Reviewerly/exHarmony huggingface dataset:
Substation Personnel Safety Detection Network Based on YOLOv4

With the development of more and more substations towards the direction of unattended, the substation in the field of personnel safety protection is becoming more and more automatic and intelligent. A video surveillance system is used to monitor the daily operating conditions of the substation. The application of more efficient video image analysis technology and detection technology to the monitoring system can effectively protect the substation staff in manual safety inspection. By more quickly detecting the movement track and position of personnel in the station, it is particularly important to give safety warnings to personnel who have entered the dangerous work area by mistake. Based on whether the substation personnel wear safety helmets as a benchmark, the article detects the wearing of the safety helmets of those entering high-risk areas to effectively distinguish between workers and non-workers. And through Yolo4's detection technology, managers can clearly monitor the situation in the dangerous area, realize the effect of predicting the occurrence of danger in advance and minimizing the danger. Therefore, in response to the above problems, this paper simplified the feature fusion part based on Yolov4 and achieved the target detection effect with a more lightweight feature extraction backbone network. The improvement of the above method has effectively improved the detection speed of the network and has strong robustness.
LDA-Reg: Knowledge Driven Regularization Using External Corpora

While recent developments of neural network (NN) models have led to a series of record-breaking achievements in many applications, the lack of sufficiently good datasets remains a problem for some applications. For such a problem, we can however exploit a large number of unstructured text corpora as an external knowledge to complement the training data, and most prevailing neural network solutions employ word embedding methods for such purposes. In this paper, we propose LDA-Reg, a novel knowledge driven regularization framework based on Latent Dirichlet Allocation (LDA) as an alternative to the word embedding methods to adaptively utilize abundant external knowledge and to interpret the NN model. For the joint learning of the parameters, we propose EM-SGD, an effective update method which incorporates Expectation Maximization (EM) and Stochastic Gradient Descent (SGD) to update parameters iteratively. Moreover, we also devise a lazy update and sparse update method for the high-dimensional inputs and sparse inputs respectively. We validate the effectiveness of our regularization framework through an extensive experimental study over real world and standard benchmark datasets. The results show that our proposed framework not only achieves significant improvement over state-of-the-art word embedding methods but also learns interpretable and significant topics for various tasks.
High-Security Sequence Design for Differential Frequency Hopping Systems

Differential frequency hopping (DFH) technique is widely used in wireless communications by exploiting its capabilities of mitigating tracking interference and confidentiality.However, electronic attacks in wireless systems become more and more rigorous, which imposes a lot of challenges on the DFH sequences designed based on the linear congruence theory, fuzzy and chaotic theory, etc.In this paper, we investigate the sequence design in DFH systems by exploiting the equivalence principle between the G-function algorithm and the encryption algorithm, in order to achieve high security.In more details, first, the novel G-function is proposed with the aid of the Government Standard (GOST) algorithm and the Rivest-Shamir-Adleman (RSA) algorithm.Then, two sequence design algorithms are proposed, namely, the G-function assisted sequence generation (GF-SG) algorithm, which takes the full advantages of the symmetric and asymmetric encryption algorithms, and the high order Gfunction aided sequence generation (HGF-SG) algorithm, which is capable of enhancing the correlation of the elements in a DFH sequence.Moreover, the security and ergodicity performance of the proposed algorithms are analyzed.Our studies and results show that the DFH sequences generated by the proposed algorithms significantly outperform the sequences generated by the reversible hash algorithm and affine transformation in terms of the uniformity, randomness, complexity and the security.
Partial Modal Conditioned GANs for Multi-modal Multi-label Learning with Arbitrary Modal-Missing

Multi-modal multi-label (MMML) learning serves an important framework to learn from objects with multiple representations and annotations. Previous MMML approaches assume that all instances are with complete modalities, which usually does not hold for real-world MMML data. Meanwhile, most existing works focus on data generation using GAN, while few of them explore the downstream tasks, such as multi-modal multi-label learning. The major challenge is how to jointly model complex modal correlation and label correlation in a mutually beneficial way, especially under the arbitrary modal-missing pattern. Aim at addressing the aforementioned research challenges, we propose a novel framework named Partial Modal Conditioned Generative Adversarial Networks (PMC-GANs) for MMML learning with arbitrary modal-missing. The proposed model contains a modal completion part and a multi-modal multi-label learning part. Firstly, in order to strike a balance between consistency and complementary across different modalities, PMC-GANs incorporates all available modalities during training and generates high-quality missing modality in an efficient way. After that, PMC-GANs exploits label correlation by leveraging shared information from all modalities and specific information of each individual modality. Empirical studies on 3 MMML datasets clearly show the superior performance of PMC-GANs against other state-of-the-art approaches.
A Politeness Control Method for Conversational Agents Considering Social Relationships with Users

In this paper, we propose an autonomous linguistic consideration (politeness) control method for conversational agents. As an approach to realize the proposed method, we examined input information based on politeness theory in sociolinguistics that is considered to be effective for agents to control politeness. Specifically, we adapted a questionnaire study to evaluate the politeness and comfortableness of utterances in different situations of requests. The participants evaluated stimulus utterances in terms of three social factors on the politeness theory: social distance/social status difference/conversation scene. Based on the dataset collected in this questionnaire study, a regression model was constructed to predict the politeness and comfortableness rated by the participants of the experiment, adapted three social factors and politeness of utterances as explanatory variables. As a result of this questionnaire study and multiple regression analysis, a valid model of politeness was obtained. The results contribute as a model to select appropriate politeness of utterance according to three social factors on the politeness theory with the user in the proposed method.
Heisenberg-limited quantum interferometry with multiphoton-subtracted twin beams

We propose a Heisenberg-limited quantum interferometer whose input is twin optical beams from which one or more photons have been indistinguishably subtracted. Such an interferometer can yield Heisenberg-limited performance while at the same time giving a direct fringe reading, unlike for the twin-beam input of the Holland-Burnett interferometer. We propose a feasible experimental realization using a photon-number correlated source, such as nondegenerate parametric down-conversion, and perform realistic analyses of performance in the presence of loss and detector inefficiency.
Privacy-Preserving Publication of Time-Series Data in Smart Grid

The collection of fine-grained consumptions of users in the smart grid enables energy suppliers and grid operators to propose new services (e.g., consumption forecasts and demand-response protocols) allowing to improve the efficiency and reliability of the grid. These services require the knowledge of aggregate consumption of users. However, an aggregate can be vulnerable to reidentification attacks which allow revealing the users’ individual consumption. Revealing an aggregate data is a key privacy concern. This paper focuses on publishing an aggregate of time-series data such as fine-grained consumptions, without indirectly disclosing individual consumptions. We propose novel algorithms which guarantee differential privacy, based on the discrete Fourier transform and the discrete wavelet transform. Experimental results using real data from the Irish Commission for Regulation of Utilities (CRU) demonstrate that our algorithms achieve better utility than previously proposed algorithms.
Reference frame independent twin field quantum key distribution with source flaws

Abstract The trade-off between distance and secret key generation rate remains one of the major challenges in the practical implementation of quantum key distribution (QKD). As a solution, a twin field QKD protocol was proposed by Lucamarini et al (2018) to address this challenge. In this protocol, the achievable secret key rate scales with the square root of channel transmittance and can surpass the secret key capacity for repeaterless QKD. However, the protocol exploits phase to encode information which presents the problem of active stabilization of interferometers. We propose a reference frame independent twin field quantum key distribution (RFITF QKD), which does not require the reference frames’ alignment. Thus, this reduces the complexity of practical QKD systems in achieving active stabilization of phase. Moreover, we employ the loss-tolerant method proposed by Tamaki et al (2014) which allows us to prove the security of the protocol by considering imperfections in the state preparation. Our simulation results show that our proposed protocol can extract a secure key over a transmission distance of l = 505 km, l = 516 km and l = 530 km for deviation of 8.42°, 7.28° and 5.15°, respectively from the desired phase encoding angle. These results demonstrate that despite the state preparation flaws, the key rates achieved are still comparable to the perfect encoding scenario. When our proposed protocol is implemented with an imperfect source, it achieves a transmission distance beyond the secret key capacity bound for repeaterless QKD.
Leverage External Knowledge and Self-attention for Chinese Semantic Dependency Graph Parsing

Chinese semantic dependency graph (CSDG) parsing aims to analyze the semantic relationship between words in a sentence. Since it is a deep semantic analysis task, the parser needs a lot of prior knowledge about the real world to distinguish different semantic roles and determine the range of the head nodes of each word. Existing CSDG parsers usually use part-of-speech (POS) and lexical features, which can only provide linguistic knowledge, but not semantic knowledge about the word. To solve this problem, we propose an entity recognition method based on distant supervision and entity classification to recognize entities in sentences, and then we integrate the category information of entities as external knowledge feature into our CSDG parser. Furthermore, there are many long sentences in some domains, which makes it difficult for the parser to deal with long-distance dependence. In this paper, we combine self-attention mechanism with Bi-LSTM, which significantly improved the performance of the parser on long texts. We also adopt Bert model to generate more powerful sentence representation and alleviate the problem of unknown words. Experiment results show that both external knowledge and self-attention are beneficial for improving the accuracy of CSDG parser and our parser achieves state-of-the-art performance in the datasets of SemEval-2016 Task 9: Chinese Semantic Dependency Parsing.
Understanding Geologic Uncertainty in Mining Studies

Editor’s note: The Geology and Mining series, edited by Dan Wood and Jeffrey Hedenquist, is designed to introduce early-career professionals and students to a variety of topics in mineral exploration, development, and mining, in order to provide insight into the many ways in which geoscientists contribute to the mineral industry. Abstract The role of geology in advanced mining studies, such as feasibility studies, is commonly dwarfed by the technical inputs from mining, metallurgical, and social license issues. Understanding and planning for geologic risk in the feasibility process is often overlooked for the higher-profile aspects required to establish an ore reserve. If the geologic model of a deposit cannot be reliably forecast, then there will be lower confidence in many of the modifying factors (which include mining, processing, environmental, social, governmental, and economic factors that influence the conversion of identified mineral resources into economic reserves). Understanding geologic risk requires characterization of all the chemical, physical, and spatial properties of mineralization and waste that form part of the mined material. It is essential to understand the scope of the professionals who use geoscientific data in order to assist the outcomes of the study, with the data types first identified, then collected in a comprehensive manner, and finally interpreted at the appropriate time to contribute to the outcomes of the study. If the study is not comprehensive, remedial collection of data is required at a cost to development timeline and budget; a worse scenario is that the development fails economically after it is built. Developing projects to a construction stage after a mining study typically involves international standards of assessment and verification, although the standards of geoscientific data collection differ between companies and countries. For this reason, recent efforts by international bodies such as the Committee for Mineral Reserves International Reporting Standards (CRIRSCO) are assisting many countries to work toward a standardized terminology in a feasibility study. There are many examples where the mining outcomes have not met the feasibility study forecast, with variable causes for a failure to deliver to plan; geoscientific data shortfalls often contribute significantly to these negative outcomes. Examination of case histories, knowledge of international standards for risk reporting, advances in measurement technology, and an understanding of the end users of geoscientific data will help geologists to better prepare the scope of a feasibility study for a potential mine, in order to deliver a product with lower risk related to geologic uncertainty.
Mining software architecture knowledge: Classifying stack overflow posts using machine learning

Abstract Software Architectural Process (SAP) is a core and excessively knowledge intensive phase of software development life cycle, as it consumes and produces knowledge artifacts, simultaneously. SAP is about making design decisions, and the changes in these verdicts may pose adverse effects on software projects. The performance and properties of software components are fundamentally influenced by the design decisions. The implementation of immature and abrupt design decisions seriously threatens the development process of SAP. Moreover, software architectural knowledge management (AKM) approaches offer systematic ways to support SAP through versatile architectural solutions and design decisions. However, the majority of software organizations have limited access to data and still depend upon manually created and maintained AKM process. In this paper, we have utilized the one of the most prominent online community for software development (i.e., Stack Overflow) as a source of SAP knowledge to support AKM. In order to support AKM, we have proposed a supervised machine learning‐based approach to classify the architectural knowledge into predefined categories, that is, analysis, synthesis, evaluation, and implementation. We have employed different combinations of feature selection technique to achieve the optimal classification results of the used classifiers (Support Vector Machine [SVM], K‐Nearest Neighbor, Random Forest, and Naive Bayes [NB]). Among these classifiers, SVM with Uni‐gram feature set provides best classification results and attains 85.80% accuracy. For evaluating the proposed approach's effectiveness, we have also computed the suitability of the classifiers, that is, the cost of computation along with its accuracy, and NB with Uni‐gram feature set proved to be the most suitable.
Thumb Inclination-Based Manipulation and Exploration, a Machine Learning Based Interaction Technique for Virtual Environments

In the context of Virtual Reality (VR), interactions refer to the plausible actions in a Virtual Environment (VE). To have an engrossing interface, interactions by the gestures of hand are becoming prominent. With thisresearch work, a novel interaction technique is proposed where interactions are performed on the basis of the position of thumb in dynamic image stream. The technique needs no expensive tracker but an ordinary camera to trace hand movements and position of thumb. The interaction tasks are enacted in distinct interaction states, where the Angle of Inclination (AOI) of thumb is used for state-to-state transition. The angle is computed dynamically between the tip-of-thumb and the base of the Region of Interest (ROI) of an input image. The technique works in two phases: learning phase and application phase. In the learning phase, user-defined fist-postures with distinct AOI are learnt. The Support Vector Machine (SVM) classifier is trained by the AOI of the postures. In the application phase, interactions are performed in distinct interaction states whereas a particular state is activated by posing the known posture. To follow the trajectory of thumb, dynamic mapping is performed to control a virtual hand by the position of thumb in the input image. The technique is implemented in a Visual Studio project called Thumb-Based Interaction for Virtual Environments (TIVE). The project was evaluated by a group of 15 users in a moderate lighting condition. The 89.7% average accuracy rate of the evaluation proves suitability of the technique in the wide range VR applications.
Locally Constructing Product Taxonomies from Scratch Using Representation Learning

Given a domain-specific set of concepts, local taxonomy construction (LTC) is the problem of `locally' inducing the neighborhood of a concept (from the set of target concepts) without being given any example links. The problem, despite having practical importance, has received little research attention due to its difficulty (in contrast with link prediction, a problem that resembles it and has undergone broad study). In this paper, we present a formalism and deep empirical study on the LTC problem. In particular, we show that an innovative application of representation learning approaches from the natural language community could be adapted to tackle the problem, often quite effectively. We also present a detailed information retrieval (IR)-based methodology for evaluating these solutions on three real- world product datasets of varying sizes. To the best of our knowledge, this is the first paper to introduce the LTC problem, especially for e-commerce applications, and offer effective, nearly unsupervised, solutions, for addressing it on real-world data.
Identification of Cyber Bullying by using Clustering Methods on Social Media Twitter

Abstract In today’s era social media is very important for some people, because it is the nature of social media that can make people addicted to using social media, this has been widely proven in terms of medical, social interaction has been reduced due to social media, things related to the physical to be reduced, for example going out, and playing outdoors for children, for adults social media can be positive and negative, positive if used to offer old friends who don’t meet for long, but negative things can be used to crime or things that are not good, what else will soon be the presidential election, social media becomes a place or means of defamation of one party with another, the initial goal of social media is to build good relations Actually, there is a sentence that says “Yout thumb your tiger” with comments that are not good on social media. If we take us to the criminal section, and our words on social media can be legally justified, one example is intimidation, intimidation is one of the ITE Law articles, intimidation will be lifted from Twitter’s social media, with Twitter we can see examples how much of this intimidation. In retrieving data on social media there are several methods used in retrieving data, the method used is the clustering method or data grouping method. The study used the Naive Bayes and Decision Tree J48 classification method. The results to be obtained in this study are the Naive Bayes method which obtained an average value of the overall scenario of 92% success rate and 8% not detected and the result of Decision Tree J48 method with an accuracy value of 100%. The most widely used type of cyberbullying is psychology related bullying or related to one’s ignorance or nature.
Hybrid Path Planning Algorithm Based on Improved Dynamic Window Approach

Path planning is the hot topic in the autonomous robot navigation research area. Different from the former researchers, namely using the static obstacle environments, in this paper, we propose a hybrid path planning algorithm based on improved dynamic windows approach. The proposed algorithm can be shown as 1 Use the Astar algorithm to plan the path to avoid static obstacles in the operating environment, 2 Design a stage target selection strategy to guide the mobile robot to move on the global optimal planning path, 3 Propose an improved dynamic window approach that integrates the speed obstacle method, and the mobile robot is facing When moving obstacles, it can flexibly avoid, and maintain the smoothness of the path and the executable of speed control commands. Finally, the feasibility and effectiveness of the algorithm proposed in this paper are verified through the design algorithm simulation experiment, and the simulation result analysis verifies the obstacle avoidance of the improved algorithm Effect and real-time path optimality.
Exploding TV Sets and Disappointing Laptops: Suggesting Interesting Content in News Archives Based on Surprise Estimation

Many archival collections have been recently digitized and made available to a wide public. The contained documents however tend to have limited attractiveness for ordinary users, since content may appear obsolete and uninteresting. Archival document collections can become more attractive for users if suitable content can be recommended to them. The purpose of this research is to propose a new research direction of Archival Content Suggestion to discover interesting content from long-term document archives that preserve information on society history and heritage. To realize this objective, we propose two unsupervised approaches for automatically discovering interesting sentences from news article archives. Our methods detect interesting content by comparing the information written in the past with one created in the present to make use of a surprise effect. Experiments on New York Times corpus show that our approaches effectively retrieve interesting content.