Reviewerly/exHarmony huggingface dataset:
An Observational Study of Deep Learning and Automated Evaluation of Cervical Images for Cancer Screening

(Abstracted from J Natl Cancer Inst 2019; doi: 10.1093/jnci/djy225) Ninety-nine percent of cervical cancers cases are caused by infections with a human papillomavirus (HPV). This cancer can be prevented in most women by cervical cancer screening and HPV vaccination.
Cybercrime has evolved: it's time cyber security did too

On August 6, 1991 the web went live to the world. At the time, most people didn't even know what the Internet was, let alone imagine what it would come to mean. What was initially a network of static web pages has become an ever-evolving information ecosystem. From streaming media to banking online to turning the lights on in our homes, the web is now an integral part of our daily lives. The Internet has changed the way we communicate and has helped create a global economy. But with great change comes great opportunity – one that malicious hackers have not been slow to exploit. As the Internet, and what we use it for, evolves every day, so do the tools and techniques used by these hackers. And so organisations need to evolve too. There are five key elements that play a part in ensuring the advancement of a business's cyber security strategy: collaboration, assessment, development, application and apprenticeships, says Laura Lee of Circadence.
ThisIsCompetition at SemEval-2019 Task 9: BERT is unstable for out-of-domain samples

This paper describes our system, Joint Encoders for Stable Suggestion Inference (JESSI), for the SemEval 2019 Task 9: Suggestion Mining from Online Reviews and Forums.JESSI is a combination of two sentence encoders: (a) one using multiple pre-trained word embeddings learned from log-bilinear regression (GloVe) and translation (CoVe) models, and (b) one on top of word encodings from a pre-trained deep bidirectional transformer (BERT).We include a domain adversarial training module when training for outof-domain samples.Our experiments show that while BERT performs exceptionally well for in-domain samples, several runs of the model show that it is unstable for out-ofdomain samples.The problem is mitigated tremendously by (1) combining BERT with a non-BERT encoder, and (2) using an RNNbased classifier on top of BERT.Our final models obtained second place with 77.78% F-Score on Subtask A (i.e.in-domain) and achieved an F-Score of 79.59% on Subtask B (i.e.out-of-domain), even without using any additional external data.
Role of Soft Computing Techniques in Software Effort Estimation: An Analytical Study

In developing software, software effort estimation plays an important role in the success of a software project. Inaccurate, inconsistent, and unreliable estimation of a software leads to failure. Because of various special specifications and changes in the requirements, accurate Software Effort Estimation (SEE) for developing software is a difficult task. This software effort estimation must be calculated effectively to avoid unforeseen results. At early development stages, these inabilities to maintain certainty, inaccurate, unreliability are the limitations of expert judgment and algorithmic effort estimation models. After that, attention was turned to machine learning and soft computing methods. Soft computing is an association with the methodologies centering on fuzzy logic, artificial neural networks, and evolutionary computation. These methods will provide flexible information processing capability for handling real-life situations. The main aim of the study is to provide an in-depth review of software effort estimation from the initial stages that included expert judgment-based SEE to the latest techniques of soft computing.
An Optimized Approach for Secure Data Transmission Using Spread Spectrum Audio Steganography, Chaos Theory, and Social Impact Theory Optimizer

Being easy to understand and simple to implement, substitution technique of performing steganography has gain wide popularity among users as well as attackers. Steganography is categorized into different types based on the carrier file being used for embedding data. The audio file is focused on hiding data in this paper. Human has associated an acute degree of sensitivity to additive random noise. An individual is able to detect noise in an audio file as low as one part in 10 million. Given this limitation, it seems that concealing information within audio files would be a pointless exercise. Human auditory system (HAS) experiences an interesting behavior known as masking effect, which says that the threshold of hearing of one type of sound is affected by the presence of another type of sound. Because of this property, it is possible to hide some data inside an audio file without being noticed. In this paper, the research problem for optimizing the audio steganography technique is laid down. In the end, a methodology is proposed that effectively resolves the stated research problem and finally the implementation results are analyzed to ensure the effectiveness of the given solution.
MEDIATING ROLE OF EMPLOYEE COMMITMENT IN THE RELATIONSHIP BETWEEN TRANSFORMATIONAL LEADERSHIP STYLE AND EMPLOYEE PERFORMANCE

Purpose: This study intends to highlight the importance of Employee Commitment as a bridge between Transformational Leadership and Employee Performance in the construction companies.&#x0D; Methodology: The respondents were selected randomly from the list of construction workers registered with the construction firms in Jordan. The selected respondents were given a self-administered questionnaire to attend at their own convenient time. The studies employed Structural Equation Modelling in IBM-SPSS-AMOS 24.0 to model and estimate the inter-relationships among the constructs in the study.&#x0D; Result: The results showed transformational leadership has a positive and significant influence on both employee commitment and employee performance. The results also indicated that employee commitment has a positive and significant influence on employee performance. More importantly, the study found that employee commitment partially mediates the relationship between transformational leadership style and employee performance.&#x0D; Applications: The results of his study highlighted the importance of employee commitment in generating their work performance, especially in the construction industry.&#x0D; Novelty/Originality: At present, the study on the mediating role of employee commitment in the relationship between transformational leadership style and employee performance is limited, especially in the construction industry where the employee commitment is extremely important to ensure the project can be delivered on schedule.
Worst Cases Policy Gradients

Recent advances in deep reinforcement learning have demonstrated the capability of learning complex control policies from many types of environments. When learning policies for safety-critical applications, it is essential to be sensitive to risks and avoid catastrophic events. Towards this goal, we propose an actor-critic framework that models the uncertainty of the future and simultaneously learns a policy based on that uncertainty model. Specifically, given a distribution of the future return for any state and action, we optimize policies for varying levels of conditional Value-at-Risk. The learned policy can map the same state to different actions depending on the propensity for risk. We demonstrate the effectiveness of our approach in the domain of driving simulations, where we learn maneuvers in two scenarios. Our learned controller can dynamically select actions along a continuous axis, where safe and conservative behaviors are found at one end while riskier behaviors are found at the other. Finally, when testing with very different simulation parameters, our risk-averse policies generalize significantly better compared to other reinforcement learning approaches.
An Efficient Victim Prediction for Sybil Detection in Online Social Network

With the rapid development of Online Social Networks (OSNs), OSNs have become a rewarding target for attackers. One particularly representative attack is the Sybil attack, Sybil accounts create a lot of malicious activities, which poses a serious threat to the safety of normal users. Many existing Syibl detection mechanisms have preconditions or assumptions, for example, limiting the number of attacking edges. But in general, the assumption is only a handful, often does not hold in real life scenarios. When the assumption is not established, these mechanisms perform poorly. In this work, We propose a scheme that uses victim prediction to improve Sybil detection accuracy. And our solution does not need to be based on any assumptions. First, we designed a victim classifier to predict victims. Then, based on the prediction results, the edge weights in the graph model are modified. Next, trust propagation is performed on the graph model. Finally, sorting all accounts. The experimental results show that our scheme can ensure that the majority of normal users rank higher than Sybils, thus classifying normal users and Sybils.
Internet of Things Health Detection System in Steel Structure Construction Management

In recent years, the development of computer network technology, especially the hot Internet of things, has brought great convenience and comfort to people's life. Steel structure is also more and more widely used in the field of construction in China. The purpose of this study is to explore the application of Internet of things health monitoring system in steel structure construction management. In this study, through the use of example analysis, comprehensive data analysis and other methods, combined with steel structure health monitoring system and construction process management analysis. The results show that the relative deviation of the data within 1-33 is mostly less than 10%, but the relative deviation of 34-40 suddenly increases to 60%, and the actual data suddenly decreases, resulting in the deviation of about 50%. It is concluded that the time series model established in this study can objectively reflect the change trend and law of strain data, has certain prediction accuracy, and has certain value significance for monitoring data analysis and construction management of steel building structures.
Data Security Defense and Algorithm for Edge Computing Based on Mean Field Game

With the development of the Internet of Things, the edge devices are increasing. Cyber security issues in edge computing have also emerged and caused great concern. We propose a defense strategy based on Mean field game ... | Find, read and cite all the research you need on Tech Science Press
Blockchain-based cross-domain authentication strategy for trusted access to mobile devices in the IoT

In this paper we propose a blockchain-based cross-domain authentication strategy. This strategy uses the cosmos network model to enable mobile devices to reliably access external domain networks when moving across domains. Our test results prove the feasibility of this strategy and have better performance than other cross-domain authentication schemes.
Stereo Dense Image Matching by Adaptive Fusion of Multiple-Window Matching Results

Traditional stereo dense image matching (DIM) methods normally predefine a fixed window to compute matching cost, while their performances are limited by the matching window sizes. A large matching window usually achieves robust matching results in weak-textured regions, while it may cause over-smoothness problems in disparity jumps and fine structures. A small window can recover sharp boundaries and fine structures, while it contains high matching uncertainties in weak-textured regions. To address the issue above, we respectively compute matching results with different matching window sizes and then proposes an adaptive fusion method of these matching results so that a better matching result can be generated. The core algorithm designs a Convolutional Neural Network (CNN) to predict the probabilities of large and small windows for each pixel and then refines these probabilities by imposing a global energy function. A compromised solution of the global energy function is utilized by breaking the optimization into sub-optimizations of each pixel in one-dimensional (1D) paths. Finally, the matching results of large and small windows are fused by taking the refined probabilities as weights for more accurate matching. We test our method on aerial image datasets, satellite image datasets, and Middlebury benchmark with different matching cost metrics. Experiments show that our proposed adaptive fusion of multiple-window matching results method has a good transferability across different datasets and outperforms the small windows, the median windows, the large windows, and some state-of-the-art matching window selection methods.
Deep Transferring Quantization

Network quantization is an effective method for network compression. Existing methods train a low-precision network by fine-tuning from a pre-trained model. However, training a low-precision network often requires large-scale labeled data to achieve superior performance. In many real-world scenarios, only limited labeled data are available due to expensive labeling costs or privacy protection. With limited training data, fine-tuning methods may suffer from the overfitting issue and substantial accuracy loss. To alleviate these issues, we introduce transfer learning into network quantization to obtain an accurate low-precision model. Specifically, we propose a method named deep transferring quantization (DTQ) to effectively exploit the knowledge in a pre-trained full-precision model. To this end, we propose a learnable attentive transfer module to identify the informative channels for alignment. In addition, we introduce the Kullback-Leibler (KL) divergence to further help train a low-precision model. Extensive experiments on both image classification and face recognition demonstrate the effectiveness of DTQ.
Towards Playing Full MOBA Games with Deep Reinforcement Learning

MOBA games, e.g., Honor of Kings, League of Legends, and Dota 2, pose grand challenges to AI systems such as multi-agent, enormous state-action space, complex action control, etc. Developing AI for playing MOBA games has raised much attention accordingly. However, existing work falls short in handling the raw game complexity caused by the explosion of agent combinations, i.e., lineups, when expanding the hero pool in case that OpenAI's Dota AI limits the play to a pool of only 17 heroes. As a result, full MOBA games without restrictions are far from being mastered by any existing AI system. In this paper, we propose a MOBA AI learning paradigm that methodologically enables playing full MOBA games with deep reinforcement learning. Specifically, we develop a combination of novel and existing learning techniques, including curriculum self-play learning, policy distillation, off-policy adaption, multi-head value estimation, and Monte-Carlo tree-search, in training and playing a large pool of heroes, meanwhile addressing the scalability issue skillfully. Tested on Honor of Kings, a popular MOBA game, we show how to build superhuman AI agents that can defeat top esports players. The superiority of our AI is demonstrated by the first large-scale performance test of MOBA AI agent in the literature.
Investigating Tree Family Machine Learning Techniques for a Predictive System to Unveil Software Defects

Software defects prediction at the initial period of the software development life cycle remains a critical and important assignment. Defect prediction and correctness leads to the assurance of the quality of software systems and has remained integral to study in the previous years. The quick forecast of imperfect or defective modules in software development can serve the development squad to use the existing assets competently and effectively to provide remarkable software products in a given short timeline. Hitherto, several researchers have industrialized defect prediction models by utilizing statistical and machine learning techniques that are operative and effective approaches to pinpoint the defective modules. Tree family machine learning techniques are well-thought-out to be one of the finest and ordinarily used supervised learning methods. In this study, different tree family machine learning techniques are employed for software defect prediction using ten benchmark datasets. These techniques include Credal Decision Tree (CDT), Cost-Sensitive Decision Forest (CS-Forest), Decision Stump (DS), Forest by Penalizing Attributes (Forest-PA), Hoeffding Tree (HT), Decision Tree (J48), Logistic Model Tree (LMT), Random Forest (RF), Random Tree (RT), and REP-Tree (REP-T). Performance of each technique is evaluated using different measures, i.e., mean absolute error (MAE), relative absolute error (RAE), root mean squared error (RMSE), root relative squared error (RRSE), specificity, precision, recall, F-measure (FM), G-measure (GM), Matthew’s correlation coefficient (MCC), and accuracy. The overall outcomes of this paper suggested RF technique by producing best results in terms of reducing error rates as well as increasing accuracy on five datasets, i.e., AR3, PC1, PC2, PC3, and PC4. The average accuracy achieved by RF is 90.2238%. The comprehensive outcomes of this study can be used as a reference point for other researchers. Any assertion concerning the enhancement in prediction through any new model, technique, or framework can be benchmarked and verified.
BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction

We study the challenging task of neural network quantization without end-to-end retraining, called Post-training Quantization (PTQ). PTQ usually requires a small subset of training data but produces less powerful quantized models than Quantization-Aware Training (QAT). In this work, we propose a novel PTQ framework, dubbed BRECQ, which pushes the limits of bitwidth in PTQ down to INT2 for the first time. BRECQ leverages the basic building blocks in neural networks and reconstructs them one-by-one. In a comprehensive theoretical study of the second-order error, we show that BRECQ achieves a good balance between cross-layer dependency and generalization error. To further employ the power of quantization, the mixed precision technique is incorporated in our framework by approximating the inter-layer and intra-layer sensitivity. Extensive experiments on various handcrafted and searched neural architectures are conducted for both image classification and object detection tasks. And for the first time we prove that, without bells and whistles, PTQ can attain 4-bit ResNet and MobileNetV2 comparable with QAT and enjoy 240 times faster production of quantized models. Codes are available at https://github.com/yhhhli/BRECQ.
TexSmart: A System for Enhanced Natural Language Understanding

This paper introduces TexSmart, a text understanding system that supports finegrained named entity recognition (NER) and enhanced semantic analysis functionalities.Compared to most previous publicly available text understanding systems and tools, TexSmart holds some unique features.First, the NER function of TexSmart supports over 1,000 entity types, while most other public tools typically support several to (at most) dozens of entity types.Second, TexSmart introduces new semantic analysis functions like semantic expansion and deep semantic representation, that are absent in most previous systems.Third, a spectrum of algorithms (from very fast algorithms to those that are relatively slow but more accurate) are implemented for one function in TexSmart, to fulfill the requirements of different academic and industrial applications.The adoption of unsupervised or weakly-supervised algorithms is especially emphasized, with the goal of easily updating our models to include fresh data with less human annotation efforts.
Maria: A Visual Experience Powered Conversational Agent

Arguably, the visual perception of conversational agents to the physical world is a key way for them to exhibit the human-like intelligence.Image-grounded conversation is thus proposed to address this challenge.Existing works focus on exploring the multimodal dialog models that ground the conversation on a given image.In this paper, we take a step further to study image-grounded conversation under a fully open-ended setting where no paired dialog and image are assumed available.Specifically, we present Maria, a neural conversation agent powered by the visual world experiences which are retrieved from a large-scale image index.Maria consists of three flexible components, i.e., text-to-image retriever, visual concept detector and visual-knowledge-grounded response generator.The retriever aims to retrieve a correlated image to the dialog from an image index, while the visual concept detector extracts rich visual knowledge from the image.Then, the response generator is grounded on the extracted visual knowledge and dialog context to generate the target response.Extensive experiments demonstrate Maria outperforms previous state-of-the-art methods on automatic metrics and human evaluation, and can generate informative responses that have some visual commonsense of the physical world.
A User-Adaptive Layer Selection Framework for Very Deep Sequential Recommender Models

Sequential recommender systems (SRS) have become a research hotspot in recent studies. Because of the requirement in capturing user's dynamic interests, sequential neural network based recommender models often need to be stacked with more hidden layers (e.g., up to 100 layers) compared with standard collaborative filtering methods. However, the high network latency has become the main obstacle when deploying very deep recommender models into a production environment. In this paper, we argue that the typical prediction framework that treats all users equally during the inference phase is inefficient in running time, as well as sub-optimal in accuracy. To resolve such an issue, we present SkipRec, an adaptive inference framework by learning to skip inactive hidden layers on a per-user basis. Specifically, we devise a policy network to automatically determine which layers should be retained and which layers are allowed to be skipped, so as to achieve user-specific decisions. To derive the optimal skipping policy, we propose using gumbel softmax and reinforcement learning to solve the non-differentiable problem during backpropagation. We perform extensive experiments on three real-world recommendation datasets, and demonstrate that SkipRec attains comparable or better accuracy with much less inference time.
A Comparative Study on Machine Learning Algorithms for Predicting Breast Cancer Prognosis in Improving Clinical Trials

In recent years, machine learning algorithms have been more and more used in healthcare industry, especially in research areas involving human participants such as clinical trials and areas where the data is too expensive to collect. This research project has conducted a comparative study on three well-known machine learning methods: Logistic Regression (LR), Support Vector Machine (SVM), and Naïve Bayes (NB) against the same dataset for predicting breast cancer prognosis in improving clinical trials. The experiment results have provided a comprehensive view of the patient's risk levels and risk factors to clinicians that benefit in effective and efficient treatment. This research has also demonstrated that different machine learning algorithms against the same dataset for breast cancer prognosis can have a difference in both performance and accuracy. Therefore, the comparative study on different machine learning algorithms can identify the most suitable machine learning algorithm to achieve cost-effective clinical trials.