Reviewerly/exHarmony huggingface dataset:
Neutral Free Logic: Motivation, Proof Theory and Models

Abstract Free logics are a family of first-order logics which came about as a result of examining the existence assumptions of classical logic (Hintikka The Journal of Philosophy , 56 , 125–137 1959; Lambert Notre Dame Journal of Formal Logic , 8 , 133–144 1967, 1997, 2001). What those assumptions are varies, but the central ones are that (i) the domain of interpretation is not empty, (ii) every name denotes exactly one object in the domain and (iii) the quantifiers have existential import. Free logics reject the claim that names need to denote in (ii). Positive free logic concedes that some atomic formulas containing non-denoting names (including self-identity) are true, negative free logic treats them as uniformly false, and neutral free logic as taking a third value. There has been a renewed interest in analyzing proof theory of free logic in recent years, based on intuitionistic logic in Maffezioli and Orlandelli ( Bulletin of the Section of Logic , 48 (2), 137–158 2019) as well as classical logic in Pavlović and Gratzl ( Journal of Philosophical Logic , 50 , 117–148 2021), there for the positive and negative variants. While the latter streamlines the presentation of free logics and offers a more unified approach to the variants under consideration, it does not cover neutral free logic, since there is some lack of both clear formal intuitions on the semantic status of formulas with empty names, as well as a satisfying account of the conditional in this context. We discuss extending the results to this third major variant of free logics. We present a series of G3 sequent calculi adapted from Fjellstad ( Studia Logica , 105 (1), 93–119 2017, Journal of Applied Non-Classical Logics , 30 (3), 272–289 2020), which possess all the desired structural properties of a good proof system, including admissibility of contraction and all versions of the cut rule. At the same time, we maintain the unified approach to free logics and moreover argue that greater clarity of intuitions is achieved once neutral free logic is conceptualized as consisting of two sub-varieties.
Combating Adversarial Covert Channels in Wi-Fi Networks

Physical layer covert channels exploit the characteristics of radio signals to convey secret messages while remaining inconspicuous within wireless channels.With specifically designed modulation schemes, covert channels effectively disguise secret messages as noise.Since the intentionally embedded noise dissipates when the radio signal is decoded as a bitstream, adversaries can maintain a stealthy breach in communication systems.IoT devices, particularly, often utilize vendor-specific firmware and hardware whose security verification is too complex for everyday users.Hence, these devices can easily be compromised to transmit their data to unauthorized adversaries via the covert channels.To this end, we propose a novel countermeasure system, Ghost-Fi Detector, which detects the covert channels reliably and comprehensively.Our attack models reflect a real-world wireless network technology, Wi-Fi, and cover three aspects of its radio signal characteristics including amplitude, phase, and frequency.Since each model induces distinct manifestations in the received signal, there is no dominant universal detection mechanism that detects all the attack models simultaneously.Instead, Ghost-Fi detector consists of six precisely designed complementary defense mechanisms that perform passive radio-frequency analyses.Evaluation results show that Ghost-Fi Detector achieves an average hit rate of 95% with an almost zero false-positive rate for arbitrary Wi-Fi frames.
Masked Face Detection Using Transfer Learning

COVID-19 pandemic has created chaos all over the world thereby reducing productivity and affecting day to day life of people. Wearing a face mask has become the need of the hour. In the future, it is expected that masks will become an integral part of human lifestyle and people have to wear masks to avail certain services. Therefore, the detection of face mask has become a global problem to avoid the spread of this virus. This paper presents a deep learning approach to detect faces with masks from an image or from a video stream, which can be used in surveillance tasks. The method attains accuracy up to 99% on Simulated Face Mask Dataset. We have manually tested this model on webcam, and it detects the presence of masks correctly without overfitting.
IRIS Position-Based Wheelchair Maneuver Using Semantic Segmentation

The paper presents a novel system for wheelchair maneuver to aid people suffering from limb-related paralysis. It features Iris Center Localization using Hough Transform for tracking the direction of eye gaze. The limitations observed with Hough transform in previous research works were resolved using Semantic Segmentation for improved detection of the Iris against the Sclera background. Hence, we have demonstrated how Deep Learning models can perform Semantic Segmentation for Object Detection. Our approach gave an accuracy of 99.2% and allowed the wheelchair to move with more precision due to the scope of calibration of the motor torque/speed with respect to the determined eye gaze estimate. Moreover, different methods to avoid object collision were studied and implemented. Further, we provide an abstract design for the proposed wheelchair.
Asymptotic Evaluation of Classification in the Presence of Label Noise

In a practical classification problem, there are cases where incorrect labels are included in training data due to label noise. We introduce a classification method in the presence of label noise that idealizes a classification method based on the expectation-maximization (EM) algorithm, and evaluate its performance theoretically. Its performance is asymptotically evaluated by assessing the risk function defined as the Kullback-Leibler divergence between predictive distribution and true distribution. The result of this performance evaluation enables a theoretical evaluation of the most successful performance that the EM-based classification method may achieve.
Improvements to Vanilla Implementation of Q-Learning Used in Path Planning of an Agent

Bhuiya, Aritra Satapathy, Suresh C.The Vanilla Reinforcement Learning algorithm for path planning in a 2-D world suffers from high mean path length and large iterations to optimize its path. An approach involving multi agents sharing common knowledge of the world has been simulated to improve the results. Multi agents increase the exploration area of the map without affecting the explore–exploit dilemma. Keeping in mind that in the real world agents may have vision, a simple yet effective vision-based technique has been proposed where an agent can locate its goal if it lies in its line of sight and not blocked by obstacles. An environment is designed in Python 3 that uses dynamically generated maps of different sizes to simulate the results.
RNN-Based Deep Learning Model for Generating Caption of an Image

Image captioning refers to a task wherein an image is seen by a computer to give an output as “what it sees.” Image captioning would mean giving an intelligent system an image, say of a dog running across a field to receive a statement back as “dog running across a field” or something similar down its lines. The image caption generator is a deep learning model built using the power of recurrent neural networks (RNNs) which are powered by the long-term and short-term (LTSM) units. The model also uses transfer learning to enable build a better and faster deep learning model. The model is built using the “Flicker8k” dataset and uses the inherent power of a convolutional neural network to predict suitable caption for an image. The seamless integration of computer vision into the model and its high-quality prediction makes it highly usable and in-demand with respect to the current trends in the industry.
Markerless Location-Based Augmented Reality Application for Showcasing Deals

The development and execution of an online mobile app suitable for detecting offers and showing relevant details on a digital augmented reality display via a smart phone’s camera are discussed in this paper. The client–server design of this system is two-tiered. Initially, a deal should be recorded on our platform by an entity that is distinct from users. Deal Teal AR uses the camera view to overlay electronic data on areas around you in the current world, based on the position you are facing. Deal Teal AR displays a picture via your phone’s camera when you touch it. Deal Teal’s database of businesses-restaurants, hotels, places of interest, and much more is often used to generate cards that display information or bargains. The bargains are placed in the current environment and become active, thanks to powerful augmented reality technology like computer vision and object recognition. A virtual terrain modeling platform with deep learning was also employed to increase the application’s productivity and construction recognition abilities.
Performance of Deconvolution Network and UNET Network for Image Segmentation

Jayesh Kothari, Jash Racha, Sai Sandesh Sengupta, JoydeepIn this paper, we have discussed the architecture of certain deep learning algorithms namely, Deconvolutional Neural Network and UNET Network. These are compared to the performances of models based on each of them using various mathematical parameters. Briefly, in both of these architectures, the image is passed through several convolution layers, each followed by a rectified linear unit (ReLU) and a max-pooling operation in the contraction path. This enables to capture the feature information. A similar symmetric expanding path helps find spatial information by passing the image through some up-convolution layers. In UNET though, in the expanding path, the spatial information obtained is concatenated with feature information that was obtained from the contraction path. For the CityScapes Dataset, we can see that models based on UNET clearly outperform the prior models based on Deconvolution Network by evaluating and comparing their IOU values. The network was trained on Google Colab’s GPU.
A Universal Dependency Treebank for Definitely Endangered Low-Resource Kangri Language

In this paper, we have present the first universal dependency treebank for low resource definitely endangered Himachali Kangri (ISO 639-3xnr) language. The goal of the Kangri Universal Dependency Treebank (KDTB) is to establish a large, syntactically annotated treebank that will aid cross-lingual learning and typological research as well as serve as a significant resource in the development of language technology tools. KDTB data contain syntactic annotation according to dependency-constituency schema, as well as morphological tags. The Kangri UD treebank (KDTB) consists of 2249 tokens and 1108 vocabulary (288 sentences).
Underwater Image Segmentation Using Fuzzy-Based Contrast Improvement and Partition-Based Thresholding Technique

Underwater images suffer from haziness due to the presence of particles and changes in water density in the marine environment. Underwater image segmentation is one of the challenging areas due to the blurriness of the images. Image preprocessing is necessary before image segmentation due to unclear image, so the work is divided into three parts: contrast improvement, sharpening, and segmentation of an image. The fuzzy-based contrast improvement technique is proposed with Contrast Limited Adaptive Histogram Equalization to enhance contrast. An unsharp mask is used to sharpen an image. The proposed partition-based thresholding segmentation is used to segment the image. In this method, the partition is made to calculate an appropriate threshold value to segment each partition. Quantitative and qualitative analysis has been shown in results and discussions part using entropy as a measurement parameter. 75.67% accuracy was received by the work. Also, the work is compared with existing work.
Development of a Web-Based Corona Emergency Portal

As the present world is devastated by the massive outbreak of coronavirus (COVID-19), Bangladesh is experiencing significant damage in all sectors, especially as a developing country. This paper presents the development of a Web-based corona emergency portal, which will help ordinary people find out all the necessary emergency information in this regard. The Web portal comprises the COVID-19 tracker, corona e-commerce, and blood plasma bank, which are developed with React.js, Node.js and Firebase. The COVID-19 tracker is a much-needed option for this pandemic where daily updates on COVID-19 will be posted. The blood plasma bank aims to serve people with emergency blood plasma, which is very rare to find in critical situations. The developed Web-based system is user-friendly and efficient. Under these ongoing circumstances, this portal aims to be a one-stop service for all types of corona-related services for Bangladesh during this unexpected period of the COVID-19 pandemic.
Knowledge Graph Based Question-Answering System for Effective Case Law Analysis

Thomas, Anu Sangeetha, S.Case Law Analysis is a critical step in legal research, as every lawyer has to acquire the skill set to read, understand and apply case laws to augment their arguments and pleadings. We found that the interpretations of case laws provided by existing prominent legal tools are not complete in terms of providing some of the essential details required for effective case law analysis. This paper overcomes the above-mentioned challenge by proposing a judicial knowledge graph based question-answer system. Representing the judicial data in the form of knowledge graph ultimately benefits the legal professionals in analyzing case laws. The proposed approach supports querying the judicial knowledge graph in natural language text and then returns the results directly in natural language. We model the task of answering input queries as a cypher query pattern selection problem. Experimental results are quite satisfying in terms of efficiency and effectiveness in comparison to existing knowledge graph based question answering systems. To the best of the authors’ knowledge, this work is the first to offer legal question answering based on knowledge graph.
Medical Image Protection Using Blockchain for E-healthcare System

Basically, the modern E-healthcare system is a Cloud-based system where all the details of Patient Health Information (PHI) on disease diagnosis and scan reports are stored and shared among the stockholders of the system. However, the security of these sensitive records against unauthorized access and misuse is of major concern. In this context, a Blockchain-based record-sharing system could play an important role for secure transactions of medical information. In this paper, a smart contracts and Inter Planetary File System (IPFS)-based secure medical image protection system has been proposed to encrypt the session data. Moreover, it records every transaction of patient data in the Blockchain for better traceability. The performance of the proposed framework is evaluated in simulation to show its effectiveness.
RoMaPla: Using t-Test for Evaluating Robustness of Marathi Plagiarism

Identifying plagiarism of the document is a mandatory task in the academic domain. Generally online available tools are used to check plagiarism. These tools calculate similarity between the documents using a sequence of the tokens/words present in the documents which are to be compared. A semantic relationship between the words for eg., word and its synonym are treated as different, while calculating the similarity between the documents. Few tools may be available for checking the similarity of English documents. But checking the plagiarism of Marathi documents is comparatively untouched field. Information present in the Marathi language is growing due to multilingual processing. The existing MaPla (Marathi Plagiarism checker) proved that Document synset matrix for Marathi (DSMM) similarity results are near to readings observed using cognitive ability of humans and it was performed on 4 documents. To further confirm robustness of MaPla, we experimented with 24 documents to calculate the similarity between all pairs of documents using cosine measure. Thus two, 24 × 24 matrices are formulated using DSMM and manual readings. Paired t-test, which was not carried out in MaPla, proves that there is no significant difference between two matrices and hence proves the robustness of the proposed technique.
Eyes Say It All: Deep fake Detection Method Analysis Using Different Metrics

With the advancement of artificial intelligence (AI) techniques, fake digital content has mushroomed in recent years. The production of very realistic fake content, even by a novice computer literate, has become a cakewalk with the latest deep learning and generative adversarial network (GAN) models. The objectionable content created by the use of deepfake technology is getting exploited by bad elements to tarnish the image of celebrities. With the advancement of digital technologies, it is almost impossible to detect fake content with naked eyes and is posing a big challenge for the public. In this manuscript, we use a method to detect deepfakes by using eye reflection and comparing both eyes in terms of different metrics such as structural similarity index measure (SSIM), intersection over union (IoU), and mean-squared error (MSE). We used Flickr-Faces-HQ (FFHQ) dataset for real human eye images, and the thispersondosentexist.com Web site for GAN-synthesized images. Next, we used automatic detection of the corneal region and compared pixels of both eyes. Despite their simplicity, the methods can achieve AUC values of up to 0.9.
Recovering ROI of Medical Image Through Curvelet Transform-Based Watermarking Method

Telemedicine is a forward-thinking research area that is gaining traction. A remote specialist relies heavily on scan images or therapeutic images and data of the patient for diagnostic purposes. While sending a scan image to a remote specialist, intruders may change the Region of Interest (ROI). If the ROI in a clinical picture has been modified, the remote specialist is responsible for restoring it. This paper uses a novel robust watermarking method based on Curvelet Transform to retrieve the ROI in a medical picture. The proposed method hides ROI information in a diagnostically unimportant part of the medical picture (Region of Non-Interest) using Curvelet Transform. Experiments show that using this novel method, the ROI in a medical image can be restored to its original state.
Subgraph Adaptive Structure-Aware Graph Contrastive Learning

Graph contrastive learning (GCL) has been subject to more attention and been widely applied to numerous graph learning tasks such as node classification and link prediction. Although it has achieved great success and even performed better than supervised methods in some tasks, most of them depend on node-level comparison, while ignoring the rich semantic information contained in graph topology, especially for social networks. However, a higher-level comparison requires subgraph construction and encoding, which remain unsolved. To address this problem, we propose a subgraph adaptive structure-aware graph contrastive learning method (PASCAL) in this work, which is a subgraph-level GCL method. In PASCAL, we construct subgraphs by merging all motifs that contain the target node. Then we encode them on the basis of motif number distribution to capture the rich information hidden in subgraphs. By incorporating motif information, PASCAL can capture richer semantic information hidden in local structures compared with other GCL methods. Extensive experiments on six benchmark datasets show that PASCAL outperforms state-of-art graph contrastive learning and supervised methods in most cases.
Few-Shot Training Gan for Face Forgery Classification and Segmentation Based on the Fine-Tune Approach

There are many techniques for faking videos that can alter the face in a video to look like another person. This type of fake video has caused a number of information security crises. Many deep learning-based detection methods have been developed for these forgery methods. These detection methods require a large amount of training data and thus cannot develop detectors quickly when new forgery methods emerge. In addition, traditional forgery detection refers to a classifier that outputs real or fake versions of the input images. If the detector can output prediction of the fake area, i.e., a segmentation version of forgery detection, it will be a great help for the forensic work. Thus, in this paper, we propose a GAN-based deep learning approach that allows detection of forged regions using a smaller number of training samples. The generator part of the proposed architecture is used to synthesize predicted segmentation which indicates the fakeness of each pixel. To solve the classification problem, a threshold on the percentage of fake pixels is used to decide whether the input image is fake. For detecting fake videos, frames of the video are extracted and it is detected whether they are fake. If the percentage of fake frames is higher than a given threshold, the video is classified as fake. Compared with other papers, the experimental results show that our method has better classification and segmentation.
Practically Motivated Adaptive Fusion Method with Tie Analysis for Multilabel Dispersed Data

The goal of this paper is to propose an adaptive fusion method that aggregates the results of predictions obtained for set of classifiers. The problem of selecting the best fusion method for a given data set is an important but hard task. For different data sets, different fusion methods work best, and it is not possible to predict in advance which method will be appropriate for a given data set. In the proposed adaptive method the weights assigned to the fusion methods are adjusted iteratively. The proposed method is based on empirically developed formulas. The results obtained using this method were compared with the results obtained for the online learning method - the Multiplicative Weights approach - which is asymptotically optimal (Arora et al., 2012). However, the results obtained with the use of the proposed method turn out to be better when applied to a finite set of test objects. In addition, a comparative analysis is provided when fusion methods are used simultaneously. It was shown that the results obtained by the adaptive fusion method are much better.
Discrimination Method of Biomass Slagging Tendency Based on Particle Swarm Optimization Deep Neural Network (Dnn)

The slagging problem that occurs during the combustion of biomass fuels is difficult to deal with, affecting the safe operation of the boiler and reducing the combustion efficiency. Therefore, predicting the slagging tendency of biomass combustion can guide the selection of fuel, reduce the experimental cost and improve the production efficiency of the boiler. In this paper, 114 kinds of biomass are selected as the data set. Through the visualization of the overall distribution of the data set. And the quantitative relationship between slag and each element was obtained by Spearman correlation analysis. Next, taking 13 kinds of chemical elements and biomass ash content (%) as input and slagging type as output, and the network models of DNN, RNN and LSTM-NN were built by python 3.6 . The four indexes of Accuracy, Precision, Recall and F1_score were used to evaluate the model. Among them, the accuracy of DNN model was highest. After that, PSO algorithm is used to optimize the highest precision DNN model, and the optimization accuracy is improved to 0.9130434. Then the optimized model was used to predict the slagging types of other 571 biomass, and the results provide guidance for the actual operation of biomass fuel boilers.