Reviewerly/exHarmony huggingface dataset:
Deduction with uncertain conditionals (revised &amp; simplified, with examples)

Section 1 of this paper provides an introduction to this new “algebra of conditionals”, addresses various plausibility tests for such an algebra, provides a Venn diagram disproving a supposed counter-example, and answers various other objections raised in the literature about the efficacy of this algebraic extension of logic and conditional probability. Section 2 greatly simplifies the calculation of the implications of a set of conditional propositions or conditional events. These results depend on defining a deductive relation for conditionals (actually two have been found) with the property that the conjunction of two conditionals implies each of its components. That seemingly innocuous property assures that the deductively closed set implied by a finite set of n conditionals with respect to the deductive relation is implied by the single conditional formed by conjoining all n of them. The results are illustrated by solving several examples of deduction with several uncertain conditionals.
Five-Class Classification of Cervical Pap Smear Images: A Study of CNN-Error-Correcting SVM Models

Objectives: Different complex strategies of fusing handcrafted descriptors and features from convolutional neural network (CNN) models have been studied, mainly for two-class Papanicolaou (Pap) smear image classification. This paper explores a simplified system using combined binary coding for a five-class version of this problem.Methods: This system extracted features from transfer learning of AlexNet, VGG19, and ResNet50 networks before reducing this problem into multiple binary sub-problems using error-correcting coding. The learners were trained using the support vector machine (SVM) method. The outputs of these classifiers were combined and compared to the true class codes for the final prediction.Results: Despite the superior performance of VGG19-SVM, with mean ± standard deviation accuracy and sensitivity of 80.68% ± 2.00% and 80.86% ± 0.45%, respectively, this model required a long training time. There were also false-negative cases using both the VGGNet-SVM and ResNet-SVM models. AlexNet-SVM was more efficient in terms of running speed and prediction consistency. Our findings also showed good diagnostic ability, with an area under the curve of approximately 0.95. Further investigation also showed good agreement between our research outcomes and that of the state-of-the-art methods, with specificity ranging from 93% to 100%.Conclusions: We believe that the AlexNet-SVM model can be conveniently applied for clinical use. Further research could include the implementation of an optimization algorithm for hyperparameter tuning, as well as an appropriate selection of experimental design to improve the efficiency of Pap smear image classification.
Few-Shot Charge Prediction with Data Augmentation and Feature Augmentation

The task of charge prediction is to predict the charge based on the fact description. Existing methods have a good effect on the prediction of high-frequency charges, but the prediction of low-frequency charges is still a challenge. Moreover, there exist some confusing charges that have relatively similar fact descriptions, which can be easily misjudged. Therefore, we propose a model with data augmentation and feature augmentation for few-shot charge prediction. Specifically, the model takes the text description as the input and uses the Mixup method to generate virtual samples for data augmentation. Then, the charge information heterogeneous graph is introduced, and a novel graph convolutional network is designed to extract distinguishability features for feature augmentation. A feature fusion network is used to effectively integrate the charge graph knowledge into the fact to learn semantic-enhanced fact representation. Finally, the semantic-enhanced fact representation is used to predict the charge. In addition, based on the distribution of each charge, a category prior loss function is designed to increase the contribution of low-frequency charges to the model optimization. The experimental results on real-work datasets prove the effectiveness and robustness of the proposed model.
Can You Perceive the Size Change? Discrimination Thresholds for Size Changes in Augmented Reality

Existing psychophysical experiments show that size changes of objects could influence the human perception of objects properties (e.g., shape or weight) in the physical surroundings. On the other hand, Augmented Reality (AR) is an interactive toolkit that allows the integration of virtual objects and real-world environments. However, the absolute detection threshold of size changes on virtual objects is not clear in AR, limiting human perception and experience in AR. In this paper, we designed a pilot study and two psychophysical experiments to explore the humans’ ability towards the range of the detection threshold of size changes on virtual objects in AR. Our experimental results show that the range of the detection threshold of size changes is 3.10%–5.18% (reference is a 10 * 10 * 10 cm\(^3\) cube). We also calculated the value of the point of subjective equality (PSE = 4.00%), which means that users react ‘same’ with 50% probability upon the given stimulus.
Reflections on Designing in the Wild: How Theories of Design Information Manifest in Practice

Abstract Information acquisition, utilization and communication are integral to the design process, but systematic investigation of information behavior is complicated by its variety and the ways in which designers engage with information throughout the design process. Our previous work developed a theoretical framework to categorize the various types of information used during the design process, known as the Information Archetypes Framework. This paper explores how these information dimensions manifest in design practice, as reflected on by experienced practicing designers. Deep qualitative analysis of eight interviews with practicing designers revealed that the designers intentionally adapt their behavior to match situation specific needs, and navigate the tensions between information dimensions through trajectories and loops.
Research on College English Multimedia Teaching Model Driven by Wireless Communication Network Environment

English teaching has also gradually introduced computer network-assisted teaching methods. Compared with other subjects, English teaching has many constraints, including lower teacher-student interaction time, lack of English speaking training environment, single learning method, and other problems leading to the phenomenon that English teaching tends to emphasize theory but not practice. In terms of the current practical situation, the development and research of computer network multimedia courses are still far from meeting the actual needs of English network teaching. English network teaching mainly involves the transmission and use of video streams on the network, but the current video stream transmission conditions are not very ideal. Therefore, this paper focuses on the practical development of computer-assisted teaching in English teaching based on the browser/server (B/S) architecture mode of network multimedia teaching system, so that the teaching reform of English network courses can be further improved and developed. The research content of this paper is based on the development technology of the B/S mode, theoretical study and research on the production method of the English network teaching system, and practical evaluation and development application of this teaching system. A comparison of the educational effects of computer network education models and traditional education models is carried out to determine the implementation and effectiveness of web-based English multimedia education systems. Based on a web browser-based network education model, the Internet is used as a means of transmitting media education content with synchronised English and playback lessons as well as related online tests and online question and answer sessions on the Internet, enabling timely and efficient educational activities. The teaching system is also evaluated and applied. The research results show that the network multimedia teaching system provides a variety of forms of materials, making the original more abstract content a more vivid image and concrete. Changing the original traditional teaching mode of English makes students’ English learning change from simple memory to concrete understanding; increasing students’ initiative to information not only improves the utilization of time but also changes the monotonous information exchange from teachers to students, providing a large number of English environment materials, which is conducive to students’ oral practice and promotes the successful completion of teaching tasks.
Key Point Matching with Transformers

This work aims at describing a solution for the Track 1 of the KPA 2021 shared task, analyzing different methodologies for the specific problem of key point matching. The analysis focuses on transformer based architectures, experimentally investigating the effectiveness of variants specifically tailored to the task.
Development of Digital Libraries with Software Product Line Engineering

Digital Libraries have become popular nowadays since important libraries all over the world started distributing their collections online, properly classified, and, in many cases, with access to the digital version of the resource. These programs have been beneficial to the general population as well as research groups in fields such as language and literature. Nonetheless, since their creation is a time-consuming and costly process, small organizations are forced to rely on obsolete or poorly designed software. However, most of the features, including the data model, are shared by this type of system, with minor variations depending on the type of resources to be handled. This article presents a Software Product Line (SPL) for the semi-automatic generation of Digital Libraries (DL). Our SPL allows developers to specify which DL features are required, which will define the data model variationand the generated source code. The specification is then transformed into a fully functional DL application with the specified features that is ready for deployment. We present the feature model, the SPL implementation, and acase study on three sample projects that enabled us to evaluate the resulting software, with a focus on development effort savings.
Neural Machine Translation for Sinhala-English Code-Mixed Text

Code-mixing has become a moving method of communication among multilingual speakers.Most of the social media content of the multilingual societies are written in code-mixed text.However, most of the current translation systems neglect to convert code-mixed texts to a standard language.Most of the user written code-mixed content in social media remains unprocessed due to the unavailability of linguistic resource such as parallel corpus.This paper proposes a Neural Machine Translation(NMT) model to translate the Sinhala-English code-mixed text to the Sinhala language.Due to the limited resources available for Sinhala-English code-mixed(SECM) text, a parallel corpus is created with SECM sentences and Sinhala sentences.Srilankan social media sites contain SECM texts more frequently than the standard languages.The model proposed for code-mixed text translation in this study is a combination of Encoder-Decoder framework with LSTM units and Teachers Forcing Algorithm.The translated sentences from the model are evaluated using BLEU(Bilingual Evaluation Understudy) metric.Our model achieved a remarkable BLEU score for the translation.
Multi-class Classification of Ovarian Cancer from Histopathological Images using Deep Learning - VGG-16

Ovarian cancer (OC) is the primary gynecological malignancy. Precise prediction and subtype classification of OC (Serous, Mucinous, Endometrioid, Clear cell) are vital within the diverse diagnosis. Pathologists currently rely on Computer Aided Diagnosis (CAD) to correctly work out the diagnosis. Our study demonstrates a pre-trained Deep Convolutional Neural Networks (DCNN) architecture Visual Geometry Group – 16 (VGG16) to automatically detect, predict & classify the subtypes of ovarian cancers from histopathological images.VGG-16 comprises 16 layers (13 convolution layers, three fully-connected layers, five max-pooling layers, and one softmax layer). Initially, we trained the model with 500 images, 100 of each class, and achieved an accuracy of 50%. We then augmented the dataset of 500 images by image rotation, flipping, enhancing, and zooming to generate 24742 images, multiplying our input dataset. After training the model with this augmented dataset, the classification accuracy increased from 50% to 84.64%. The proposed work is the first attempt to use VGG16 to detect, predict and classify subtypes of ovarian cancer from histopathological images.
The Power of Selecting Key Blocks with Local Pre-ranking for Long Document Information Retrieval

On a wide range of natural language processing and information retrieval tasks, transformer-based models, particularly pre-trained language models like BERT, have demonstrated tremendous effectiveness. Due to the quadratic complexity of the self-attention mechanism, however, such models have difficulties processing long documents. Recent works dealing with this issue include truncating long documents, in which case one loses potential relevant information, segmenting them into several passages, which may lead to miss some information and high computational complexity when the number of passages is large, or modifying the self-attention mechanism to make it sparser as in sparse-attention models, at the risk again of missing some information. We follow here a slightly different approach in which one first selects key blocks of a long document by local query-block pre-ranking, and then few blocks are aggregated to form a short document that can be processed by a model such as BERT. Experiments conducted on standard Information Retrieval datasets demonstrate the effectiveness of the proposed approach.
A Texture Features-Based Robust Facial Expression Recognition

Facial expression plays an important role in communicating emotions. In this paper, a robust method for recognizing facial expressions is proposed using the combination of appearance features. Traditionally, appearance features mainly divide any face image into regular matrices for the computation of facial expression recognition. However, in this paper, we have computed appearance features in specific regions by extracting facial components such as eyes, nose, mouth, and forehead, etc. The proposed approach mainly has five stages to detect facial expression viz. face detection and regions of interest extraction, feature extraction, pattern analysis using a local descriptor, the fusion of appearance features and finally classification using a Multiclass Support Vector Machine (MSVM). Results of the proposed method are compared with the earlier holistic representations for recognizing facial expressions, and it is found that the proposed method outperforms state-of-the-art methods.
A Clean, Green Haven?- Examining the Relationship between Clean Energy, Clean and Dirty Cryptocurrencies

Is clean energy a safe haven for cryptocurrencies, or vice versa? In this paper, we investigate the hedge and safe haven property of a wide range of clean energy indices against two distinct types of cryptocurrencies based on their energy consumption levels, termed black or ’dirty’ and green or 'clean'. Statistical evidence shows that clean energy is not a direct hedge for either type of cryptocurrency. However, it serves as at least a weak safe haven for both in extreme bearish markets. Moreover, clean energy is more likely to be a safe haven for ’dirty’ cryptocurrencies than ’clean’ cryptocurrencies during increased uncertainty. We further study the spillover patterns among clean energy, cryptocurrency, stock, and gold markets. Weak connectedness is found between clean energy and cryptocurrencies which implies the potential use of clean energy as a hedge and diversification tool for cryptocurrencies in the future.
A Novel Image Coding Through the Chaos Theory and Compressed Sensing

Patel, Saumya Vaish, AnkitaAn image coding scheme is presented in the paper, based on compressive sensing (CS) and chaos theory. At first, the plain image is processed through discrete wavelet transform (DWT) to obtain the transformed data of the signal. The measurement matrix (MM) is originated through logistic-tent system which provides better reconstruction quality and also reduces the bandwidth utilization since it requires only initial parameter to generate MM at the receiver end. To provide more security, row- and columnwise confusion and diffusion has been applied through logistic map. Experimental results verify the expediency of the proposed scheme over the existing algorithm.
Communication Strategies for Interaction in Social Networks

This chapter critically reviews communication strategies used on social networks from a multilingual perspective. Communication strategies are necessary for effective communication between interlocutors, especially those with different language competencies. Advances in technology have initiated preferences for communication on social networks, where interlocutors come from different linguistic, social-cultural backgrounds. This chapter first discusses communication strategies from different perspectives before conducting an in-depth review of communication strategies from a multilingual perspective. It then sheds light on the nature of communication on social networks as a multilingual and multicultural environment. Finally, it gives implications for educating second language communication in a multilingual world.
Visualizing Data with Pandas and Matplotlib

In the previous chapter, you learned how to read the data stored in various file formats into Python variables using NumPy, Pandas, and Matplotlib.
Helpdesk Ticket Support System Based on Fuzzy Tahani Algorithm

The broad perspective of the academic service procedure could lead to severe matters if there is no concrete procedure to handle each academic request. Lousy and unclear instructions may confuse the users and officers to address the problem. Therefore, the development of educational service applications can help to enhance the quality of service in a particular work organization. To solve the problem, we developed a helpdesk system through a ticket support system to accommodate administrative and technical student issues in the Department of Electrical Engineering, Universitas Negeri Malang. The online system has been running for 19 months produces 2.164 datasets comprised of three types of completion time, i.e., slow, normal, and fast. Based on the result, the success rate of fast classification is 52.9%, medium classification is 9.3%, and slow classification is 37.8%. The reliability and functional testing refer to ISO/IEC 25010:2011 standard. Hence, the application can 100% work properly, which performs 86.1 % of usability testing.
Mimicking an Information Reservoir by Superconducting Quantum Circuits

The dissipative model of quantum computation is proven to be equivalent to its circuit model. Particularly, multi-qubit gates require time-dependent control with optimized parameters for some specific problems. One such problem is the simulation of a quantum version of a perceptron that classifies quantum information as binary using the framework of open quantum systems. In this scheme, a probe qubit is in contact with multiple, distinct quantum information-bearing environments and returns a binary decision depending on its amplitude parameter in its steady state. We refer to these environments as quantum information reservoirs. We choose a standard quantum collisional model in which the reservoir parameters can be defined in detail. In this study, we present the analytical results of the proposed classifier with an application to the superconductor quantum circuits for a single information reservoir. We exploit the additivity of quantum dynamic maps for dissipative processes in the weak coupling regime where optimized time-dependent control is not required to achieve the classification result. We show that the current state-of-the-art for superconducting circuits allows for the physical implementation of dissipative quantum information processing in the presence of information reservoirs with realistic parameters.
Hybrid recommendation based on graph embedding

In recent years, online reservation systems of country hotel have become increasingly popular in rural areas. How to accurately recommend the houses of country hotel to the users is an urgent problem to be solved. Aiming at the problem of cold start and data sparseness in recommendation, a Hybrid Recommendation method based on Graph Embedding (HRGE) is proposed. First, three types of network are built, including user-user network based on user tag, house-house network based on house tag, and user-user network based on user behavior. Then, by using the method of graph embedding, three types of network are respectively embedded into low-dimensional vectors to obtain the characterization vectors of nodes. Finally, these characterization vectors are used to make a hybrid recommendation. The datasets in this paper are derived from the Country Hotel Reservation System in Guizhou Province. The experimental results show that, compared with traditional recommendation algorithms, the comprehensive evaluation index (F1) of the HRGE is improved by 20% and the Mean Average Precision (MAP) is increased by 11%.
Machine Learning and Fuzzy Logic in Electronics: Applying Intelligence in Practice

The paper presents an analysis and summary of the current research state concerning the application of machine learning and fuzzy logic for solving problems in electronics. The investigated domain is conceptualized with aim the achievements, trending topics and future research directions to be outlined. The applied research methodology includes a bibliographic approach in combination with a detailed examination of 66 selected papers. The findings reveal the gradually increasing interest over the last 10 years in the machine learning and fuzzy logic techniques for modeling, implementing and improving different hardware-based intelligent systems.
GREEN-Cloud Computing (G-CC) Data Center and Its Architecture toward Efficient Usage of Energy

The improvements in worker ranches can be attributed to the advancements in scattered enrolling, which have led to considerable increase in energy utilization and the impact on the climate in terms of carbon impressions. To minimize the usage of power, a few basic approaches are required to be framed for sustainability of green cloud. Green cloud architecture (GCA) is being derived from these models driving, not exclusively, to energy proficiency yet likewise to a carbon spread consideration thought. To minimize energy use, a greener climate is considered a solution. Among all associations, the data correspondence headway (ICT) industry is clearly responsible for more prominent improvements in the overall energy use. The objective of this chapter is to determine green circled listing to drive the recyclability or biodegradability of dead matter and present day office wastage by minimizing the utilization of unsafe materials and growing energy consumption during the lifetime.