nils-herrmann/clean_scirepeval huggingface dataset:
An image based approach for speech perception

Classification of speech signal is one of the most vital problems in speech perception and spoken word recognition. Although, there have been many studies on the classification of speech signals but the results are still limited. In this paper, we propose an image based approach for speech signal classification based on the combination of Local Naïve Bayes Nearest Neighbor (LNBNN) and Scale-invariant Feature Transform (SIFT) features. The proposed approach allows training feature vectors to have different sizes and no re-training is needed for additional training data after training phase. With this approach, achieved classification results are very satisfactory. They are 72.8%, 100% and 95.0% on the ISOLET, Digits and Places databases, respectively.
A New Effective Learning Rule of Fuzzy ART

Unsupervised neural networks are known for their ability to cluster inputs into categories based on the similarity among inputs. Fuzzy Adaptive Resonance Theory (Fuzzy ART) is a kind of unsupervised neural networks that learns training data until satisfying a given need. In the learning process, weights of categories are changed to adapt to noisy inputs. In other words, learning process decides the quality of clustering. Thus, updating weights of categories is an important step of learning process. We propose a new effective learning rule for Fuzzy ART to improve clustering. Our learning rule modifies weights of categories based on the ratio of the input to the weight of chosen category and a learning rate. The learning rate presents the speed of increasing/decreasing the weight of chosen category. It is changed by the following rule: the number of inputs is larger, value is smaller. We have conducted experiments on ten typical data sets to prove the effectiveness of our novel model. Result from experiments shows that our novel model clusters better than existing models, including Original Fuzzy ART, Complement Fuzzy ART, K-mean algorithm, Euclidean ART.
Efficient Binary Arithmetic Encoder for HEVC with multiple bypass bin processing

The increasing amount of digital video with supreme quality requires more efficient compression. As the complexity of video coding algorithm is rising, there are more demands for hardware accelerators and customized hardware. Context-based Adaptive Binary Arithmetic Coding (CABAC) is the only entropy coding method adopted in the latest video compression standard, High Efficiency Video Coding (HEVC). Binary Arithmetic Encoder (BAE) is an essential component in CABAC, where the compression process happens. Because of the high data dependency and sequential coding characteristic, it is challenging to parallelize BAE. In this work, we proposed a low-cost and high-throughput hardware architecture for one core of BAE in HEVC. Our 4-stage pipelined BAE architecture is capable of processing one regular bin and up to 4 bypass bins per clock cycle with 30% reduction in terms of area when compared with the designs for one-core CABAC architecture. The design can compress an average of 1.4 bins per cycle. It achieves a throughput of 1 Gbin/s at the maximum operating frequency of 810 MHz with the area of 2.2 kGEs and the power consumption of 2.0 mW in Nangate 45nm technology.
Automated Posture Segmentation in Continuous Finger Spelling Recognition

Recognizing continuous finger spelling plays an important role in understanding sign language. There are two major phases in recognizing continuous finger spelling, which are posture segmentation and posture recognition. In the former, a continuous gesture sequence is decomposed into segments, which are then used for the latter to indentify corresponding characters. Among all the segments, beside valid postures corresponding to characters, there are also many movement epentheses, which appear between pairs of postures to move the hands from the end of one posture to the beginning of the next. In this paper, we propose a framework to split a continuous movement sequence into segments as well as to identify valid postures and movement epentheses. By using the velocity and signing rate based filter, we can obtain very good result with both high recall and precision rate.
Speech classification by using binary quantized SIFT features of signal spectrogram images

SIFT features are not only widely used in content based image retrieval, but also used in speech perception problem by using SIFT features combination with LNBNN classifier. With the combination of SIFT and LNBNN, speech perception problem is quite highly accurate. In this approach, each speech signal is converted into an spectrogram image, then SIFT features are extracted from this image. Typically, a few thousand key-points are extracted from each image spectrogram. This approach retains all discriminative features of speech signals. However, the big limitation of this approach is that the need of memory to store all SIFT features. In order to reduce the need of memory, we quantize SIFT features from 128 bytes to 128 bits and encode them into 16 bytes for each SIFT features. We show that SIFT features perform surprisingly well even after quantizing each component to binary, when the medians are used as the quantization thresholds. Quantized features preserve both distinctiveness and matching properties. The LNBNN perform searching K-nearest neighbor in classification, thus it uses the KD-TREE structure to speed up searching in classification, however, KD-TREE cannot perform with binary SIFT. Therefore, we propose using a multi index hashing to improve the KNN search problem.
Improvements on a simple muscle-based 3D face for realistic facial expressions

Facial expressions play an important role in face-to-face communication. With the development of personal computers capable of rendering high quality graphics, computer facial animation has produced more and more realistic facial expressions to enrich human-computer communication. In this paper, we present a simple muscle-based 3D face model that can produce realistic facial expressions in real time. We extend Waters' (1987) muscle model to generate bulges and wrinkles and to improve the combination of multiple muscle actions. In addition, we present techniques to reduce the computation burden on the muscle model.
Classifying Online Handwriting Characters under Cosine Representation

The natural way of handwriting to enter data into computer is still preferable in many tasks. However, handwriting character recognition is not a trivial task for computer. Based on the presentation of the input, handwriting recognition can be divided into two classes: offline and online. The main advantage of online handwritten data over offline data is the availability of stroke segmentation and order of writing. Utilizing this information rather than static image only can obtain higher recognition rate [11]. In this paper, we extend the method proposed in [13] to represent multiple strokes of a character together in a single set of features using cosine transformation. Using this representation, we have developed an online writer-independent character recognition system with MultiLayer Perceptron (MLP) classifiers, one classifier for each single character. We have tested our system on Section 1a (isolated digits) of the Unipen data set  and have obtained very competitive results.
High-performance adaption of ARM processors into Network-on-Chip architectures

The demand for system scalability, reusability, and the decoupling between computation and communication have motivated the growth of Network-on-Chip (NoC) paradigm in the recent years. The system design has changed from the computation centric design to the communication centric design. Researchers have proposed a number of NoC architectures. Most of these works focus on network architectures and routing algorithms, however, the interfaces between network architectures and processing units also need to be addressed to improve the overall performance of the system. This paper presents an efficient AXI (Advanced eXtensible Interface) compliant network adapter for 2D mesh Wormhole-based NoC architectures, named AXI-NoC adapter. The proposed network adapter achieves high frequency of 650MHz with a low area footprint (952 cells, approximate to 2; 793μm2 with a CMOS 45nm technology) by using an effective micro-architecture and with zero latency by using the mux-selection method.
Impact of Cache Partitioning on Multi-tasking Real Time Embedded Systems

Cache partitioning techniques have been proposed in the past as a solution for the cache interference problem. Due to qualitative differences with general purpose platforms, real-time embedded systems need to minimize task real-time utilization (function of execution time and period) instead of only minimizing the number of cache misses. In this work, the partitioning problem is presented as an optimization problem whose solution sets the size of each cache partition and assigns tasks to partitions such that system worst-case utilization is minimized thus increasing real-time schedulability. Since the problem is NP-Hard, a genetic algorithm is presented to find a near optimal solution. A case study and experiments show that in a typical real-time embedded system, the proposed algorithm is able to reduce the worst-case utilization by 15% (on average) if compared to the case when the system uses a shared cache or a proportional cache partitioned environment.
Towards an ontology for Vietnamese water puppetry

In this paper we propose the first steps for building a Vietnamese water puppetry ontology. The creation of the puppetry ontology would allow not only a more informed and productive professional training, but also the capability of preserving and promoting water puppetry. Gathering water puppetry expert knowledge requires a deep study of puppetry history, background knowledge and performance. Hence, we analyze stories, background knowledge and performance of the water puppetry in the context of the Vietnamese general culture and legends. Especially, we emphasize on the issue of inconsistencies - a key challenge in ontology building. We also present a specific case study using DL-Lite for representation, reasoning and querying in presence of inconsistencies.
Real-Time Scheduling of Concurrent Transactions in Multidomain Ring Buses

We address the problem of scheduling concurrent periodic real-time transactions on Multidomain Ring Bus (MDRB). The problem is challenging because although the bus allows multiple nonoverlapping transactions to be executed concurrently, the degree of concurrency depends on the topology of the bus and of executed transactions. To solve this problem, first, we propose two novel efficient scheduling algorithms for topographically acyclic transaction sets. The first algorithm is optimal for transaction sets under restrictive assumptions while the second one induces a good sufficient schedulable utilization bound for more general transaction sets. Then, we extend these two algorithms for the scheduling of topographically cyclic transaction sets. Extensive simulations show that the proposed algorithm can schedule transaction sets with high bus utilization and is better than that of related works in most practical settings. The implementation of the algorithms in a real testbed shows that they have relatively low execution-time overhead.
Student ability estimation based on IRT

Most of the assessment systems are now using the Classical Test Theory (CTT), the real ability of students is not exactly revealed because they rely only on counting the number of true responses without awaring other characteristics like the difficulty of each item. Several testing software are applied weighted questions but they depend on the sentiment of teachers. The modern testing theories nowadays are built on a mathematical model which can calculate the latent trait of students. The Rasch model is the probability model which promotes interaction between an item and a student. We have constructed a system to estimate students' ability basing on Item Response Theory (IRT) and applying K-Means to classify student ranking. In this paper we present a model to categorize students' levels and compare them to traditional assessment methods. The results indicate that the methods we proposed have shown some significant improvement and they could be effectively applied for other tutoring systems. The result is also meaningful in customizing content and testing ways. Beside, the research is a guide line for teachers or test makers to give other testing approaches for various examinations.
Improvement of Single-Trial EEG Classifier Accuracy Based on Combination of Optimal Spatial Filters and Time-Domain Features

Common spatial pattern (CSP) is a popular techinique in feature extraction for brain-computer interface (BCI). However, CSP algorithm itself does not perform well since the estimation of covariance matrices is quite sensitive to training data. This causes over fitting in some cases, especially when the training set is small. Moreover, this method may result in poor outcomes because it just computes features on spatial domain but omit those on other domains. In this paper, we propose a simple yet effective approach. Through improving the CSP algorithm, optimal spatial filters with highest discriminative ability will be extracted. Concurrently, we also incorporate some time-domain information into feature vectors to make the signal presentation become more sufficient. Experiment results show that this is a promising method for an electroencephalography (EEG) - based brain-computer interface system.
Predicting students' performance based on learning style by using artificial neural networks

In recent years, many education researchers concentrated on learning styles and its implications. They relied that people who have different personality types tend to have different learning styles, which in turn influence to students' performance in each type of subjects. To measure the relationship between learning styles and student performance in a subject or entire course, we conducted an online survey with a participation of students in various courses to analysis and show the effects of different learning styles on students' performance. This test based on the Felder-Soloman questionnaire with 44 test questions divided into four dimensions. We have filtered data to select 316 undergraduate students for the analysis. According questionnaire results, we have applied statistical methods to analyze data and issued some important notes. Besides these, we have built an artificial neutral network to predict academic performance based on students' learning style. We have shown some significant relationships between learning style and performance of the testers, which have implications for the studying of a course.
Soft Real-Time Chains for Multi-Hop Wireless Ad-Hoc Networks

Prioritized MAC protocols are needed to support soft real-time communication in wireless networks. In this paper, we introduce real-time chain, a new prioritized MAC protocol to support soft real-time data flows in multi-hop wireless ad-hoc networks. By avoiding packet collisions and limiting the effect of priority inversions, real-time chain is able to provide soft real-time and bandwidth guarantees. Furthermore, the use of multiple channels enables high spatial reuse and transmission rates. Finally, our approach can be integrated with a slightly modified version of the IEEE 802.15.4 standard. The protocol has been fully implemented on Crossbow MICAz hardware and its performance has been validated with a large set of both indoor and outdoor experiments
On the problem of classifying Vietnamese online handwritten characters

Recognizing circumflexes and diacritics in a Latin-based tonal language of Vietnamese is important because they are used to distinguish words. There are two main approaches to solve the diacritics recognition problems. The first approach tries to separate diacritics from their main character and recognizes them independently. This approach, however, should only be applied with printed documents as the diacritics of printed characters are separable. With free-styled handwritten characters, diacritics are very hard to separate and recognize because they may overlap main characters. The second approach tries to recognize both main character and diacritics without separation step. In this paper, we want to show that our approach with an appropriate feature extraction method and subclass diving strategy, a robust recognition can be obtained with high recognition rate.
Exception triggered DoS attacks on wireless networks

Security protocols are not as secure as we assumed. In this paper, we identified a practical way to launch DoS attacks on security protocols by triggering exceptions. Through experiments, we show that even the latest strongly authenticated protocols such as PEAP, EAP-TLS and EAP-TTLS are vulnerable to these attacks. Real attacks have been implemented and tested against TLS-based EAP protocols, the major family of security protocols for Wireless LAN, as well as the Return Routability of Mobile IPv6, an emerging lightweight security protocol in new IPv6 infrastructure. DoS attacks on PEAP, one popular TLS-based EAP protocol were performed and tested on a major university's wireless network, and the attacks were highly successful. We further tested the scalability of our attack through a series of ns-2 simulations. Countermeasures for detection of such attacks and improvements of the protocols to overcome these types of DoS attacks are also proposed and verified experimentally.
Region-Aware 3-D Warping for DIBR

In 3-D video (3DV) applications, depth-image-based rendering (DIBR) has been widely employed to synthesize virtual views. However, this approach is performed in a frame-based way, meaning each whole frame is dealt with and the characteristics of different regions in the frame are ignored. As a result, redundant pixels in some regions are abused during the subsequent warping and blending stage. This paper proposes a region-aware 3-D warping approach for DIBR in which warped frames are reasonably divided beforehand so that only the indispensable regions are used. With the proposed scheme, it is possible to avoid noneffective and repeated pixels during the warping stage. In addition, the blending process is also saved. The experimental results show that compared to the state-of-the-art VSRS3.5 and VSRS-1D-fast algorithms, our approach can achieve significant computation savings without sacrificing synthesis quality.
Multiple Description Video Coding Based on Frame-Rotation

Multiple Description Coding is an effective way to improve the robustness of real-time data transmission over unreliable channels. In this paper, we propose a novel MDC scheme based on rotation which is compatible with the current video standard. In the MDC approach, two or more independently decodable descriptions of the same data are generated. In the case of two descriptions, one of the descriptions is formed by the original video sequence and the other is formed by the rotated video sequence. The decoder will reconstruct the video sequence according to the different content received. Experimental results show that the performance of the proposed scheme is superior to the classical RS-MDC scheme about 1 dB.
Main vertical objects detecting algorithm for real-time 2D-to-3D conversion

Lacking of enough 3D contents has become the bottleneck of 3D technology development. 2D-to-3D conversion can not only solve this problem, but also be compatible with the 2D video format. However, most of the real-time 2D-to-3D conversion methods are limited to some special images/videos. This article proposes a scheme that can be applied to all kinds of images/videos. Firstly, the motion vectors are obtained by using bi-directional motion estimation. Combining the motion information and the RGB values of the image, a new image segmentation technology is proposed. With each segmented region, the main vertical objects are detected. Finally, the main vertical objects are assigned the same disparity value based on its bottom vertical position. The disparities of other pixels are determined on its vertical position, which can be seen as a safety methods that uses 3D cues implicitly. The experimental results show its stronger 3D effect.
Fast bottom-up pruning for HEVC intraframe coding

In intraframe coding of the High Efficiency Video Coding (HEVC) standard, up to 35 modes are defined for intra prediction and the quadtree structure is used for adaptive block partition. While such flexibility leads to more efficient compression, it also dramatically increases the encoder complexity. In this paper, a simple yet effective fast bottom-up pruning algorithm is proposed to reduce the computational cost. Mode decision at a large coding unit (CU) is selectively skipped based on the block structures of its sub-CUs. Our experimental results show that the proposed scheme can effectively reduce the encoder complexity without compromising the compression efficiency.
Lateral Stability Control of Car-Trailer Combination Based on 4WS

Rollover and jack-knifing of car-trailer combination on overtaking or obstacle avoidance under emergency are serious threats for motorists. A car-trailer combination model was built in this paper. The kinematics of car-trailer combination has been analyzed, and a 4 DOF model was built to act as the controller predictive model. The dynamics simulation for yaw rate following and anti-rollover has been performed on the dynamic car-trailer combination model. The results show that the lateral stability control based on four-wheel steering proposed in this paper can stabilize the car-trailer combination, rollover and jack-knifing are prevented from and the car-trailer combination more can follows accurately the driver's desired path.
A Fast Fractal Image Coding Algorithm Based on FGSE

In fractal image coding, the main consuming time is the process of finding the best matched domain block in a large domain pool for a given range block. In this paper, we propose a fast algorithm for fractal image coding based on the following lemma and fine granularity successive elimination (FGSE). The lemma describes the relationship between fractal image coding and FGSE. This algorithm can decrease about half domain pool before range-domain matching computation and further improve the speed by combining with other fast algorithms of fractal image coding. Simulation results show that our algorithm takes less coding time, has absolutely the same fidelity measured by PSNR as that of Saupe's and achieves super quality compared with Fisher's algorithm
Research on Suspension System Based on Neural Network Algorithm

In this paper, a six degree of freedom half body vehicle suspension system is developed. The neural network algorithm is used to control the suspension system. With the aid of software Matlab/Simulink, the simulation model is achieved. With changing of neural network coefficients, such as changing of training epoch and changing of the network structure, a lot of simulation work is done. Simulation results demonstrate that the proposed active suspension system proves to be effective in vibration reduction and drive stability enhancement of the suspension system.