Reviewerly/exHarmony huggingface dataset:
60‐2: Self‐supervised Perceptual Motion Deblurring using a Conditional Generative Neural Network Guided by Optical Flow

The sample‐and‐hold characteristic of flat‐panel displays causes motion blur. Hardware compensation, such as frame‐rate doubling, is expensive, and existing methods of software compensation are slow. We propose a display motion deblurring network (DMDnet) which compensates for motion blur using a neural network trained on pairs of images with a synthetic random displacement between them. We assess the compensated images by convolving them with a kernel produced by a perceptual blur estimation algorithm, to simulate what is seen by a viewer. This technique is approximately 87 times faster than a state‐of‐the‐art optimization technique which produces an equivalent level of compensation; and its average PSNR is 29.70 dB, against 26.68 dB for the optimization.
Reducing Perturbation of Adversarial Examples via Projected Optimization Method

Deep neural networks are vulnerable to the adversarial example. So far, the primary way of generating the adversarial example is comparing the success rates of the adversarial attack. However, the distance between the made example and the original example is also an essential indicator. In this paper, it is demonstrated that the optimization algorithm could reduce the perturbation of adversarial example generated by using the extremum loss function to obtain the perturbation. This paper introduces the OPA optimization algorithm and uses it to find the best advantage on the model decision boundary as the adversarial example. This paper tests four attack methods FGSM, BIM, MI-FGSM and C&W, and measures the perturbation between the original sample and the generats sample by the Euclidean distance. And it is found that the noise of the sample image is significantly reduced by OPA optimization. It should be set in 12-point font size.
Safe functional systems through integrity types and verified assembly

Building a trustworthy life-critical embedded system requires deep reasoning about the potential effects that sequences of machine instructions can have on full system operation. Rather than trying to analyze complete binaries and the countless ways their instructions can interact with one another — memory, side effects, control registers, implicit state, etc. — we explore a new approach. We propose an architecture controlled by a thin computational layer designed to tightly correspond with the lambda calculus, drawing on principles of functional programming to bring the assembly much closer to myriad reasoning frameworks, such as the Coq proof assistant. This approach allows assembly-level verified versions of critical code to operate safely in tandem with arbitrary code, including imperative and unverified system components, without the need for large supporting trusted computing bases. We demonstrate that this computational layer can be built in such a way as to simultaneously provide full programmability and compact, precise, and complete semantics, while still using hardware resources comparable to normal embedded systems. To demonstrate the practicality of this approach, our FPGA-implemented prototype runs an embedded medical application which monitors and treats life-threatening arrhythmias. Though the system integrates untrusted and imperative components, our architecture allows for the formal verification of multiple properties of the end-to-end system. We present a proof of correctness of the assembly-level implementation of the core algorithm in Coq, the integrity of trusted data via a non-interference proof, and a guarantee that our prototype meets critical timing requirements.
Research and Implementation of Teaching and Experiment System of Artificial Intelligence Specialty

With the rapid development of information technology, the voice of artificial intelligence is increasing. At present, the talents of artificial intelligence in our country are in short supply, which cannot meet the demand of talents in the market. Therefore, it is very important for colleges and universities to set up the major of artificial intelligence to train talents. However, the traditional teaching method is not suitable for the major of artificial intelligence. Based on this, this paper proposes the research and implementation of the teaching and experiment system of artificial intelligence. The teaching and experiment system proposed in this paper is based on B/S architecture. Teachers and students can complete teaching tasks and experiments on the browser. Considering the increasing number of users and courses, scheduling algorithm is used to ensure the efficiency of task execution and resource utilization. Through the user experience survey of the teachers and students of the system, the results show that 61.2% of the teachers and 65.87% of the students are satisfied with the system, which shows that the system has achieved good results and is deeply loved by teachers and students. Hope this system can provide reference for the teaching of related subjects.
A Comparative Study on Three Multi-Label Classification Tools

Many science, technology and innovation (STI) resources are attached with several different labels, such as IPC and CPC for patents, and PACS (Physics and Astronomy Classification Scheme) numbers for scientific publications. This problem is well known as the multi-label classification. Though there are a number of approaches and open-source tools for this task in the literature that work well on benchmark datasets, real-world is more complex in terms of both the number and hierarchy of labels. This work aims to compare comprehensively the performance of three state-of-the-art tools, Dependency LDA, Scikit-Multilearn and Neural Classifier on Scigraph of academic resource data. It is found that Neural Classifier works better on an unbalanced distribution dataset with more complex hierarchical structure and a larger number of label scale in terms of Micro F1, Micro F1 and Hamming Loss than the other two tools. On the basis of our comparisons, several directions are suggested in the near future.
An Efficient DFS Algorithm to Compute Least Longest Schedule Path of Software Projects

The requirements of the ensuing software provided by the client are studied by software developers before the preparation of the project charter. The schedule, cost and person-days estimation are made by the developer using an activity chart. This activity chart is transformed into the graphical form using standard semiotics then manually the critical path, i.e. least long path from start to end is identified. This procedure may have several shortfalls (i) Time-consuming (ii) Manual process. In addition to this, a manual process is an error-prone approach. When the number of activities is increased, the determination of the critical path is a herculean task. In such a case, there is quintessence to develop an automated methodology that determines the critical path. This paper contains a proposed automated methodology for the determination of the critical path through a modified depth-first search (DFS) algorithm. Instead of activity chart representation, we have transformed the tabular information of dependencies and time taken by each activity into n × n matrix form and then developed an algorithm to compute critical path it takes time complexity of O(n2).
Qualitative Software Process Management for Developing Countries- An Exploratory Study

Engineering is all about developing innovative products. Software engineering is the engineering branch that deals with the production of innovative software product. Development of a software product is incredibly challenging and complicated process in this high-tech world. There are several models or methodologies for the development of software like waterfall model, build and fix model, spiral model etc. With changing technology and resources, many advancements have been made in software engineering tools and models. However, developing countries are still struggling to compete in the IT world. Developed countries have maintained their position to a large extent. Hence, it is the need of the hour to take qualitative measures by the developing countries in order to cope up with that stress and to produce globally acceptable and reliable software product. This paper presents a complete guide to Software engineering basics and "How to improve software quality" measures using exploratory study on qualitative software process management for developing countries.
Improvement of SPIDAR-HS and Construction of Visual Rod Tracking Task Environment

The final goal of this study is to elucidate the contribution of haptics and vision to the Sense of embodiment (SoE) in a virtual reality (VR) environment through physiological behavioral measurements. To achieve the objective, a rod-tracking task was employed in which a rod held in the hand is passed along a sinusoidal path in VR environment using HMD and haptic device. The problems identified in the previous system were the device positional accuracy and insufficiency of the force sensation. In order to solve these problems, the control circuit of the haptic device was modified. Then, the positional accuracy of the device and the force output characteristics of the device were evaluated. Through these evaluations, it was confirmed that the accuracy of the device was improved, and the strength of the force presentation was improved. In addition, we evaluated the experimental environment by measuring physiological behavior. The evaluation of the experimental environment by the physiological behavioral measurements was carried out by a questionnaire survey on the Rod Tracking Task, which included measurements of muscle potential and subjective evaluation. In the future, we will improve the accuracy of the device, improve the control circuit, and change the force-sensing model in consideration of the system construction.
Viewer’s Age Dependence in Subjective Evaluation of Low-Quality Images Presented on High-Definition Displays

In recent years, rapid advances in display technology bring about the possibility of viewing old video contents reproduced by state-of-the-art display devices such as 4K/8K ultra-high-definition TVs. The mismatch between low-quality content images and reproducing them on high-definition displays may cause visually induced human health problems. We carried out an experiment and a questionnaire to measure participants' feelings of strangeness, discomfort and sickness in viewing a low-quality video clip presented on a high-definition display. Then we compared the results with the case of a high- quality video clip using the same display. We found that low- quality images can cause certain health problems depending on viewers' ages. For older people such images can cause eye focusing problems rather than general symptoms, whereas for young and middle-aged people they cause both eye focusing problems and general symptoms such as discomfort and fatigue.
Domain Ontology Construction for Intelligent Anti-Telephone-Fraud Applications

Domain ontology should reflect real data and support intelligent applications. However, existing methods for ontology construction focus on the conceptual architecture while ignore characteristics of domain data and application demands. To enhance the domain applicability of ontology, we propose a method of Data and Application Driven Ontology Construction (DaDoc), integrating data characteristics and application demands into the entire construction lifecycle. Our method includes three main phases: data and demands analysis, ontology construction and evaluation. Then, we apply this method to the anti-telephone-fraud field and construct the anti-fraud domain ontology based on cross-domain data. And this ontology represents anti-fraud domain knowledge and it can support intelligent anti-fraud applications, including semantic-based fraud identification and global situation analysis. We finally use quantitative indicators to evaluate the quality of anti-fraud domain ontology. Furthermore, this modeling practice validates the effectiveness of our method for ontology construction and this method can be applied to other domains.
Route Planning Using Nature-Inspired Algorithms

There are many different heuristic algorithms for solving combinatorial optimization problems that are commonly described as Nature-Inspired Algorithms (NIAs). Generally, they are inspired by some natural phenomenon, and due to their inherent converging and stochastic nature, they are known to give optimal results when compared to classical approaches. There are a large number of applications of NIAs, perhaps the most popular being route planning problems in robotics—problems that require a sequence of translation and rotation steps from start to the goal in an optimised manner while avoiding obstacles in the environment. In this chapter, we will first give an overview of Nature-Inspired Algorithms followed by their classification and common examples. We will then discuss how the NIAs have applied to solve the route planning problem.
A survey of contend-based filtering technique for personalized recommendations

The number of choices on the internet is devastating. In recent years, internet services have grown exponentially and it is unavoidable that a number of services suffer from information overload, which makes it difficult for users to find the information they are in need of. This is where recommender systems or RSs come in handy by delivering personalized suggestions from a set of choices that is presented to users to pinpoint items or services of interest. Recommender systems among other practices are mostly used for music, movies, jokes, restaurants, financial services, life insurance, Instagram Facebook and twitter followers. This makes recommender systems a fundamental part of websites and e-commerce applications. In this paper, we present a survey of content-based filtering recommender systems. We further provide the landscape of different recommendation methods and their basic approaches.
How to Draw Monoidal Categories

In this chapter we take a short break from Soergel bimodules to learn about diagrammatics. We illustrate the method of string diagrams for drawing morphisms in 2-categories and contrast it with the way that they are typically drawn. By viewing a monoidal category as a 2-category with a single object, we are also able to draw morphisms in monoidal categories. With these diagrams in hand, we then define the Temperley–Lieb category. In subsequent chapters we will use string diagrams to understand the morphisms in the monoidal category of Soergel bimodules.
CRWMS: Bipartite Network Embedding based on Constrained Random Walk and Mixed-Skipgram

Abstract A bipartite network is a basic representation model in recommendation systems, in which the explicit links (e.g., representing user-item rating information) only exist between heterogeneous nodes (e.g., users and items). Most traditional bipartite network embedding methods only consider the explicit network structure, but ignore semantic relations and rating information therein. To solve this deficiency, we designed a new bipartite network embedding approach based on the Constrained Random Walk and Mixed-Skipgram (CRWMS). Specifically, we propose a constrained random walk strategy that produces the mixed local sampling sequences, and further adopt the splitting operation to preserve both explicit and implicit structural information. Furthermore, we propose a novel and effective mixed-skipgram model to discern node representation vectors through joint training of explicit and implicit structural sequences. We compare CRWMS with five state-of-the-art network embedding methods on three real-world bipartite networks. Experiments show that our approach improves performance in edge classification, edge clustering, recommendation and link reconstruction tasks.
Construction of Isodual Codes over Finite Rings

This paper considers the construction of isodual cyclic codes over finite rings. We give conditions on the existence of isodual cyclic codes, used to obtain isodual cyclic codes. We also provide a construction for isodual cyclic codes.
Computer Simulation on the First Secretary of Poverty Alleviation

This paper takes the first secretary of poverty alleviation as the research object, uses computer simulation technology to analyze the competency of the first secretary of poverty alleviation, and builds the competency model of the first secretary of poverty alleviation. The simulation model was used to analyze the E country in Yunnan Province, and the SPSS computer software was used to verify the correlation hypothesis between the demographic characteristics of the first secretary of poverty alleviation and competency, then the corresponding research conclusions were obtained. From the perspective of simulation technology, how to improve the first secretary of poverty alleviation is only effective in reducing poverty.
DEAMER: A Deep Exposure-Aware Multimodal Content-Based Recommendation System

Modern content-based recommendation systems have greatly benefited from deep neural networks, which can effectively learn feature representations from item descriptions and user profiles. However, the supervision signals to guide the representation learning are generally incomplete (i.e., the majority of ratings are missing) and/or implicit (i.e., only historical interactions showing implicit preferences are available). The learned representations will be biased in this case; and consequently, the recommendations are over-specified. To alleviate this problem, we present a Deep Exposure-Aware Multimodal contEnt-based Recommender (i.e., DEAMER) in this paper. DEAMER can jointly exploit rating and interaction signals via multi-task learning. DEAMER mimics the expose-evaluate process in recommender systems where an item is evaluated only if it is exposed to the user. DEAMER generates the exposure status by matching multi-modal user and item content features. Then the rating value is predicted based on the exposure status. To verify the effectiveness of DEAMER, we conduct comprehensive experiments on a variety of e-commerce data sets. We show that DEAMER outperforms state-of-the-art shallow and deep recommendation models on recommendation tasks such as rating prediction and top-k recommendation. Furthermore, DEAMER can be adapted to extract insightful patterns of both users and items.
An Investigation Into Product Owner Practices in Scrum Implementations

The utilization of the Scrum methodology delineates a separation of roles for a product team, with the Product Owner being responsible for identifying and describing product backlog items and making decisions regarding the priority of these items, ensuring business requirements are being met, and providing feedback throughout the project to the team to ensure that there is success in the deployment of the IT solution. Despite the importance of this role, there is scant research to examine the effectiveness of the Product Owner in the outcomes of the Scrum effort. In this chapter, the authors study and empirically evaluate the efficacy of the Product Owner and the practices and procedures that are inherent to the Scrum methodology, as well as the intervening effects of the challenges of the development process and the changing requirements. They conclude by presenting the results of the analysis and the implications of the findings for future work in Scrum, as well as what the research means for Product Owners within organizations that are employing the Scrum methodology.
Rule-based Classifiers for Suspect Detection from CCTV Footages

Background: A significant work has been presented to identify suspects, gathering information and examining any videos from the CCTV Footage. This exploration work expects to recognize suspicious exercises, i.e., object trade, the passage of another individual, peeping into other's answer sheet and individual trade from the video caught by a reconnaissance camera amid examinations. This requires the procedure of face acknowledgment, hand acknowledgment and distinguishing the contact between the face and hands of a similar individual and that among various people. Methods: Segmented frames have been given as input to obtain a foreground image with the help of Gaussian filtering and background modeling method. Such foreground images have been given to Activity Recognition model to detect normal activity or suspicious activity. Result: Accuracy rate, Precision and Recall are calculated for activities detection, contact detection for Best Case, Average Case and Worst Case. Simulation results are compared with performance parameter such as Material Exchange, Position Exchange, and Introduction of a new person, Face and Hand Detection and Multi Person Scenario. Conclusion: In this paper, a framework is prepared for suspect detection. This framework will absolutely realize an unrest in the field of security observation in the training area.
Non-hierarchical Clustering for Large Data Without Recalculating Cluster Center

The k-means non-hierarchical clustering is one of the most widely used methods to partition a dataset into groups of patterns. An advantage of the k-means calculation process is that it is simple and convenient. However, the k-means method converges to one of many local minima, in which the final result depends on the initial starting points. Thus, it is necessary to duplicate the analysis using a different initial value. To obtain the optimum solution for each pair of initial values, the algorithm repeatedly calculates the cluster centers to minimize the average squared distance between objects in the same cluster, so the computational resources required can be costly. Thus, it is important to decrease the number of iterations to reduce processing costs. To solve this problem, we performed k-means analysis without recalculation of the full dataset using the number and the centers of clusters obtained from the analysis of sampling data. The proposed method can be used to simplify and reduce calculation costs by removing the cluster center recalculation step.
Nonrigid Registration of Multimodal Images Using Local Structural Descriptors

Nonrigid registration plays an important role in reducing potential intensity and geometric differences across individual images with varying modalities. In this paper, we propose a new registration method by introducing two local structural descriptors and a similarity measure. The descriptors are used to highlight small intensity variations and represent the texture characteristics of desirable objects, while the measure estimates image differences based on the introduced descriptors. With these descriptors and measure, multimodal images can be aligned in the free form deformation (FFD) registration framework. Experimental results on two public magnetic resonance (MR) image datasets demonstrated that the developed method can achieve reasonable registration accuracy, and outperformed some existing methods.
Safety Evaluation of Training Airspace Environment Based on Wavelet Neural Network

In order to improve the safety level for avoidance collision, a wavelet neural network safety evaluation method of airspace environment is proposed for military flight training. In the method, the airspace environment safety evaluation index system is first established for military flight training, and then wavelet neural network is introduced to establish the problem of airspace environment safety evaluation for military flight training. The actual example shows that the proposed method has a good evaluation result for airspace environment safety evaluation.