Reviewerly/exHarmony huggingface dataset:
Break the Data Barriers While Keeping Privacy: A Graph Differential Privacy Method

The booming development of Internet of Vehicles (IoV) has brought new vitality to the construction of intelligent transportation systems (ITS). At the same time, a huge amount of data has been generated due to the gradual development of IoV toward large scale, complex, and diversified. These data are owned by the companies that vehicles belonging to or service providers, such as taxi companies own taxi data. Due to interest and privacy considerations, data owners are not willing to share data, thus a serious data isolated island problem is created, which is detrimental to the development of ITS. Therefore, this article focuses on how to prevent privacy disclosure of vehicles while sharing vehicle data to improve the service. Considering the amount of interactive data and privacy disclosure during data release, vehicle data are abstracted from text form into a graph-structured data form. At the same time, graph differential privacy (DP) together with anonymity protection is proposed innovatively to firmly protect vehicle privacy. Moreover, to solve the high complexity of big data graph-structure transformation, an accelerated nodes and edges combined graph DP (ACGDP) algorithm is proposed. Based on the simulations of real-world data that combine electric and nonelectric taxies, it is verified that our proposed scheme has a tradeoff between information availability and privacy protection. With the graph DP processed data, our proposed scheme reduces the average wasted mileage for charging by 3.87% and achieves a 44.28% increase in drivers’ income. Drivers’ satisfaction of receiving orders and charging preference reaches 68% after the graph-structured data reuse.
Microsoft Teams desktop application forensic investigations utilizing <scp>IndexedDB</scp> storage

While the COVID-19 virus remolded the routines of the establishments, remote collaboration and distant communication gained more popularity. As the way electronic communications are handled changes drastically, new applications and storage mechanisms are introduced. Microsoft Teams is an application offered within the scope of Microsoft Office 365 that offers services for hosting virtual meetings, team communication, and comprehensive team resource management. It is prevalently used by organizations and indicates a great potential to be a source of digital forensic investigations. This paper scrutinizes the artifacts created by Microsoft Teams in IndexedDB persistent storage. IndexedDB is a fast-growing client-side storage technology that is relatively new as a source for digital forensic investigations. A single-case pretest-posttest quasi experiment was conducted to produce artifacts in Microsoft Teams IndexedDB storage. The artifacts were extracted without user credentials indicating security flaws in the application. Extracted artifacts were processed based on signature patterns and evaluated for their significance. Traditional database queries were utilized to link and present the information clustered according to their relevancy. A time-frame analysis was constructed to display information in a suitable format for investigators. The results indicate that Microsoft Teams IndexedDB storage artifacts contain significant potential for digital investigations with extraction of complete contents of private chat messages, voice mails, and team extensions with efficient time-frame analysis.
The Design and Realization of a Novel Intelligent Drying Rack System Based on STM32

With the development and progress of society, people have higher and higher requirements for the intelligence of the drying rack system. Therefore, it is essential practical significance to improve the level of intelligence of the drying rack system in people's daily life. Traditional drying rack systems are mainly divided into four types: outdoor, floor-standing, hand-operated, and electric. However, they all have the disadvantages of consuming human resources, time, and space. In this paper, we design a novel intelligent drying rack system based on STM32, consisting of many sensors, a horizontal rotation mechanism, and a lifting mechanism. This intelligent drying rack system first uses raindrop sensors, photoresistors, and wind speed sensors to collect external environmental information. Then, the system monitors the surrounding environment changes in real-time. When the environment changes, the system will drive the motor to realize collecting and drying clothes. Finally, we can realize all intelligent control functions through voice module, button module, and infrared remote control. In addition, we also develop an APP that displays the system's control interface and can realize all intelligent control functions. Through debug-running and system testing, the simulation results demonstrate the accuracy and efficacy of the designed drying rack system.
Feature Enhancement-Based Ship Target Detection Method in Optical Remote Sensing Images

Ship targets in ORSIs (Optical Remote Sensing Images) have the characteristics of various scales, and most of them are medium and small-scale targets. When the existing target detection algorithms are applied to ship target detection in ORSIs, the detection accuracy is low. There are two main reasons for the above problems, one is the mismatch of the receptive fields, and the other is the lack of feature information. For resolving the problem that multi-scale ship targets are difficult to detect, this paper proposes a ship target detection algorithm based on feature enhancement. Firstly, EIRM (Elastic Inception Residual Module) is proposed for feature enhancement, which can capture feature information of different dimensions and provide receptive fields of different scales for mid- and low-level feature maps. Secondly, the SandGlass-L block is proposed by replacing the ReLu6 activation function of the SandGlass block with the Leaky ReLu activation function. Leaky ReLu solves the problem of 0 output when ReLu6 has negative input, so the SandGlass-L block can retain more feature information. Finally, based on SandGlass-L, SGLPANet (SandGlass-L Path Aggregation Network) is proposed to alleviate the problem of information loss caused by dimension transformation and retain more feature information. The backbone network of the algorithm in this paper is CSPDarknet53, and the SPP module and EIRM act after the backbone network. The neck network is SGLPANet. Experiments on the NWPU VHR-10 dataset show that the algorithm in this paper can well solve the problem of low detection accuracy caused by mismatched receptive fields and missing feature information. It not only improves the accuracy of ship target detection, but also achieves good results when extended to other categories. At the same time, the extended experiments on the LEVIR dataset show that the algorithm also has certain applicability on different datasets.
Airlines based Twitter Sentiment Analysis Using Deep Learning

The airlines industry has been a competitive marketplace that has grown rapidly over the last few decades. Mostly, customers like family members, businessman, sportsman and youngsters are traveling through Airlines. If people are participating, their feedback is extremely important. Customer direct feedback may be favorable or negative, but understanding their Tweets is critical for improvement. This serves as a conduit for open-source inactive communication between promoters and customers coincidentally exercising their time and duties on the same platform for a variety of reasons. Sentiment Analysis on the social networking sites like Twitter or Facebook that bridge the gap between information and real time feedback, has become an amazing method for finding out about a user's feelings and has a wide scope of utilizations. It focuses on polarity (positive, negative, and neutral), sentiments and emotions (urgent, not urgent), and even intents (interested not interested). In this paper, the idea is to analyses the tweets emerging from social site such as Twitter, necessarily focused around the airline industry, its customers and employees, current as well as imminent. So ultimately, the objective is to deploy the deep learning algorithms on dataset of 14641 total tweets regarding U.S airlines, collected from Kaggle repository. The similar data set is utilized for both training and testing because there is more possibility for errors, which raises the likelihood of inaccurate predictions. Therefore, train_test_split function of scikit-learn python library has been used. It allows to breaking a dataset with ease while pursuing an ideal model. To prevent over fitting associated with the co-adaptation of feature detectors, the dropout learning algorithm has been called on to remarkable results.
Physical Metadata Visualisation: The Knitted Personal Library

Personal collections, be they books, music, or artefacts have significance to the owner, and provide conversation and engagement with visitors. For example visitors of physical libraries may browse the spines, select a book, and begin a conversation. When the collection is digital it cannot be engaged with in the same manner and it becomes more difficult to share and explore with others. Physical data visualisation is becoming more popular, supported by new technologies, such as 3D printing, and the resurgence of crafting. This paper reports our investigation of digital library and eBook metadata visualisation using knitting. We report on our design considerations, a digital prototype to explore knitted library shawls, and a knitted prototype for a personal digital library.
Deep Learning-Based Classification of Cancer Cell in Leptomeningeal Metastasis on Cytomorphologic Features of Cerebrospinal Fluid

It is a critical challenge to diagnose leptomeningeal metastasis (LM), given its technical difficulty and the lack of typical symptoms. The existing gold standard of diagnosing LM is to use positive cerebrospinal fluid (CSF) cytology, which consumes significantly more time to classify cells under a microscope.This study aims to establish a deep learning model to classify cancer cells in CSF, thus facilitating doctors to achieve an accurate and fast diagnosis of LM in an early stage.The cerebrospinal fluid laboratory of Xijing Hospital provides 53,255 cells from 90 LM patients in the research. We used two deep convolutional neural networks (CNN) models to classify cells in the CSF. A five-way cell classification model (CNN1) consists of lymphocytes, monocytes, neutrophils, erythrocytes, and cancer cells. A four-way cancer cell classification model (CNN2) consists of lung cancer cells, gastric cancer cells, breast cancer cells, and pancreatic cancer cells. Here, the CNN models were constructed by Resnet-inception-V2. We evaluated the performance of the proposed models on two external datasets and compared them with the results from 42 doctors of various levels of experience in the human-machine tests. Furthermore, we develop a computer-aided diagnosis (CAD) software to generate cytology diagnosis reports in the research rapidly.With respect to the validation set, the mean average precision (mAP) of CNN1 is over 95% and that of CNN2 is close to 80%. Hence, the proposed deep learning model effectively classifies cells in CSF to facilitate the screening of cancer cells. In the human-machine tests, the accuracy of CNN1 is similar to the results from experts, with higher accuracy than doctors in other levels. Moreover, the overall accuracy of CNN2 is 10% higher than that of experts, with a time consumption of only one-third of that consumed by an expert. Using the CAD software saves 90% working time of cytologists.A deep learning method has been developed to assist the LM diagnosis with high accuracy and low time consumption effectively. Thanks to labeled data and step-by-step training, our proposed method can successfully classify cancer cells in the CSF to assist LM diagnosis early. In addition, this unique research can predict cancer's primary source of LM, which relies on cytomorphologic features without immunohistochemistry. Our results show that deep learning can be widely used in medical images to classify cerebrospinal fluid cells. For complex cancer classification tasks, the accuracy of the proposed method is significantly higher than that of specialist doctors, and its performance is better than that of junior doctors and interns. The application of CNNs and CAD software may ultimately aid in expediting the diagnosis and overcoming the shortage of experienced cytologists, thereby facilitating earlier treatment and improving the prognosis of LM.
Resource Scheduling Algorithms in Cloud Computing: A Big Picture

With the increase in digital footprint, a computing model that can process the data parallel without increasing the overall cost has been sought. For this, many computing models have been proposed. Cloud computing is one of them that provides a good solution for a variety of applications. By utilizing a network of remote servers, cloud computing manages and processes a variety of data over virtualized resources making scheduling of tasks critical for performance. In the cloud, to completely execute the task, several virtualized resources may be required. Thus, making manual scheduling an unfeasible solution. For maximizing resource utilization and minimizing the makespan of the task, task scheduling schedules tasks in cloud computing. Various researchers have proposed their research for scheduling tasks in past. The task scheduling in the cloud over virtual resources can be either static and dynamic in nature. This paper presents a comparative analysis of FIFS, RR, SJF, Priority, Max-Min, EDF scheduling algorithms in cloud platforms on parameters such as response time, makespan, throughput, and energy efficiency and tries to identify an algorithm that is most suitable for the cloud environment. CloudSim is used as the simulator tool For analyzing the performance of various algorithms and the results are discussed in detail.
Improved Spoken Language Representation for Intent Understanding in a Task-Oriented Dialogue System

Successful applications of deep learning technologies in the natural language processing domain have improved text-based intent classifications. However, in practical spoken dialogue applications, the users' articulation styles and background noises cause automatic speech recognition (ASR) errors, and these may lead language models to misclassify users' intents. To overcome the limited performance of the intent classification task in the spoken dialogue system, we propose a novel approach that jointly uses both recognized text obtained by the ASR model and a given labeled text. In the evaluation phase, only the fine-tuned recognized language model (RLM) is used. The experimental results show that the proposed scheme is effective at classifying intents in the spoken dialogue system containing ASR errors.
Comparison of different sample-based motion planning methods in redundant robotic manipulators

Abstract The main objective of a motion planning algorithm is to find a collision-free path in the workspace of a robotic manipulator in a point-to-point motion. Among the various motion planning methods available, sample-based motion planning algorithms are easy to use, quick and powerful in redundant robotic systems applications. In this study, different sampling-based motion planning algorithms are employed to select the most appropriate method for efficient collision-free motion planning. As a case study, finding a collision-free robotic displacement for welding a main pipe with other intersecting pipes and joints is considered. The robotic manipulator employed in this study has seven degrees of freedom, where six degrees are related to the manipulator joints and one degree is related to its base linear movement suspended from ceiling. Five criteria, time, path length, path time, path smoothness and process time are used to evaluate the efficiency of different sample-based motion planning algorithms. Finally, a smaller set of more efficient algorithms are introduced based on the defined efficiency evaluation criteria.
Implementation of Mathematical Morphology Technique in Binary and Grayscale Image

Morphological picture supervision is a collection of non-direct responsibilities acknowledged with the shape or morphology of high points in an image. Morphological responsibilities depend just on the inclusive in treating of pixel respects, not on their arithmetical qualities, and in this way are predominantly fit to the handling of corresponding images. Morphological tasks can equally be functional to grayscale images with the culmination goal that their light exchange dimensions are ambiguous. Subsequently, their absolute pixel reverences are of no or minor scheming. The proposed work focused 3 on enhancing the image by utilizing the morphological technique: erosion, dilation, opening and closing. The arrangement of elements determined the increase of binary pixel in an image. When erosion is applied to the grayscale image, it enhanced the intensity of an image through enchanting the region extreme after fleeting the constructing part over the object. Outcomes of the technique carried out to several sides of an object indicate that abstract data of the reference object can be effortlessly obtained. The proposed technique can be utilized to develop an appropriate islanding method and can be used to recognize the weak associations in the object.
Handling Dynamic User Preferences Using Integrated Point and Distribution Estimations in Collaborative Filtering

Change in users’ preferences over time is a challenging issue in collaborative filtering models. Recent temporal recommender systems (RSs) use latent temporal features or probabilistic latent transition models. The models designed using latent temporal features remain confined to point estimation only, i.e., considering single predicted values (e.g., rating) and completely ignore the likelihood of other possible items being generated due to change in users’ preferences. The latent transitive models represent users’ preference over different states using distribution estimation but ignore user–item features. This article proposes an integrated approach for designing collaborative filtering models using both point and distribution estimation techniques together to address this issue. The matrix factorization-based point estimation strategy is applied to integrate user–item correlations and feature information, whereas the hidden Markov model is used for users’ preference distribution over time. Three benchmark datasets were used to evaluate and compare the proposed model’s performance with related state-of-the-art temporal RS models. The proposed model makes the system more robust in handling dynamic users’ preferences.
Decentralized ME-Centric Framework—A Futuristic Architecture for Consumer IoT

The Internet of Things (IoT) landscape is synergistic with various participating domains like smart healthcare, industrial IoT, and smart cities. With its massive potential, the Consumer Internet of Things (CIoT) landscape occupies a sizable chunk of the future Internet and is still immature due to its lack of interoperability and data sustainability among IoT applications. Most of the existing CIoT deployments are vertical solutions comprising CIoT devices, gateways, and centralized cloud services, resulting in fragmented silos of data. We need a contemporary, decentralized, semantic, data-centric, and interoperable approach to overcome the integration issues and provide a seamless global ecosystem for CIoT applications. In this article, we introduce a futuristic decentralized ME-centric state-of-the-art architecture for CIoT that can overcome the challenges of and enhance the agility, scalability, resiliency, and security of CIoT systems. In a decentralized ME-centric prototype, we integrate edge computing with decentralized storage solutions and new cutting-edge technologies like digital twins and blockchain with semantic ontologies to design a comprehensive beneficial outlook for consumers.
An efficient IoT forensic approach for the evidence acquisition and analysis based on network link

Abstract In an Internet of Things (IoT) environment, IoT devices are typically connected through different network media types such as mobile, wireless and wired networks. Due to the pervasive nature of such devices, they are a potential evidence source in both civil litigation and criminal investigations. It is, however, challenging to identify and acquire forensic artefacts from a broad range of devices, which have varying storage and communication capabilities. Hence, in this paper, we first propose an IoT network architecture for the forensic purpose that uses machine learning algorithms to autonomously detect IoT devices. Then we posit the importance of focusing on the links between different IoT devices (e.g. whether one device is controlled or can be accessed from another device in the system), and design an approach to do so. Specifically, our approach adopts a graph for modelling IoT communications’ message flows to facilitate the identification of correlated network traffic based on the direction of the network and the associated attributes. To demonstrate how such an approach can be deployed in practice, we provide a proof of concept using two IoT controllers to generate 480 commands for controlling two IoT devices in a smart home environment and achieve an accuracy rate of 98.3% for detecting the links between devices. We also evaluate the proposed autonomous discovering of IoT devices and their activities in a TCP network by using real-world measurements from a public dataset of a popular off-the-shelf smart home deployed in two different locations. We selected 39 out of 81 different IoT devices for this evaluation.
Identifying Extract Method Refactorings

Extract method refactoring identifies and extracts a set of statements implementing a specific functionality within a method. Its application enhances the structure of code and provides improved readability and reusability. This paper introduces Segmentation, a new approach for identifying extract method opportunities focusing on achieving higher performance with fewer suggestions. Evaluation of the approach includes six case studies from the open-source domain, and performance is compared against two state-of-the-art approaches. The findings suggest that Segmentation provides improved precision and F measure over both the approaches. Further, improved performance is reflected over long methods too.
Space-Heuristic Navigation and Occupancy Map Prediction for Robot Autonomous Exploration

Efficient autonomous exploration in unknown environments is a challenging and basic problem in the field of robotics. Due to the lack of priori information of the environment, the robot cannot clearly select the region with high information gain for exploration. In addition, the existing greedy exploration strategy leads to repeated back-and-forth movements of the robot and inefficient exploration. Intelligent bodies like humans often rely on past experience to infer the structural characteristics of the environment which are used to assist exploration. To give the robot spatial awareness, we propose a method of predicting map occupancy by using Generative Adversarial Networks (GANs). The adversarial loss and the feature extraction loss used in the training of GANs are introduced in detail. Further, we propose a novel utility function for the evaluation of exploration goals based on path length and structural characteristics of the environment. The utility function is used to guide the robot to efficiently explore step by step. We also propose a space-heuristic path planning method named CI-RRT* for robot navigation. We demonstrate the superiority of the proposed methods through comparison and ablation experiments in simulation environments. The experimental results prove that our method is superior to the existing methods.