Reviewerly/exHarmony huggingface dataset:
<i>Proof and Falsity: A Logical Investigation</i> By <scp>Nils Kürbis</scp>

Nils Kürbis’s Proof and Falsity takes place against the backdrop of the philosophy of logic developed in Dummett’s The Logical Basis of Metaphysics. Kürbis introduces Dummett’s framework for evaluating the correct logic (Chapters 1 and 2) and shows how, according to it, intuitionistic logic is to be preferred. Kürbis then presents a well-known problem for Dummett’s account (Chapter 3) and considers and argues against various putative solutions (Chapters 4–6), before explaining and defending his own preferred approach (Chapter 7). The main conclusion is that, in Kürbis’ modified framework, classical (not intuitionistic) logic is justified. Chapters 1 and 2 provide a thorough introduction to Dummett’s proof-theoretic semantics. The different aspects of the view are clearly explained and carefully considered. Together these first two chapters serve as an excellent introduction to proof-theoretic semantics, accessible to anyone with minimal knowledge of logic. Logic as a discipline strives to provide a precise account of deductively validity of argument, that is, of the notion of following from in virtue of the meaning of the logical terms (whatever they are). Each account (i.e. each logic) must articulate (the relevant aspects of) the meaning of the logical terms in some way or other and indicate what it is for the conclusion of an argument to follow from the premisses in virtue of these meanings. Proof-theoretic semantics articulates the meaning of logical terms via their characteristic rules of inference. A logic is then given by a calculus, consisting of these rules (perhaps plus further ‘structural’ rules). An argument is deductively valid in the logic – that is, the conclusion follows from the premisses in virtue of the meaning of the logical terms – just in case the conclusion can be obtained exclusively from the premisses by successive applications of the inference rules; i.e. if there is a proof.
A Novel Image Encryption Algorithm Based on Bernoulli Shift Map

Abstract This paper puts forward a chaotic encryption algorithm based on Bernoulli Shift Map. This algorithm adopts dynamic encryption key and uses plaintext image parameters to control generation of chaotic key, and enhance connection between chaotic key and plaintext data; the iterative operation precision of chaotic map during encryption is high, avoiding cycle of chaotic system iteration process. The NPCR and UACI analysis, adjacent pixels correlation analysis and image information entropy analysis on encrypted image show this encryption algorithm has favorable encryption characteristics such as larger space for key, sensitivity to plaintext and random distribution of ciphertext.
SqueezeBioBERT: BioBERT Distillation for Healthcare Natural Language Processing

Healthcare text mining attracts increasing research interest as electronic health record (EHR) and healthcare claim data have skyrocketed over the past decade. Recently, deep pre-trained language models have improved many natural language processing tasks significantly. However, directly applying them to healthcare text mining won’t generate satisfactory results, because those models are trained from generic domain corpora, which contains a word distribution shift from healthcare corpora. Moreover, deep pre-trained language models are generally computationally expensive and memory intensive, which makes them very difficult to use on resource-restricted devices. In this work, we designed a novel knowledge distillation method, which is very effective for Transformer-based models. We applied this knowledge distillation method to BioBERT [5], and experiments show that knowledge encoded in the large BioBERT can be effectively transferred to a compressed version of SqueezeBioBERT. We evaluated SqueezeBioBERT on three healthcare text mining tasks: named entity recognition, relation extraction and question answering. The result shows that SqueezeBioBERT achieves more than 95% of the performance of teacher BioBERT on these three tasks, while being 4.2X smaller.
Second-Order Estimation Based Attention Network for Metric Learning

Mapping image data into the embedding space where objects of the same class or label have a short distance in-between and objects of different classes have long margins, has been an essential task for many computer vision applications. However, current approaches struggle to map image data into a proper embedding space due to the difficulty of constructing discriminative features from among numerous features from the original data. Existing approaches include finding effective loss and new sampling methods, which do not consider improving the embedding space by selecting fine features extracted by the network. In this work, we proposed a new attention approach by exploiting the variance of features. The method can improve the performance of the current metric learning. Our approach consists of a variance estimation module(VEM) and fusion stage for applying channel-wise attention on extracted features. It is easy to implement and fast for training. Unlike other traditional second-order based methods, the variance estimation module does not embed second-order calculation in the network itself, and cost no large extra computation time in the evaluation stage. The experiment shows promising performance while compared with current SOTA approaches on multiple metric learning benchmark datasets such as CUB200-2011, CARS196, In-shop Clothes. Contribution-We design a new attention module by using estimation of variance in the features and achieve SOTA results in several benchmarks with almost no extra time cost in the test stage.
Definition Extraction Feature Analysis: From Canonical to Naturally-Occurring Definitions

Textual definitions constitute a fundamental source of knowledge when seeking the meaning of words, and they are the cornerstone of lexical resources like glossaries, dictionaries, encyclopedia or thesauri. In this paper, we present an in-depth analytical study on the main features relevant to the task of definition extraction. Our main goal is to study whether linguistic structures from canonical (the Aristotelian or genus et differentia model) can be leveraged to retrieve definitions from corpora in different domains of knowledge and textual genres alike. To this end, we develop a simple linear classifier and analyze the contribution of several (sets of) linguistic features. Finally, as a result of our experiments, we also shed light on the particularities of existing benchmarks as well as the most challenging aspects of the task.
3D Image Conversion of a Scene from Multiple 2D Images with Background Depth Profile

The computer vision and augmented reality industry focuses on reconstructing the 3D view of an object with the goal of improving visual effects. There are various applications that require 3D models such as street view, film industry, sports industry. 3D models cannot be captured directly using video or still cameras. Cameras are capable of obtaining high-quality 2D images at different angles. This paper will discuss about how the 2D images captured from various camera are converted using in-depth analysis to obtain a 3D view of an object or image. The last received 3D visual input is a dense reconstruction of the 2D image. This reconstruction method is less complex than traditional mathematical modeling methods. This technique can be easily integrated into any application domains such as 3D, such as facial reconstruction.
Data-Driven E-commerce Techniques and Challenges in the Era of the Fourth Industrial Revolution

The E-commerce industry has a significant role in the national and international economy. E-commerce is vital in the implementation of the fourth industrial revolution, where information and communication technologies are tools in creating digital channels of trade. Understanding e-commerce is essential for it is development. The objective of this paper is to explore the popular techniques and data sources of e-commerce in addition to the current challenges that face e-commerce in the last five years. We used a literature review as a method for this research.Â  According to the literature, sales records are the most popular data source used in the research community for e-commerce analytics, then followed by big data and social media. Besides, detecting and predicting customer behavior is the most used technique in e-commerce research followed by personalized recommendations. Also, we reported the main three challenges that face researchers in the field of e-commerce currently: First, e-commerce Security and privacy is a major concern for consumers and industries. Second, understanding the collected data from e-commerce systems and how to create business value from it efficiently. Third, providing personalized offers with the most appropriate items, still a difficult task.
Purchase Predictive Design Using Skeleton Model and Purchase Record

The depth camera has enabled the skeleton and joints of the human body to use skeleton data in 3D space. Behavior recognition using skeleton data is mainly based on artificial neural networks such as RNN. This study classifies behaviors observed by the consumer into four categories using skeleton model learning for purchase predictive design. Skeleton model learning collects 25 skeleton joints using several Kinect v2s in unattended stores where four racks of items can be purchased. Torso, left arm, right arm, left leg, and right leg to five body joints are performed by BRNN, and as the layer becomes deeper, each part is then joined to the body. Finally, the 25 joints are grouped together and BRNN-LSTM is performed to solve the vanishing gradient problem (Jun et al. in J Korea Multimedia Soc 21:369–381, 2018, [1]). Supervised learning involves four behaviors used as input and the purchase record status as output. A GRU is employed to reduce computational complexity while maintaining the benefits of LSTM.
DSnow

Data center played a key role in main internet infrastructure with the development of cloud computing. However, the traditional structure of data center networks has the problems of poor scalability, limited number of ports, and high equipment cost etc. Therefor this paper proposed a novel structure called DSnow, which based on the Durer pentagon that has fractal characteristics. Firstly, compared with DPBA(Durer- Fractal-Pentagon-Based Datacenter Network Architecture), DSnow made full use of the advantages of fractal graphics and improves its scalability; secondly, after analysis, this proved the under the same conditions, it can provide more servers than most network topologies. At last, the paper proved a routing algorithm included unicast and multicast.
A Brief Simulation Method for Coded Structured Light Based 3D Reconstruction

Coded structured light based 3D reconstruction is one of the most reliable methods to recover a scene. The errors in the reconstruction are mainly caused by indirect illumination. So it is necessary to suppress the effect of indirect illumination to improve the reconstruction accuracy. In this paper, a simulation method of coded structured light based 3D reconstruction is presented. A simulated point cloud with high accuracy is generated for the noise reduction for the actual point cloud. A down-sampling method based on edge detection is also proposed to improve the efficiency of loading and transporting a point cloud. The experiment results verify the performance of simulation and down-sampling.
Stack-Based Scale-Recurrent Network for Image Deblurring

Quite a few researches are devoted to eliminating motion blur using "coarse-to-fine" architecture. And it has shown its superiority in removing motion blur in some cases. However, there are still exiting problems: Complex network structure and large number of parameters, which makes the model difficult to train and result in expensive runtime. Poor quality and insufficient deblurring performance of images when simply using this architecture to remove motion blur. To solve the above problems, In this paper, we utilize the "coarse-to-fine" architecture and combine the benefits of stacked structure, proposing a new structure "Stack-based Scale-recurrent Network" (SSRN) for image deblurring task. Different from other traditional multi-scale methods with multiple losses evaluation, we only have one loss evaluation, and Scale-recurrent network (SRN) is employed in our model since it has simpler network structure and the results recovered through it are better than those via other networks. We choose the LFW face dataset for testing in our experiment. Due to the particularity of face images, in this way, we can verify whether our model has good deblurring performance for blurred face images. On the other hand, it can also laterally reflect whether the model can be applied to face recognition task (make the blurred face image clear so that it can be used for face recognition, the face recognition method will be described in the following chapters). We evaluate our model by means of calculating PSNR/SSIM value and accuracy of face recognition on a large blurring dataset with complex motion. As we expected, compared with other deblurring methods, both the PSNR/SSIM value and face recognition accuracy of deblurred image obtained by our model have improved significantly, which confirms the superiority of our proposed model in image deblurring task. At the end of this article, we display some deblurred images to show the high deblurring performance of our model in deblurring task.
A Systematic Review of Authentication Methods for Internet of Things

Internet of things is becoming the most important technology now a days and it is next era of communication. By the use of IOT, various physical things can communicate, interact and exchange data seamlessly. IOT brings intelligence and automation in different areas like agriculture, transportation, industry, health and many more. The ultimate goal of the IOT applications is to increase comfort and efficiency of the users. IOT architecture comprises of various objects and things which are connected to open network. This openness/this exposure provides fertile ground for various types of security attacks. Security and Protection are the significant perspective of IOT Network. Conventional Security approaches can't be applied directly to the resource constrained network. Authentication confirms the identity of each individual registered entity in the network. It is very much important to verify and validate identity of device as compromised device can damage to the network. IOT applications can significantly be useful to society when these security issues at each layer (Perception, Network and Application) are analyzed and solved. In this paper, a detailed review of the security related challenges specially related to Authentication and source of threats in IOT applications is discussed.
Improved Mean Shift Algorithm for Maximizing Clustering Accuracy

Clustering is a machine learning method that can group similar data points. Mean Shift (MS) is a fixed window-based clustering algorithm, which calculates the number of clusters automatically but cannot guarantee the convergence of the algorithm. The main drawback of the Mean Shift Algorithm is that the algorithm requires to set a stopping criterion (threshold point) otherwise all clusters move towards one cluster and fixed bandwidth is used here. It cannot define the upper bound of iteration numbers and need to set the iteration numbers. This paper proposed a new Mean Shift Algorithm, called Improved Mean Shift (IMS) algorithm, which overcomes the all defined pitfalls of Mean Shift Algorithm. The IMS process KD-tree data structure was used to sort the dataset and all data points as initial cluster centroids without a random selection of initial centroids. In each iteration, it shifts the variable bandwidth sliding window to the actual data point nearest to the mean using k-nearest neighbours (kNN) algorithm and finds the number of clusters automatically. Also, this paper handles the missing values using Mean Imputation (MI). The IMS algorithm produces better results than the Mean Shift Algorithm on both synthetic and real datasets.
Incorporating Multi-Type External Information for Document-Level Sentiment Classification

Some researchers have recently realized that besides the document text, User preferences and Product characteristics are also useful for document-level sentiment classification. To make full use of them, previous studies mainly regarded them as ID-type information and incorporated them into research models. This paper defines such information related with user preferences and product characteristics as external information, and proposes a new type of external information, DOT (the Descriptive text of Opinion Target). Also, this research presents a Multi-EI (Multi-type External Information) neural model, which involves two different attention mechanisms for Id-type External Information (i.e., User preference and Product characteristics) and Description-type External Information (i.e., DOT). We also build a new dataset, the SC_EXT (Sentiment Classification EXTention) dataset, based on IMDB and Yelp movie reviews with various document-user ratios. Experimental results on SC_EXT show that the model can effectively integrate Multi-EI into a deep neural network. Moreover, with the help of DOT information, the model achieves state-of-the-art performance.
Searching for Distress of Similar Situation from CQA Content

In this study, we tackle the problem of retrieving questions from a corpus archived in a Community Question Answering service that a consultant having distress can feel empathy with them. We hypothesize that the consultant feels empathy with the questions having a similar situation with that of the consultant’s distress, and propose a method of retrieving similar sentences focusing on the situation of the distress. Specifically, we propose two approaches to fine-tuning the pre-trained BERT model so that the learned model better captures the similarity of the situation between distress. One tries to extract only the words representing the situation of the distress, the other tries to predict whether the two sentences show the same situation. The data for training the models are gathered by the crowdsourcing task where the workers are asked to gather the sentences whose situation is similar to the given sentence and to annotate the words in the sentences that represent the situation. The data is then used to fine-tune the BERT model. The effectiveness of the proposed methods is evaluated with the baselines such as TF-IDF, Okapi BM25, and the pre-trained BERT. The results of the experiment with 20 queries showed that one of our methods achieved the highest nDCG@5 while we could not observe any significant differences among the methods.
A Comparison Analysis of Mobile Forensic Investigation Framework

As of now, where every single person is having smart phone, day by day it will increase, and with increasing smart phones, it is obvious that crime will not remain same with the use of mobile phones, due to various activities are done by this device only like calling, chatting, Web browsing, online transactions, mailing, entertainment, and much more. Therefore, it will not be an easy job for professional forensic investigators to find out the criminal form several aspects of smart phones attribute to their many varieties of mobile device manufacturers, unlike hardware components, different operating systems, and many programs used. Numerous tools, process models, and frameworks are designed and developed for the purpose of investigation where all are unable to have a secure solution for investigating all the different mobile devices. Here, we will have reviews of some current process models which are used for mobile device forensic investigation and find out a better result for selecting a process model that should meet all features of mobile devices which have maximum security that should not be modified or be hacked during, and after the investigation this will bring full, accurate results from the evidence, for doing such a database based on the private or federated blockchain technology which will be recommended.
Model-Based Motion Control and Drift Compensation through Pose Estimation

This contribution is about controlling the pose of an automated logistics vehicle by use of a Light Detection and Ranging sensor with the help of the Robot Operating System. A drift caused by the mounted Mecanum wheels and the inhomogeneous mass distribution is reduced while driving by the proposal drift compensation. This is achieved by using the estimated robot pose provided by a Simultaneous Localization and Mapping approach. Based on the Robot Operation System, the data transfer and automated functions are realized. Utilizing the model-based method of Conceptual Design Specification Technique for the Engineering of Mechatronic Systems partial implementation of this system is simplified.
The role of a multimedia textbook in the priority national project “education”

In the article, the authors consider the role of a multimedia textbook in modern education. The priority national project "Education" is being implemented in Russia - the creation and implementation of modern electronic textbooks is one of the priority concepts in this project. Electronic publications enhance interactive features such as modeling, surveys, discussions, and additional multimedia materials. Advantages of electronic textbooks: mobility, large capacity of information; the ability to constantly update content, without purchasing a new textbook and financial expenses; level of visibility: you can only put text, illustrations, diagrams and graphs in a printed publication, but you can also put animation in an electronic publication, which is convenient for visual display of academic subjects. The electronic textbook provides more opportunities for learning within the existing programs, as well as for the development and optimization of the educational process.
Organic geochemical characteristics of diatomite in Nadanhada terrane

Abstract Based on the geochemical characteristics of the siliceous rocks in the Nadanhada terrane, the abundance, type and maturity of organic matter are studied, and the source rocks in Fuyuan, Raohe and Hongqiling are comprehensively evaluated. The type of organic matter in this area is mainly type III kerogen, which is in the high over mature stage with low metamorphic degree. At present, liquid hydrocarbon can still be produced. The Qianjin depression (Fuyuan area) of Sanjiang basin is a favorable area for the exploration of Lower Jurassic cherts with high organic matter abundance, low maturity and good preservation conditions.
Text-to-Painting on a Large Variance Dataset with Sequential Generative Adversarial Networks

Converting text descriptions to images using Generative Adversarial Networks has become a popular research area. Visually appealing images were generated in recent years successfully. We investigated the generation of artistic images on a custom-built large variance dataset, which includes training images with variations, for example, in shape, color, and content. These variations in images provide originality, which is an important factor for artistic essence. One major characteristic of our work is that we used keywords as image descriptions, instead of sentences. As a network architecture, we proposed a sequential Generative Adversarial Network model, which utilizes several techniques like Wasserstein loss, spectral normalization, and minibatch discrimination to have stable training curves. Ultimately, we were able to generate painting images, which have a variety of styles. We evaluated the quality of generated paintings by using Fréchet Inception Distance score.