nils-herrmann/clean_scirepeval huggingface dataset:
Network-Clustered Multi-Modal Bug Localization

Developers often spend much effort and resources to debug a program. To help the developers debug, numerous information retrieval (IR)-based and spectrum-based bug localization techniques have been devised. IR-based techniques process textual information in bug reports, while spectrum-based techniques process program spectra (i.e., a record of which program elements are executed for each test case). While both techniques ultimately generate a ranked list of program elements that likely contain a bug, they only consider one source of information—either bug reports or program spectra—which is not optimal. In light of this deficiency, this paper presents a new approach dubbed Network-clustered Multi-modal Bug Localization (NetML), which utilizes multi-modal information from both bug reports and program spectra to localize bugs. NetML facilitates an effective bug localization by carrying out a joint optimization of bug localization error and clustering of both bug reports and program elements (i.e., methods). The clustering is achieved through the incorporation of network Lasso regularization, which incentivizes the model parameters of similar bug reports and similar program elements to be close together. To estimate the model parameters of both bug reports and methods, NetML employs an adaptive learning procedure based on Newton method that updates the parameters on a per-feature basis. Extensive experiments on 355 real bugs from seven software systems have been conducted to benchmark NetML against various state-of-the-art localization methods. The results show that NetML surpasses the best-performing baseline by 31.82, 22.35, 19.72, and 19.24 percent, in terms of the number of bugs successfully localized when a developer inspects the top 1, 5, and 10 methods and Mean Average Precision (MAP), respectively.
Hardware implementation for entropy coding and byte stream packing engine in H.264/AVC

Entropy coding and data packing are the major phases in video coding. The new video coding standard, H.264 Advanced Video Coding (H.264/AVC), has adopted Exp-Golomb and Context-Adaptive coding methods to increase data compression ratio. In this paper, we propose hardware architecture of entropy encoding and byte stream data packing engines for the H.264/AVC. Our entropy coding engine, that contains Exp-Golomb and Context-Adaptive Variable Length Coding (CAVLC), supports baseline and main profile of the standard. The proposed architecture is implemented using 180nm technology from AMS. The design consumes only 1.56mW at the operating frequency of 100MHz.
3D Facial Reconstruction System from Skull for Vietnamese

Reconstructing the 3D facial model of an unidentified individual from his skull contributes considerable benefits to terms of archaeology, anthropology and forensic investigation but it is still significantly complicated matter. Computer aided system of 3D facial reconstruction based on skull has a great advantage of reduction in time consumption. Nevertheless, existing results either contain graphical artifacts or reflect incorrectly differences among reconstructed faces. In this paper, we propose a Vietnamese 3D facial reconstruction system from skull that can overcome existing problems. Machine learning is applied to CT image database of living Vietnamese to learn the relationship between skull measurements and soft tissue thicknesses. Then, the soft tissue thicknesses for each individual are calculated from his/her skull measurements. Moreover, instead of using a fixed number of dowels, extra dowels are added to deform a 3D template face, which ensures the smoothness of the 3D reconstructed face as well as represents best the characteristics of the face to be reconstructed.
A Vietnamese 3D taking face for embodied conversational agents

Conversational agents are receiving significant attention from multi-agent and human computer interaction research societies. Many techniques have been developed to enable these agents to behave in a human-like manner. In order to do so, they are simulated with similar communicative channels as humans. Moreover, they are also simulated with emotion and personality. In this work, we focus on issue of expressing emotions for embodied-agents. We present a three dimensional face with ability to speak emotional Vietnamese speech and naturally express emotions while talking. Our face can represent lip movements during emotionally pronouncing Vietnamese words, and at the same time it can show emotional facial expressions while speaking. The face's architecture consists of three parts: Vietnamese Emotional Speech Synthesis module, Emotions to Facial Expressions module, and Combination module which creates lip movements when pronouncing Vietnamese emotional speech and combines these movements with emotional facial expressions. We have tested the face in the football supporter domain in order to confirm its naturalness. The face is simulated as the face of a football supporter agent which experiences emotions and expresses emotional expressions in his voice as well as on his face.
Ultra low-power and low-energy 32-bit datapath AES architecture for IoT applications

In this paper, we propose a novel AES microarchitecture with 32-bit datapath optimized for low-power and low-energy consumption targeting IoT applications. The proposed design uses simple shift registers for key/data storage and permutation to minimize the area, and the power/energy consumption. These shift registers also minimize the control logics in the key expansion and the encryption path. The proposed architecture is further optimized for area and/or power/energy consumption by selecting a suitable implementation of S-boxes and applying the clock gating technique. The implementation results in TSMC 65nm technology show that our design can save 20% of area or 20% of energy per bit at the same area when compared with the current 32-bit datapath designs. Our design also occupies smaller core area with lower energy per bit and at least 4 times higher in throughput in comparison with other 8-bit designs in the same technology node.
A Fast Template-Based Approach to Automatically Identify Primary Text Content of a Web Page

Search engines have become an indispensable tool for browsing information on the Internet. The user, however, is often annoyed by redundant results from irrelevant web pages. One reason is because search engines also look at non-informative blocks of web pages such as advertisement, navigation links, etc. In this paper, we propose a fast algorithm called FastContentExtractor to automatically detect main content blocks in a web page by improving the ContentExtractor algorithm. By automatically identifying and storing templates representing the structure of content blocks in a website, content blocks of a new web page from the website can be extracted quickly. The hierarchical order of the output blocks is also maintained which guarantees that the extracted content blocks are in the same order as the original ones.
Real-Time Communication for Multicore Systems with Multi-domain Ring Buses

We address the problem of scheduling real-time data transactions on a multicore processor bus. In particular, to in-crease system predictability and tighten WCET estimation, we propose to employ a software-controllable Multi-Domain Ring Bus (MDRB) architecture. The problem of scheduling periodic real-time transactions on MDRB is challenging because the bus allows multiple non-overlapping transactions to be executed concurrently, and because the degree of concurrency depends on the topology of the bus and of executed transactions. We propose a practical abstraction mechanism for the scheduling problem together with two novel scheduling algorithms. The first algorithm is optimal for transaction sets under restrictive assumptions while the second one induces a competitive sufficient schedulable utilization bound for more general transaction sets.
Learning relationship between speech and image

Currently, studies on learning relationship between objects focus on the text domain. There are a few researchers who focus on relationship learning between objects in other domains. In these researches, they have tried to represent the qualitative description of structure of objects, and the symbolic relationship between them. This output provides symbolic meaning to the inter-object relationships which are useful for subsequent common sense reasoning and decision making. In this paper, we propose an approach to learn the relationship between a speech signal and an image. In other words, this approach can classify objects based on speech input.
Near-Duplicates Detection for Vietnamese Documents in Large Database

Near-duplicate documents exacerbate the problem of information overload. Research in detecting near-duplicates has attracted a lot of attention from both industry and academia. In this paper, we focus on addressing this problem for Vietnamese documents which, to the best of our knowledge, has not been done before. Most of the current algorithms have been designed for English which are not directly applicable to Vietnamese - a monosyllabic language. We propose to combine Charikarpsilas algorithm (Alexander Andoni et al., 2006) with a ldquoweighting schemerdquo and Vietnamese specific features to address the language intricacy. Experimental results indicate that our scheme is effective for detecting near-duplicates in a corpus of Vietnamese documents.
Supervising an Unsupervised Neural Network

Machine learning is the field that is dedicated to the design and development of algorithms and techniques that allow computers to “learn”. Two common types of learning that are often mentioned are supervised learning and unsupervised learning. One often understands that in supervised learning, the system is given the desired output, and it is required to produce the correct output for the given input, while in unsupervised learning the system is given only the input and the objective is to find the natural structure inherent in the input data. We, however, suggest that even with unsupervised learning, the information inside the input, the structure of the input, and the sequence that the input is given to the system actually make the learning “supervised” in some way. Therefore, we recommend that in order to make the machine learn, even in a “supervised” manner, we should use an “unsupervised learning” model together with an appropriate way of presenting the input. We propose in this paper a simple plasticity neural network model that has the ability of storing information as well as storing the association between a pair of inputs. We then introduce two simple unsupervised learning rules and a framework to supervise our neural network.
Shift Error Analysis in Image Based 3D Skull Feature Reconstruction

3D skull is crucial in skull-based 3D facial reconstruction [1, 2, 3, 4, 5, 6, 7, 8]. In 3D reconstruction, especially in skull-based 3D facial reconstruction, features usually play an important role. Because, the accuracy in feature detection strongly affects the accuracy of the 3D final model. In this paper, we concentrate on accuracy of 3D reconstructed skull, one important part in skull-based 3D facial reconstruction. We discuss a cause of errors called shift errors when taking sequence of skull images. In addition, we analysis the effect of shift error in 3D reconstruction and propose solution to limit the effect.
Coscheduling of CPU and I/O Transactions in COTS-Based Embedded Systems

Integrating COTS components in critical real-time systems is challenging. In particular, we show that the interference between cache activity and I/O traffic generated by COTS peripherals can unpredictably slow down a real-time task by up to 44%. To solve this issue, we propose a framework comprised of three main components: 1) a COTS-compatible device, the peripheral gate, that controls peripheral access to the system; 2) an analytical technique that computes safe bounds on the I/O-induced task delay; 3) a coscheduling algorithm that maximizes the amount of allowed peripheral traffic while guaranteeing all real-time task constraints. We implemented the complete framework on a COTS-based system using PCI peripherals, and we performed extensive experiments to show its feasibility.
Combination of facial movements on a 3D talking head

Facial movements play an important role in interpreting spoken conversations and emotions. There are several types of movements, such as conversational signals, emotion displays, etc. We call these channels of facial movement. Realistic animation of these movements improves the realism, liveliness of the interaction between human and computers using embodied conversational agents. To date, no appropriate methods have been proposed for integrating all facial movements. We propose in this paper a scheme of combining facial movements on a 3D talking head. First, we concatenate the movements in the same channel to generate smooth transitions between adjacent movements. This combination only applies to individual muscles. The movements from all channels are then combined taking into account the resolution of possible conflicting muscles
AES Datapath Optimization Strategies for Low-Power Low-Energy Multisecurity-Level Internet-of-Things Applications

Connected devices are getting attention because of the lack of security mechanisms in current Internet-of-Thing (IoT) products. The security can be enhanced by using standardized and proven-secure block ciphers as advanced encryption standard (AES) for data encryption and authentication. However, these security functions take a large amount of processing power and power/energy consumption. In this paper, we present our hardware optimization strategies for AES for high-speed ultralow-power ultralow-energy IoT applications with multiple levels of security. Our design supports multiple security levels through different key sizes, power and energy optimization for both datapath and key expansion. The estimated power results show that our implementation may achieve an energy per bit comparable with the lightweight standardized algorithm PRESENT of less than 1 pJ/b at 10 MHz at 0.6 V with throughput of 28 Mb/s in ST FDSOI 28-nm technology. In terms of security evaluation, our proposed datapath, 32-b key out of 128 b cannot be revealed by correlation power analysis attack using less than 20 000 traces.
Accurate and low complex cell histogram generation by bypass the gradient of pixel computation

Histogram of Oriented Gradient (HOG) is a popular feature description for the purpose of object detection. However, HOG algorithm requires a high performance system because of its complex operation set. In HOG algorithm, the cell histogram generation is one of the most complex part, it uses inverse tangent, square, square root, floating point multiplication. In this paper, we propose an accurate and low complex cell histogram generation by bypass the gradient of pixel computation. It employs the bin's boundary angle method to determine the two quantized angles. However, instead of choosing an approximate value of tan, the nearest greater and the nearest smaller of each tan value from the ratios between pixel's derivative in y and x direction are used. The magnitudes of two bins are the solutions of a system of two equations, which represents the equality of the gradient of a pixel and its two bins in both vertical and horizontal direction. The proposed method spends only 30 addition and 40 shift operations to caculate two bins of a pixel. Simulation results show that the percentage of error when reconstructing the differences in x and y direction are always less than 2% with 8-bit length of the fractional part. Additionally, manipulating the precision of gradient magnitude is very simple by pre-defined sine and cosine values of quantized angles. The synthesis results of a hardware implementation of the proposed method occupy 3.57 KGEs in 45nm NanGate standard cell library. The hardware module runs at the maximum frequency of 400 MHz, and the throughput is 0.4 pixel/ns for a single module. It is able to support 48 fps with 4K UHD resolution.
A max-min learning rule for Fuzzy ART

Fuzzy Adaptive Resonance Theory (Fuzzy ART) is an unsupervised neural network, which clusters data effectively based on learning from training data. In the learning process, Fuzzy ARTs update the weight vector of the wining category based on the current input pattern from training data. Fuzzy ARTs, however, only learn from patterns whose values are smaller than values of stored patterns. In this paper, we propose a max-min learning rule of Fuzzy ART that learns all patterns of training data and reduces effect of abnormal training patterns. Our learning rule changes the weight vector of the wining category based on the minimal difference between the current input pattern and the old weight vector of the wining category. We have also conducted experiments on seven benchmark datasets to prove the effectiveness of the proposed learning rule. Experiment results show that clustering results of Fuzzy ART with our learning rule (Max-min Fuzzy ART) is significantly higher than that of other models in complex datasets.
Reducing temporal redundancy in MJPEG using Zipfian estimation techniques

Motion JPEG simply compresses each frame in a video sequence using JPEG still image compression standard. This video codec satisfies power consumption and real-time properties of embedded systems while having very low complexity. However, the downside is its inefficiency in reducing the bit-rate because there are still redundancies between encoded frames. In this paper, we focus on reducing temporal redundancies in MJPEG using a motion detection algorithm, called Zipfian estimation. The Zipfian estimation helps MJPEG extract separately the moving blocks and the stationary blocks then removes temporal redundancies by minimizing encoded bit-stream of stationary blocks and encoding only the residuals of the moving blocks between adjacent frames. Experimental results show that the proposed method can provide twice compression ratio as much as the conventional MJPEG, and an approximate quality and bit-rate as the H.264/AVC (Intra-mode). Even the proposed method uses the Zipfian estimation, it still has smaller number of operations than the conventional MJPEG if the percentage of the static scene is equal or greater than 60%. Compared with the H.264/AVC Intra mode when turning off the rate-distortion optimization, the running time of the proposed method is still only a half.
Recognizing Postures in Vietnamese Sign Language With MEMS Accelerometers

In this paper, we discuss the application of microelectronic mechanical system (MEMS) accelerometers for recognizing postures in Vietnamese Sign Language (VSL). We develop a similar device to the Accele Glove for the recognition of VSL. In addition to the five sensors as in the Accele Glove, we placed one more sensor on the back of the hand to improve the recognition process. In addition, we use a completely different method for the classification process leading to very promising results. This paper concentrates on signing with postures, in which the user spells each word with finger signs corresponding to the letters of the alphabet. Therefore, we focus on the recognition of postures that represent the 23 Vietnamese-based letters together with two postures for "space" and "punctuation". The data obtained from the sensing device is transformed to relative angles between fingers and the palm. Each character is recognized by a fuzzy rule-based classification system, which allows the concept of vagueness in recognition. In addition, a set of Vietnamese spelling rules has been applied to improve the classification results. The recognition rate is high even when the postures are not performed perfectly, e.g., the finger is not bended completely or the palm is not straight
Fast and Realistic 2D Facial Animation Based on Image Warping

Facial animation has contributed to many fields such as movies, computer games, education and e-commerce. Facial animation is obtained in two ways: pre-calculated animation and real-time animation. Pre-calculated animation often produces very realistic results with very powerful computer systems and is often used for animated movies. Real-time animation often produces lower quality results due to the real-time requirements and is often used for interactive applications. We propose in this paper a real-time animation method which operates on a 2D image of face. We simulate the effect of facial muscles with image warping techniques. Our method can produce realistic facial expressions and can be easily deployed on not very strong environments such as web pages, mobile phones and PDAs.
Classifying human body postures by a two-neuron fuzzy neural network

Human behaviour analysis is an important step of developing a surveillance system. Classifying postures is a attractive topic of human behaviour analysis. Many classifiers and features extracting from human body are developed. However, complex computing is a difficult for implementing models and understanding extracted features. In this paper, we proposed a fuzzy neural network including two neurons to implement easily. Moreover, two features extracted by counting pixels of human body's silhouette are presented. Experiments classify four postures including standing, lying, sitting, and bending. To prove the effectiveness, our model is compared to competing models. Results show the proposed model is better than compared models and improve significantly the accuracy of classifying.
A codeword-based indexing scheme for fingerprint identification

Fingerprint authentication system is now one of the most reliable personal identification methods. However, it is difficult to design a system such that it satisfies requirements in both accuracy and lookup speed due to large fingerprint database and complicated fingerprint measures. Therefore, fast and accurate fingerprint indexing plays very important role in fingerprint authentication system. In this paper, we present a new approach which is able to improve the performance of fingerprint indexing process. Moreover, this technique itself provides privacy property for fingerprint system which is not mentioned in previous indexing techniques.
Recognizing Vietnamese Online Handwritten Separated Characters

Vietnamese alphabet is based on the Latin alphabet with the addition of nine accent marks or diacritics - four of them to create additional sounds, and the other five to indicate the tone of each word. Because Vietnamese is a tonal language that uses tone to distinguish words, recognizing diacritics is an important part in recognizing Vietnamese word. However, in written form, diacritics are much smaller then the characters, which make very them hard to recognize. Previous works on Vietnamese characters recognition often pre-process input with a graph-based approach by trying to separate the main characters with their diacritics by determining connected regions at pixel level. This approach, however, only works well where the input contains only characters with separable diacritics, for example, scanned image of printed documents. We propose in this paper a robust method to recognize online Vietnamese characters with diacritics. Using cosine transformation with appropriated sampling algorithms, we represent multiple strokes of a character together in a single set of features. This set of features is then used as the input for a well designed machine learning based system. We have tested our system on the combination of Vietnamese characters with diacritics and Section 1c (isolated characters) of the Unipen data set, and have obtained very competitive results.