Reviewerly/exHarmony huggingface dataset:
Research on Integrated Management Platform of Sports Service Industry Based on WebGIS

WebGIS is the geographic information system software of GIS system running on the Web side. It is the extension and development of GIS on the Internet. It uses two parts, the server and the client, to realize the transmission and expression of GIS information. WebGIS technology is applied to the sports service industry, and the unique spatial analysis and query function of WebGIS is used to organically combine WebGIS technology with the sports service industry. Build a comprehensive management platform for sports service industry based on WebGIS, make full use of new technologies such as HTML5, Java Script and CGI; and A* algorithm for optimal path planning, which is faster and saves space than the traversal path planning algorithm; the comprehensive management platform of the sports service industry is designed into several subsystems to provide services for the relevant departments of the sports service industry.
Bilateral Association Mining with Machine Learning to Predict Bitcoin Price Changing Trends

In this paper, we attempt to predict the price of Bitcoin from the emotion changes of investors and sentiment analysis is implemented to reflect the public response toward the Bitcoin market fluctuation. Meanwhile, the impact of the price variation on peopleś mood are discussed and abstracted into a backward prediction model. By establishing a closed-loop association mining network, this paper deeply explores the bidirectional interaction between the investor emotion and the bitcoin price, and with the help of this bilateral model, more decisive factors in changing the bitcoin price are considered. Consequently, the price tendency can be more accurately predicted. Compared with the existing bitcoin price prediction method, our network has higher degree of preciseness and robustness.
Statistical Span Property of Binary Run Sequences

We define run sequences of period <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2^{n}-1$ </tex-math></inline-formula> as the binary sequences where the distribution of runs of 0’s and runs of 1’s is exactly same as that for the maximal length linear shift resister sequences of period <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2^{n}-1$ </tex-math></inline-formula> . We first count the number of all the cyclically distinct run sequences of period <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2^{n}-1$ </tex-math></inline-formula> . For each <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> -tuple, we consider the average number of occurrences over all the run sequences of period <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2^{n}-1$ </tex-math></inline-formula> . We identify the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> -tuples with average number 1 and, in particular, those that occur exactly once in every run sequence of period <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2^{n}-1$ </tex-math></inline-formula> . We finally prove that, as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> increases, the average number of every non-zero <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> -tuple approaches to 1.
Remote Sensing Image Retrieval Using Multilingual Texts

Recently, multimodal retrieval has attracted increasing attention in remote sensing community. in particular, text-image retrieval showed a promising research topic due to its ability to enable a flexible retrieval experience. To achieve this end, we propose a transformer-based multilingual text-image retrieval approach. Specifically, we employ the transformer encoder for both textual and visual modalities. At the text encoder, we jointly train four languages: English, Arabic, French, and Italian. We conduct experiments on fine-grained multimodal datasets named RSITMD. The experimental studies of the proposed method demonstrate its superior performance on both single and multiple languages modalities compared with the state-of-the-art methods.
Bidirectional Relation Attention for Entity Alignment Based on Graph Convolutional Network

Entity alignment is an effective solution to integrate resources from different knowledge graphs (KGs). It aims to establish link between entities in different KGs that refer to the same object in the real world. Most of the existing embedding-based methods embed entities and relations into the same vector space or utilize a graph neural network (GNN) for nodes aggregation to learn the topology of the KG. However, these approaches do not sufficiently distinguish the effects of different relations on node features and ignore the dependencies between relations and entities. Some latent semantic information hidden in the relations is underutilized. This paper proposes a bidirectional relation attention mechanism to jointly learn entity and relation embeddings. We employ a graph convolutional network (GCN) to obtain the structure embedding for the KGs, then the attention mechanism used in relation embeddings facilitates the flow of information from entities to relations and strengthen the dependency between them. Finally the relation attention exploited in the aggregation stage integrates both direction information and connected relation semantics of neighborhoods that capture different impacts of relation on the neighborhood nodes. Experiments conducted on the DBP15K dataset validate the effectiveness of our proposed model and show that our model outperforms the baseline methods.
Cyber-Awareness for Dummies

“Great Powers comes with Great Responsibility”—this line from Spider-Man suits better today while using Internet responsibly. When, during pandemic, people were caged inside their own houses and apartments, the only means of communication was Internet (social media), but it is also a hive for the crimes and criminals who are mentally evil rather than physically. Cyber-frauds have increased a lot after lockdown, and it is raging high cyberattacks and security issues. In this paper, we try to point out some of those attacks.
TE-BiLSTM: Improved Transformer and BiLSTM on Fraudulent Phone Text Recognition

The number of mobile phone users become extremely large, at the same time telephone fraud cases occur frequently throughout the world, which makes people’s property and security destroyed. For this reason, a method to identify fraud phone text based on text classification is proposed in this paper. A hybrid neural network TE-BiLSTM (Transformer-Encoder-Bidirectional Long Short-Term Memory) based on improved Transformer combined with BiLSTM is proposed. Besides, a fraud phone text dataset with multiple types of fraud terms is built. Word2Vec is used to represent the fraud phone text. Multi-head attention mechanism can extract deep semantic information from text in different subspaces and BiLSTM (Bidirectional Long short-term Memory) can exploit the long-distance dependency of text. TE-BiLSTM is used to extract multi-level information, and Softmax layer is used to classify and recognize fraud phone text. Experiments show that our model has the highest experimental results compared to other comparison models. The recognition accuracy of fraud phone text based on TE-BiLSTM is improved by 5.25% compared with Transformer, and increased by 5.58%, 3.92% and 3.67% respectively compared with other traditional models - LSTM, BiLSTM and BiLSTM-ATT (BiLSTM-attention).
The controllability analysis of a discrete-time reservoir engineering system

This paper is concerned with a discrete-time reservoir engineering consisting of a sequence of qubits which are used to manipulate the quantum state of a harmonic oscillator. We consider the controllability problem regarding such a system both in the finite time and infinite time scenarios, with detailed proofs. In more concrete terms, we discuss how the quantum state of the harmonic oscillator evolves step by step, and provide the final state after <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> steps. The role of entanglement involved in the reservoir is also discussed by demonstrating photon numbers. In terms of infinite time controllability analysis, we take advantage of the approximated Lindblad master equation, to explore under what circumstances can we stabilize coherent states, squeezed states and Schrödinger cat states. Stabilization of certain quantum states as well as the corresponding convergence rates are proved by means of Lyapunov functions. These results shed light on a systematic framework towards controllability analysis in quantum reservoir engineering.
Image Compression and Decompression with Configurable Architecture on FPGA platform

Images forms a dominant percentage of information available on the internet and on the storage devices. Compression of images saves bandwidth in transmission and saves storage space and bandwidth. A hybrid method is proposed to compress images on Field Programmable Gate Array using Haar Transformation followed by Autoencoder. The Haar Transformation is implemented on FPGA to attain higher throughput compared existing architecture. In addition, Auto-encoder reduces the distortion and provides a higher throughput while consuming minimum power. The result is compared to JPEG 2000.
Defending Against Adversarial Denial-of-Service Data Poisoning Attacks

Data poisoning is one of the most relevant security threats against machine learning and data-driven technologies. Since many applications rely on untrusted training data, an attacker can easily craft malicious samples and inject them into the training dataset to degrade the performance of machine learning models. As recent work has shown, such Denial-of-Service (DoS) data poisoning attacks are highly effective.
A General Inference Framework for Deep Neural Network of Modulation Recognition

Modulation recognition is one of the crucial tasks in intelligent communications. With the development of deep learning, modulation recognition based on deep neural networks has attracted significant attention. Meanwhile, with development of internet of things as well as edge computing, various embedded devices have emerged. Consequently, how to deploy the deep neural network of modulation recognition on embedded devices becomes a research hotspot. Existing inference frameworks for the deep neural network of modulation recognition are highly dependent on the hardware platform, suffer from weak universality, and cannot be widely transplanted into various embedded devices. To solve this problem, this paper proposes a general inference framework for the modulation recognition network. The framework is built with the standard C language library, which is generally supported by embedded devices, to construct all the operators in the deep neural network, so as to ensure that the deployment of the framework is not limited by the hardware platform. Test results show that the inference framework proposed in this paper can run well on various embedded devices and achieve modulation recognition without accuracy loss.
Improved Robustness and Safety for Pre-Adaptation of Meta Reinforcement Learning with Prior Regularization

Meta Reinforcement Learning (Meta-RL) has seen substantial advancements recently. In particular, off-policy methods were developed to improve the data efficiency of Meta-RL techniques. \textit{Probabilistic embeddings for actor-critic RL} (PEARL) is a leading approach for multi-MDP adaptation problems. A major drawback of many existing Meta-RL methods, including PEARL, is that they do not explicitly consider the safety of the prior policy when it is exposed to a new task for the first time. Safety is essential for many real-world applications, including field robots and Autonomous Vehicles (AVs). In this paper, we develop the PEARL PLUS (PEARL$^+$) algorithm, which optimizes the policy for both prior (pre-adaptation) safety and posterior (after-adaptation) performance. Building on top of PEARL, our proposed PEARL$^+$ algorithm introduces a prior regularization term in the reward function and a new Q-network for recovering the state-action value under prior context assumptions, to improve the robustness to task distribution shift and safety of the trained network exposed to a new task for the first time. The performance of PEARL$^+$ is validated by solving three safety-critical problems related to robots and AVs, including two MuJoCo benchmark problems. From the simulation experiments, we show that safety of the prior policy is significantly improved and more robust to task distribution shift compared to PEARL.
WinDrift: Early Detection of Concept Drift Using Corresponding and Hierarchical Time Windows

In today's interconnected society, large volumes of time-series data are usually collected from real-time applications. This data is generally used for data-driven decision-making. With time, changes may emerge in the statistical characteristics of this data - this is also known as concept drift. A concept drift can be detected using a concept drift detector. An ideal detector should detect drift accurately and efficiently. However, these properties may not be easy to achieve. To address this gap, a novel drift detection method WinDrift (WD) is presented in this research. The foundation of WD is the early detection of concept drift using corresponding and hierarchical time windows. To assess drift, the proposed method uses two sample hypothesis tests with Kolmogorov-Smirnov (KS) statistical distance. These tests are carried out on sliding windows configured on multiple hierarchical levels that assess drift by comparing statistical distance between two windows of corresponding time period on each level. To evaluate the efficacy of WD, 4 real datasets and 10 reproducible synthetic datasets are used. A comparison with 5 existing state-of-the-art drift detection methods demonstrates that WinDrift detects drift efficiently with minimal false alarms and has efficient computational resource usage. The synthetic datasets and the WD code designed for this work have been made publicly available at https://github.com/naureenaqvi/windrift .
Design and Implementation of Distributed Image Recognition App with Federal Learning Techniques

In recent years, machine learning technology has been widely used in many fields, such as smart transportation, smart healthcare, smart finance and smart cities. Although machine learning technology has brought people a lot of convenience, the privacy problem of user data has also emerged [1]. Considering that users are not necessarily willing to upload personal privacy data to the cloud for deep learning training, therefore, instead of consuming a lot of bandwidth to upload data to the cloud, it is better to train on the local device and then use the model parameters obtained after training. (For example: weights and bias, etc.) upload to the server for aggregation. This emerging machine learning technology is called federated learning. In this way, the privacy and security of data can be guaranteed, and the purpose of decentralized learning can be achieved through aggregation. This study uses the architecture of federated learning technology and convolutional neural network algorithms to implement distributed image recognition mobile applications. This application allows users to use their mobile devices and the central servers for repeated training. After multiple rounds of repeated training, the convergence will be stabilized, and the accuracy will be significantly improved. At the same time, it can take into account privacy and achieve the machine the purpose of learning.
Using Ontology-Centric Knowledge Integration Framework as a Foundation to Innovative Problem-Solving

Knowledge is a power which considered an important factor to solve any problems. Integrating knowledge becomes a management challenge as it can lead to creating new knowledge or innovation knowledge which magnifies this power. Cognition knowledge of individuals can be classified into three types of knowledge: declarative, procedural, and strategic knowledge which can be regulated to solve problems. Formulation of knowledge opens a chance for computers to perform complex knowledge integration and knowledge regulation process that can produce creative and innovative problem-solving. The majority of knowledge integration frameworks integrate declarative knowledge to enrich knowledge about topic of interest. In this research we proposed a problem-solving framework by integrating different types of cognition knowledge and use ontology as a foundation for formulating different types of knowledge. We applied the framework to a software design problem and take the software creational design pattern as a case study. The results of using the framework shows that it generates the most of the known design patterns in addition to innovating new software creational design pattern which can be used to solve complex design problems.
An inter-partition communication mechanism in multi-domain isolation operating system

Multi-domain isolation operating system is a highly secure and trusted operating system architecture. By dividing the system into several partitions, each partition focuses on a class of functions and operates independently, reducing coupling and negative effects between different partitions. Inter-partition communication is a key component of multi-domain isolation operating system architecture, and inter-partition communication can greatly affect the overall system availability, security, and reliability. However, there are relatively limited research results on inter-partition communication of multi-domain isolation oper-ating system architecture, so there is an urgent need for research in this area. Based on Linux operating system and referring to multiple independent levels of security and safety (MILS) architecture, the implementation of multi-domain isolation op-erating system in this paper provides resource access control service for partitions with different security levels. This paper designs an inter-partition communication mechanism of multi-domain isolation operating system which helps different partitions communicate with each other and records inter-partition communication information so that users can understand the inter-partition communication status in system runtime.
“To Clean Code or Not to Clean Code” A Survey Among Practitioners

Context: Writing code that is understandable by other collaborators has become crucial to enhancing collaboration and productivity. Clean Code has become one of the most relevant software craftsmanship practices and has been widely embraced as a synonym for code quality by software developers and software development organizations all over the world. However, very little is known regarding whether developers agree with Clean Code principles and how they apply them in practice. Objectives: In this work, we investigated how developers perceive Clean Code principles, whether they believe that helps reading, understanding, reusing, and modifying Clean Code, and how they keep their code clean. Methods: We conducted a Systematic Literature Review in which we screened 771 research papers to collect Clean Code principles and a survey among 39 practitioners, some of them with more than 20 years of development experience. Results: So far, the results show a shared agreement with Clean Code principles and their potential benefits. They also show that developers tend to write “messy” code to be refactored later.
Gabor Filter-Embedded U-Net with Transformer-Based Encoding for Biomedical Image Segmentation

Medical image segmentation involves a process of categorization of target regions that are typically varied in terms of shape, orientation and scales. This requires highly accurate algorithms as marginal segmentation errors in medical images may lead to inaccurate diagnosis in subsequent procedures. The U-Net framework has become one of the dominant deep neural network architectures for medical image segmentation. Due to complex and irregular shape of objects involved in medical images, robust feature representations that correspond to various spatial transformations are key to achieve successful results. Although U-Net-based deep architectures can perform feature extraction and localization, the design of specialized architectures or layer modifications is often an intricate task. In this paper, we propose an effective solution to this problem by introducing Gabor filter banks into the U-Net encoder, which has not yet been well explored in existing U-Net-based segmentation frameworks. In addition, global self-attention mechanisms and Transformer layers are also incorporated into the U-Net framework to capture global contexts. Through extensive testing on two benchmark datasets, we show that the Gabor filter-embedded U-Net with Transformer encoders can enhance the robustness of deep-learned features, and thus achieve a more competitive performance.
Machine Learning-Based Non-Intrusive Digital Forensic Service for Smart Homes

Security and privacy concerns keep growing with the successful development of Internet of Things (IoT) and the booming deployment of smart homes. IoT devices are utilized cooperatively to enable the interactions between home surroundings and users’ daily lives, containing forensically-valuable information about what happens in smart homes, which can help introduce digital forensics into smart homes to alleviate the growing concerns. However, current IoT devices, apps, and platforms usually do not provide built-in capabilities for digital forensics. To overcome this limitation, we propose a non-intrusive solution (i.e., bringing no modification to IoT devices, apps, and platforms) of digital forensic service to provide Forensics-as-a-Service (FaaS) for smart homes. First, it leverages side-channel analysis on sniffed network traffic to monitor commands, actions, and states of IoT devices. Then, it introduces provenance graphs (i.e., causal graphs) for smart home modeling to provide a holistic and overall explanation of smart homes. Machine learning (ML) techniques are applied to overcome the deficiency of a non-intrusive solution as it suffers from challenges in data collection and smart home modeling. Finally, it conducts forensic analysis based on scalable, reusable policies that are designed for graph-based smart home modeling. We implement a prototype of our forensic service and evaluate it in a real-world smart home. The evaluation results show that our forensic service can effectively collect forensic data for smart home modeling and conduct forensic analysis to explain security risks in smart homes.