Reviewerly/exHarmony huggingface dataset:
Research on Prediction the Development Trend of Weibo Topics Based on Fuzzy Neural Network

Abstract The people who use the Weibo application are widely available, and Weibo has the characteristics of instant openness, strong interaction, fast transmission speed and wide influence range. Therefore, predicting the development trend of Weibo topics quickly and accurately is a topic worth studying. In this paper, the total number of posts in a Weibo topic is used as a quantitative indicator of the development the Weibo topic. Fuzzy neural network is used to predict the hot trend of Weibo topic, and the improved annealing algorithm is used to optimize the membership function of the fuzzy neural network and its quantity to improve the prediction accuracy. The superiority of the proposed method is verified by the comparison experiment of the collected Sina Weibo dataset. The method proposed in this paper effectively solves the problem that the parameters in the membership function are complex and easy to fall into the local optimum, which improves the accuracy of the development trend of Weibo public.
Boat in the pool – lab work

Abstract This article describes a non-traditional lab focused on applying Archimedes’ principle in a situation that is, for students, new and unusual. Two main interconnected features of this lab are important. Firstly, it is not a “standard” lab experiment in which students are instructed to measure something and then to interpret results. Here, a very important element is prediction. Students have to predict the result first, calculate it using Archimedes’ principle, and only then do the measurement. Secondly, it is not the teacher who tells students whether their calculation is right or wrong, it is the experiment itself. This article presents the lab work itself (instructions and results), some methodological comments, and my experience in school.
An Improved BING/NMS Algorithm for Aircraft Detection

The detection method of the aircraft target should meet the requirements of real-time and precise. Due to the high concealment and clustering of aircraft, the traditional BING algorithm has a lot of false positives and false negatives, which greatly reduces the detection probability of targets. Aiming at the problem, an improved BING/NMS algorithm based on Otsu-HSV color model for aircraft detection is presented in this paper. Firstly, in the case of complex dynamic environment background, the RGB color model is not conducive to image detection of aircraft. Therefore, the reconstruction Otsu-HSV model combing HSV color model with Otsu algorithm for image preprocessing is proposed. Then aiming at the BING algorithm and the NMS algorithm, which will lead to high false detection and miss detection, we propose to improve the detection precision and real-time by using overlap and weighted score. The results demonstrate that the proposed algorithm can improve AP and ABO, reduce false alarm and has a good detection effect.
Various Face Annotation Techniques: Survey

Basic idea behind the Face Annotation is to detect the facial expression and process further on it for various applications. Techniques of face annotation are used to give an appropriate name to the face image. In this research work, first the face notations are saved in the database and it can be retrieved any time for further processing and then it compares two different images of a same person and finds out whether those images belongs to the same person only. In this paper we described various techniques of face annotation such as Content based, Retrieval based, Search Based, Cluster Based and Caption Based face annotation. Based on the study we present the parametric evaluation of existing techniques.
Proposal of Environment Adaptive Software

Recently, heterogeneous hardware such as GPU and FPGA is used in many systems and also IoT devices are increased repidly. However, to utilize heterogeneous hardware, the hurdles are currently high because of much technical skills. In order to break down such a situation, we think it is required in the future that application programmers only need to write logics to be processed, then software will adapt to the environments with heterogeneous hardware, to make it easy to utilize heterogeneous hardware and IoT devices. Therefore, in this paper, we propose environment adaptive software to operate an once written application with high performance by automatically converting the code and configuring setting so that we can utilize GPU, FPGA and IoT devices in the location to be deployed. We explain a processing flow and elemental technologies to achieve environment adaptive software. We also describe the details of elemental technologies such as automatic GPU offloading which are already under considered.
Lehmer's dance: a lecture performance

We describe our experiences with the development, preparation, and execution of a lecture performance that combines mathematics, dance, sound, and live visuals. We present some plans for future projects that combine choreography and mathematics, also in an educational setting.
An Agile Approach Applied to Intense Maintenance Projects

This paper proposes an approach to managing software projects that are susceptible to frequent and intense change requests demanding quick response time. Highly influenced by agile thinking, in particular, SCRUM, Kanban and Lean Software development, this solution directly captures the philosophy and the main techniques of these popular agile "methodologies". Moreover, it includes the use of specific measures to manage the project's health and give accurate information to the top level management. This proposal's focus is on improving and optimizing the maintenance process of large-scale information systems which daily handle the change requests of thousands of users. The application of this approach in a real study case allowed to obtain significant results that indicate the potential of the proposal.
Gradient Based Prediction for High Fidelity Reversible Data Hiding with Pairwise Embedding

This paper investigates the use of gradient based prediction for pairwise reversible data hiding. Pairwise based approaches split the host image into two pixel sets. The pixels in each set are processed based on the information from the other set. Host pixels from the same set are grouped into pairs based on their prediction error. Hidden data is then embedded into each pair. The proposed approach introduces a five set distribution combined with a twelve pixel rhombus shaped prediction context, improving the prediction precision and maintaining the independent prediction required for pixel pairing. This approach shows promising results on images with large textured regions.
Secure Data Compression Scheme for Scalable Data in Dynamic Data Storage Environments

With the continuous and exponential increase of the number of users and the size of their data, data deduplication becomes more and more a necessity for cloud storage providers. By storing a unique copy of duplicate data, cloud providers greatly reduce their storage and data transfer costs. These huge volumes of data need some practical platforms for the storage, processing and availability and cloud technology offers all the potentials to fulfill these requirements. Data deduplicationis referred to as a strategy offered to data providers to eliminate the duplicate data and keeps only a single unique copy of it for storage space saving purpose. This paper, presents a scheme that permits a more fine-grained trade-off. The intuition is that outsourced data may require different levels of protection, depending on how popular content is shared by many users. A novel idea is presented that differentiates data according to their popularity. Based on this idea, an encryption scheme is designed that guarantees semantic security for unpopular data and also provides the higher level security to the cloud data. This way, data de-duplication can be effective for popular data, whilst semantically secure encryption protects unpopular content. Also, the backup recover system can be used at the time of blocking and also analyze frequent login access system.
Why Bother with Object Orientation?

The pervious four chapters have introduced the basic concepts behind object orientation, the terminology and explored some of the motivation. This chapter looks at how object orientation addresses some of the issues that have been raised with procedural languages. To do this it looks at how a small extract of a program might be written in a language such as C, considers the problems faced by the C developer and then looks at how the same functionality might be achieved in an object-oriented language such as Python. Do not worry too much about the syntax you will be presented with; it is mostly a form of pseudo code and it should not detract from the legibility of the examples.
An Exploratory Study on Detection of Cloned Code in Information Systems

Code clones are source code parts that are identical or have some degree of similarity to another part of the code. Cloning arises for a variety of reasons, including copy and paste and the reuse of ad-hoc code by programmers. Detection of information system clones is aimed at propagating changes by all clones at the development, maintenance and evolution stages, preserving data consistency, correcting errors, and so on. Clones can be classified as 1, 2, 3 and 4, depending on their similarity and characteristics that classify them as such. Several techniques and tools have been created with the objective of detecting code clones, and for this, they use techniques of representation of the source code in text, token, tree, graphic, hybrid and metrics. This systematic mapping work presents answers to the four research questions, which aim to identify, count and catalog, data from a set of 875 articles, of which 128 were selected, for the selection of relevant information seeking to provide content for the collection of data objectified. In all, 52 clone detection tools were identified, which reinforce the current theme; 26 ways of presenting source code to detect clones, where the commonly used ones stand out for ease of understanding and handling; 13 programming languages in 6 paradigms and the identification, highlighting the great presence of clones detection in object oriented information systems, of all 4 types of clones, as well as semantic and syntactic clones, which reinforces the current questioning of authors of this division search line into four types.
Medical image grading learning based on active and incremental fine-tuning

The development of convolutional neural network has brought great achievements to image classification in recent years. However, the classification performance is good only for natural images rather than medical images. An important reason is that the medical image database used for training is always deficient. So how to use these limited data to acquire more extensive features has become a hot research focus. In this paper, we first update the order and number of the whole training data every time in active and incremental fine-tuning. Then we set different contribution rate for the data selected in our model, which based on the information quantity of the data in training stage and make our model converge steadily. After that, a pre-trained model and our preprocessed datasets are employed, which allows us to further fine-tune our models. The experiments evaluated on two different biomedical datasets shows that our model can achieve promising results.
An overview of profiling side channel analysis based on machine learning

Abstract It has made a real issue in cryptographic security with the widespread use of them which attracts more attention now. This paper summarizes the most distinguishing profiling type attack in the side channel of cryptographic chips.The principle steps of the traditional profiling side channel analysis are introduced firstly,then the exposed problems are directed in discussion.It fits well to solve the problems using the algorithm in machine learning. So what undoubtedly added in is that the profiling side channel analysis is based on machine learning. Also the situation and the progress are both the significance in the paper.The disadvantages and the future of its development are mentioned at the end.
A Modified Artificial Bee Colony Algorithm for Function Optimization Strategy

Abstract Artificial Bee Colony (ABC) algorithm has received wide attention due to the outstanding performance in solving complex optimization problems. However, the further application of ABC is still hindered due to some bottlenecks, such as slow convergence velocity, poor global search ability and low accuracy of solution. To overcome these problems, an improved ABC (CT-ABC) is proposed in this paper by using both a chaotic map and a tolerance based search equation (TSE). Some standard test functions are chosen to verify the performance by implementing the proposed modified ABC algorithm, basic ABC and another modified ABC (WABC). Simulation results reveal the proposed CT-ABC algorithm is able to obtain the faster convergence speeds and better accurate approximate solution.
Modelling Standard Work with Simple Virtual Agents

Performing repetitive tasks in an industrial setting can typically be accomplished in a variety of ways. In this paper we train virtual agents with kinematic constraints to visit a variety of locations in a manner that optimizes their efficiency. The agents are trained in two ways. The first optimizes only the time required to perform the task, while the second also rewards the agents for the degree to which that perform the work in a particular order, representing a standard for the work. This is an instance of an industrial practice called standard work, and this study shows how to use simple virtual agents as a sandbox for experimentation with standard work. Two techniques for encouraging standard work are tested. The system divorces the agent's control parameters, which are the focus of their training, from their performance, simulating the type of training that must be performed on human workers. The virtual agents can be trained to standard work at a very high rate but, in some agents, with a loss of speed. This is analogous to workers that are difficult to train because of preconceptions about the correct technique for performing a task, another good feature for an experimental sandbox system.
On the Impact of Order Information in API Usage Patterns

Many approaches have been proposed for learning Application Programming Interface (API) usage patterns from code repositories. Depending on the underlying technique, the mined patterns may (1) be strictly sequential, (2) consider partial order between method calls, or (3) not consider order information. Understanding the trade-offs between these pattern types with respect to real code is important in many applications (e.g. misuse detection), given that APIs often have usage constraints, such as restrictions on call order. API misuses, i.e., violations of these constraints, may lead to software crashes, bugs and vulnerabilities. In this paper, we present the results of a work that addresses this need. We have constructed a benchmark based on an episode mining algorithm that can be configured to learn three type of patterns: sequential, partial, and no-order patterns. We use the benchmark in two ways. First, we use it to empirically study the different types of the mined API usage patterns based on three well-defined metrics: expressiveness, consistency and generalizability. Second, we evaluate the effect of the different pattern types within the real application context of using them as an input to a misuse detector. We run the benchmark on two existing datasets consisting of: (1) 360 C# code repositories, and (2) four Java projects. We use the C# data set to empirically study the resulting API usage patterns, and the Java data set to evaluate the effect of different pattern types on the application context of misuse detection. For this purpose, we build EMDetect for detecting API misuses in Java projects. Our results show practical evidence that not only do partial-order patterns represent a generalized super set of sequential-order patterns, partial-order mining also finds additional patterns missed by sequence mining, which are used by a larger number of developers across code repositories. Additionally, our study empirically quantifies the importance of the order information encoded in sequential and partial-order patterns for representing correct co-occurrences of code elements in real code. On the application context of misuse detection, our results show that sequential-order patterns perform better in terms of precision by ranking true positives higher in the top findings, while partial-order patterns perform better in terms of recall by being able to find more misuses in the source code. Last but not least, our benchmark can be used by other researchers to explore additional properties of API patterns, and for building-up other applications based on API usage patterns.
Deep Regression for Precise Geometric Dimension Measurement

A planar-dimensions vision measurement method is proposed by developing a Neural Network to measure real-world distance between any two points on the plane. The system leveraging Neural Network ability to search in the solution space is a highly non-linear model that could map points’ location on the pixel plane of image(s) with the actual distance between them considering the non-uniform geometric distortion in captured images caused by the entocentric lens in a common camera. The method was tested with a printed calibration chessboard, placed in different locations on the plane, with measured distance between tested points. Experimental results show the proposed method’s mean absolute error is 1.24 × 10SUP-2/SUP mm and standard deviation is 1.63 × 10SUP-3/SUP mm, tested with 10-folds cross-validation method.
A Collusion Avoidance Node Selection Scheme for Social Network-Based Distributed Data Storage

Motivated by the privacy protection requirement of a traffic accident emergency rescue system, we propose to select trustworthy nodes in one's social network as the data keepers and store pieces of encoded data in a distributed way. Specifically, we utilize Fountain Code to encode data and generate sufficient amount of coding symbols meanwhile select sufficient number of data keepers to ensure the success of recovery of data in such an unstable environment. Besides, we further consider the concept of gaming theory to prevent the collusion between data keepers, we derive a trust score function to evaluate nodes in one's social network and propose a Collusion Avoidance Node Selection Scheme to improve the reliability and avoid collusion. By this approach, we solve the problems of privacy preserving and the instability of nodes in the social network. In the experiments, we use real datasets to study the performance of our proposed approach. The experimental results show that our approach outperforms the Best Score Selection and Random Selection methods and our approach can avoid collusion effectively and improve the data privacy.
Implementation Study of Parachute Training Simulator

Parachute training simulator is a powerful simulation tool that gives training starting from the position of free fall to jumpers with scenarios and graphics very close to reality and can perform this education close to reality. Parachute simulator is a cost-effective solution for paratroopers to enhance training, planning, practicing skills and mission readiness in a short notice and safe environment. Parachute training simulator designed parachute training with 3D virtual images are an effective addition to mastering all the necessary parachute dynamics and control by providing a real jump to the staff. The system provides real-time practicing the crucial skills under a wide range of weather conditions, wind and emergency situations while improving mission readiness and performance by using virtual reality with high resolution visual database. Parachute simulator training starts with free fall motion. After the parachute is turned on by the user, it is aimed to be controlled by the parachute control rivers (ripcord) to land at the specified point. A parachute simulator training system generally includes parachute training unit, visual system, instructor console, environmental voice and intercom are the basic units. In this study, the example design procedure is explained for a parachute simulator.
Reduced-reference image quality assessment method based on wavelet feature extraction and fusion

Abstract Virtual reality has many characteristics such as multi-sensation, interactivity, and presence. Virtual reality images, also known as 360-degree panoramic images, provide users with an immersive experience and have been widely used in education, medical, military and many other fields in recent years. 360-degree panoramic images have high resolution and are subject to varying degrees of distortion during transmission and storage. Therefore, the evaluation of virtual reality images has practical significance. To this end, we propose a method for evaluating the quality of images in the new CVIQD database. The proposed image quality assessment model is developed based on Haar wavelet and Db4 wavelet feature extraction and fusion. The method is compared with a single wavelet feature extraction method. Experiments show that the method has better performance.
Hotspot Detection in Social Media Based on Improved Strategy Clustering

With the rapid growth of social media platform, hotspot detection has become an increasing important issue in the We media era. As for researchers, the emergence of all kinds of public opinion crises is one of the greatest challenges. In order to make a response to public opinion precisely, a more effective approach for hotspot detection is necessary. Clustering algorithm is one of the best choices for distinguishing hotspots. However, the existing methods still need some improvement in filtering data and calculating text similarity. Therefore, we propose a method which combines the two strategies to improve the performance of clustering algorithm for hotspot detection. One strategy is message importance which is used for measuring the value of information from social media. The other strategy is an improved text similarity based on feature words which is applied to solve sparse vector from the short texts. Through improving the accuracy of clustering, we can obtain more accurate hotspots. We validate the proposed method is feasible through the experiment based on the datasets from Weibo online.
Single shot relation detector for pedestrian detection

Although it is well believed for years that contextual information and relation between pedestrians would help pedestrians recognition, but this idea is rarely used in the deep learning era. This is due to the fact that the convolution method of deep neural networks is not easy to fuse related features and will increase the amount of computation. In this paper, we propose a single shot proposal relation based approach for pedestrian detection. We get the proposal on the image features of different scales, and use these proposal relationships to extend the features of each proposal. Finally, the position of the pedestrian is obtained through the convolutional neural network. Its computational cost is small and it is easy to embed into existing networks. Our detector is trained in an end-to-end fashion, experimental results on the Caltech Pedestrian dataset show that our approach achieves state-of-the-art performance.