Reviewerly/exHarmony huggingface dataset:
Deep Learning-Based Detection for Traffic Control

Often urban intersections have a problem with long queues, but the traffic flow during the green light is relatively small. In this paper, we propose an integrated, machine-vision control mode process for controlling traffic lights. The method proposed in this paper is to obtain real-time video images of the road through the monitoring camera, detect the number of vehicles in the different areas, and dynamically control the traffic signal. Our real-time system fundamentally guarantees the smooth traffic flow through the intersection to avoid frequent traffic-congestion.
Accuracy of ultrasonic artificial intelligence in diagnosing benign and malignant breast diseases

Abstract Background: Artificial intelligence system is a deep learning system based on computer-assisted ultrasonic image diagnosis, which can extract morphological features of breast mass and conduct objective and efficient image analysis, thus automatically intelligent classification of breast mass, avoiding subjective error and improving the accuracy of diagnosis. [1–2] A large number of studies have confirmed that artificial intelligence (AI) has high effectiveness and reliability in the differential diagnosis of benign and malignant breast diseases. [3–4] However, the results of these studies have been contradictory. Therefore, this meta-analysis tested the hypothesis that artificial intelligence system is accurate in distinguishing benign and malignant breast diseases. Methods: We will search PubMed, Web of Science, Cochrane Library, and Chinese biomedical databases from their inceptions to the November 20, 2021, without language restrictions. Two authors will independently carry out searching literature records, scanning titles and abstracts, full texts, collecting data, and assessing risk of bias. Review Manager 5.2 and Stata14.0 software will be used for data analysis. Results: This systematic review will determine the accuracy of AI in the differential diagnosis of benign and malignant breast diseases. Conclusion: Its findings will provide helpful evidence for the accuracy of AI in the differential diagnosis of benign and malignant breast diseases. Systematic review registration: INPLASY2021110087.
An Online Active Multi-label Classification Algorithm Based on a Hybrid Label Query Strategy

Online active learning can both effectively reduce the labeling cost and process large-scale or streaming data, and thus it has become an important research area in machine learning. However, there are few online active learning studies regarding multi-label classification tasks. And the existing online active multi-label classification algorithms either ignore the label correlation and the label cardinality inconsistency, or can only be applied to a particular application field. In this paper, we propose a novel online active multi-label classification algorithm, termed MSGDA to overcome the drawbacks of the existing algorithms. Our hybrid label query strategy in MSGDA takes account of both the max-margin predictive uncertainty and the label cardinality inconsistency for measuring the importance of an unlabeled instance. Meanwhile, our online updating rule exploits the label correlation. The experimental results on six multi-label datasets demonstrate the superiority of our proposed algorithm.
Automated Adequacy Assessment of Cervical Cytology Samples Using Deep Learning

Cervical cancer has been among the most common causes of cancer death in women. Screening tests such as liquid-based cytology (LBC) were responsible for a substantial decrease in mortality rates. Still, visual examination of cervical cells on microscopic slides is a time-consuming, ambiguous and challenging task, aggravated by inadequate sample quality (e.g. low cellularity or the presence of obscuring factors like blood or inflammation). While most works in the literature are focused on the automated detection of cervical lesions to support diagnosis, to the best of our knowledge, none of them address the automated assessment of sample adequacy, as established by The Bethesda System (TBS) guidelines. This work proposes a new methodology for automated adequacy assessment of cervical cytology samples. Since the most common reason for rejecting samples is the low count of the squamous nucleus, our approach relies on a deep learning object detection model for the detection and counting of different types of nuclei present in LBC samples. A dataset of 41 samples with a total of 42387 nuclei manually annotated by experienced specialists was used, and the best solution proposed achieved promising results for the automated detection of squamous nuclei (AP of 82.4%, Accuracy of 79.8%, Recall of 73.8% and F1 score of 81.5%). Additionally, by merging the developed automated cell counting approach with the adequacy criteria stated by the TBS guidelines, we validated our approach by correctly classifying an entire subset of 12 samples as adequate or inadequate.
Optimizing microservices with hyperparameter optimization

In the last few years, the cloudification of applications requires new concepts and techniques to fully reap the benefits of the new computing paradigm. Among them, the microservices architectural style, which is inspired by service-oriented architectures, has gained attention from both industry and academia. However, decomposing a monolith into multiple microservices also creates several challenges across the application’s lifecycle. In this work, we focus on the operation aspect of microservices, and present our novel proposal to enable self-optimizing microservices systems based on grid search and random search techniques. The initial results show our approach is able to optimize the latency performance of microservices to up to 10.56%.
Probabilistic Dialogue Model Combined With VAE For Task-oriented Dialogue System

Dialogue state tracking and dialogue response generation are two crucial modules in a task-oriented dialogue system. Recent works on task-oriented dialogue systems have mainly focused on studying the two modules separately without considering the end-to-end joint learning of them. For dialogue state tracking, many methods analyze the entire dialogue history, and this may result in larger memory usage and computational costs with the increase of dialogue histories. For dialogue response generation, oracle labels of the dialogue state are directly used to generate the response regardless of the vital impact the dialogue state tracking on it. In this paper, we propose a Probabilistic Graph-based end-to-end model combined with Variational Auto-Encoder(PGVAE), which jointly trains the dialogue state tracking and the dialogue response generation using the simplified dialogue context rather than the entire dialogue history. We address the issue of the loss of contextual information because of ignoring the history of dialogue by utilizing the dialogue state of the last turn as a summary of the history. Experimental results on real-world datasets of MultiWOZ 2.0 and MultiWOZ 2.1 demonstrate the effectiveness of the proposed model for task-oriented dialogue systems.
Assessing the Value of Executive Leadership Coaches for Cybersecurity Project Managers

With the complex nature of impacts of cybersecurity breaches, it is critical that organizational have cybersecurity project managers that can make sound managerial and leadership decisions. Often cybersecurity project managers act quickly with managerial decisions at work. When time is of the essence, strategic thinking, strategic communication, and strategic decision making are critical to organizational effectiveness and productivity. Decision making and strategic communications are just a few skills that executive leadership coaches can teach. This article explores the values and potential benefits of executive coaching as a leadership development tool for information technology and cybersecurity project managers.
Structure Synthesis for Extended Robot State Automata

Opening up untapped potential in smaller enterprises requires methods for robot programming usable by non-experts. In a previous work on an automata-based programming approach without a graphical interface, linking to previous states to close loop structures was identified as challenging for users. In this paper, we propose an approach to generate such structures automatically from small overlap within the programmed instructions. To this end, a marking function for state pairs is calculated, which evaluates whether the structures starting from two states are conflicting or in agreement (and in the latter case, how much). Algorithms for calculating and utilizing this marking function are presented. Our experiments on an example task confirm the effectiveness of the approach.
Unit-Modulus Wireless Federated Learning Via Penalty Alternating Minimization

Wireless federated learning (FL) is an emerging machine learning paradigm that trains a global parametric model from distributed datasets via wireless communications. This paper proposes a unit-modulus wireless FL (UMWFL) framework, which simultaneously uploads local model parameters and computes global model parameters via optimized phase shifting. The proposed framework avoids sophisticated baseband signal processing, leading to both low communication delays and implementation costs. A training loss bound is derived and a penalty alternating minimization (PAM) algorithm is proposed to minimize the nonconvex nonsmooth loss bound. Experimental results in the Car Learning to Act (CARLA) platform show that the proposed UMWFL framework with PAM algorithm achieves smaller training losses and testing errors than those of the benchmark scheme.
A Realtime Lightweight Detection Network Framework for UAV Identification

Detection Neural networks are memory intensive, making them difficult to deploy on embedded system with limited hardware resources. To solve the real-time problem of airborne recognition, we introduce a lightweight detection network framework based on the YOLOv3-tiny, that work together to reduce the volume of network by 3× with accuracy increasing by 1% and detection speed reaching 15 fps. The method is based on the YOLOv3-tiny, the size of which is 33.1 MB. Its reference time and mAP are 0.085 s and 78.6%, respectively. Next, we use the structure compression and improved channel pruning strategy to reduce the network volume to 10.33 MB without affecting their accuracy. Considering that model compression will have an impact on the performance of the network, we also adopt some tricks, such as adjusting learning rate and adding attention mechanism, to further improve the identification accuracy, from 78.6% to 79.3%. These measures enable the model to run better and faster on the resource-constrained airborne embedded devices or mobile applications.
Fuzzing Deep Learning Models against Natural Robustness with Filter Coverage

Coverage-guided fuzzing on deep learning (DL) models can generate natural adversarial variants. However, no existing fuzzing work can show that covering or uncovering a coverage element of a test adequacy criterion can significantly change the accuracy of the DL model under test. This paper proposes a novel testing criterion, Filter Coverage, and a novel fuzzing technique FilterFuzz guided by this criterion. To the best of our knowledge, Filter Coverage is the first test adequacy criterion able to identify such a coverage element-blamed filter-in a DL model using convolutional filters by demonstrating a cause-and-effect chain. Both Filter Coverage and Neuron Coverage measure whether individual neurons are activated, but Filter Coverage is more selective and collective and higher in the abstraction level. FilterFuzz, guided by Filter Coverage, tackles the challenge of achieving a higher rate of generating natural adversarial variants against natural robustness. Our case study shows that FilterFuzz is significantly more effective than fuzzing guided by Neuron Coverage by 33% and produces more diverse kinds of such variants. Moreover, when applying to the problem of labeling cost reduction on generated natural variants, Filter Coverage identifies 4.3x to 4.7x of natural adversarial variants than random reordering. Our work also calls for a re-examination of the previous ineffective conclusions of Neuron Coverage.
Anomaly Detection on Static and Dynamic Graphs Using Graph Convolutional Neural Networks

Anomalies represent rare observations that vary significantly from others. Anomaly detection intended to discover these rare observations and has the power to prevent detrimental events, such as financial fraud, network intrusion, and social spam. However, conventional anomaly detection methods cannot handle this problem well because of the complexity of graph data (e.g., irregular structures, relational dependencies, node/edge types/attributes/directions/multiplicities/weights, large scale, etc.) (Ma X, Wu J, Xue S, Yang J, Zhou C, Sheng QZ, Xiong H, Akoglu L. IEEE Trans Knowl Data Eng, 2021 [1]). Thanks to the rise of deep learning in solving these limitations, graph anomaly detection with deep learning has obtained an increasing attention from many scientists recently. However, while deep learning can capture unseen patterns of multi-dimensional Euclidean data, there is a huge number of applications where data are represented in the form of graphs. Graphs have been used to represent the structural relational information, which raises the graph anomaly detection problem—identifying anomalous graph objects (i.e., vertex, edges, sub-graphs, and change detection). These graphs can be constructed as a static graph, or a dynamic graph based on the availability of timestamp. Recent years have observed a huge efforts on static graphs, among which Graph Convolutional Network (GCN) has appeared as a useful class of models. A challenge today is to detect anomalies with dynamic structures. In this chapter, we aim at providing methods used for detecting anomalies in static and dynamic graphs using graph analysis, graph embedding, and graph convolutional neural networks. For static graphs we categorize these methods according to plain and attribute static graphs. For dynamic graphs we categorize existing methods according to the type of anomalies that they can detect. Moreover, we focus on the challenges in this research area and discuss the strengths and weaknesses of various methods in each category. Finally, we provide open challenges for graph anomaly detection using graph convolutional neural networks on dynamic graphs.
An improvement on Schmidt’s bound on the number of number fields of bounded discriminant and small degree

Abstract We prove an improvement on Schmidt’s upper bound on the number of number fields of degree n and absolute discriminant less than X for $6\leq n\leq 94$ . We carry this out by improving and applying a uniform bound on the number of monic integer polynomials, having bounded height and discriminant divisible by a large square, that we proved in a previous work [7].
Deep Discrete Hashing for Label Distribution Learning

Label Distribution Learning (LDL) can better describe the real-world data by learning a set of label distributions instead of discrete binary labels. Particularly, hashing-based LDL has achieved promising performance due to its desirable advantages of fast similarity computation and extremely low storage cost. However, existing hashing-based LDL methods are still shallow learning methods, which cannot deeply capture the implicit data semantics, and meanwhile fail to fully model the semantic data relations. In this letter, we propose an effective and efficient Deep Discrete Hashing for Label Distribution Learning (DDH-LDL) method, which develops the first deep hashing framework for LDL. Specifically, DDH-LDL captures implicit semantic information by multi-layer non-linear transformation, and simultaneously preserves the modeled semantic relations of instances into hash codes via semantic message aggregation on Graph Convolutional Network (GCN). Furthermore, we elaborately design a discrete optimization module that is seamlessly integrated into our proposed deep hashing framework to reduce the binary quantization errors. Experiments on several widely tested datasets verify the superiority of the proposed method on both learning accuracy and efficiency.
End-to-End Pre-trained Dialogue System for Automatic Diagnosis

With the development of medical technology, Chinese medical resources are extremely scarce. At this necessary time, the development of dialogue agents to interact with patients and provide clinical advice has attracted more and more attention. In the task of generative medical dialogue, the end-to-end method is often used to establish the model. However, traditional end-to-end models often generate deficient relevance to medical dialogue. Towards this end, we propose to integrate medical information into initial pre-trained model and use the division of sentence based on “words and expressions” to improve the accuracy of medical entity recall, which will make the model have a deeper understanding of medical field. Finally, we use the Chinese medical dialogue MedDG [1] to fine-tune the model, so that the model can give the reply to the doctor’s clinical inquiry for the disease content from the patient. The experimental results show that our framework achieves higher accuracy in disease diagnosis, which won the fourth place in the 2021 medical dialogue generation task containing Chinese.
A Deep Convolutional Neural Network-Based Speech-to-Text Conversion for Multilingual Languages

Designers have been processing speech for decades for a wide variety of applications, from mobile communications to automatic reading machines, among others. By eliminating the need for alternative communication methods, speech recognition saves time and money. In the world of electronics and computers, speech is rarely employed because of the complexity and variety of voice signals and noises. Today's technologies allow us to process speech signals quickly and accurately and recognize the text. A real-time translation of speech into written language requires specific techniques, as it must be extremely rapid and nearly error-free to make sense. A person's speech is the most natural and important form of communication. This system converts human speech into a string of words using the speech-to-text (STT) technology. This system's goal is to extract, classify, and recognize information about speech in a variety of ways. Using convolutional neural networks (CNNs) for voice classification, the proposed system is developed. The input signals are classified by CNN on its own since it is a self-optimizing neural network. In addition, high-level features are extracted by convolutional and pooling layer, where the data is classified using fully connected (FC) layer. A database contains pre-recorded speech. Testing and training are the two key aspects of the database. In the training phase, samples from the training database are run through a series of tests to determine their characteristics. Each sample's features are combined to create a feature vector that is stored for future reference. When a sample is supplied to the system for analysis, its features are extracted. There is a comparison between these features and the reference feature vector, and the words with highest similarity are output. MATLAB (V2018a) environment is used to design the system.
A Variational Graph Autoencoder for Manipulation Action Recognition and Prediction

Despite decades of research, understanding human manipulation activities is, and has always been, one of the most attractive and challenging research topics in computer vision and robotics. Recognition and prediction of observed human manipulation actions have their roots in the applications related to, for instance, human-robot interaction and robot learning from demonstration. The current research trend heavily relies on advanced convolutional neural networks to process the structured Euclidean data, such as RGB camera images. These networks, however, come with immense computational complexity to be able to process high dimensional raw data. Different from the related works, we here introduce a deep graph autoencoder to jointly learn recognition and prediction of manipulation tasks from symbolic scene graphs, instead of relying on the structured Euclidean data. Our network has a variational autoencoder structure with two branches: one for identifying the input graph type and one for predicting the future graphs. The input of the proposed network is a set of semantic graphs which store the spatial relations between subjects and objects in the scene. The network output is a label set representing the detected and predicted class types. We benchmark our new model against different state-of-the-art methods on two different datasets, MANIAC and MSRC-9, and show that our proposed model can achieve better performance. We also release our source code https://github.com/gamzeakyol/GNet.
Comparative Study of Production of Three Grid-Connected PV Systems in two Cities of Morocco

Solar energy technologies do not produce air pollution or greenhouse gases when they operate. The use of solar energy can have a positive effect on the environment when it replaces or reduces the use of other energy sources that have greater environmental impacts. However, there are environmental issues related to the production and use of solar energy technologies to select the most suitable technologies for each location. In this work, a comparative study was conducted of the various photovoltaic technologies for one year in each of the two Moroccan cities, Rabat and Mohammedia, located approximately 57.33 km apart. The three panel technologies studied are monocrystalline technology (m-Si), polycrystalline technology (p-Si) and amorphous technology (a-Si) with a power of 2.04 KWh each. The photovoltaic (PV) systems were installed in order to determine which technology produces the most clean electricity. It is based on a comparison of the monthly production of the three technologies in the two cities, and the justification of the behaviour of the three technologies using two similar weather stations installed near the solar panels. In such a way, the determination of the climatic conditions will justify the behaviour of the three technologies. The comparative analysis included annual energy efficiency, performance ratio, annual energy density and system efficiency. Thus, with these performances, we can observe that photovoltaic installations with polycrystalline are appropriate in Mohammedia, whereas in Rabat they are monocrystalline.
WordNet Based Hybrid Model for Query Expansion

An important task of the retrieval system is to maximize precision and recall value. Several methods have been introduced to achieve this goal. Query expansion is one of the methods that try to maximize the mean average precision value. The proposed method expands the original query by appending the mixture of terms retrieved from the local method (top "k" ranked initially retrieved documents) and global method respectively. FIRE 2011 adhoc English test collection has been used to evaluate the performance of the proposed method. The mean average precision of the proposed method is 0.3037. An improvement of 1.71% and 2.25% is observed with respect to the original query and BM25 model respectively.