Reviewerly/exHarmony huggingface dataset:
Design of A Bit-Serial Artificial Neuron VLSI Architecture with Early Termination

In this paper, a VLSI design of a bit-serial artificial neuron circuit is proposed. Different from the ordinary bit-serial architectures which usually start from the least significant bit (LSB), the proposed design will start from processing the most significant bit (MSB). For the MSB-first approach, the more significant part of results will be generated earlier, and the intermediate results will be progressively refined by processing the less significant bits. An artificial neuron is equipped with an activation function at the output, and many common used activation functions such as sigmoid, a rectified linear unit (ReLU) etc will saturate to 0 for large negative inputs. Some will saturate to 1 for large positive inputs. Therefore, when the intermediate results are positive or negative enough, the remaining processing of less significant bits can be neglected. Our preliminary results shows that the approximation results due to the proposed early termination can still lead to the same classification accuracy as the full precision, but the processing cycles can be reduced by more than 25%. The proposed methodology can be applied to the design of hardware accelerators for those machine learning networks based on neurons such as neural network (NN) and convolution NN (CNN).
Pedestrian Tracking in the Compressed Domain Using Thermal Images

The video surveillance of sensitive facilities or borders poses many challenges like the high bandwidth requirements and the high computational cost. In this paper, we propose a framework for detecting and tracking pedestrians in the compressed domain using thermal images. Firstly, the detection process uses a conjunction between saliency maps and contrast enhancement techniques followed by a global image content descriptor based on Discrete Chebychev Moments (DCM) and a linear Support Vector Machine (SVM) as a classifier. Secondly, the tracking process exploits raw H.264 compressed video streams with limited computational overhead. In addition to two, well-known, public datasets, we have generated our own dataset by carrying six different scenarios of suspicious events using a thermal camera. The obtained results show the effectiveness and the low computational requirements of the proposed framework which make it suitable for real-time applications and onboard implementation.
Texture image retrieval based on directional filter selection and statistical modeling

This paper presents a new approach to texture image retrieval based on the statistical modeling in the multidirectional complex transform domain for effectively improving the performance of the retrieval system. Firstly the basic directional filters are selected and used for the complex transform. Then the coefficients of highpass subband are modeled as generalized Gaussian distribution; the magnitude coefficients of complex directional bandpass subbands are modeled as Gamma distribution; and their distribution parameters are efficiently combined with the statistical features of relative phases of complex bandpass subband coefficients. Finally the texture images of VisTex standard database are retrieved using a new combined form of similarity measurements. The experimental results show that, in comparison with the existing texture image retrieval methods, the proposed approach obtains a superior average retrieval rate.
A saliency-weighted orthogonal regression-based similarity measure for entropic graphs

Various measures are used to determine similarity ratios among images before and after image registration. Image registration methods are based on finding the translation, rotation, and scaling parameters that maximize the similarity between two images by taking advantage of the feature points and densities that are found. While the similarity criterion is calculated, it is possible and advantageous to use approximation methods on the graphs based on information theory. The current study proposes a new similarity measure based on saliency-weighted orthogonal regression derived from the weighted sums of the saliency map of the orthogonal regression residuals formed on the entropic graph. It is evaluated in terms of both quantitative and qualitative methods and compared with other graph-based similarity measures.
Overlapping shadows inpainting based on multispectral lighting reproduction

As an important technology in the film industry, Multispectral Lighting Reproduction is used widely to provide real and vivid images for audiences. The overlapping shadows cause the redundant part of the film images which lead to uncoordinated visual effects. This problem is confusing the film industry. To avoid this problem, light sources have to irradiate from a far distance inside the gigantic building, meanwhile a high cost of equipment is required. Hence, it is important to propose a better method which can shorten the illuminative distances and save a huge amount of cost while maintaining the good accuracy of lighting reproduction. In this paper, we propose a novel system to solve the overlapping shadows problem, which can remarkably reduce the power and hardware costs in multispectral lighting reproduction system. The system consists of two workflows. First, the non-negative least squares solution is used to solve the illuminance coefficient. The accurate light is achieved by the illuminance coefficient. Second, images are categorized as the full shadow, overlapping shadows and non-shadow by different luminance and color features. Finally, overlapping shadows are removed by the inpainting algorithm. Experimental results show that our method can effectively detect the overlapping shadows and images inpainting of the dataset.
Exploration of student’s flexibility in solving linear equation system and linear inequality problems

Student's flexibility was an important characteristic of problem solving. Flexibility could be explained as a skill to work flexibly in various situations. This study aimed to analyze, describe and provide information about student's flexibility in solving linear equation system and linear inequality problems that could be used as a basis for improving the learning process. This study was a descriptive qualitative including twenty 11thhigh school students in Grobogan as its subjects. The study variables were indicators of IDEAL problem solver which students performed including identified problems, defined the problem, explored solution, acted on the strategies, and look back and evaluate. The data were taken of test and interview methods. The results showed that students with very good category in solving problem (25%) used two types of strategies and made a few mistakes, students with good category (15%) used four types of strategies and had difficulties to finish the third problem, students with enough category (35%) used three types of strategies and had problems in designing a plan to solve the third problem and students with poor category (25%) used two types of strategies.Most students were not able to design strategies to solve an unfamiliar problem.
Research on long-distance hand recognition based on depth information

This paper proposes a long distance gesture recognition algorithm based on Kinect. First, we use Kinect to capture human skeleton and depth information, track and extract hand information. For the characteristics of the depth image determine that, the experimental results will be not affected by the background, light, skin color and clothing. Then the initial obtained data of hand shape is denoised and smoothed, and the contour and skeleton of hand shape are extracted. When the hand is at long distance, the accuracy of Kinect is not sufficient to get the detail hand shape-information. So we combine the hand depth information with the color information to get hand shape. Finally, we use the Hu moment of the hand shape contour binary image and the hand skeleton binary image as data feature, and utilize SVM to train and identify hand gesture. The experimental results show that the Hu moment of the hand skeleton binary image is more advantageous than the Hu moment of the hand contour binary image, and the proposed long-distance hand recognition algorithm also has the recognition accuracy similar to the close-distance.
Type-2 Fuzzy Controller for Stability of a System

Type-2 Fuzzy sets (T2FSs) handle a greater modeling and uncertainties which exist in the real world applications especially control systems. To avoid mathematical complexity, interval T2FSs (IT2FSs) have been pertained in majority of the fields. One of the important components which influence the fuzzy controller is the t-norm. For obtaining the stability of a control system, t-norm operator can be preferred for better results and in this paper the minimum and maximum operations have been used to simplify the work of the system with Gaussian interval type-2 membership function (GIT2MF). Also proposed Gaussian interval type-2 weighted geometric (GIT2WG) operator and mathematical properties of aggregation operator have been proved using the proposed operator. The goal of this work is to analyze the stability of an inverted pendulum using interval type-2 fuzzy logic controller (IT2FLC) and the results are compared with Proportional Integrated Derivative (PID) Controller. It is observed that IT2FL controller gives the better stability.
CRRCNN: cascade rotational RCNN for dense arbitrary-oriented object detection

In this work, we present a novel network named CRRCNN (Cascade Rotational Region-based CNN) to detect dense objects with oriented bounding boxes. The CRRCNN consists of a Faster RCNN and a Cascade RCNN with Rotational RoIAlign. The Faster RCNN consists of RPN (Region Proposal Network) and RCNN (Region-based CNN). RPN generates horizontal bounding boxes. Rotational region proposals are generated through quadrilateral vertices regression of RCNN, and therefore Faster RCNN is regarded as a Rotational Region Proposal Network (RRPN). To generate accurate rotational bounding boxes, a Cascade RCNN with Rotational RoIAlign is proposed following the Faster RCNN, which will be demonstrated to be crucial for accurate arbitrary-oriented object detection, especially for dense objects. Feature Pyramid Network is also employed to obtain rich context information. The two networks mentioned above are unified and learned end-to-end by jointly optimizing. Experiments on the challenging DOTA dataset demonstrate the effectiveness of our approach.
A new stereo based on effective cost aggregation

This paper presents a new stereo based on effective cost aggregation. Firstly, a color-based image segmentation algorithm is used to segment the reference image into blocks with different colors, and they are taken as the matching window to search the similar region in the target image. Secondly, in order to eliminate the mismatching of brightness difference and noise, Census transform is conducted with both reference image and target image to obtain the bit strings. At the same time, for the sake of matching efficiency, it is of great necessary to rectify the dynamic disparity scopes of the pixel to be matched by the disparity of neighborhood pixels. At last, the bit strings can be used to execute the cost calculation by a new kind of cost aggregation function. By moving the window in the horizontal direction, we can figure out the percentage of the mismatch pixels in every segmented block respectively. The minimum value means which corresponding the horizontal shift is expressed as the best disparity of this segmented block. The experimental results demonstrate that our method can not only improve the accuracy of the depth discontinuities, but also has the high calculation efficiency. Besides, it has good robustness in different brightness environments.
A Linear Logic Based Method for Deadlock-Freeness Scenarios Monitoring in Web Services Composition

This paper presents a method for the monitoring of deadlock-freeness scenarios in Web Services composition. This method considers the Petri net theory and is based on the analysis of Linear Logic proof trees with dates. In the proposed method, a Linear Logic proof tree with date is built for each different deadlock-freeness scenario in the composed system. These proof trees are analysed and the data obtained from them are used to guide the execution of such scenarios, avoiding the deadlock situations. The CPN Tools is used for the implementation and analysis of a monitored model to evaluate the efficacy of the proposed method.
Thoughts on the Orientation of Mathematics Education in Colleges and Universities

Mathematics is a science of structure, order, and relationship. It evolved from the basic practices of counting, measuring, and describing the shape of objects. It involves logical reasoning and quantity calculation. Since the 17th century, mathematics has been an indispensable aid to physical science and technology, and is considered to be the basic language of science. In the mathematics education work of colleges and universities, accurately positioning the education direction and improving the classroom teaching mode can effectively shape the students' mathematical thinking, stimulate students' innovative consciousness, and strengthen students' ability to solve problems. This article will focus on the classroom teaching mode for the university mathematics education orientation, in order to provide theoretical reference for relevant teaching and research personnel.
Quantitative Comparison of White Matter Segmentation for Brain MR Images

The volume of white matter in brain MR image is important for medical diagnosis, therefore, it is critical to obtain an accurate segmentation of the white matter. We compare quantitatively the up-to-date versions of three software packages: SPM, FSL, and FreeSurfer, for brain MR image segmentation, and then select the package that performs the best for white matter segmentation. Dice index (DSC), Hausdorff distance (HD), and modified Hausdorff distance (MHD) are chosen as the metrics for comparison. A new computational method is also proposed to calculate HD and MHD efficiently.
Human-Centered Methods to Boost Productivity

Since programming is a human activity, we can look to fields which have already developed methods for better understanding the details of human interactions with technologies. In particular, the field of human-computer interaction (HCI) has dozens, if not hundreds, of methods that have been validated for answering a wide range of questions about human behaviors. (And many of these methods, in turn, have been adapted from methods used in psychology, ethnography, sociology, etc.) For example, in our research, we have documented the use of at least 10 different human-centered methods across all the phases of software development, almost all of which have impacts on programmer productivity.
Using Belief Propagation-Based Proposal Preparation for Automated Negotiation over Environmental Issues

Automated negotiation is used as a tool for modeling human interactions with the aim of making decisions when participants have conflicting preferences. Although automated negotiation is extensively applied in different fields (e.g., e-commerce), its application in environmental studies is still unexplored. This paper focuses on negotiation in environmental resource-management projects. The primary objective of this study is to reach agreement/conclusion as fast as possible. To achieve this objective, an agent-based model with two novel characteristics is proposed. The first is automating the process of proposal offering using Markov Random Fields and belief propagation (BP). The second is the ability to estimate stakeholders’ preferences through an argument handling (AH) model. The experiments demonstrated that the AH model and the BP-based proposal preparation (BPPP) approach improve the performance of the negotiation process. A combination of these two modules outperforms the conventional utility-based approach by decreasing the number of negotiation rounds up to 50%.
An adaptive smooth variable structure filter based on the static multiple model strategy

Estimation theory is an important field in mechanical and electrical engineering, and is comprised of strategies that are used to predict, estimate, or smooth out important system state and parameters. The most popular and well-studied estimation strategy was developed over 60 years ago, and is referred to as the Kalman filter (KF). The KF yields the optimal solution in terms of estimation error for linear, well-known systems. Other variants of the KF have been developed to handle modeling uncertainties, non-Gaussian noise, and nonlinear systems and measurements. Although KF-based methods typically work well, they lack robustness to uncertainties and external disturbances – which are prevalent in signal processing and target tracking problems. The smooth variable structure filter (SVSF) was introduced in an effort to provide a more robust estimation strategy. In an effort to improve the robustness and filtering strategy further, this paper introduces an adaptive form of the SVSF based on the static multiple model strategy.
Image Shaking Calibration Algorithm for The Vehicle Image-based Surveillance System

In this paper, we proposed an image shaking calibration algorithm for the vehicle image based surveillance system. The proposed algorithm uses Vertical Edge Detection to select the confidence region of extracted image and Lucas-Canada Optical Flow technique to estimate motion. And we implemented the image correction algorithm for the selected region in the difference image between two frames in the digital image. In order to verify the efficiency of the proposed algorithm, the extracted video image was divided into 8×6 format so as to have a size of 160 pixels in width × height in 1280 × 960 size. As to the binarization result, the white boundary frequency was obtained for each area in the state of the monochrome image. Thereafter, the simulation was performed by selecting the region as the confidence region when it is above a certain threshold value. As a result, a similar restoration angle of about 85% was obtained in the frame between the original image and the difference image.
Speaker identification based on deep learning in FX iDeal system

The FX (foreign exchange) iDeal system, which provides instant foreign exchange information for inter bank traders, with the rapidly increased number of users, there are urgently needs to meet the higher security requirements and improve the compliance of transactions in the inter bank market. In this paper, we make a research on speaker recognition to improve the security of the system. According to the historical sound data sent by the user, we extract the Mel cepstrum features, and then construct the speaker's identification model based on the deep neural network (DNN). Based on the model, we can verify the user. In order to prevent the DNN from over-fitting or falling into the local minimum, we add a dropout mechanism and a dynamic learning rate adjustment algorithm to the network. To maximize the data mining capabilities, we use the restricted Boltzmann machine (RBM) to realize unsupervised training which can make the network initialization parameters are closer to the global minimum, and then optimize the parameters by supervised training. Verification experiment shows that the model can effectively improve the speaker confirmation ability and is more effective than the general BP neural network and GMM model.
Co-saliency detection via cluster-based structured matrix decomposition

Aiming at automatically discovering the common objects among a group of relevant and similar images as foreground, co-saliency has become a hot topic in recent years. Previous works utilize low-rank matrix recovery on the single image, but neglect the relationship between a set of images. In this paper, we propose a novel framework to capture the coherence of common salient objects, and solve the problem when the background is clatter. The model include a novel cluster-based tree-structured sparsity-including regularization that make regions from same class have identical saliency value, and a Laplacian constraint regularization is also integrated into the model, the propose is to enlarge the gaps between common objects and background in original feature space and smooth the saliency value in same cluster. Furthermore, to facilitate the efficient, a coherence weight is identified and integrated into the model. Experiment results on three benchmark datasets demonstrate are the performance of our method compared to other stateof-the-art co-saliency models.
Sentiment evaluation of forex news

Sentiment analysis is significant for excavating text opinion. There are two issues in the foreign exchange (Forex) field. 1) In sentiment orientation, most researches focus on product reviews, lack fine-grained sentiment analysis for Forex news. 2) In sentiment intensity, most works consider the intensity of sentiment words but ignore the significance of field characteristics. Aiming at the two problems, a fine-grained Sentiment Analysis model (shorted as WD-SA) is established, which integrates with the Weight of sentiment words and Domain features. First, the semantic information of text is embedded into a vector based on word2vec. Then, sentiment orientation is detected by a method, which combines machine learning algorithm and the weight of sentiment words. Finally, features are extracted to investigate the intensity of news. The experimental results show that our algorithm outperforms the state-of-the-art.
Designing a Dependency Execution for Oracle Jobs

Oracle Database includes a job scheduler. It can schedule jobs to run at a designated date and time. Though the Scheduler also includes chains, which are named groups of steps that work together to accomplish a task, there are some disadvantages: job dependency is not easy to set, job concurrency is not manageable, and it has no notice by text-message. The researchers designed, built and tested the Dependency Execution for Oracle Jobs for a long time. It is running smoothly after trial and trial. It is much easy to set a job which depends on other jobs by inserting a row into a configuration table, and the job can be scanned and run by background processes which are designed by researchers. On this way, this system can also record who is the job's creator, how to contact, and the time of created and estimated expires. It has concurrency control and the priority and private channel for the jobs execution of short time. Many companies have a lot of reports to do every day. It depends on the data from all over the systems to be extracted and processed. The database is so stressed every morning that it is needed to property schedule job execution. The designed system shall make a more convenient and easier configuration to create and rerun Oracle jobs. The system administrators can also analysis the log to remind the job creator to optimize the script and improve the execution efficiency and performance of the Oracle database.