Reviewerly/exHarmony huggingface dataset:
Fast Fault Diagnosis in Industrial Embedded Systems Based on Compressed Sensing and Deep Kernel Extreme Learning Machines

With the complexity and refinement of industrial systems, fast fault diagnosis is crucial to ensuring the stable operation of industrial equipment. The main limitation of the current fault diagnosis methods is the lack of real-time performance in resource-constrained industrial embedded systems. Rapid online detection can help deal with equipment failures in time to prevent equipment damage. Inspired by the ideas of compressed sensing (CS) and deep extreme learning machines (DELM), a data-driven general method is proposed for fast fault diagnosis. The method contains two modules: data sampling and fast fault diagnosis. The data sampling module non-linearly projects the intensive raw monitoring data into low-dimensional sampling space, which effectively reduces the pressure of transmission, storage and calculation. The fast fault diagnosis module introduces the kernel function into DELM to accommodate sparse signals and then digs into the inner connection between the compressed sampled signal and the fault types to achieve fast fault diagnosis. This work takes full advantage of the sparsity of the signal to enable fast fault diagnosis online. It is a general method in industrial embedded systems under data-driven conditions. The results on the CWRU dataset and real platforms show that our method not only has a significant speed advantage but also maintains a high accuracy, which verifies the practical application value in industrial embedded systems.
Multipartite stationary entanglement generation in the presence of dipole-dipole interaction in an optical cavity

Engineering a stationary entanglement between atoms or ions placed at small distances is a challenging problem in quantum information science. In this paper, the stationary and dynamics of entanglement in a system of dipole-coupled qubits interacting with a single-mode optical cavity in the strong coupling regime are theoretically investigated. We find that the entanglement in the steady state can be induced and tuned in nonresonance cases by considering the dipole-dipole interaction (DDI) between the qubits. We also point out that the novel measure used in this study can quantify the net multiqubit entanglement in the system. By increasing the DDI intensity, the behavior of the system depends on the number of qubits. Interestingly, with only two qubits, the amounts of steady-state entanglement enhances as the DDI intensity increases. For a system with more than two qubits, we find that DDI in the weak range of intensity plays a constructive role in the entanglement between qubits. However, it could destroy the stationary entanglement as the interaction between qubits intensifies. The entanglement of a system consisting of more than two qubits tends to disappear for an ensemble of qubits with smaller interatomic distances that lead to stronger dipolar interaction between nonadjacent qubits. Finally, we study the atomic population transfer as well as the transmitted spectrum.
M-YOLO based Detection and Recognition of Highway Surface Oil Filling with Unmanned aerial vehicle

Pavement oil repair is a kind of repair scheme for damaged pavement. Due to the problems of difficult and low detection efficiency in oil repair area, a light network architecture algorithm (M-YOLO) based on Mobilenet V3-YOLOv5S is proposed in this paper. MobileNetv3 structure was introduced to replace YOLOv5s backbone network to reduce model size and improve target detection speed. SPPNet network structure was introduced to remove repetitive features and improve target detection accuracy. After experiments, the accuracy of M-YOLO algorithm is up to 98.3%, the average accuracy is up to 95.5%, and the detection speed is up to 96.6fps. Compared with YOLOv3, the p-value, mAP value and FPS of the proposed algorithm are effectively improved by 10.4%, 5.1% and 30.8F*s-1, respectively. It can be seen that the algorithm proposed in this paper greatly reduces the number of model parameters and computation, and improves the detection accuracy and detection speed.
Application of Artificial Intelligence Technology in Music Education Supported by Wireless Network

With the increasing application and use of information technology in college music classrooms getting deeper and deeper, the role of music informatization teaching classrooms becomes more and more prominent. In this context, it will be an important research topic to strengthen information technology education, promote the innovation and development of music education in colleges and universities, and build a rich information technology music classroom. Artificial intelligence technology’s implementation in the area of music education is developing and becoming more common, and to some extent, it has become a stimulant of music teaching, although there are certain negatives. The arrival of wireless networks, especially 5G, has drawn strong attention from all walks of life, and its characteristics of “large bandwidth, ultralow latency, and connectivity of everything” have been applied and explored in the field of music education, making the teaching mode, teaching methods, and interactive experience develop in a better direction. At the same time, VR, AR, AI, big data, and other technologies based on a wireless network have also been optimized and applied to the field of music education, which will further promote the development of intelligent music education. This study examines the impact of informatization on music education creativity in academic institutions and then goes on to address informatization tactics in music education innovation practice in order to develop a new model of music education digitization that is current. Focusing on music education, the applications of artificial intelligence in music education are listed, and a music education framework based on artificial intelligence algorithms and wireless networks is proposed. Finally, the proposed framework’s usefulness is demonstrated in relevant tests, and the prospects for artificial intelligence technology in the realm of music are examined.
Chinese Named Entity Recognition Based on Knowledge Based Question Answering System

The KBQA (Knowledge-Based Question Answering) system is an essential part of the smart customer service system. KBQA is a type of QA (Question Answering) system based on KB (Knowledge Base). It aims to automatically answer natural language questions by retrieving structured data stored in the knowledge base. Generally, when a KBQA system receives the user’s query, it first needs to recognize topic entities of the query, such as name, location, organization, etc. This process is the NER (Named Entity Recognition). In this paper, we use the Bidirectional Long Short-Term Memory-Conditional Random Field (Bi-LSTM-CRF) model and introduce the SoftLexicon method for a Chinese NER task. At the same time, according to the analysis of the characteristics of application scenario, we propose a fuzzy matching module based on the combination of multiple methods. This module can efficiently modify the error recognition results, which can further improve the performance of entity recognition. We combine the NER model and the fuzzy matching module into an NER system. To explore the availability of the system in some specific fields, such as a power grid field, we utilize the power grid-related original data collected by the Hebei Electric Power Company to improve our system according to the characteristics of data in the power grid field. We innovatively make the dataset and high-frequency word lexicon in the power grid field, which makes our proposed NER system perform better in recognizing entities in the field of power grid. We used the cross-validation method for validation. The experimental results show that the F1-score of the improved NER model on the power grid dataset reaches 92.43%. After processing the recognition results by using the fuzzy matching module, about 99% of the entities in the test set can be correctly recognized. It proves that the proposed NER system can achieve excellent performance in the application scenario of a power grid. The results of this work will also fill the gap in the research of intelligent customer-service-related technologies in the power grid field in China.
The Problems of the COVID-19 Pandemic in Higher Education

Since the outbreak of the pandemic COVID-19, many studies have been conducted to examine how education has responded to the challenges of a completely new situation that has led to the spread of distance education as the only form of instruction. In this study, data were collected and analyzed to understand the difficulties of distance education that higher education students faced during the pandemic. Our goal was to present the results of a socio-psychological study of accessibility, educational resources, applications, and distance learning technologies. A total of 160 students from different Moscow universities participated in the study. A qualitative research method was used for the study. For this purpose, mainly in-depth interviews were conducted to find out the participants’ views on distance education. The data obtained were analyzed by the researchers using qualitative analysis methods. The results showed that all students faced technical difficulties during distance learning, such as poor internet connection, lack of access to online platforms due to the high number of users, lack of necessary equipment, and individual space for online learning. The results also showed low technical readiness for distance education and low quality of online resources, as well as cyber threats during online courses. In addition, the results showed that most students indicated that they would prefer a hybrid form of instruction that combines distance and face-to-face instruction. Implications for further studies are drawn in the conclusion.
Evaluating the Effect of Enhanced Text-Visualization Integration on Combating Misinformation in Data Story

Misinformation has disruptive effects on our lives. Many researchers have looked into means to identify and combat misinformation in text or data visualization. However, there is still a lack of under-standing of how misinformation can be introduced when text and visualization are combined to tell data stories, not to mention how to improve the lay public's awareness of possible misperceptions about facts in narrative visualization. In this paper, we first analyze where misinformation could possibly be injected into the production-consumption process of data stories through a literature survey. Then, as a first step towards combating misinformation in data stories, we explore possible defensive design methods to enhance the reader's awareness of information misalignment when data facts are scripted and visualized. More specifically, we conduct a between-subjects crowdsourcing study to investigate the impact of two design methods enhancing text-visualization integration, i.e., explanatory annotation and interactive linking, on users' awareness of misinformation in data stories. The study results show that although most participants still can not find misinformation, the two design methods can significantly lower the perceived credibility of the text or visualizations. Our work informs the possibility of fighting an infodemic through defensive design methods.
Using Twitter to Detect Hate Crimes and Their Motivations: The HateMotiv Corpus

With the rapidly increasing use of social media platforms, much of our lives is spent online. Despite the great advantages of using social media, unfortunately, the spread of hate, cyberbullying, harassment, and trolling can be very common online. Many extremists use social media platforms to communicate their messages of hatred and spread violence, which may result in serious psychological consequences and even contribute to real-world violence. Thus, the aim of this research was to build the HateMotiv corpus, a freely available dataset that is annotated for types of hate crimes and the motivation behind committing them. The dataset was developed using Twitter as an example of social media platforms and could provide the research community with a very unique, novel, and reliable dataset. The dataset is unique as a consequence of its topic-specific nature and its detailed annotation. The corpus was annotated by two annotators who are experts in annotation based on unified guidelines, so they were able to produce an annotation of a high standard with F-scores for the agreement rate as high as 0.66 and 0.71 for type and motivation labels of hate crimes, respectively.
Trajectory tracking for quadrotors: An optimization‐based planning followed by controlling approach

Abstract We present an optimization‐based reference trajectory tracking method for quadrotor robots for slow‐speed maneuvers. The proposed method uses planning followed by the controlling paradigm. The basic concept of the proposed method is an analogy with linear quadratic Gaussian in which nonlinear model predictive control (NMPC) is employed for predicting optimal control policy in each iteration. Multiple‐shooting is suggested over direct‐collocation for imposing constraints when modeling the NMPC. Incremental Euclidean distance transformation map is constructed for obtaining the closest free distances relative to the predicted trajectory; these distances are considered obstacle constraints. The reference trajectory is generated ensuring dynamic feasibility. The objective is to minimize the error between the quadrotor's current pose and the desired reference trajectory pose in each iteration. Finally, we compared the proposed method with two other approaches and showed that the proposed method outperforms the said approaches in terms of reaching the goal without any collision. Additionally, we published a new data set that can be used for evaluating the performance of trajectory tracking algorithms.
Social Engineering Attack and Scam Detection using Advanced Natural Langugae Processing Algorithm

The Method of convincing anyone to share their knowledge is known as social engineering. Social engineers rely on people's ignorance of the consequence of sharing their valuable information, as well as their lack of knowledge for securing their systems and IT infrastructure from security attacks. These attacks may be carried out by the employees of an organization, through a third-party agency. They violate the rules of the organization for financial gain or revenge. The attacker uses different tactics to gather sensitive information of the victims, this itself is a method of social engineering attack. The process of gaining confidential information illegally is a criminal act. The proposed research study has developed a framework to find the message received from an unknown source or URL is a spam or legitimate by using natural language process (NLP). During COVID-19, many peoples started using Internet for their daily activities without the knowledge of security risks in Internet. This rise in the number of individuals using the Internet is virtually never accompanied by knowledge about cyber security and other forms of Internet based attacks. This attracted the attackers to target these victims and launch their attacks. SEA is a form of advanced cyber security attack, which use people's natural curiosity to break surveillance systems and have a high success rate. The goal of the study is to look at the details of how the COVID-19 pandemic has paved the way for an expansion of social engineering attacks, as well as various methods for detecting and mitigating these attacks.
Enhancing Spam Detection on SMS performance using several Machine Learning Classification Models

The Short Message Service (SMS) is swiftly turning into the most stable and secured mode of interaction because of its wide-ranging global coverage, reliability, dependability, and power efficiency. Because A2P messaging is more secure in comparison to P2P messaging, anybody can send a message, which leads to an attack. Spammers make use of this opportunity to spread harmful information, participate in disruptive behaviour, and harass others. In our work, we used machine learning classifiers for instance Multinomial-Naive-Bayes algorithm, Support-Vector-Machine models (SVM), Logistic-Regression, and Decision Trees to detect SMS spam from a dataset of nearly six thousand messages, taking into account combinations of Term-Frequency-Inverse-Document-Frequency (TF-IDF) and Count-Vectorization features to investigate the trade off between F1-score, accuracy -score, and computing time. Following that, we conducted a comparison study of the accuracy of various models for spam detection in order to determine the most accurate model that can be employed in this situation.
Actor-Critic with Transformer for Cloud Computing Resource Three Stage Job Scheduling

Cloud computing is widely used in academia and industry. Users can provide jobs to cloud service providers that provide pay-as-you-go methods by cloud for calculation and obtaining results. Cloud service providers make reasonable scheduling for different jobs of users and in the case of successful operation, the cost of energy consumption becomes a key factor. Prior works proposed various algorithms based on reinforcement learning on job scheduling. However, due to the lack of online training mode and refined container decision-making, this paper makes some improvements and extensions. This paper presents the algorithm ACT4JS, an actor-critic with transformer for three stage job scheduling algorithm, that finely manages data centers, server nodes, and application containers. The job scheduling algorithm is designed to select the best long-term decision by learning from cloud computing resource model that accepts single-task multi-instance jobs and directed acyclic graph jobs with dependencies. To learn key features and loss optimization, use the transformer network structure and the proximal policy optimization method. To minimize energy consumption costs for large scale job scheduling and running, designs a reward function to reflect optimized energy consumption targets and independent simulation experiments. To achieve low energy consumption cost and low task reject rate, use training methods such as experience replay and advantage estimate. Compared with the mainstream baseline algorithm, the results show that the ACT4JS algorithm is better than the baseline in the online training model. In addition, the effect of ACT4JS is more effective in the long-term experimental.
Identifying multivariate geochemical anomalies via tensor dictionary learning over spatial-elemental dimensionalities

Geochemical data is a typical geospatial-data with high-dimensional elemental properties. Existing methods for recognizing multivariate geochemical anomalies are limited in fully profiting from the inherent spatial-elemental structures of the geochemical data. This paper presents a novel method for the identification of multivariate geochemical anomalies, which leverages tensor dictionary learning over spatial-elemental dimensionalities to learn the joint representation of the spatial-elemental structures of geochemical data. The learned representations in spatial and elemental dictionaries are capable of capturing both the non-local features over space and the high-correlated distributed features across elements. The sparse representations over the spatial and elemental dimensions are exploited to reconstruct the geochemical background values, which permits us to identify multivariate geochemical anomalies considering the spatial-elemental structure of geochemical data. The proposed method is applied to recognize geochemical anomalies related to Au mineralization in the northwest Jiaodong Peninsula, Eastern China. By comparing with the anomalies recognized by deep autoencoder and local singularity analysis, we conclude that the tensor dictionary learning method is more effective to identify the mineralization-associated anomalies attribute to its ability to fuse spatial-elemental information of the geochemical data.
Balanced knowledge distillation for one-stage object detector

The latest knowledge distillation (KD) methods have successfully supervised a student model to have a better representation using intermediate layers of a teacher model. However, the previous KD methods did not obtain generalized knowledge for various object scales from a one-stage object detector because the one-stage object detector has a structural property that uses several intermediate layers to extract objects of various scales. In other words, the previous KD methods could not distill and transfer knowledge to intermediate layers of one-stage object detectors in a balanced way. Therefore, we propose a shared knowledge encoder and an averaged prototype transfer to remove or mitigate the distillation and transfer imbalances that adversely affect the KD process. Experimental results show that the proposed KD method outperforms the state-of-the-art methods. For instance, the proposed method provides about 1.3% and 2.2% higher accuracy than the baseline on the PASCAL VOC and MS COCO datasets, respectively.
Peel | Pile? Cross-Framework Portability of Quantum Software

In recent years, various vendors have made quantum software frameworks available. Yet with vendor-specific frameworks, code portability seems at risk, especially in a field where hardware and software libraries have not yet reached a consolidated state, and even foundational aspects of the technologies are still in flux. Accordingly, the development of vendor-independent quantum programming languages and frameworks is often suggested. This follows the established architectural pattern of introducing additional levels of abstraction into software stacks, thereby piling on layers of abstraction. Yet software architecture also provides seemingly less abstract alternatives, namely to focus on hardware-specific formulations of problems that peel off unnecessary layers. In this article, we quantitatively and experimentally explore these strategic alternatives, and compare popular quantum frameworks from the software implementation perspective. We find that for several specific, yet generalisable problems, the mathematical formulation of the problem to be solved is not just sufficiently abstract and serves as precise description, but is likewise concrete enough to allow for deriving framework-specific implementations with little effort. Additionally, we argue, based on analysing dozens of existing quantum codes, that porting between frameworks is actually low-effort, since the quantum- and framework-specific portions are very manageable in terms of size, commonly in the order of mere hundreds of lines of code. Given the current state-of-the-art in quantum programming practice, this leads us to argue in favour of peeling off unnecessary abstraction levels.