Reviewerly/exHarmony huggingface dataset:
Quantitative Indicators of Retraction Phenomenon on an Automated Breast Volume Scanner: Initial Study in the Diagnosis and Prognostic Prediction of Breast Tumors

Retraction phenomenon is a unique sign on an automated breast volume scanner coronal plane image and has high specificity in differentiating benign lesions from malignant breast cancer. The purpose of this study was to quantify the retraction phenomenon by setting different rules to describe connected regions from different dimensions. In total, six quantitative indicators (FΩ1,FΓ,FS,FΩ2,FΩ3and FL) were obtained. FΩ1, FΩ2 and FΩ3 represent the relative areas of the connected region under different rules. FΓandFS represent the number ratio and absolute area of the connected region, respectively. FL represents the ratio of edge numbers. Two hundred fourteen patients with 214 lesions (90 benign and 124 malignant) were enrolled in this study. All quantitative indicators in the malignant group were significantly higher than those in the benign group (all p values <0.001). The indicator FΓ achieved the highest area under the receiver operating characteristic curve (AUC) (0.701, 95% confidence interval: 0.631-0.771). Both FΓ and FS had significant associations with axillary lymph node metastasis (p = 0.023 and 0.049). Compared with the classic texture feature gray-level co-occurrence matrix, retraction phenomenon quantization improved the AUC by 8.3%. The results indicate that retraction phenomenon quantitative indicators have certain value in distinguishing benign and malignant breast lesions and seem to be associated with axillary lymph node metastasis.
BITCOIN ANONYMITY AND ITS CHALLENGES

Bitcoin emerged as the most successful cryptocurrency due to the fact that it evaluates in 2009. Other than its security tightness, the principal settlements are probably to be its key to achievement: anonymity and isolation. In this paper, we offer an entire description of the info that make this type of cryptocurrency a thrilling problem for studies in a private society. We're carrying out an intensive overview of nameless bitcoin research papers published up to now and describe a number of the conditions that require research on that topic. Keywords Bitcoin, Blockchain statistics, Mining channel, E-pockets, and so on.
Correlational instance feature embedding for sample specificity learning

Sample specificity learning aims to treat every single sample as a separate class and mine the underlying class-to-class visual similarity relationship, thus learning discriminative feature embeddings without using category labels. We introduce a correlational instance feature embedding approach to improve the representation ability of deep neural networks. It exploits the self-correlation and cross-correlation of instances in each training batch by learning a feature embedding with intrainstance variation and interinstance interpolation, resulting in stronger discriminability and better generalizability. The exhaustive experiments on several benchmarks show the performance advantages of our proposed method over the existing methods.
Text Messages with Emoticons Analysis on Sentiment Tools

Emoticons have been widely used in virtual communications to represent facial expressions of emotion. It often contains a hidden value when it is used along with text messages. However, not all sentiment tools can measure the sentiment of emojis. This paper investigates how emojis play their roles in text messaging by evaluating the available set of sentiment tools online. Prototype that can analyse the sentiment on text messaging containing emojis is created. The comparison is made on text messaging with emojis with other sentiment analysis tools available online. This study presented the comparison of the results and feedback from responses about the accuracy, consistency, usefulness, satisfaction of the mechanism.
Chip-Firing Reservoir Computing

Spike neural networks (SNNs), as a network structure closer to biological neurons, have been applied to many machine learning fields because of their high computing capability. However, due to its high hardware requirements and difficulty in training, the application cost of SNNs in practice is higher than that of traditional artificial neural networks and the accuracy is usually lower. In order to build a brain-like neural network with lower hardware requirements and reduce training requirements, we propose a new computing network structure called chip-firing reservoir (CFR), which can be considered as a type of more discretized SNNs. CFR computing (CFRC) can extract spatiotemporal information to improve computing capability as SNNs, while only the output layer is trained as in conventional reservoir computing (RC), reducing the difficulty of training. Furthermore, since CFRC only use integer calculations, we hope that the computational cost can be significantly reduced compared to SNNs based on floating-point computations. In the experiment, we test and analyze the CFRC for image classification tasks. The results show that CFRC has functional performance on the MNIST image data set.
Virtual Kitchen Scene Modelling Based on 3ds Max With Rendering View

With the quick improvement of modem innovation, representing objects and scenes virtually is one of those major considerations made in technological sectors. A virtual three-dimensional representation of objects is not just to have a glimpse of the structure, but also to have a realistic view of the scene. This chapter mainly describes the usage and applications of 3ds Max related to which creating a 3D model of a kitchen is briefed. The chapter also includes the methodologies of designing and implementing various features of 3ds Max like editors and materials. The manner in which patterns are applied to objects, lighting effects, usage of modifiers like edit poly, edit mesh, turbo-smooth, mesh smooth bend, etc. are briefly described. This chapter can guarantee a variety of usage on texture maps with the help of rendering material editors which improves the quality of the rendering image and the structure.
Towards Polyvalent Adversarial Attacks on URL Classification Engines

Clicking on the wrong link and downloading a malware or leaking confidential information has been a common threat to everyone. Novel defense mechanisms against web threats have emerged, from URL parsing via Machine Learning to JavaScript deep graph analysis. In this study, we consider the problem of generating Adversarial Examples to evade URL classifiers. We propose a neural network architecture that will be able to generate synthetic URLs for a variety of attacks that will evade existing security classifiers. The main focus of the literature is on classifiers for phishing URLs, but in this work we aim to extend it to other attacks like Drive-by-Download. Our system splits the URL at the word level before substituting words with symbols. This approach contrasts with the existing literature that works on the character level without semantic considerations. We also envision a combination of the word level generation with a character level analysis by a Convolutional Neural Network to improve the evasion capacity of the synthetic samples.
Classification of Chinese Text in the Power Field Using FastText with Electric Power Keywords

Text classification has always been a hot research topic in the field of natural language processing. Fasttext is a model which is simple in structure and has the advantage of fast and efficient. In order to analyze the situation of electric power intellectual property in recent years, this paper use the model of FastText to process Chinese electric power core journals and patents in recent five years. The text information in the company's intellectual property rights is divided into a series of phrases representing individual meanings though word segmentation basing on the Jieba word segmentation tool. The stop words unrelated to the power industry in the phrases are removed, and the word related the power industry frequency corresponding to the phrases and the frequency of occurrence in the text are counted. According to the characteristics of power industry word aggregation, the phrase can be divided into 26 categories. The text classification model based on keywords of power industry was designed, and the machine learning algorithm Fasttext is used to complete the text classification. Based on each subdivision field of power, the publication situation of intellectual property is analyzed and counted, and the results of power system intellectual property are visualized.
3D Image Creation With Standard Light Effects

The goal of this research work is to investigate the process of producing standard lighting for a bookshelf, as well as the process of rendering that scene. Lights in reality enable you to see things, and lights in 3ds Max do the same job. Furthermore, you may give characteristics to 3ds Max's lighting tools, allowing them to cast shadows and even control atmospheric lighting effects. If you haven't defined any lights, 3ds Max will use the default lighting. This allows you to observe any object you make without using any lights in the scene. The default lights vanish as soon as another light is added to the scene, and they mysteriously reappearance if all other lights in the area are removed. The 3D scene has comprised the boxes of varied parameters and a compound object named ProBoolean. Boxes of lesser length, width, and height are placed over the box of greater parameters, and smaller boxes are cut using ProCutter also named as ProBoolean compound object.
Hand Signal Recognition of Workers on Construction Sites Using Deep Learning Networks

Hand signals, as one of the common ways to communicate, are widely used on construction sites due to their simple but effective nature. However, they may not always be captured timely or interpreted correctly in construction fields, which easily leads to worker injuries/fatalities, work interruption, and stoppage, etc. This paper investigated whether construction hand signals could be captured and interpreted automatically with deep learning networks. A new data set containing 11 classes of hand signals for instructing tower crane operations is created under different scenes. The created data set is employed to compare two state-of-the-art 3D convolutional neural networks (CNNs), namely ResNeXt-101 and Res3D+ConvLSTM+MobileNet, and measure their hand signal recognition performance. The comparison results indicate that a high classification accuracy (99.0%) and a short inference time (0.21 s/gesture) could be achieved, which illustrates the feasibility of using deep learning networks to achieve hand signal recognition in construction.
Improving the Prediction Accuracy of Data-Driven Fault Diagnosis for HVAC Systems by Applying the Synthetic Minority Oversampling Technique

Faulty operation of heating, ventilation, and air conditioning (HVAC) systems in a building can lead to thermal discomfort, wasted energy, and shorter equipment life. Early fault diagnosis in HVAC systems is critical to maintaining indoor environmental comfort, saving energy, and preventing further deterioration of the system. Data-driven predictive models have emerged as a popular approach to fault diagnosis of HVAC systems. For an HVAC system, failures are a small probability event. Therefore, the prediction accuracy of these data-driven approaches is affected by imbalanced data sets. The normal data far outweigh the fault data. Predictive models trained with these imbalanced data sets will perform poorly in fault diagnosis of an HVAC system. To address this limitation, this study aims to examine the potential of the synthetic minority oversampling technique (SMOTE) for sampling fault data points to improve the accuracy of the fault predictive model. To that end, six months of operational data (for example, humidity and temperature) were collected from embedded sensors in the HVAC system. A SMOTE algorithm was applied to increase the ratio of failure to normal data from 0.02 to 0.3. Both the original and improved data sets were used to train the fault predictive model based on different supervised learning algorithms. Results indicated that data sets improved through the SMOTE algorithm increased the accuracy of the predictive model by about 20%. This improvement can lay the groundwork for increasingly proactive facility maintenance.
Research on Automatic Generation of Comment Labels Oriented to Users’ Individualized Needs

As the scale of online shopping users continues expanding, comments and feedback to users of their opinions after purchase and use are of great significance to assist users in purchasing decisions. In order to encourage users to actively comment, some websites provide comment tags for users to choose. These labels only summarize the common features of the products, but do not consider the differences among different products, it is even more unable to meet the personalized expression needs of user comments.. Based on the above reasons, the paper proposes an automatic generation algorithm of comment tags oriented to the personalized needs of users. This algorithm takes a single user as the research object, combines the Seq2Seq (Sequence to sequence learning) model and the part-of-speech-syntax features analysis algorithm to comment on users analyze, capture the description style and product characteristics of a single user in the comment language, combine the user's personalized language with the specific product characteristics, and generate comment tags from the two perspectives of user needs and product characteristics. The paper verifies the effectiveness of the algorithm through real data, compared with the existing algorithm, the algorithm in this paper is more suitable for user needs.
Siamese anchor-free network based on dual attention mechanism for visual tracking

In order to improve the ability of extracting discriminant features and add valid location information, a new Siamese anchor-free network based on dual attention mechanism is proposed&#x2014;DASN. The method introduces the coordinate channel attention module and the spatial attention module to get the context features that contain the precise location information of the target. DASN can enhance the feature extraction by modeling the dependency between channels and positions, thus achieve the improvement of the accuracy of classification and locating and the robustness of tracking. Experimental results show that the proposed method achieves the performance improvement on datasets VOT2018, OTB-2013 and OTB-2015, and also meets the real-time requirements.
Graph Alignment for Cross-Domain Text-to-SQL

Text-to-SQL, the task of translating the natural language utterance into SQL, has attracted much attention recently. Under the cross-domain setting, the traditional semantic parse model is difficult to adapt to the invisible database schema. The key to being able to better handle cross-domain issues lies in the encoding method for modeling the natural language utterance and the database schema and establishing alignment between them. We propose a Graph Alignment for cross-domain Text-to-SQL (GASQL) to provide a method that unified encodes the natural language utterance and the database schema. Following the unified encoding method, we propose a well-designed graph alignment module to further learn the alignment between the natural language utterance and the database schema. We conducted experiments on the challenging Spider benchmark, and the results proved that our model can align the natural language utterance and database schema well, and achieved good results.
A Behavior-Aware Scheme for Personalized Credit Computing

Quality of Service (QoS) and Quality of Experience (QoE) are employed to characterize network performance to improve network resource utilization in web/internet-based services. However, user credit that also contributes to efficient network resources allocation is ignored. Analyzing users’ behaviors to estimate user credit is a common denominator studied in decades, yet they analyze incomplete attributes of users’ behaviors. A general user credit computing method is urgent and challenging. Thus, we propose Quality of Credit (QoC) and define three metrics to systematically achieve user credit computing. Detailed QoC calculation and differential pricing scheme are proposed to regulate users’ behaviors to improve network resource utilization, which is defined as Credit Level Agreement (CLA). We conduct extensive experiments to demonstrate the effectiveness and feasibility of QoC. Actually, QoS and QoE describe network performance metrics, while QoC depicts the credibility of users, so that they can complement each other, and jointly provide support for collaborative computing and various applications at a higher perspective than ever.
Design and implementation of intelligent recommendation Online Autonomous Learning System

The knowledge atlas private cloud teaching assistant system realizes the registration, login, addition, deletion, change and query functions of information and course experimental homework, the behavior data collection of teachers and students to exchange and discuss information, and timely recommend to the course related papers that may be of interest to each other. The system can generate a report from the information for teachers and students and provide it to the dean of the department or the administrative teacher of curriculum management for teaching resource integration and information management.
Website Link Structure Optimization Based on SEO Algorithm

Search engine is an indispensable part of people's life now, and it is also an important function to complete users' normal daily life. It plays a very important role in today's society with developed network technology. Using the SEO algorithm to optimize the link structure of the website, and to a certain extent, optimize the relevant search engine algorithm, which solves the redundant and complex algorithms between traditional websites, which is a leap in the convenient and concise website link structure in the future. Based on the SEO algorithm, this paper optimizes the structure of the relationship between the website link structures and solves the problems existing between the traditional link structures. The experimental results show that after using the SEO algorithm to optimize the link structure of the website, the deficiencies of the traditional link structure are solved, the importance before optimization increases from 0 to 100, and the accessibility of the web page increases from 0 to 400.
Design of Interactive English-Chinese Machine Translation System Based on Mobile Internet

The era of mobile Internet has gradually matured. Facing the trend of globalization, language communication has become an indispensable part. Since the current level of education has not been universalized to everyone's basic grasp, the design of English-Chinese translation software is particularly important when communicating with foreign friends. Therefore, this paper studies an interactive English-Chinese machine translation system. The purpose of this time is to design a translation software that can bring convenience and provide a tool for people to communicate. This paper mainly uses the experimental method and the comparative method to design and test the interactive English-Chinese machine translation system. Experimental data show that the system designed in this paper has better translation ability in attributive post-terms, with an error of only 0.3862.
Lightweight Network Based Real-time Anomaly Detection Method for Caregiving at Home

Using data-driven technologies to support the healthcare of the elderly has been largely celebrated as an effective means. This paper focuses on the issue of using video-based sensing technologies to remotely monitor the activities and conditions of the elderly. Although it is a widely explored field, the high cost and high infrastructural requirements of most existing technologies usually challenge their effectiveness and efficiency in practical caregiving context. To address these challenges, we propose a lightweight network based real-time anomaly detection system, which consists of video-based ADL sensing and pre-processing, AI streaming aggregating and cluster computing. We examine our method by implementing and deploying it into a real-world care facility for the elderly in Shanghai China. The results show that our method has good performance in expansibility, reliability, bandwidth availability, accuracy and privacy protection.
Meeting Summarizer using Natural Language Processing

There is a demand for meeting summary, when entering the virtual world. Meeting transcripts, such as Microsoft Teams transcripts and Google Meet Transcripts, are already available. It's tough to read long transcripts, thus summary transcript production makes it easier to acquire a concise document. A summary can assist in reaching a meeting's key conclusion. The main purpose of this project is to construct a summary from the transcript. This is based on the methodical integration of different natural language processing (NLP) approaches, such as meeting summarization, that were previously developed individually and tested offline using standard datasets. This project takes into account Microsoft Teams transcripts. The Term Frequency Inverse Document Frequency (TF-IDF) method and the Page Rank algorithm were employed in this research as NLP tools.
DH-Kmeans: An Improved K-means Clustering Algorithm based on Dynamic Initial Cluster Center Determination and Hierarchical Clustering

As a classical clustering algorithm, the K-means is widely applied and studied in the information retrieval. However, the performance of this algorithm is sensitive to the selection of initial clustering clusters. Improper selection of these centers will trap the algorithm into the problem of local optimum. Meanwhile, this algorithm cannot process the non-convex datasets effectively. In order to ease these problems, this paper proposes the DHKmeans, an improved K-means based on the dynamic initial clustering center determination and the hierarchical clustering. In the DH-Kmeans, a comprehensive measure which is based on the density parameter and the cluster distance is firstly designed to dynamically determine the initial clustering centers. By this, the problem of local optimum is avoided. Then, the Agglomerative Hierarchical Clustering (AHC) is integrated into the iteration procedures of the DH-Kmeans. In the iteration procedures, the similar small clusters are merged gradually to endow the DH-Kmeans with the ability of precisely processing more types of datasets. Experimental results on testing 25 artificial datasets and six real datasets have demonstrated that the DH-Kmeans is fast, stable, and accurate in data clustering.