Reviewerly/exHarmony huggingface dataset:
Gaze-Pinch Menu: Performing Multiple Interactions Concurrently in Mixed Reality

Performing an interaction using gaze and pinch has been certified as an efficient interactive method in Mixed Reality, for such techniques can provide users concise and natural experiences. However, executing a task with individual interactions gradually is inefficient in some application scenarios. In this paper, we propose the Hand-Pinch Menu, which core concept is to reduce unnecessary operations by combining several interactions. Users can continuously perform multiple interactions on a selected object concurrently without changing gestures by using this technique. The user study results show that our Gaze-Pinch Menu can improve operational efficiency effectively.
Interactive Multi-Model Independent Joint Probabilistic Data Association Filtering Algorithm

Abstract Aiming at the problem of redundant calculation of the Interactive Multi-Model-Joint Probabilistic Data Association Filtering (IMM-JPDAF) algorithm, an Interactive Multi-Model Independent Joint Probabilistic Data Association Filtering algorithm is proposed. The algorithm uses model probabilities as priori information to reliably estimate the parameters of the maneuvering target motion model, and then uses them in the JPDA module, so that the JPDA can exist independently of the model filters. The simulation results show that the proposed algorithm can save about 20% of the calculation amount without reducing the accuracy of the target tracking, effectively reducing the redundancy.
CED-BGFN: Chinese Event Detection via Bidirectional Glyph-Aware Dynamic Fusion Network

Event Detection is an essential task in information extraction. However, most existing studies on event detection are designed for English text. There is still a lack of efficient algorithm for Chinese event detection, which is expected to be greatly improved. Recent work has shown that enhanced text representation, such as introducing glyph information, can significantly improve downstream tasks in natural language processing. In this paper, we propose a novel method for Chinese Event Detection via Bidirectional Glyph-aware Dynamic Fusion Network, called CED-BGFN. We use two representations: glyph-aware information and pre-trained language model. To integrate the heterogeneous representation modules, we propose a creative fusion network Bidirectional Glyph-aware Fusion Network, named BGFN. Considering the dynamic interaction of the two expressions, BGFN adaptively learns the fusion weights for the downstream event detection task. We conduct extensive experiments to investigate the validity of the proposed method on the ACE 2005 Chinese corpus. Results demonstrate that compared with the previous state-of-the-art methods, our approach obtains transcendent performance in both event trigger identification task and classification task, with an increase of 5.48 (7.46%) and 5.03 (7.1%) in F1-score, respectively.
On the Hardness of Sparsely Learning Parity with Noise

Abstract The Learning Parity with Noise (LPN) problem represents the average-case analogue of the NP-Complete problem “decoding linear codes”, and it has been extensively studied in learning theory, coding theory and cryptography with applications to quantum-resistant cryptographic schemes. However, LPN also suffers from large public key size which is the common drawback that hinders code-based cryptography from being practical. In this paper, we study a sparse variant of LPN whose public matrix consists of sparse vectors instead of following uniform distribution. We show a win–win argument that at least one of the following assumption is true: (i) either the hardness of sparse LPN is implied by that of the standard LPN under the same noise rate; (ii) or there exists new black-box constructions of public-key encryption schemes and oblivious transfer protocols from standard LPN. Since the second assumption relies on the infeasible noise regimes for LPN-based public-key cryptography, we believe that the first assumption is more likely to hold, i.e. sparse LPN is as hard as standard LPN. Finally, we give a (heuristic) method to further compress the sparse public matrix by evaluating pseudorandom functions with keys made public, whose security again resorts to the aforementioned win–win technique.
Review on The Collection Technology of Station Network Equipment Status Based on Power Plant Side

The safety protection of new energy plants is an important link in the power monitoring system. This paper summarizes the collection technology of network equipment status in new energy plants, mainly from three aspects: illegal network channel analysis and isolation technology, security status collection index and collection technology, and honeypot-based illegal network traffic traction technology. First, we study and analyze illegal network channels based on the obtained equipment status information and network topology information. Then, based on the discovered illegal network channel link information, we discuss the illegal network channel isolation technology based on firewalls and Illegal network traffic traction technology based on honeypots. Finally, in view of the problems of multiple external information interaction interfaces for network equipment and complex terminal communication, We discuss the collection index system and collection technology of the security status of network equipment.
Use of the video problem book in preparation of future teachers

The subject of this research is the development of professional competencies of future elementary school teachers in the process of using video problem book (based on the material of math classes). The article discusses different methods of using video materials in preparation of students. An overview of foreign research on the topic is provided. It is noted that currently the methods of using video content are not sufficiently covered in the literature. The questions of which video fragments are better to use, how to include them into the educational process, what particular tasks they pursue, etc. remain unanswered. The article describes the experience of using video problem book as technique of teaching math to students, as well as future elementary school teachers. The author analyzes the work with the video problem book, an essential element of which is the use of &amp;ldquo;stop motion&amp;rdquo; technique in a problematic spots of the lesson. The article also presents the results of comparative research of academic performance of the groups of students: an experimental group that used video problem book, and a control group that that did not use video problem book in the educational process. The author picked the following competencies: diagnostic competence of the teacher, ability to make pedagogical decisions, and reflexivity. The implementation of video problem book in the educational process develops in students and future elementary school teacher the skills to make deliberate decisions in different learning situations.
Incrementally Zero-Shot Detection by an Extreme Value Analyzer

Human beings not only have the ability to recognize novel unseen classes, but also can incrementally incorporate the new classes to existing knowledge preserved. However, zero-shot learning models assume that all seen classes should be known beforehand, while incremental learning models cannot recognize unseen classes. This paper introduces a novel and challenging task of Incrementally Zero-Shot Detection (IZSD), a practical strategy for both zero-shot learning and class-incremental learning in real-world object detection. An innovative end-to-end model - IZSD-EVer was proposed to tackle this task that requires incrementally detecting new classes and detecting the classes that have never been seen. Specifically, we propose a novel extreme value analyzer to detect objects from old seen, new seen, and unseen classes, simultaneously. Additionally and technically, we propose two innovative losses, i.e., background-foreground mean squared error loss alleviating the extreme imbalance of the background and foreground of images, and projection distance loss aligning the visual space and semantic spaces of old seen classes. Experiments demonstrate the efficacy of our model in detecting objects from both the seen and unseen classes, outperforming the alternative models on Pascal VOC and MSCOCO datasets.
Research on Mechanical Design and Manufacture Based on Multi-Agent and Its Automation Application

With the rapid development of science and technology in China, the design and manufacture of machinery and its automation have gradually penetrated into our country. Nowadays, this mechanical technology has been accepted by many industries, and more and more mechanical design enterprises are gradually developing towards automation. The realization of mechanical automation means that we can save a lot of manpower, and the industrial output will increase. People need to pay attention to rest while machines don't use it, so the realization of mechanical automation has always been our goal. Compared with traditional mechanical equipment, automatic mechanical equipment has the advantages of simple operation and low dependence on human resources, which is of great significance to the promotion of economic benefits of various production enterprises in society, and further promotes the continuous acceleration of social and economic development. This paper mainly analyzes the actual situation of design, manufacture and automation of multi-agent machinery, explores its existing problems, and puts forward some corresponding solutions, hoping to promote the development of science and technology and improve China's comprehensive national strength.
Overview of Energy Saving Technologies for Wireless Sensor Networks in the Internet of Things

The energy saving technology of wireless sensor networks in the Internet of Things (IoT) is to extend the life cycle of the network and solve the problem of network energy consumption optimization. In recent years, with the rapid development of IoT technology, wireless sensor network energy saving technology has also received extensive attention and research. Based on the development status of network energy saving technology, this article first describes the wireless sensor network model under the framework of the IoT. Secondly, it focuses on the five common algorithms such as ant colony algorithm, bee colony algorithm, low energy adaptive clustering hierarchy (LEACH) algorithm, fruit fly optimization algorithm, genetic algorithm, etc., and elaborates on each algorithm in detail, and further elaborates various algorithm improvements and strategies. In the end, it summarized the direction that the IoT energy saving technology needs to be improved and developed in the future.
Standardization of Big Data and Its Policies

Big data is defined as massive sets consisting of a broader variety of data, further complicated as well as dynamic structure with challenges in collecting, storing, examining, and then applying additional procedures or extracting results then visualizing further the outcomes. The phrase big data analytics is utilized to delineate the aforementioned method of studying vast volumes of complex data to discover hidden trends or to find secret associations. There is, nevertheless, a strong inconsistency seen between privacy, security, and the widely accepted use of big data. This article deals with the use of privacy by adapting established techniques, like k-anonymity, HybrEx, T-closeness, and L-diversity, and introducing them in trade and commerce. A variety of privacy-preservation frameworks are being geared toward the preservation of solitude at various levels (such as production, storage, and processing of data) of the big data lifetime. This paper aims to include a detailed summary of frameworks for protecting the privacy and also to address some barriers to current frameworks. This paper also covers different policies related to big data standards. At least, a brief review of the Indian Personal Data Protection Bill is done.
WeightAlign: Normalizing Activations by Weight Alignment

Batch normalization (BN) allows training very deep networks by normalizing activations by mini-batch sample statistics which renders BN unstable for small batch sizes. Current small-batch solutions such as Instance Norm, Layer Norm, and Group Norm use channel statistics which can be computed even for a single sample. Such methods are less stable than BN as they critically depend on the statistics of a single input sample. To address this problem, we propose a normalization of activation without sample statistics. We present WeightAlign: a method that normalizes the weights by the mean and scaled standard derivation computed within a filter, which normalizes activations without computing any sample statistics. Our proposed method is independent of batch size and stable over a wide range of batch sizes. Because weight statistics are orthogonal to sample statistics, we can directly combine WeightAlign with any method for activation normalization. We experimentally demonstrate these benefits for classification on CIFAR-10, CIFAR-100, ImageNet, for semantic segmentation on PASCAL VOC 2012 and for domain adaptation on Office-31.
The Covariance Matrix Evolution Strategy Algorithm Based On Cloud Model And Cholesky Factor

The covariance matrix adaptive evolution strategy (CMA-ES) is a random search evolution strategy with superior performance and high accuracy. However, when faced with multimodal complex functions, it also has the shortcomings of converging too fast and easily falling into local optimization. Matrix operations in high dimensions also greatly reduce the performance of the algorithm. This paper proposes an improved CMA-ES algorithm based on the cloud model and Cholesky factor update. The cloud model has a good ability to deal with uncertain problems, and the step size is controlled by cloud reasoning, which can better avoid falling into problems such as local optimization and premature convergence. At the same time, the Cholesky factor greatly reduces the computational cost of the algorithm by effectively updating the covariance, especially in high dimensions. Through multiple function tests, multiple experimental verifications and compared with CMA-ES and its Cholesky variant algorithm, the algorithm has the advantages of higher efficiency and more accurate convergence.
Dynamic Multi-path Neural Network

Although deeper and larger neural networks have achieved better performance, due to overwhelming burden on computation, they cannot meet the demands of deployment on resource-limited devices. An effective strategy to address this problem is to make use of dynamic inference mechanism, which changes the inference path for different samples at runtime. Existing methods only reduce the depth by skipping an entire specific layer, which may lose important information in this layer. In this paper, we propose a novel method called Dynamic Multipath Neural Network (DMNN), which provides more topology choices in terms of both width and depth on the fly. For better modelling the inference path selection, we further introduce previous state and object category information to guide the training process. Compared to previous dynamic inference techniques, the proposed method is more flexible and easier to incorporate into most modern network architectures. Experimental results on ImageNet and CIFAR-100 demonstrate the superiority of our method on both efficiency and classification accuracy.
The types of library websites

The websites can be classified by various characteristics. Classifying is essential for drawing up requirement specification presented to webmaster and achieving contractor - customer understanding on the resulting website. Every website type has its unique specific structure. Based on the study of the existing Internet sites of Russian libraries of different types and affiliation, the author attempts to identify their key features. The classifications by implementation technology, affiliation, purpose and target age, or by resource content type and status are discussed. The classification proposed in the article is based on a speculative study of national and foreign experience and the author's suggestions on how the libraries’ traditional management, bibliographic, and methodological activities can be transformed on the Web. Other classifications are proposed: by user group age, content type, or status.
Semantic Enrichment for Non-factoid Question Answering

Non-factoid question answering system answers questions which are not fact-based and difficult to answer. The paper discusses different taxonomies of semantic relations that are utilized for answering different types of non-factoid questions. Various adverbial clauses are explored for why-type and how-type questions. Different challenges for identifying semantic relations with their approaches to address them are also discussed. Further, the paper briefs researches that used semantic relations in modules of QAS with some future directions.
Technoscience Academy Crossmark Policy Statement

In today's world there are millions of diseases with various symptoms foreach, no human can possibly know about all of these diseases and the treatmentsassociated with them. So, the problem is that there isn’t any place where anyone can have the details of the diseases or the medicines/treatments. What if there is a placewhere you can find your health problem just by entering symptoms or the currentcondition of the person. It will help us to deduce the problem and to verify thesolution. The proposed idea is to create a system with artificial intelligence that canmeet these requirements. The AI can classify the diseases based on the symptomsand give the list of available treatments. The System is a text-to-text diagnosis chatbot that will engage patients in conversation with their medical issues and provides apersonalized diagnosis based on their symptoms and profile. Hence the people canhave an idea about their health and can take the right action.
Blood Vessel Structure Analysis in Endoscopic Images for Computer-Aided Diagnosis

The number of people with colorectal cancer is increasing, and the establishment of a computer-aided diagnosis support system using endoscopic images is desired in medical practice. It is desired to realize computer-aided diagnosis for automated classification of benign/malignant polyps from the endoscope image. For cancer to grow, it is necessary to grow blood vessels, which serve as a supply path for supplying oxygen and nutrients to cancer cells so that accurate blood vessel structure analysis has potential importance. In this paper, by using the latent geometric information of blood vessels, a novel blood vessel analysis method is proposed for aiming to realize the computer-aided diagnosis of endoscopy.
Transferable Adversarial Attacks for Deep Scene Text Detection

Scene text detection (STD) aims to locate text in images and plays an important role in many computer vision tasks including automatic driving and text recognition systems. Recently, deep neural networks (DNNs) have been widely and successfully used in scene text detection, leading to plenty of DNN-based STD methods including regression-based and segmentation-based STD methods. However, recent studies have also shown that DNN is vulnerable to adversarial attacks, which can significantly degrade the performance of DNN models. In this paper, we investigate the robustness of DNN-based STD methods against adversarial attacks. To this end, we propose a generic and efficient attack method to generate adversarial examples, which are produced by adding small but imperceptible adversarial perturbation to the input images. Experiments on attacking four various models and a real-world STD engine of Google optical character recognition (OCR) show that the state-of-the-art DNN-based STD methods including regression-based and segmentation-based methods are vulnerable to adversarial attacks.
Collaborative Global Impact Cloud Computing Risk Assessment Framework

Currently, risk assessment is more of an art than a science. In this paper, we demonstrate how a previously proposed theoretical risk assessment framework that focuses on making risk assessment more scientific can be implemented and applied to cloud networks. Our work offers a practical risk assessment implementation for cloud networks where disparate network owners can directly measure network risks in an objective, uniform and repeatable manner across networks by allowing the network owners to collaboratively agree on risk metrics and continuously monitor their cloud networks with the same tool, which employs these agreed upon metrics. The end result is a science-based risk metric (SBRM) based on the global impact of the vulnerabilities in the network. Specifically, we demonstrate the feasibility of implementing this SBRM by: (1) imposing real vulnerabilities on realistic cloud networks built from real hardware (multi-core 3 Ghz Xeon Deterlab servers), (2) demonstrating the operational use of our risk assessment implementation to measure risk across several emulated cloud networks, and (3) describing our approach.
Research on Human Behavior Recognition Based on Video Key Frame

In order to solve the problems of low recognition accuracy and high computational complexity caused by redundant video data in the existing behavior recognition process, a human behavior recognition method based on video key frame (S3DCCA) is proposed. First of all, structural similarity (SSIM) algorithm is used to calculate the difference of luminance, contrast and structure between the two frames, and the result is multiplied to attain SSIM value, then select the local and global key frame in the human motion video frame sequence according to the SSIM value. Finally, the selected key frame are used as the input of three-dimensional convolutional neural networks and attention mechanism Channel attention (3DCCA) model to recognize human behavior. Experimental results on UCF101 and HMDB51 datasets show that the proposed method has high recognition rate.
Finding Agreements: Study and Evaluation of Heuristic Approaches to Multilateral Negotiation

Negotiation is a process by which participants get an agreement that satisfies them all. Multilateral negotiation involves more than two agents. The complexity of negotiation intensifies when involving multiple agents with conflicting interests that need to reach a joint agreement. To model negotiation, we have three axes: object of negotiation, negotiation protocol, and negotiation model. There are Several negotiating approaches are available to study negotiation model. The heuristic is the most commonly used in multilateral negotiations, because it realistically simulates negotiating problems. Negotiation is made more difficult when there are multiple agents who need to reach an agreement on differing interests. An important facet of any successful negotiation is to lay out a clear and specific protocol guiding the interactions between the agents. In our research study, the negotiation protocol is based on the divide and conquer concept. In other words, agents are divided into several groups where they debate without a mediator. This article aims to evaluate and study two heuristic approaches. In the first approach, the agents negotiate on a single attribute; we will compare this approach to the case where the agents negotiate in a single group according to the results obtained. In the second approach, the agents negotiate on several attributes. This approach is compared with the first approach to determinate in which case of the second approach it is better to use the first approach to facilitate the search for agreement.