Reviewerly/exHarmony huggingface dataset:
Nonmonotonicity of R-Calculus

Nonmonotonic logics [, , ] are different from monotonic logics in that the deduction is nonmonotonic. The traditional logics, such as propositional logic, first-order logic, modal logic, etc., are monotonic. The nontraditional logics, such as default logic, R-calculi, autoepistemic logic, circumscription, etc., are nonmonotonic. The nonmonotonicity of a nonmonotonic logic follows from using a negation \(\Delta \not \vdash A\) of a monotonic deduction \(\Delta \vdash A\) []. We found that \(\Delta \not \vdash A\) occurs in each nonmonotonic logic. For example, in default logic, a formula B is deducible (or in some extension of default theory \((\Delta ,A\rightsquigarrow B)\)) from a default theory \((\Delta , A\rightsquigarrow B)\) [, , , , , ] if A is deducible in propositional logic from \(\Delta \) and \(\lnot B\) is not, that is, $$ \Delta \vdash A \& \Delta \not \vdash \lnot B,$$where \(A\rightsquigarrow B\) is a normal default \(\dfrac{A:B}{B}\).
Estimation of Imageability Ratings of English Words Using Neural Networks

The article considers the problem of imageability ratings estimation of English words using artificial neural networks. To train and test the models, we use data of several freely available psycholinguistic databases. We compared two approaches based on different vector representations of words. The first approach uses pre-trained fastText vectors. The second one utilizes explicit word vectors built on the basis of co-occurrence statistics with the most frequent words extracted from the Google Books Ngram corpus. We employed the MRC Psycholinguistic Database to obtain the value of Spearman's correlation coefficient between imageability ratings and their estimations. The highest resulting value equaled 0.882. This significantly improves the results obtained in previous works. The approach proposed in this paper can be used to create large dictionaries with imageability ratings, which is important for many practical problems.
Robust Visual Question Answering Based on Counterfactual Samples and Relationship Perception

Traditional visual question answering algorithms based on relationship perception help answer questions by modeling the relationship in the input image. Although better visual question answering performance can be obtained, the model learns the language deviation of the image appearance during training and performs slightly worse on test sets with different data distributions. A model based on Counterfactual Samples and Relationship Perception (CSRP) is proposed by us to solve this problem. The counterfactual sample generation mechanism can generate a large number of counterfactual samples by shielding key objects, forcing the model to focus on key objects to answer questions. Counterfactual samples as feature enhancements can reduce the learning appearance language bias during training. And the relationship between image objects perceives semantics. Extensive experiments on the VQA-CP v2 and VQA V2 datasets demonstrate that our proposed model outperforms most state-of-the-art methods.
Dual Histogram Based RVIN detector and Hybrid Gaussian Filter

The objective of this paper is to develop an efficient noise detector and filter for random valued impulse noise (RVIN) in gray level images. A Dual Histogram based Noise Detector (DHND) is proposed and compared with other approaches using the classical measures like False Alarm (FA) and Miss Detection (MD). Also a novel filtering approach is proposed namely Hybrid Gaussian Filtering (HGF) which combines two filters such as Gaussian Impulse Kernel (GIK) and Gaussian Weighted Median (GWM). The proposed HGF approach is validated across state-of-art filters in terms of standard performance measures such as Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM) and Feature Similarity Index Measure (FSIM). The robustness of the filter is experimented for various standard gray scale images to evaluate its global significance. Experimental results reveal the pros and cons of other algorithms along with the effectiveness of the proposed filter under varying noise conditions.
Learning algebraic structures with the help of Borel equivalence relations

We study algorithmic learning of algebraic structures. In our framework, a learner receives larger and larger pieces of an arbitrary copy of a computable structure and, at each stage, is required to output a conjecture about the isomorphism type of such a structure. The learning is successful if the conjectures eventually stabilize to a correct guess. We prove that a family of structures is learnable if and only if its learning domain is continuously reducible to the relation E0 of eventual agreement on reals. This motivates a novel research program, that is, using descriptive set theoretic tools to calibrate the (learning) complexity of nonlearnable families. Here, we focus on the learning power of well-known benchmark Borel equivalence relations (i.e., E1, E2, E3, Z0, and Eset).
Design and Application of Graphical Shunting Based on C/S Architecture in Factory and Mining Enterprises

In factories and mines station, shunting have the primary task of transportation, large and frequent shunting operation has brought serious problems to management; its timeliness and larger traffic requirements of its information integration, operational efficiency, The accuracy is higher. The current information management system is mainly based on reports and printing freight information files. Its information integration is low, and the operation of preparing a locomotive coupler plan is too cumbersome. The author proposes to adopt a graphical shunting based on C/S architecture. Module, automatic preparation of hook plan and locomotive operation diagram, better realization of human-computer interaction and system response.
User Satisfaction Analysis of Instant Transcription Application Using the System Usability Scale (SUS) Method: A Case Study on Teenaged and Adult Deaf Users

Deaf individuals experience verbal/oral communication barriers, both expressively (speaking) and receptively (understanding other people's speech). More than 100 million individuals currently use the Instant Transcription App as an assistive technology to overcome the communication barriers of deaf individuals. The Instant Transcription application is classified as an assistive technology that deaf individuals widely use, but it is still rarely studied. Therefore it is necessary to measure the usability aspect analysis using SUS (System Usability Scale). Based on the results of processing the satisfaction score using SUS, the final result is an average value of the SUS score of 65, which means that the satisfaction level of users of the Instant Transcription Application is at the High Marginal acceptance level. The level of satisfaction of users of the Instant Transcription Application already has sufficient in terms of the satisfaction felt by deaf users aged teenagers to adults. Still, it needs to be increased because the average score of 65 is still on the grade scale category “D.”
Research on Drawing Robot Based on Image Edge Detection

This paper selects the outer contour method to describe the image's features and uses industrial robots to draw. Based on the minimum description length, the Gaussian filter in the Canny operator is improved adaptively to ensure edge recognition accuracy. To highlight the primary contour in the process of drawing the image, delete the small areas. We propose an edge thinning algorithm to avoid drawing the same contour twice and obtain the edges with single-pixel width. The broken contours are connected to reduce the time of lifting the pen when the robot is drawing. To avoid contour tracking error resulting in incomplete image rendering, removing the burr points in the image.
An Appraisal of Developmental Crises in Africa: Lessons From the Asian Tigers

This study compares Africa’s developmental crises with those of Asian ’Tigers’ success economic story, and how it can serve as a lesson for Africa, still trudging on development. The work observes, contrary to all pretenses that Africa, in spite of her much publicized theoretical ambition for development, is still caught in the web of under-development. It examines the various obstacles to Africa’s development and identified them as systemic pathological defects. The paper argues Asian Tigers that shared similar experiences with Africa have moved away from under-development and poverty to rival the levels of prosperity in the Global North. Using data generated from secondary sources and comparative method of analysis, the paper articulates that; Africa’s developmental challenges such as bribery and corruption, bad governance, weak political institutions and undemocratic values are serious, real and many but could be meet through improvement on governance. The paper noted that today, African leaders still excusably blame the failure of the region on its colonial past. While these features cannot be said to be the libel of Asia Tigers. Interesting, they are now in revolt against bondages of the past, against poverty, ignorance disease, under-development and dependency syndrome. In light of the above finding, a major institutional reform is needed to achieve sustainable development in Africa.
Control Variates for Similarity Search

We present an alternative technique for similarity estimation under locality sensitive hashing (LSH) schemes with discrete output. By utilising control variates and extra information, we are able to achieve better theoretical variance reductions compared to maximum likelihood estimation with extra information. We show that our method obtains equivalent results, but slight modifications can provide better empirical results and stability at lower dimensions. Finally, we compare the various methods' performances on the MNIST and Gisette dataset, and show that our model achieves better accuracy and stability.
Fake News Polarization for Sentiment Analysis

The popularity of the internet has increased the use of e-commerce websites and news channels. Fake news has been around for many years, and with the arrival of social media and modern-day news at its peak, easy access to e-platform and exponential growth of the knowledge available on social media networks has made it intricate to differentiate between right and wrong information, which has caused large effects on the offline society already. A crucial goal in improving the trustworthiness of data in online social networks is to spot fake news so the detection of spam news becomes important. For sentiment mining, the authors specialise in leveraging Facebook, Twitter, and Whatsapp, the most prominent microblogging platforms. They illustrate how to assemble a corpus automatically for sentiment analysis and opinion mining. They create a sentiment classifier using the corpus that can classify between fake, real, and neutral opinions in a document.
Enhancing Micro-location Accuracy for Asset Tracking: An Evaluation of Two Fingerprinting Approaches Using Three Machine Learning Algorithms

Micro-location is the ability to track a user’s location or asset at a granular level and is the next evolution in location services. However, this evolution is not proceeding without challenges. Current mobile device GPS systems are not accurate enough to provide the granularity required; especially indoors when there is no line-of-sight with satellites. One promising solution involves using Bluetooth Low Energy beacons. In this paper, we present an indoor location system that uses BLE beacons and iPhones. We present the relevant background of work in this area, the architectural framework that we designed and developed, the mobile apps, the machine learning algorithms employed and report on the findings of two fingerprinting methods using three machine learning algorithms. Our indoor micro-location prediction system using beacons consistently performs at an accuracy of at least 1.7 m spherical radius from the target with convergence in under 2 s and was validated on 2,000 trials.
Enhancement of Veterinary Vaginoscopic Image of Female Canines using Color Transfer Algorithm in 1αβ Color Space

Veterinarians need knowledge of the estrus stage of the reproductive cycle to decide the exact mating time for canine breeding. Through vaginoscopy, a visual examination of the vaginal vault of female canines can be done and the mating period can also be determined. However, due to low light illumination in vaginoscopic examination, the captured images lack visual quality. This hampers accurate determination of the mating period. In this study, an attempt has been made to enhance vaginoscopic images of female canines recorded at the College of Veterinary & Animal Sciences, Mannuthy, Kerala, India. These captured images are subjected to singular value decomposition (SVD), with the aid of a color transfer algorithm in 1αβ cone space. SVD results in a singular value matrix that accommodates the majority of the intensity information of the input image. By scaling these singular values, the total intensity of the input image is improved. Further, color transfer in 1αβ cone space is carried out to get better enhanced images. These enhanced images are evaluated using various quality parameters such as mean square error (MSE), peak signal to noise ratio (PSNR), entropy and mean. The proposed algorithm has an average MSE of 20.11, while PSNR, entropy and mean values are 35.65 dB, 7.16, and 104.15, respectively. The qualitative analysis shows that this algorithm can be used to enhance the veterinary vaginoscopic images intended for further processing. This algorithm facilitates veterinarians to clearly visualize the wrinkles and bloody discharge during oestrus cycle.
Integrated Air-Ground Vehicles for UAV Emergency Landing Based on Graph Convolution Network

With unmanned aerial vehicle (UAV) technologies advanced rapidly, many applications have emerged in cities. However, those applications do not widely spread as the safety consideration hinders the UAV from integrating into the civilian environment. This work focuses on investigating the UAV emergency landing problem which is a critical safety functionality of UAV. This work proposed a graph convolution network (GCN)-based decision network to learn by imitating the human pilots’ landing strategy. To alleviate the needs of a large amount of real-world data for model training, the proposed model allows to be trained in a simulated environment and then transferred to the real-world scenario due to the separation of domain-specific terrain classes and domain-independent topological structures among down-looking camera images. The GCN-based decision network can be coupled with a topological heuristic to improve the performance of action prediction in an emergency situation. To evaluate the proposed method, this work implemented a simulation environment for collecting data and testing the UAV emergency landing. The empirical results in both simulated and real-world scenarios show that the proposed methods can outperform the state-of-the-art counterparts in terms of predictive accuracy and success landing rate.
Control architecture for a novel Leg-Based Stair-Climbing Wheelchair

Based on a recently proposed leg-based stairclimbing wheelchair, this work presents the control architecture for this mechanism. The objective of this work is to propose a step by step control methodology, which can define the positions of the wheels to climb up/down stairs. This will simplify the control of the seventeen actuators involved in climbing up and down tasks. The strategy proposed in this work makes the system robust to sensor uncertainties and small errors in the mechanical parameters, making the structure safer. This control architecture facilitates the practical implementation of the kinematic control. In addition, this strategy is also useful to optimize the configuration of the mechanism.
Feature Matching Network for Weakly-Supervised Temporal Action Localization

Weakly supervised temporal action localization needs to get the category of the action as well as the time period when the action of corresponding category occurs with only video level annotation during training. Currently, how to effectively localize the actions with weak discriminative information and distinguish background activities is the major challenge. To address these issues, we propose a feature matching network (FMNet) that can produce attention scores and perform different operations on them to represent different actions and background features. Moreover, we modify the cross-entropy loss to match different features. Experimental results on two standard datasets THUMOS14 and ActivityNet1.2 show the superiority of our methods.
Comparison of Camera Calibration and Measurement Accuracy Techniques for Phase Measuring Deflectometry

Phase measuring deflectometry (PMD) is a competitive method for specular surface measurement that offers the advantages of a high dynamic range, non-contact process, and full field measurement; furthermore, it can also achieve high accuracy. Camera calibration is a crucial step for PMD. As a result, a method based on the calibration of the entrance pupil center is introduced in this paper. Then, our proposed approach is compared with the most popular photogrammetric method based on Zhang’s technique (PM) and Huang’s modal phase measuring deflectometry (MPMD). The calibration procedures of these three methods are described, and the measurement errors introduced by the perturbations of degrees of freedom in the PMD system are analyzed using a ray tracing technique. In the experiment, a planar window glass and an optical planar element are separately measured, and the measurement results of the use of the three methods are compared. The experimental results for the optical planar element (removing the first 6 terms of the Zernike polynomial) show that our method’s measurement accuracy reached 13.71 nm RMS and 80.50 nm PV, which is comparable to accuracy values for the interferometer.
Random Sparsity Defense Against Adversarial Attack

More recently, with the development of deep learning and the expansion of its applications, deep learning became the targets of attackers. Researchers found that adversarial perturbations have excellent efficiency for attacking deep neural network. The adversarial examples that are crafted by adding tiny and imperceptible perturbations to modify pixels can make the classifier output wrong results with high confidence. This demonstrates the vulnerability of deep neural network. In this paper, we proposes a method to defend the adversarial attack, reducing output distortion owing to the attack. The proposed method, called random sparsity defense, is a combination of whitening and random sparsity. The method increases randomness in sparsity-based defense and weakens the adverse effects of randomness through whitening. Experimental results on MNIST dataset show that the proposed random sparsity defense can resist attack well and has a good ability to correct classification results.
“Progress” in Art in Terms of Semiotics of Creativity

The article gives insight into the universal principles of artistic and technical creativity. General semiotics is our methodology in discovering creative process as the activity that induces the shift of semantic, syntactic or pragmatic rule at the levels of sensory perception, mind or reason, as a result of which the new appears. The research follows the ontological framework of Friedrich Dessauer's concept of the potential cosmos, which is a modern version of Platonism. The concept of creativity as a projective semiosis implies two theses, defended in the article. The first thesis: a work of art is a specific technical object, both incomplete and augmented, incomplete due to the basic intangibility of the artistic language, augmented - in the sense of changed teleological causality. The second theses: the developmental history of literature and the process of changing artistic styles can be described in terms of general semiotics as a shift of a specific semiotic rule.
Performance evaluation of 3G (UMTS Network) for E-Learning Video Streaming

Advancement in technology has led teaching and learning to improve from traditional to electronic learning (E-Learning). E-learning consists of different multimedia including videos which facilitate learning much easier. Unfortunately, videos accessed on e-learning platform suffer in Quality of Service (QoS) and Quality of Experience (QoE). The aim of this paper is to evaluate QoS in terms of jitter, delay and packet loss, also to evaluate and compare objective and subjective QoE of e-learning video transmitted via Universal Mobile Telecommunication System (UMTS) network. A model of UMTS network was developed by using Network Simulator 2 (NS2) and EvalVid framework. The developed model was used for e-learning video streaming. The streamed video was objectively and subjectively evaluated for its QoE, also jitter, delay and packet loss were used as the parameters for QoS. The obtained results has shown that e-learning video streaming delivered through 3G/UMTS suffers packet losses that exceeds the accepted value of 1%, hence poor video streaming QoS. Subjective QoE turned out to be much worse than objective QoE. Therefore, this paper recommends preference in using subjective QoE, because it gives real feelings of users with regards to QoS. Results suggest that UMTS is not suitable for e-learning video streaming.
Location-Aware Named Entity Disambiguation

Named Entity Disambiguation (NED) and linking has been traditionally evaluated on natural language content that is both well-written and contextually rich. However, many NED approaches display poor performance on text sources that are short and noisy. In this paper, we study the problem of entity disambiguation for short text and propose a location-aware NED framework that resolves ambiguities in text with little other contextual cues. We show that the spatial dimension is crucial in disambiguating named entities and that the location inference is less utilized in many NED systems. Our proposed framework integrates (in an unsupervised manner) spatial signals that are readily available for many sources that emit short text (e.g., micro-blogs, search queries, and news streams). Our evaluation on news headlines and tweets reveals that a simple spatial embedding improves the accuracy of competitive baseline NED approaches from the literature by 8% for the news headlines and by 4% on tweets.
Comparitive study of GRU and LSTM cells based Video Captioning Models

Video Captioning task involves generating descriptive text for the events and objects in the videos. It mainly involves taking a video, which is nothing but a sequence of frames, as data from the user and giving a single or multiple sentences (sequence of words) to the user. A lot of research has been done in the area of video captioning. Most of this work is based on using Long Short Term Memory (LSTM) units for avoiding the vanishing gradients problem. In this work, we purpose to implement a video captioning model using Gated Recurrent Units(GRU's), attention mechanism and word embeddings and compare the functionalities and results with traditional models that use LSTM's or Recurrent Neural Networks(RNN's). We train and test our model on the standard MSVD (Microsoft Research Video Description Corpus) dataset. We use a wide range of performance metrics like BLEU score, METEOR score, ROUGE-1, ROUGE-2 and ROUGE-L to evaluate the performance.