Reviewerly/exHarmony huggingface dataset:
Incorporating experts’ judgment into machine learning models

Machine learning (ML) models have been quite successful in predicting outcomes in many applications. However, in some cases, domain experts might have a judgment about the expected outcome that might conflict with the prediction of ML models. One main reason for this is that the training data might not be totally representative of the population. In this paper, we present a novel framework that aims at leveraging experts’ judgment to mitigate the conflict. The underlying idea behind our framework is that we first determine, using a generative adversarial network, the degree of representation of an unlabeled data point in the training data. Then, based on such degree, we correct the machine learning model’s prediction by incorporating the experts’ judgment into it, where the higher that aforementioned degree of representation, the less the weight we put on the expert intuition that we add to our corrected output, and vice-versa. We perform multiple numerical experiments on synthetic data as well as two real-world case studies (one from the IT services industry and the other from the financial industry). All results show the effectiveness of our framework; it yields much higher closeness to the experts’ judgment with minimal sacrifice in the prediction accuracy, when compared to multiple baseline methods. We also develop a new evaluation metric that combines prediction accuracy with the closeness to experts’ judgment. Our framework yields statistically significant results when evaluated on that metric.
A Novel Processing of Scalable Web Log Data Using Map Reduce Framework

The traditional RDBMS is not sufficient to manage highly scalable web log data. Hadoop framework can overcome the problems raised in traditional systems. Hadoop is a sophisticated framework to manage gigantic amounts of scalable data. It contains a Map Reduce framework which helps in writing applications to process enormous volumes of data in parallel, on voluminous clusters of vendible hardware in a reliable manner. Analysis of Weblog data needs to be processed in a distributed environment due to its nature of huge volume and also the generation of online streaming data. Hadoop framework with pig scripting is used for extracting the dynamic patterns from weblog data in a distributed environment. Diverse situations in the data set are rendered by using status codes. Frequency of status codes is also analyzed. Managing a huge volume of data using distributed processing considerably reduces the execution time.
Synthesis and Characterization of TiO2-Chitosan Beads and Its Application as A Degradation Agent of Methylene Blue

Dye waste is an environmental problem that has a negative impact on the ecosystem. One way to overcome this is by chemically degrading dyes using photocatalyst materials. TiO2 is one of the photocatalysts that can be synthesized by utilizing microwave radiation which is transferred directly to the material. This method takes a short amount of time so it can be completed in a very short time. TiO2 powder has the disadvantage that it is difficult to separate from the waste solution. TiO2 can be modified into beads by utilizing chitosan to produce TiO2-chitosan beads (TCB). In this research, the synthesis of TiO2 was carried out using microwave, followed by modification with chitosan to produce TCB and then its application in the degradation of methylene blue dye waste at various pH, concentration and contact time variations. Material characterization was carried out by determining the surface area of the material using Brunauer-Emmet-Teller (BET), band gap energy with UV-Vis Diffuse Reflectance Spectrocopy (UV-Vis DRS), crystal phase with X-Ray Diffraction (XRD), and surface morphology with Scanning Electron Microscope (SEM). The results showed that the synthesized TiO2 has anatase crystalline phase with an average crystal size of 18.85 nm and a bandgap energy of 3.16 eV. The TCB surface morphology indicated the successful impregnation of TiO2 on the chitosan. This study showed that the optimum performance of TCB was at pH 11 within 5 min and kinetic analysis results follows 2nd order. HIGHLIGHTS Methylene blue dye waste pollutes the aquatic environment TiO2 as a photocatalyst can degrade methylene blue Chitosan is an abundant natural polymer which has high adsorption ability TiO2-chitosan beads material was synthesized to facilitate the separation of the material from the liquid after photodegradation process GRAPHICAL ABSTRACT
Cross-modality image translation: CT image synthesis of MR brain images using multi generative network with perceptual supervision

Background: Computed tomography (CT) and magnetic resonance imaging (MRI) are the mainstream imaging technologies for clinical practice. CT imaging can reveal high-quality anatomical and physiopathological structures, especially bone tissue, for clinical diagnosis. MRI provides high resolution in soft tissue and is sensitive to lesions. CT combined with MRI diagnosis has become a regular image-guided radiation treatment plan. Methods: In this paper, to reduce the dose of radiation exposure in CT examinations and ameliorate the limitations of traditional virtual imaging technologies, we propose a Generative MRI-to-CT transformation method with structural perceptual supervision. Even though structural reconstruction is structurally misaligned in the MRI-CT dataset registration, our proposed method can better align structural information of synthetic CT (sCT) images to input MRI images while simulating the modality of CT in the MRI-to-CT cross-modality transformation. Results: We retrieved a total of 3416 brain MRI-CT paired images as the train/test dataset, including 1366 train images of 10 patients and 2050 test images of 15 patients. Several methods (the baseline methods and the proposed method) were evaluated by the HU difference map, HU distribution, and various similarity metrics, including the mean absolute error (MAE), structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and normalized cross-correlation (NCC). In our quantitative experimental results, the proposed method achieves the lowest MAE mean of 0.147, highest PSNR mean of 19.27, and NCC mean of 0.431 in the overall CT test dataset. Conclusions: In conclusion, both qualitative and quantitative results of synthetic CT validate that the proposed method can preserve higher similarity of structural information of the bone tissue of target CT than the baseline methods. Furthermore, the proposed method provides better HU intensity reconstruction for simulating the distribution of the CT modality. The experimental estimation indicates that the proposed method is worth further investigation.
Text Classification Based on CNN-BiGRU and Its Application in Telephone Comments Recognition

In this paper, we proposed a deep fusion model for telephone comments recognition, named CNN-BiGRU. Traditionally, the most used algorithms in text classification are Convolutional Neural Network (CNN), Long and Short Term Memory (LSTM) and Bi-Gated Recurrent Neural Network (BiGRU). For CNN, it can extract the feature form the neighbors, and a softmax layer is followed for classification. The global feature is not included in the CNN model. LSTM introduces the gate, which can capture the information before the node. BiGRU is developed from LSTM, and it can find the features in the context. So compared to LSTM, BiGRU not only includes the information before, but also can capture the following features. Thus, LSTM and BiGRU can extract the global features, but cannot capture the local features. In order to deal with this weakness, we proposed a fusion model for comments classification, which combines the CNN and BiGRU in our model. Different from other methods, CNN and BiGRU are parallelly connected. CNN model can extract the local feature, and BiGRU can find the global feature. Then we concatenate the two kinds of features and feed to recognition layer for classification. Then we use our model to classify the telephone comments; compared with the traditional machine SVM and tow deep neural models — CNN and BiGRU — our model performed better.
Densenet121-DNN-Based Hybrid Approach for Advertisement Classification and User Identification

Online advertisement plays an important role in human society and is used to influence customers to buy a product or service.But these advertisements are not user-preferred.Sometimes unwanted and malware advertisements appear on the page and make them uncomfortable.Therefore, advertisement classification and displaying the userpreferred advertisement is an important concept in the modern era.For this, in this paper, a hybrid deep learning-based framework is implemented for advertisement classification and user-preferred advertisement.Initially, the Densenet-121-based system is implemented.To extract the features like texts and objects from the input advertisement image, Densenet-121's convolution and pooling layers are used, and then average pooling and softmax layers of Densenet-121 are used for classifying the advertisement into four categories such as automobiles, clothes, foods, and cosmetics.Then, the DNN (deep neural network) technique is implemented to recommend the advertisement based on user preference.Gather the information of the user from user profiles and then process the collected data, if the information is matched with any advertisement then it will show the advertisement as recommended to the user.To evaluate the performance of the proposed approach manually collected data will be used with the accuracy, F1-score, recall, and precision metrics.The proposed models achieve a high accuracy of 99.93%.Furthermore, the outcomes show good precision for various advertisement categories.
Task Scheduling Based on Cost and Execution Time Using Ameliorate Grey Wolf Optimizer Algorithm in Cloud Computing

An enriched computational platform has been unfolded with the introduction of cloud technology offering ensemble services to users that includes storage, database and processing power.More recently, the cloud technology has been upgraded to a federated environment that offers even more features where various service providers could interconnect for providing an integrated service in a transparent way to cloud users.Applications that demand enormous computing resources like for instance bioinformatics workflow applications could very well make use of the abundant cloud resources for effective execution.Fine tuning the task scheduling activity in cloud could further boost the overall cloud performance.In this paper, we had designed an optimal and ideal task scheduling algorithm that primarily focuses on reducing the cost and makespan QoS parameters, eventually leading to enhanced cloud performance.The proposed algorithm, which is a meta-heuristic enhanced hybrid version named, grey wolf optimizer cuckoo (GWOC) is formally designed from the existing grey wolf optimizer and cuckoo search algorithms.results obtained clearly justify the goal accomplishment of the proposed GWOC algorithm and its swiftness in achieving convergence, thereby clearly outperforming existing contemporaries like gravitational search algorithm (GSA), whale optimization algorithm (WOA) algorithm and grey wolf optimizer (GWO) algorithm.The proposed GWOC technique had produced an improvement of 2.11%, 3.5% and 5.17% for makespan and had reduced the cost to the tune of 7.71%, 11.3%, and 15.4% when compared with gravitational search algorithm (GSA), whale optimization algorithm (WOA) algorithm and grey wolf optimizer (GWO) algorithm respectively when used with 100 VMs.Detailed results have been presented in section 5.
SPSD: Semantics and Deep Reinforcement Learning Based Motion Planning for Supermarket Robot

Robot motion planning is an important part of the unmanned supermarket. The challenges of motion planning in supermarkets lie in the diversity of the supermarket environment, the complexity of obstacle movement, the vastness of the search space. This paper proposes an adaptive Search and Path planning method based on the Semantic information and Deep reinforcement learning (SPSD), which effectively improves the autonomous decision-making ability of supermarket robots. Firstly, based on the backbone of deep reinforcement learning (DRL), supermarket robots process real-time information from multi-modality sensors to realize high-speed and collision-free motion planning. Meanwhile, in order to solve the problem caused by the uncertainty of the reward in the deep reinforcement learning, common spatial semantic relationships between landmarks and target objects are exploited to define reward function. Finally, dynamics randomization is introduced to improve the generalization performance of the algorithm in the training. The experimental results show that the SPSD algorithm is excellent in the three indicators of generalization performance, training time and path planning length. Compared with other methods, the training time of SPSD is reduced by 27.42% at most, the path planning length is reduced by 21.08% at most, and the trained network of SPSD can be applied to unfamiliar scenes safely and efficiently. The results are motivating enough to consider the application of the proposed method in practical scenes. We have uploaded the video of the results of the experiment to https://www.youtube.com/watch?v=h1wLpm42NZk.
Measuring the Effect of Stereo Deficiencies on Peripersonal Space Pointing

State-of-the-art Virtual Reality (VR) and Augmented Reality (AR) headsets rely on singlefocal stereo displays. For objects away from the focal plane, such displays create a vergence-accommodation conflict (VAC), potentially degrading user interaction performance. In this paper, we study how the VAC affects pointing at targets within arm's reach with virtual hand and raycasting interaction in current stereo display systems. We use a previously proposed experimental methodology that extends the ISO 9241–411:2015 multi-directional selection task to enable fair comparisons between selecting targets in different display conditions. We conducted a user study with eighteen participants and the results indicate that participants were faster and had higher throughput in the constant VAC condition with the virtual hand. We hope that our results enable designers to choose more efficient interaction methods in virtual environments.
All-optical control of optical bistability in a hybrid system

Abstract In this paper, we study the tunneling induced optical bistability (OB) in a quantum dot (QD)-metallic nanoparticle (MNP) hybrid system via surface plasmon effects. We realized that in the presence of the tunneling effect, OB arises when the probe light is parallel to the major axis of the hybrid system. We realized the threshold of OB can be controlled by controlling the distance parameter between the QD and MNP. For appropriate distance between the QD and MNP, we find that optical multistability (OM) appears in the system. We find that the threshold of OM can be adjusted when we consider the radius effect of the MNP, respectively.
Understanding image-text relations and news values for multimodal news analysis

The analysis of news dissemination is of utmost importance since the credibility of information and the identification of disinformation and misinformation affect society as a whole. Given the large amounts of news data published daily on the Web, the empirical analysis of news with regard to research questions and the detection of problematic news content on the Web require computational methods that work at scale. Today's online news are typically disseminated in a multimodal form, including various presentation modalities such as text, image, audio, and video. Recent developments in multimodal machine learning now make it possible to capture basic “descriptive” relations between modalities–such as correspondences between words and phrases, on the one hand, and corresponding visual depictions of the verbally expressed information on the other. Although such advances have enabled tremendous progress in tasks like image captioning, text-to-image generation and visual question answering, in domains such as news dissemination, there is a need to go further. In this paper, we introduce a novel framework for the computational analysis of multimodal news. We motivate a set of more complex image-text relations as well as multimodal news values based on real examples of news reports and consider their realization by computational approaches. To this end, we provide (a) an overview of existing literature from semiotics where detailed proposals have been made for taxonomies covering diverse image-text relations generalisable to any domain; (b) an overview of computational work that derives models of image-text relations from data; and (c) an overview of a particular class of news-centric attributes developed in journalism studies called news values. The result is a novel framework for multimodal news analysis that closes existing gaps in previous work while maintaining and combining the strengths of those accounts. We assess and discuss the elements of the framework with real-world examples and use cases, setting out research directions at the intersection of multimodal learning, multimodal analytics and computational social sciences that can benefit from our approach.
Cryptanalysis of Tropical Encryption Scheme Based on Double Key Exchange

A tropical encryption scheme is analyzed in this paper, which uses double key exchange protocol (KEP). The key exchange protocol is divided into two stages: The first stage of the key exchange uses matrix power function in a tropical semiring; the obtained shared key at the first phase of the key exchange serves as an input for the second phase. This paper proves that the common secret key of the first key exchange phase can be obtained by solving linear equations, and when the order of the matrix is 50, the time to solve the shared key is less than 1 second. Finally, the common secret key of the second phase can be obtained through KU attack and common secret key of the first key exchange. So the protocol isn’t secure.
Dual-feature Fusion Attention Network for Small Object Segmentation

Accurate segmentation of medical images is an important step during radiotherapy planning and clinical diagnosis. However, manually marking organ or lesion boundaries is tedious, time-consuming, and prone to error due to subjective variability of radiologist. Automatic segmentation remains a challenging task owing to the variation (in shape and size) across subjects. Moreover, existing convolutional neural networks based methods perform poorly in small medical objects segmentation due to class imbalance and boundary ambiguity. In this paper, we propose a dual feature fusion attention network (DFF-Net) to improve the segmentation accuracy of small objects. It mainly includes two core modules: the dual-branch feature fusion module (DFFM) and the reverse attention context module (RACM). We first extract multi-resolution features by multi-scale feature extractor, then construct DFFM to aggregate the global and local contextual information to achieve information complementarity among features, which provides sufficient guidance for accurate small objects segmentation. Moreover, to alleviate the degradation of segmentation accuracy caused by blurred medical image boundaries, we propose RACM to enhance the edge texture of features. Experimental results on datasets NPC, ACDC, and Polyp demonstrate that our proposed method has fewer parameters, faster inference, and lower model complexity, and achieves better accuracy than more state-of-the-art methods.
Discovering Emerging Threats in the Hacker Community: A Nonparametric Emerging Topic Detection Framework

The prevalence and rapid growth of cybercrime are largely attributed to hacker communities on the dark web, where cybercriminals extensively exchange hacking resources, share hacking knowledge, and organize cyberattacks. Such streams of hacker-generated content constitute an invaluable data source for developing threat intelligence that can inform organizations of cybersecurity risks and facilitate proactive cyber defense. Drawing upon the design science paradigm, we propose a novel nonparametric emerging topic detection (NPETD) framework for detecting emerging topics in streams of hacker-generated content. Our framework extends the state-of-the-art nonparametric topic model to inductively model topics without having to specify the number of topics a priori. Moreover, our framework features an efficient algorithm to jointly infer topics and detect topic emergence. We conducted experiments to rigorously evaluate the effectiveness and efficiency of our framework in comparison with the state-of-the-art baseline methods. Our framework outperformed the baseline methods in detecting the listings of emerging threats in darknet marketplaces on recall, F-measure, topic coherence, and processor time. The practical utility of our framework is further demonstrated in a major hacker forum, where we identified several notable emerging topics with important implications for victim companies and law enforcement. The proposed framework contributes to cybersecurity, topic detection and tracking, and design science.
A MODEL OF USING INFORMATION AND DIGITAL TECHNOLOGIES TO EVALUATE THE EFFECTIVENESS OF PEDAGOGICAL RESEARCH

The paper clarifies the definitions of "information and digital technologies", "effectiveness of pedagogical research", and "evaluation of the effectiveness of pedagogical research using information and digital technologies". A model of using information and digital technologies to evaluate the effectiveness of pedagogical research has been developed, which contains four blocks: purpose, content, organizational and activity, and evaluation and result. The selected components are characterized. In particular, the purpose of the model is to develop the content and technologies for using information and digital technologies to evaluate the effectiveness of pedagogical research; the purpose group, and regulatory documents on which the developed model is based. The following digital tools and platforms are highlighted in the content block: scientometric databases, electronic scientific and educational libraries, altmetric tools, etc. In the organizational and activity block, the following topics are proposed as possible topics for seminars/webinars: evaluating the effectiveness of pedagogical research using statistical modules of scientific digital libraries, Google cloud services, international and domestic scientometric databases, scientific social networks, altmetric and bibliometric systems, and creating an e-portfolio. The evaluation and result block considers project-competition, scientific-publication, scientometric, altmetric, expert and representative scientific criteria and indicators for evaluating the effectiveness of pedagogical research of scholars, research and teaching staff, research institutions, and HEIs. The weight of each criterion can be adjusted by introducing coefficients of significance. The result of the developed model is the improvement of the competence of researchers and academic staff using information and digital technologies to evaluate the effectiveness of pedagogical research.