube2Text dataset show that our proposed LSTM-E achieves to-date the best published performance in generating natural sentences: 45.3% and 31.0% in terms of BLEU@4 and METEOR, respectively. Superior performances are also reported on two movie description datasets (M-VAD and MPII-MD). In addition, we demonstrate that LSTM-E outperforms several state-of-the-art techniques in predicting Subject-Verb-Object (SVO) triplets.
A Dynamic 3-D Surface Profilometer With Nanoscale Measurement Resolution and MHz Bandwidth for MEMS Characterization

Commercialization of microelectromechanical systems (MEMS) has made accurate dynamic characterization a major challenge in design and fabrication. In view of this need, a dynamic 3-D surface profilometer involving white light interferometric scanning principle with a stroboscopic LED light source was developed. The developed instrument was applied to a microcantilever beam used in atomic force microscopy (AFM) to analyze its full-field resonant vibratory behavior. The first five resonant vibration modes were fully characterized with vertical measurement accuracy of 3-5 nm and vertical measurement in the range of tens of micrometers. The experimental results were consistent with the outcomes of the theoretical simulation by ANSYS. Using stroboscopic illumination and white light vertical scanning techniques, the developed static and dynamic 3D nanoscale surface profilometry of MEMS devices can achieve measurement range of tens of micrometers and dynamic bandwidth of up to 1-MHz resonance frequency.
Applying virtual reality to control of logical control mechanism system

This paper mainly explains the applying virtual reality (VR) to control of logical control mechanism (LCM) system. VR can be utilized to control this LCM system for pick and place different materials. The user monitored the information of working site by Human Machine Interface (HMI). Therefore, we worked in a comfortable environment which can be improved efficiently. The procedure to build the system robot in VR describes in this study. With experimental tests to verify the LCM system in VR and the system could be operated and controlled to successfully complete pick and place tasks in reality. The initial response time of the VR mechanism is delayed about 0.108 sec than it of the starting time of reality mechanism. The total operation time in VR, with the synchronous automatic control process, averagely delays about 0.317 sec than it in reality.
Evaluating the Difficulty of Concepts on Domain Knowledge Using Latent Semantic Analysis

As for the field of educational research and its applications, evaluating concept difficulty is necessary but difficult to carry out the work. Two major approaches are employed in previous research to evaluate the difficulty of concepts. Both of two approaches do not take into account whether concepts are acquired by learners and readers or not. This paper will be focused on constructing basic concept list of domain knowledge with latent semantic analysis (LSA), and use the age of acquisition of concept for representing the difficulty of concept. This paper will utilize natural science texts of elementary school in Taiwan as experimental materials to verify the validity of using our proposed method for evaluating the difficulty of concept.
A-ary Tree-based Minimum Exact Cover of Leaf Set for Secure Group Communication in Grids

Secure multicast is an effective group communication technology for information sharing and message exchange in emerging grids. As the group scale in grids may be very large and group members can be very unstable (a member can join or quit the group dynamically), it is hard for conventional group key management schemes to satisfy the efficiency of group key distribution very well because of the complexity of O(c.logn)( n is the group size, and c is the total additions/evictions). This paper gives a minimum exact cover problem of leaf set and discusses its solution based on an a-ary tree model. The correctness of the solution is proven theoretically. Furthermore, the implementation and application of it is illustrated. Both complexity analysis and simulation demonstrate the efficiency and effectiveness of the achievement of group key distribution based on minimum exact cover.
Study on High-side LDMOS energy capability Improvement

Improvement of Laterally Diffused Metal Oxide Semiconductor (LDMOS) energy capability, Unclamped inductive switching (UIS) is used to characterize ruggedness in terms of the maximum avalanche energy that device can handle prior to destructive breakdown. Maximum amount of UIS energy (EAS) sustained by device before failure is evaluated and have linear relationship with number of device finger and device-width. As we know that in multi-finger LDMOS the equivalent parasitic resistance gets lowered and MOS cells hence avoiding possibility of current failure. With lowered parasitic resistance Maximum energy of avalanche in single pulse (EAS) energy increases linearly with number of fingers.[l]
Highlight Detection with Pairwise Deep Ranking for First-Person Video Summarization

The emergence of wearable devices such as portable cameras and smart glasses makes it possible to record life logging first-person videos. Browsing such long unstructured videos is time-consuming and tedious. This paper studies the discovery of moments of user's major or special interest (i.e., highlights) in a video, for generating the summarization of first-person videos. Specifically, we propose a novel pairwise deep ranking model that employs deep learning techniques to learn the relationship between high-light and non-highlight video segments. A two-stream network structure by representing video segments from complementary information on appearance of video frames and temporal dynamics across frames is developed for video highlight detection. Given a long personal video, equipped with the highlight detection model, a highlight score is assigned to each segment. The obtained highlight segments are applied for summarization in two ways: video time-lapse and video skimming. The former plays the highlight (non-highlight) segments at low (high) speed rates, while the latter assembles the sequence of segments with the highest scores. On 100 hours of first-person videos for 15 unique sports categories, our highlight detection achieves the improvement over the state-of-the-art RankSVM method by 10.5% in terms of accuracy. Moreover, our approaches produce video summary with better quality by a user study from 35 human subjects.
ZigBee Source Route Technology in Home Application

ZigBee is a new short-range wireless technology. ZigBee devices have parents and children relationship, and those devices can construct a huge mesh network. Most devices in ZigBee network can relay packets, repair route, etc. If a ZigBee device wants to send packets to another device, the source node needs to find a route to the destination. In an application, if data are sent to the concentrator form the sensing nodes and concentrator tends control the sensing nodes, the concentrator can record the path from sensing nodes to its self and send the data to the sensing node directly according to the recorded path. The relaying nodes using the receded nodes information relay packets. In this paper, we dealt about usage of the source route technology to find the best route and send packets directly according the recorded nodes information. In a huge mesh network, find a good route is very important. It can reduce the probability of packet collision and data loss. This technology is suitable in the application of user monitors the sensing area in the concentrator, .and send control packets to the sensing nodes from concentrator.
Learning Spatio-Temporal Representation with Pseudo-3D Residual Networks

Convolutional Neural Networks (CNN) have been regarded as a powerful class of models for image recognition problems. Nevertheless, it is not trivial when utilizing a CNN for learning spatio-temporal video representation. A few studies have shown that performing 3D convolutions is a rewarding approach to capture both spatial and temporal dimensions in videos. However, the development of a very deep 3D CNN from scratch results in expensive computational cost and memory demand. A valid question is why not recycle off-the-shelf 2D networks for a 3D CNN. In this paper, we devise multiple variants of bottleneck building blocks in a residual learning framework by simulating 3 x 3 x 3 convolutions with 1 × 3 × 3 convolutional filters on spatial domain (equivalent to 2D CNN) plus 3 × 1 × 1 convolutions to construct temporal connections on adjacent feature maps in time. Furthermore, we propose a new architecture, named Pseudo-3D Residual Net (P3D ResNet), that exploits all the variants of blocks but composes each in different placement of ResNet, following the philosophy that enhancing structural diversity with going deep could improve the power of neural networks. Our P3D ResNet achieves clear improvements on Sports-1M video classification dataset against 3D CNN and frame-based 2D CNN by 5.3% and 1.8%, respectively. We further examine the generalization performance of video representation produced by our pre-trained P3D ResNet on five different benchmarks and three different tasks, demonstrating superior performances over several state-of-the-art techniques.
Learning Deep Spatio-Temporal Dependence for Semantic Video Segmentation

Semantically labeling every pixel in a video is a very challenging task as video is an information-intensive media with complex spatio-temporal dependence. We present in this paper a novel deep convolutional network architecture, called deep spatio-temporal fully convolutional networks (DST-FCN), which leverages both spatial and temporal dependencies among pixels and voxels by training them in an end-to-end manner. Specifically, we introduce a two-stream network by learning the deep spatio-temporal dependence, in which a 2D FCN followed by the convolutional long short-term memory (ConvLSTM) is employed on the pixel level and a 3-D FCN is exploited on the voxel level. Our model differs from conventional FCN in that it not only extends FCN by adding ConvLSTM on the pixel level for exploring long-term dependence, but also proposes 3-D FCN to enable voxel level prediction. On two benchmarks of A2D and CamVid, our DST-FCN achieves superior results to state-of-the-art techniques. More remarkably, we obtain to-date the best reported results: 45.0% per-label accuracy on A2D and 68.8% mean IoU on CamVid.
An optimal scheduling algorithm for fork-join task graphs

The task duplication based scheduling is a new approach to the scheduling problems. This is known as an NP-complete problem. Although some algorithms are able to find an optimal schedule under certain conditions, they ignored to economize processors and minimize the total completion time. We present a task duplication based balance scheduling (TDBS) algorithm which can schedule a class of fork-join task graph with a complexity of O(|V|/sup 2/), where |V| is the number of tasks. The proposed algorithm generates an optimal schedule with high speedup and efficiency. Simulation results showed that our algorithm has better scheduling length, less completion time and less number of processors than any of compared algorithms.
Low-Operating-Voltage Ultrathin Junctionless Poly-Si Thin-Film Transistor Technology for RF Applications

In this letter, for the first time, we experimentally investigate the radio-frequency (RF) characteristics and low-frequency noise (LFN) of n-type planar junctionless (JL) poly-Si thin-film transistors (TFTs). The fabricated JL devices show remarkable dc performance with good current drive and a high on-current/off-current ratio of 8 × 107. Furthermore, with the implementation of an in situ phosphorus-doped channel architecture and a salicide process, the JL device with a channel length of 0.4 μm exhibits a cutoff frequency (ft) of 3.36 GHz and a maximum oscillation frequency (fmax) around 7.37 GHz at a drain bias of 2 V. As far as LFN is concerned, the JL device shows approximately four orders of magnitude lower drain-current noise power spectral density (Sid) over conventional inversion-mode counterparts. These results demonstrate that the JL poly-Si TFT technique is promising for RF modules implemented in system-on-panel applications.
Approximation algorithms for the optimization problems of SNPs and haplotypes

This paper studies two optimization problems in the SNP and haplotype research. The first problem asks for a minimum set of SNPs that can tolerate a certain number of missing data. The second problem asks for a minimum set of haplotypes that can explain a given set of genotypes. We show that both problems are NP-hard and design several approximation algorithms to solve them efficiently. These algorithms have been implemented and tested on both simulated and biological data. Our theoretical analysis and experimental results indicate that these algorithms are able to find solutions close to the optimal solutions.