Reviewerly/exHarmony huggingface dataset:
Dense Optic Flow-Based Key Clip Localization in Surveillance Videos

This paper proposed a key clip localization algorithm to classify the potential clip that might contain different anomalous activities from whole surveillance video. The presented technique extracts the physical motion of object by computing dense optic flow. All statistical properties of motion component in different directions were jointly encoded to represent the feature vector of frames. The feature vectors were sent to soft-margin SVM classifier for training. Finally, a double-threshold technique is applied to merge separate proposals of frame and clip. This model is evaluated on a large-scale dataset namely UCF-crime. The dataset covers 13 kind of anomalous events in real-world long surveillance videos. Ultimately, the classifier is an efficient system with high sensitivity. The high-quality video clip proposals are helpful for further anomaly detection.
An Enhanced Object Tracking Algorithm Based on Augmented Reality Markup Language (ARML) for Medical Engineering

The use of augmented reality technologies in the healthcare sector opens up renewed possibilities. AR in medicine consists predominantly of three technical components, including camera calibration, patient registration, and object tracking. Object tracking assesses the camera or marker's spatial location on surgical instruments and is an integral aspect of a medical RA device. The AR framework must be very accurate and must provide a simple structured procedure for storing metadata and different details for an AR model view. Many registration techniques lack specificity and display techniques cannot display on numerous browsers and platforms. This paper deals with the above drawbacks by introducing a marker-less medical AR method that uses intensity-based identification for liver lesions and Augmented Reality Markup Language (ARML) for the regular AR view. Morphological operations are used to promote identification and boost the environment. AR registration technique is then accompanied by the identification, which is applied using a mutual information algorithm. We suggest storing metadata and details in ARML tags in all system stages, including preprocessing. Finally, the method of tracking is regarded in previous phases for the relevant video frames. Results of the suggested method found to boost the vision of physicians with high specificity for targeted objects. Furthermore, the use of ARML to save metadata ensures standard AR show, it also increases patient safety and follow-up.
Unsupervised learning method based on pedestrian parts and head features

Although there are enough excellent and accurate models to solve the problem of pedestrian recognition, they are all based on expensive labeled images for training. In this paper, we try to overcome these shortcomings of supervised learning by unsupervised learning. We propose to use PGN and Deeper Cut network to extract the features of human body parts and pedestrians' head, and save these features in Memory Container to guide the learning process of unsupervised task. On this basis, we propose a loss function based on the nearest neighbor approach. Our experiments verify the superiority of our method in unsupervised pedestrian recognition task.
Design and Development of “VR + Arduino” Immersion Interactive Experiment Case

Abstract In order to guide students to master Arduino development board, sensors, unity3d and other software and hardware application methods, familiar with the development process of virtual reality and intelligent hardware control platform. Learning from the WeChat "jump and jump" finger game, based on virtual reality technology and Arduino intelligent hardware control platform, the virtual intelligent fitness device with good immersion, strong interactivity, and combining entertainment, exercise and virtual experience is developed and designed. The "virtual reality + Arduino" immersion interactive experimental platform provides immersive and interactive immersive helmet and desktop screen display modes, uses a variety of intelligent control methods such as in-situ jumping action or grip to replace the finger pressing action in WeChat to control the jump of the game model, and can choose the sky, ocean, land and other fitness Scene, to achieve physical exercise and intellectual purposes. In the process of case design and production, virtual reality immersion equipment, grip and running pad are combined with fitness to cultivate students’ comprehensive practical innovation ability.
A Study of Function Point Analysis: Some Suggestive Modifications

FPA (Function Point Analysis) is a standardized method aimed at establishing a software size measurement from its functional requirements, considering the features to be implemented in it. It was designed to be applied regardless of the programming language and implementation technology. The paper focuses on the requirements of the modification in the FPA. As from its origin to date there has been drastic changes occurred in the software development paradigm, but FPA exist in its previous form, so there is a need of examine the FPA with the current scenario.
Research on Image Privacy Protection Algorithm Based on Generative Adversarial Network

In view of the existing privacy protection means, the sensitive areas in the pictures, such as face, text and other parts, are changed to protect privacy, or the objects involving privacy are removed from the picture or video. However, the obvious defect of these methods is that they destroy the practicability of the photos. When the privacy information in the photos is removed, the important information contained in the photos is also removed. In order to solve this problem, this paper introduces the generative adversarial network and proposes an image privacy protection algorithm. This algorithm defines information privacy methods and optimizes limited areas to ensure that information is properly protected. Different image protection schemes are designed to adapt and not adapt to the safe path. In order to meet the needs of privacy protection, we apply the image privacy protection algorithm in the generative adversarial network. Finally, the simulation analysis of the privacy protection algorithm based on the generative adversarial network is carried out. The experimental results show that compared with the encryption algorithm, the privacy protection algorithm based on the generative adversarial network has better performance in the end-to-end delay and encryption speed.
A Proactive Decision-Driven Architecture for Information-Aware Internet-of-Things Applications

This work proposes a proactive execution architecture for the information-aware internet of things (IoT) decisions. Herein, complying to offering a proactive congestion awareness along with elevating the quality of the decisions’ supporting information is the pivotal attribute. Firstly, to establish an anticipatory plan towards the incoming different traffic congestion situations, a Proactive Differentiative Random Early Detection (PA-DRED) algorithm is proposed. PA-DRED emphasizes the role of future load forecasting at the admission judgement procedures. PA-DRED anchors on a double-phase load prediction strategy to accurately profile the future traffic load trends. This forecasted load trend is then consolidated with the decision’s degree of emergency to formulate a proactive weighted admission criteria. Secondly, in order to optimize the execution of the admitted decisions, an adaptive information-aware priority-driven scheduling algorithm is presented. The conducted simulations reveal the robustness of the proposed architecture at furnishing the scalability and high availability against various congestion degrees. Additionally, it has been clearly demonstrated that the proposed architecture outperforms the state-of-art counterparts.
Design and Implementation of C# Minesweeper Game Software

Minesweeper game is a small game that is built in the Windows operating system. C# plays a significant advantage in the field of software development and design. In this paper, the overall operation framework of C# minesweeper software is designed, algorithm analysis and system design are proposed.
Histogram Processing for Quality Enhancement of Industrial Videoscope Images

The industrial Videoscope (VS) device is designed for the endoscopic inspection of professional equipment like motors, pumps, turbines, cavities in buildings and vehicle bodies, etc. The inspection process is performed during the operation of the equipment. The videoscope device gives different outputs such as images and videos. Because the images are taken during the equipment operation and may be taken in different environments, some or most output images may suffer from different imperfections such as low contrast and poor details. This paper presents an efficient proposed approach to enhance the quality and contrast of the VS images. The proposed approach is based on Contrast Limited Adaptive Histogram Equalization (CLAHE) with adaptive gamma correction by choosing the best clip limits in the CLAHE and the optimum parameter of an adaptive gamma correction transfer function. This proposal achieves an enhancement for the imperfections in the VS images based on both quantitative and qualitative metrics. Numerical results emphasize the performance enhancement with the proposed approach. Hence, it can be recommended for industrial VS systems.
Summarizing &amp; Sentiment Analysis on Movie Critics Data

Sentiment analysis refers to the use of text analysis, natural language processing and computational semantics to extract personalized information in reference materials. Basically, it points to the frame of mind of an individual with respect to some subject. Online view has turned into a kind of e-data with the rapid increase of reviews, ratings and recommendations. In order to automate the process of eliminating the noise, understanding the conversations, recognizing the appropriate data and following appropriate actions, vast number of members are looking at the field of sentiment analysis. The major issue of algorithms related to sentiment analysis is that they use common vocabulary terms to express their emotions or sentiment about an outcome. Although, lifestyle factors, sentence contradiction, taunting, sententiousness, lingo uncertain, and difference in situation make it tremendously difficult to turn a complex text into a simple positive or negative sentiment. Web networks have brought new ways of transmitting or voicing consumer feelings. It is a very important forum for people or consumers who find the thoughts of other customers or who switch into different kinds of feeling groups and quickly grow up as a major factor in decision-making. This article provides a sentiment analysis for the classification of customers' examinations that involves the study of the data in the form of a variety of reviews in which convictions are either positive or negative or neutral. This analysis has first prepared the dataset obtained or extracted from IMDB and then removed the unique terms from this data set that have a significance that is called vector and then selected the vector list, after which Naive Bayes, SVM, and machine learning algorithms are applied. Finally, in terms of precision, the classification efficiency has been measured.
An Efficient Information Hiding Mechanism Based on Confusion Component over Local Ring and Moore-Penrose Pseudo Inverse

The basic requirement by adding confusion is to ensure the confidentiality of the secret information. In the present article, we have suggested new methodology for the construction of nonlinear confusion component. This confusion component is used for enciphering the secret information and hiding it in a cover medium by proposed scheme. The proposed scheme is based on ring structure instead of Galois field mechanism. To provide multi-layer security, secret information is first encrypted by using confusion component and then utilized three different substitution boxes (S-boxes) to hide into the cover medium
Design and Implementation of a Moving Robot Equipped with an Arm and an FPGA to Deliver Objects between Two Positions

Abstract In this paper a system is designed and implemented using a Field Programmable Gate Array (FPGA) to move objects from a pick up location to a delivery location. This transportation of objects is done via a vehicle equipped with a robot arm and an FPGA. The path between the two locations is followed by recognizing a black line between them. The black line is sensed by Infrared sensors (IR) located on the front and on the back of the vehicle. The Robot was successfully implemented by programming the Field Programmable Gate Array with the designed system that was described as a state diagram and the robot operated properly.
How Throbber Components Affect Users’ Perception of Waiting Time

Although the processing speed of computers is fast enough, users still have to wait for computers to complete tasks or respond. To cope with this, several types of visual information have been proposed as methods of presenting the current processing conditions of a computer to users when they are waiting. In this study, we focused on a throbber as an example of such visual information. A throbber is an animated graphical control element used to show that a computer program is performing an action in the background. We investigated how the components of throbbers (e.g., presented durations, rotational velocities, and size) affected users’ perception of waiting time. As a result, we observed that the participants felt that throbbers with a slower rotational velocity had a shorter duration regardless of size when the presented duration was rather short, like 5 seconds.
Design of Convolutional Fuzzy Neural Network Classifiers

In this paper, we propose a convolutional fuzzy neural network classification to alleviate the problems of processing high-dimensional data and low computational efficiency in traditional convolutional neural networks. The model proposes a convolution fuzzy C-means algorithm, in the meanwhile uses the L2-norm regularization method to estimate parameters, so that it has better generalization ability. The experimental results indicate that the proposed CFNNCs have excellent performance in classification accuracy than classic classification models such as SVM, RVM, KNN, and the experimental accuracy can be maintained above 90%.
Kalman-Wavelet Combined Filtering

In this chapter we propose to combine two well-known techniques suitable for the analysis of nonstationary process: Kalman filtering and discrete wavelet transform. Signal filtering is an inverse problem in the sense that, based on the noisy observations obtained from measurements, it intends to estimate the state variables knowing the model of the system and the statistical behavior of the intervening noises. This technique performs simultaneously estimation and decomposition of random signals through a filter bank based on the Kalman filtering approach using wavelets. The algorithm introduced in the following pages takes advantage of the relative benefits of both methods. We present some numerical results considering two special cases of wavelets: Haar and Daubechies of four coefficients.
Robot path planning based on A* algorithm and genetic algorithm

The traditional algorithm of path planning is slow in time, heavy in calculation and large in storage space. Taking the medical service robot of Raytheon hospital(in Wuhan) as an example, optimizing its path planning, this paper proposes a combination algorithm based on genetic algorithm and A* algorithm, and two-dimensional modeling of the hospital map was carried out by raster method. Through simulation experiments, comparison with traditional algorithm and model tests, the feasibility and effectiveness of the combined algorithm are confirmed. Meanwhile, the efficiency of path planning is improved, the step size is optimized by 23%, the time is increased by 135%, and the calculation amount is reduced, and the calculation time and storage space are reduced.
Semi-automatic construction method of power safety ontology based on AR-K-means

In terms of data modeling during the construction of the power safety knowledge map, the traditional manual method of constructing the power safety ontology has the problem of time-consuming and labor-intensive. Therefore, a semi-automatic construction method of power safety ontology based on Association Rules (AR) and improved K-means is proposed in this paper. First, according to the authoritative data power safety regulations issued by State Grid Corporation as the data source, the BP neural network is used to semi-automatically extract the ontology concept; Then semi-automatically extract hierarchical and non-hierarchical relationships between ontology concepts through Association Rules and an improved K-means algorithm; Finally, the Protégé ontology editor is used to visually express the power safety ontology concept, the relationship between concepts and examples, and improve the construction of the power safety knowledge graph. The analysis of the calculation examples verifies the effectiveness of the method.
Certificates of quantum many-body properties assisted by machine learning

Computationally intractable tasks are often encountered in physics and optimization. Such tasks often comprise a cost function to be optimized over a so-called feasible set, which is specified by a set of constraints. This may yield, in general, to difficult and non-convex optimization tasks. A number of standard methods are used to tackle such problems: variational approaches focus on parameterizing a subclass of solutions within the feasible set; in contrast, relaxation techniques have been proposed to approximate it from outside, thus complementing the variational approach by providing ultimate bounds to the global optimal solution. In this work, we propose a novel approach combining the power of relaxation techniques with deep reinforcement learning in order to find the best possible bounds within a limited computational budget. We illustrate the viability of the method in the context of finding the ground state energy of many-body quantum systems, a paradigmatic problem in quantum physics. We benchmark our approach against other classical optimization algorithms such as breadth-first search or Monte-Carlo, and we characterize the effect of transfer learning. We find the latter may be indicative of phase transitions, with a completely autonomous approach. Finally, we provide tools to generalize the approach to other common applications in the field of quantum information processing.
Transfer Learning-Based Image Tagging Using Word Embedding Technique for Image Retrieval Applications

In recent days, social media plays a vital role in day to day life which increases the number of images that are being shared and uploaded daily in public and private networks, it is essential to find an efficient way to tag the images for the purpose of effective image retrieval and maintenance. Existing methods utilize feature extraction techniques such as histograms, SIFT, Local Binary Patterns (LBP) which are limited by their inability to represent images in a better way. We propose to overcome this problem by leveraging the rich features that can be extracted from convolutional neural network (CNN) that have been trained on million images. The features are then fed into an Artificial Neural Net, which is trained on the image features and multi-label tags. The tags predicted by the neural net for an image is mapped on to a word embedding plane from which the most similar words for the given tag is retrieved. Along with this ANN, we include an Object detection neural net, which provides additional tags for the image. The usage of an additional neural net and a word vector model adds to the aspect of Zero-shot tagging i.e., the ability of the tagger to assign tags to images outside its own training class examples. The dataset utilized here is the Flickr-25k dataset.
Design and development of environmental protection intelligent second-hand trading system in universities

The increasing popularity of online shopping not only facilitates people's life, but also stimulates people's desire for consumption, causing college students to buy a lot of idle items, resulting in a waste of resources. By studying the relatively common trading methods of second-hand items that currently exist, it is found that they all have certain limitations and are not convenient and practical enough for the special needs group of college students. Therefore, designing a convenient second-hand trading platform for college students has market potential and practical significance. This article has developed and designed a set of offline intelligent trading cabinets based on Raspberry Pi. The system is mainly composed of the master Raspberry Pi, 4G wireless communication module, WeChat applet platform and intelligent trading cabinets. The entire system processes data in the cloud, which is relatively stable in operation. The operation page is simple and beautiful, and it is convenient and time-saving to use. The system can help college students use the old things around them and make a practical contribution to green environmental protection. At the same time, direct income can also help students earn the first pot of gold in their lives.
Analyzing Students' Problem-Solving Abilities in a Virtual Network on the Cloud

While cybersecurity competitions are invaluable learning experiences for the individuals partaking in them, the competition environment might be able to help in understanding how individuals solve security problems. With this project, we have created a virtual network on the cloud that mimics a cybersecurity competition environment and will be used to log and analyze how students go about finding and resolving security challenges on Windows and Linux systems. This study will help educators to better train students in identifying security vulnerabilities and mitigating risk.
Analysis of online posts to discover student learning challenges and inform targeted curriculum improvement actions

Past research on analysing end-of-term student feedback tend to result in only high-level course improvement suggestions, and some recent research even argued that student feedback is a poor indicator of teaching effectiveness and student learning. Our intelligent Q&A platform with machine learning prediction and engagement features allow students to ask self-directed questions and provide answers in an out-of-class informal setting. By analysing such high quality and truthful posts which represent the students' queries and knowledge about the course content, we can better identify the exact course topics which the students face learning challenges. We have implemented our Q&A platform for an undergraduate spreadsheets modelling course, and analysed 1025 meaningful posts to identify the hot areas represented as topic tags, map the identified hot tags progression over time, to direct instructors towards targeted improvement actions. Our proposed approach can be applied to other courses where students' self-directed Q&A can be implemented.
Comments on “Identity-Based Revocation From Subset Difference Methods Under Simple Assumptions”

An identity-based revocation (IBR) scheme is a useful one-to-many cryptographic message transmission method in which a message can be encrypted using receivers' identities such as e-mail addresses as public keys and a trusted message sender who holds users' private keys is not required. Recently, a construction method for an IBR scheme was presented with symmetric broadcast encryption (SBE) schemes called SD or LSD. In this article we clarify that the SBE schemes are completely different from the original subset difference (SD) scheme by Naor, Naor, and Lotspietch or the layered SD (LSD) by Halevy and Shamir. To be precise, we show that the IBR schemes built on top of the original SD or the original LSD scheme is insecure so that even revoked users can easily decrypt a ciphertext generated for a user group excluding the revoked users.