Reviewerly/exHarmony huggingface dataset:
An enhanced particle swarm optimization with distribution fields appearance model for object tracking

This paper presents an improved Particle Swarm Optimization tracker, representing target with Distribution Fields (DFs) to solve problems of how to effectively represent the appearance model and adapt to the change of environment. First, Categorized Particle Swarm Optimization (CPSO) makes good use of identifying selected object model by integrating based on the performance of categorized particles and it achieves an efficient algorithm in object tracking. Then, compared with the traditional target representation such as color, gradient, filter responses, etc. DFs build image descriptors that are able to make object smoothing and keep pixel values. To provide a better tracking result in different videos, this paper finds a more efficient way by using an appearance model which adopts the DFs layer with CPSO to build tracking procedure. The experiments show higher performances than another four state-of-the-art approaches on several sequences videos.
A conjecture of Cameron and Kiyota on sharp characters with prescribed values

Let $ \chi $ be a virtual (generalized) character of a finite group $ G $ and $ L=L(\chi)$ be the image of $ \chi $ on $ G-\lbrace 1 \rbrace $. The pair $ (G, \chi) $ is said to be sharp of type $ L $ if $|G|=\prod _{ l \in L} (\chi(1) - l) $. If the principal character of $G$ is not an irreducible constituent of $\chi$, the pair $(G,\chi)$ is called normalized. In this paper, we first provide some counterexamples to a conjecture that was proposed by Cameron and Kiyota in $1988$. This conjecture states that if $(G,\chi)$ is sharp and $|L|\geq 2$, then the inner product $(\chi,\chi)_G$ is uniquely determined by $ L $. We then prove that this conjecture is true in the case that $(G,\chi) $ is normalized, $\chi$ is a character of $ G $, and $ L $ contains at least an irrational value.
Metalearning Applied to Multi-label Text Classification

Data Mining and Machine Learning fields have many techniques that can support data analysts in the text classification task. However, finding the most adequate techniques require advanced technical knowledge, exhaustive computational experiments and, consequently, time. To address this issue, researchers have proposed different approaches for selecting such techniques to be employed in classification tasks and the dynamic selection of classifiers is one of them. Therefore, this work proposes an approach that uses metalearning to automate the process of selecting the best classifier for each instance of a given multi-label textual dataset. Experiments were performed with multi-label text datasets and showed that the proposed approach is promising.
APGS: An Efficient Source-Accountable and Metadata-Private Protocol in the Network Layer

Due to the revelations of global-scale pervasive surveillance programs, Internet users have an increasing demand for privacy. However, this is usually undesirable for network service providers because attackers would be able to anonymize themselves and avoid regulation while conducting network attacks. Therefore, network service providers want to hold users accountable and it has been widely considered as a tussle to find a good balance point between the accountability and privacy for the Internet. In this work, we first show that existing representative approaches mainly suffer from narrow-range accountability, low efficiency or risky key management. Motivated by these observations, we propose an efficient network layer protocol called APGS to balance the accountability and privacy. At the core of our APGS is the group signature which, however, is not trivial to apply for the network layer mainly due to the efficiency, revocation, and privacy issues. We manage to overcome these challenges via proposing some novel approaches, including challenge-based cache strategy, scalable verifier-local revocation strategy, and Onion-then-Case strategy. We then evaluate the efficiency of APGS and conclude that in our environment, APGS can generate packets up to 20k pkts/s on a desktop and achieve approximately 80% of IP's goodput at most on a software router.
Introduction to Computer Vision and Realtime Deep Learning-based Object Detection

The main focus of object detection, one of the most challenging problems in computer vision (CV), is to predict a set of bounding boxes and category labels for each object of interest in an image or in a point cloud. As such, object detection has a variety of exciting downstream applications such as self-driving cars, checkout-less shopping, smart cities, cancer detection, and more. This field has been revolutionized by deep learning over the past five years, where during this time, two-stage approaches to object detection have given way to simpler, more efficient, one-stage models. Mean average precision (mAP) on benchmark problems such as the COCO Object Detection dataset has improved almost 4X over the course of five years from 15% (Fast RCNN, a two-stage approach) to 55% (EfficientDet7x, a one-stage approach). This tutorial looks under the hood of state-of-the-art object detection systems, such as two-stage, one-stage, and also more recent approaches based upon transformers. It builds out some of their associated detection pipelines in a Jupyter Notebook using Python, OpenCV, PyTorch, Keras and Tensorflow. While the primary focus is on object detection in digital images from cameras and videos, this tutorial will also introduce object detection in 3D point clouds.
Effectiveness of Mobile Assisted Language Learning towards Students’ Achievement and Motivation in Learning English Prepositions

Mobile Assisted Language Learning or MALL is a new approach of language learning that deals with the use of mobile devices. Mobile devices such as smartphones, PDAs, computer tablets are the example of devices that provide easy access for every learner without the constraints of both place and time. The purpose of this study is to examine the effects of MALL towards students' achievement and motivation in learning English prepositions. Initially, the researcher identifies the readiness and motivation level of students in using mobile applications in learning English prepositions. A quantitative research study was conducted to 30 respondents from a Chinese vernacular primary school in the southern region of Peninsular Malaysia. The data analysis involved performance tests (pre and post-test) and also a questionnaire. Data was analyzed using paired sample t-test and descriptive analysis. Results from this study revealed that there is a significant difference on students' achievement level in the post-test (t=11.833, df =29, p<; 0.05). Moreover, student's motivation gradually improved as a result of this intervention. Thus, this study suggests that the use of mobile applications, the Mobile Assisted Language Learning (MALL) is important to improve motivation and enhance students' performance in English grammar, particularly in learning English prepositions.
Optimal Bipolar Lead Placement in Electrooculography (EOG): A Comparative Study with an Emphasis on Prolonged Blinks

Electrooculography (EOG) is the measurement of potentials generated by the ocular muscle family during the exhibition of various eye movements. Such potentials initially sensed using electrodes placed at specific positions around the eyes, are conditioned for detection and analysis of these movements. However, the characteristics of EOG signals obtained substantially depend on the electrode placement. In this paper, a 3-electrode EOG setup was used to compare among 30 lead configurations enveloping different ocular muscles by studying their idiosyncrasies. For each combination, signals were acquired for a fixed set of eye movements executed by the same subject; including prolonged-blinks, a characteristic feature of drowsiness. A summary of the leads that would be favorable for the detection of each movement is then presented. Furthermore, such readings were recorded separately for different reference electrode positions to compendiously determine the optimal electrode placement for the detection of EOG signals corresponding to drowsiness.
Towards a Visual Grammar for IoT Systems Representation and their Cybersecurity Requirements

In this paper we present progress towards visual iconography (elements) and a grammar for Internet of Things (IoT) system representations and their cybersecurity requirements. Our visual representation of IoT systems aims to facilitate the identification of the IoT attack surface and the vulnerabilities that an attacker may exploit. The paper first outlines the basic visual elements and the associated grammar, which are then applied to a series of smart home IoT use cases to demonstrate how these can be used to represent these networks and their cybersecurity requirements in a visual and intuitive way. The motivation behind this work is to improve our ability to reason about IoT attack surfaces towards improving our defense capabilities for those systems.
How Can Modifications of Meaning Influence Argumentation? The Concept and Typology of Semantic Arguments

Abstract The aim of this article is to show how modifications of meaning can influence argumentation. I present the basic concept of so-called ‘semantic argumentation,’ its definition, and its different variants. I analyse the various kinds of argument in which meanings of terms are modified in support of a persuasive goal. The analysis of different semantic arguments reveals certain structures and patterns that are needed to construct a typology of such arguments. I thus outline a basic concept of argumentation based on modifications of meaning, before presenting various examples of such arguments together with an analysis of their structures, and then, finally, constructing a typology for them.
Exploiting Category Similarity-Based Distributed Labeling for Fine-Grained Visual Classification

The fine-grained visual classification (FGVC) which aims to distinguish subtle differences among subcategories is an important computer vision task.However, one issue that limits model performance is the problem of diversity within subcategories.To this end, we propose a simple yet effective approach named category similarity-based distributed labeling (CSDL) to tackle this problem.Specifically, we first obtain the feature centers for various subcategories and utilize them to initialize the label distributions.Then we replace the ground-truth labels in a Deep Neural Network (DNN) with the distributed labels to calculate the loss and perform the optimization.Finally, the joint supervision of a softmax loss and a center loss is adopted to update the parameters of the DNN, the deep feature centers, and the distributed labels for learning discriminative deep features.Comprehensive experiments on three publicly available FGVC datasets demonstrate the superiority of our proposed approach.
A Digital Modulation Recognition Method Based on Uniform Linear Array

Classical digital modulation recognition methods will suffer from performance deterioration when SNR decreases, a method based on Uniform Linear Array (ULA) was proposed to solve above problem in this paper. The SNR of received signal will be increased by spatial filtering using ULA, and then the performance of modulation recognition at low SNR will be improved. The proposed modulation recognition method employes ULA as signal receiver, uses high-order cumulants as classification features, and finally Back-propagation Neural Network (BPNN) is used for digital modulation classification. Simulation results showed that the proposed method can recognize typical digital modulations including 2ASK, 4ASK, 4FSK and 4PSK efficiently with satisfactory recognition rate at low SNR. And meanwhile influence factors including number of array elements, structure of neural network and data scale of training and testing were studied and discussed.
Observing the evolution of fatigue damage and associated strain fields in a correlative, multiscale 3D time-lapse study of quasi-unidirectional glass fibre composites

Abstract This research is focused on studying the tension-tension fatigue behaviour of a unidirectional (UD) glass-fibre wind turbine composite. The damage features, their progression and the associated strain fields are tracked in a representative volume by employing a novel correlative approach bringing together x-ray computed tomography (XCT) and digital image correlation (DIC). The focus is on studying ex situ the evolution of damage features (fibre breaks and micro cracks) in an interrupted time-lapse manner. The major drops in stiffness are correlated to the number and location of the damage features in the bulk (XCT) and at the surface (DIC). Results from XCT highlight a localized cluster of fibre breaks and matrix cracks near backing bundles along with axial macro-cracks, while DIC shows that the backing bundles cause regions of higher strain. This highlights the relation between the damage features and strain localisation and their effect on the progressive degradation in stiffness during high cycle fatigue (HCF) cycling.
Simple Data Augmented Transformer End-To-End Tibetan Speech Recognition

The Tibetan language belongs to the Tibetan branch of the Sino-Tibetan language family, and which is the common language of the Tibetan people in China, mainly containing the Ando, U-Tsang and Kham dialects. The Tibetan language has its own phonetic system, grammatical structure, rich vocabulary, and perfect expressive ability. This paper builds a corpus of U-Tsang Tibetan and designs corresponding textual annotations. Based on this corpus, a Transformer network-based method for Tibetan speech recognition is proposed and a Transformer end-to-end speech recognition model for Tibetan is trained. We compared the Transformer-based approach to Tibetan speech recognition with the convolutional neural network (CNN)-based model and introduced a data augmentation algorithm SpecAugment in training. The results show that under the use of 36000 Tibetan utterances corpora with a total length of 53h, transformer model and CNN model respectively obtain 29.3% and 32.6% of WER. After using the data augmentation algorithm SpecAugment, we got 25.8% and 28.1% in Transformer model and CNN model respectively.
Parallel Algorithm of Flow Data Anomaly Detection Based on Isolated Forest

The isolated forest algorithm is improved and applied to the hydrological field. The parallel anomaly detection algorithm (Flink-iForest) is proposed. At the same time, the k-means algorithm is combined to solve the problem of Flink-iForest threshold division and improve the stability of anomaly detection results. Through various experiments and real hydrological data, first of all, the Flink-iForest algorithm is verified in terms of accuracy, efficiency and scalability, and compared with the standard SKlearn-iForest and PIFH algorithm; finally, the effectiveness and efficiency of Flink-iForest algorithm are proved by experiments.
Deep Surrogate Q-Learning for Autonomous Driving

Open challenges for deep reinforcement learning systems are their adaptivity to changing environments and their efficiency w.r.t. computational resources and data. In the application of learning lane-change behavior for autonomous driving, the number of required transitions imposes a bottleneck, since test drivers cannot perform an arbitrary amount of lane changes in the real world. In the off-policy setting, additional information on solving the task can be gained by observing actions from others. While in the classical RL setup this knowledge remains unused, we use other drivers as surrogates to learn the agent's value function more efficiently. We propose Surrogate Q-learning that deals with the aforementioned problems and reduces the required driving time drastically. We further propose an efficient implementation based on a permutation equivariant deep neural network architecture of the Q-function to estimate action-values for a variable number of vehicles in sensor range. We evaluate our method in the open traffic simulator SUMO and learn well performing driving policies on the real highD dataset.
DOING WITHOUT ACTION TYPES

Abstract This paper explores the analysis of ability, where ability is to be understood in the epistemic sense—in contrast to what might be called a causal sense. There are plenty of cases where an agent is able to perform an action that guarantees a given result even though she does not know which of her actions guarantees that result. Such an agent possesses the causal ability but lacks the epistemic ability. The standard analysis of such epistemic abilities relies on the notion of action types—as opposed to action tokens—and then posits that an agent has the epistemic ability to do something if and only if there is an action type available to her that she knows guarantees it. We show that these action types are not needed: we present a formalism without action types that can simulate analyzes of epistemic ability that rely on action types. Our formalism is a standard epistemic extension of the theory of “seeing to it that”, which arose from a modal tradition in the logic of action.
Autonomous Vehicle Simulation Using Deep Reinforcement Learning

The reinforcement learning algorithms have been proven to be extremely accurate in performing a variety of tasks. These algorithms have outperformed humans in traditional games. This paper proposes a reinforcement learning based approach to autonomous driving. The autonomous vehicles must be able to deal with all external situations to ensure safety and to avoid undesired circumstances such as collisions. Thus, we propose the use of deep deterministic policy gradient (DDPG) algorithm which is able to work in a complex and continuous domain. To avoid physical damage and reduce costs, we choose to use a simulator to test the proposed approach. The CARLA simulator would be used as the environment. To fit the DDPG algorithm to the CARLA environment, our network architecture consists of critic and actor networks. The performance would be evaluated based on rewards generated by the agent while driving in the simulated environment.
A Study of Workpiece Targeting Based on Improved Blob Analysis

In order to meet the accurate positioning of the target workpiece by the industrial robot and obtain the workpiece coordinate information. In this paper, taking a circular workpiece as an example, a targeting study based on the Blob analysis algorithm is presented. Image threshold segmentation, connectivity analysis and other methods are used to complete the positioning of the workpiece target. To further improve the target localization accuracy, edge detection operators and curve fitting methods are introduced to improve the original Blob analysis algorithm. The experimental results show that this method can obtain the coordinate information of the target workpiece more accurately, and the accuracy can meet the robotic grasping needs of the industrial field.
Ubiquitous Power Internet of Things Architecture Construction Plan

In order to effectively realize the construction of Ubiquitous Power Internet of Things, a communication technology architecture plan for the construction of sensing layer and the network layer is proposed, the idea of optimizing energy allocation is also proposed in the plan. The aim of the plan is to solve the data sensing and interaction problems of household electrical appliances, distribution network equipment, factories and so on, the plan also attributes to solving the load data transmission and interaction problems. The idea of intelligent power consumption is also put forward in the article.
An efficient mining algorithm for maximal frequent patterns in uncertain graph database

Mining maximal frequent patterns is significant in many fields, but the mining efficiency is often low. The bottleneck lies in too many candidate subgraphs and extensive subgraph isomorphism tests. In this paper we propose an efficient mining algorithm. There are two key ideas behind the proposed methods. The first is to divide each edge of every certain graph (converted from equivalent uncertain graph) and build search tree, avoiding too many candidate subgraphs. The second is to search the tree built in the first step in order, avoiding extensive subgraph isomorphism tests. The evaluation of our approach demonstrates the significant cost savings with respect to the state-of-the-art approach not only on the real-world datasets as well as on synthetic uncertain graph databases.
Design and Implementation of Sliding Window with Circular Linked List for Dynamic Data Flow

Sliding Windows can transmit frames as well as byte data. Dynamic data flow has the characteristics of magnanimity, timeliness and continuity, among which data and knowledge will change with the passage of time, the application of sliding window can effectively limit the operation of dynamic data flow. The traditional vector sliding window has high cost and low efficiency in processing dynamic data flow. In this paper, the cyclic linked list is introduced into the sliding window design, and the location information of each child window is stored in the linked list. When the sliding window receives the dynamic data flow, the newly added data is directly inserted into the child window, which avoids the disadvantage that the sliding window needs to move the data in the window frequently when processing the dynamic data flow. Meanwhile, design clustering algorithm: Dclustream. Through efficiency analysis and experimental verification, the method proposed in this paper can effectively reduce the calculation cost of sliding window, obtain better clustering effect and improve the processing efficiency of dynamic data flow. Also, it can save memory.
Construction of public safety knowledge graphs

The public safety knowledge graph combines piecemeal data into systematic information. It shows all the aspects of the events. This paper builds public knowledge graphs on 1000 triples to show the area, nation, location, resource, begin date, end date, description and casualties of all the emergency events. With the assistance of the public safety knowledge graphs, the problem of public safety can be alleviated to some extent.
Automatic Contextual Storytelling in a Natural Language Corpus

Storytelling is an ancient art and science of conveying wisdom through generations for centuries. Data-driven storytelling in the context of a natural language corpus has a huge potential for conveying fast valuable insights about the corpus for better decision making. But high dimensional unstructured nature of natural language text makes automatic extraction of stories extremely difficult. This PhD research project believes that modern storytelling is a hand in hand approach of contextual topic visualization and contextual summarization. While exploratory data visualization can provide valuable insights into the data, these insights can be used to understand and design models for producing abstract summarization. In this project, the context of a story is defined from three perspectives: a single document, a collection of multiple documents about a topic of interest and the whole corpus. In this project, exploratory data visualization is used to understand the context better and now with the achieved insights, research is focusing on abstract summarization for automatic contextual storytelling.