Reviewerly/exHarmony huggingface dataset:
Online Path Planning of Mobile Robots Based on African Vultures Optimization Algorithm in Unknown Environments

Autonomous mobile robots developed using metaheuristic algorithms are increasingly becoming a hot topic in control and computer sciences. Specifically, finding the shortest root to the goal and avoiding hurdles are current subjects of autonomous mobile robots. The main drawbacks of classic methods are the incapacity to move the robot in a dynamic and unknown environment, deadlock in a local minimum and complicated environments, and incapacity to foretell the speed vector of obstacles and non-optimality of the route. This article exhibits a recent path planning approach that utilizes the African Vultures Optimization (AVOA) for navigation of the mobile robot in static and dynamic unknown environments with a dynamic target. The proposed online optimization approach is used in three different environments including an environment with unknown static obstacles, an environment with unknown dynamic obstacles, and an environment with a dynamic target. The proposed approach can solve a local minima problem in the environment with static obstacles. The online optimization method is performed using two phases which are the sensors’ reading phase and the path calculation phase and the results are given based on computer simulation in different unknown environments. A comparative study was conducted between the suggested algorithm and two other algorithms and the results showed that the AVOA algorithm was better in avoiding obstacles successfully including the local minima situation. Finally, the average enhancement rates in the path length compared with the Adaptive Particle Swarm Optimization (APSO) and the Hybrid Fuzzy-Wind Driven Optimization (WDO) are 2.21% and 1.02207%, respectively.
Artificial Techniques Based on Neural Network and Fuzzy Logic Combination Approach for Avoiding Dynamic Obstacles

The autonomous mobile robot must be capable of avoiding static and dynamic obstacles in the environment and navigating towards the target without any human effort. A valid low-cost path from start to goal is obtained by A* algorithm. Neural network used for Zone classification. The relative values between mobile robot and obstacle are used for classification which are distance, velocity, and angle. Zone1 is very dangerous while zone 5 is not dangerous. If the neural network classifies the obstacle as a dangerous obstacle and activates the controller. The fuzzy logic makes a decision as a reaction of mobile robot to prevent collision. There are three inputs to the fuzzy logic (relative velocity, relative distance, and relative angle) between mobile robot and obstacle. The outputs of fuzzy logic are velocity and steering angle of mobile robot. Static obstacles have been added to the environment in addition to dynamic obstacles to make the environment more complex. Three dangerous dynamic obstacles to the mobile robot are tested. While mobile robot is avoiding one obstacle, another obstacle enters critical zone and becomes dangerous to mobile robot. The mobile robot avoids the second obstacle while it is avoiding the first obstacle. Then the velocities of mobile robot and obstacles have been increased to prove that the proposed system can handle cases with faster velocities. The simulation results for the tested cases shows the capability of the proposed method for avoiding static and dynamic obstacles in fully known environment.
Proposal for an integrative performance framework based on Distributed Ledger Technology dedicated to higher education students entering the labor market

Recognition of the skills that students acquire during their training is essential for them to access to more complex activities or to pursue further studies. An integrative student-centric framework is proposed in order to fade the boundaries between students, educational institutions and potential employers by making block-chain certification issuers and HR software interoperable. The contributions and demands of stakeholders taking part in the process are investigated. Key drivers of performance are identified for Distributed Ledger Technology (DLT) aiming to improve the chances for students in higher education system entering the labor market.
IIT Dhanbad @LT-EDI-ACL2022- Hope Speech Detection for Equality, Diversity, and Inclusion

Hope is considered significant for the wellbeing, recuperation and restoration of human life by health professionals.Hope speech reflects the belief that one can discover pathways to their desired objectives and become roused to utilise those pathways.Hope speech offers support, reassurance, suggestions, inspiration and insight.Hate speech is a prevalent practice that society has to struggle with everyday.The freedom of speech and ease of anonymity granted by social media has also resulted in incitement to hatred.In this paper, we work to identify and promote positive and supportive content on these platforms.We work with several machine learning models to classify social media comments as hope speech or nonhope speech in English.This paper portrays our work for the Shared Task on Hope Speech Detection for Equality, Diversity, and Inclusion at LT-EDI-ACL 2022.
A Study on the Performance of Digital Economy Companies based on DEA-malmquist Model

With the third technological revolution, the invention and use of electronic computers has completely pushed human development to a new stage.the arrival of the post-2020 epidemic has accelerated the pace of digital transformation, and the digital economy has become the third largest industry after agriculture and industry, becoming the new engine of national economic development.The rise of digital economy also forces the transformation and upgrading of enterprises.This paper takes the main financial indicators of listed companies in digital economy as samples and considers DEA-malmquist index in addition to traditional CCR and BCC models to discuss the linear relationship between corporate R&D inputs and revenues.The study data are more significant and can clearly illustrate the positive linear relationship between R&D investment and revenue.It is an important reference for future strategic decisions and financial expenditure allocation of economic digital companies.
Identifying relevant common sense information in knowledge graphs

Knowledge graphs are often used to store common sense information that is useful for various tasks.However, the extraction of contextuallyrelevant knowledge is an unsolved problem, and current approaches are relatively simple.Here we introduce a triple selection method based on a ranking model and find that it improves question answering accuracy over existing methods.We additionally investigate methods to ensure that extracted triples form a connected graph.Graph connectivity is important for model interpretability, as paths are frequently used as explanations for the reasoning that connects question and answer.
An Empirical Study on Explanations in Out-of-Domain Settings

Recent work in Natural Language Processing has focused on developing approaches that extract faithful explanations, either via identifying the most important tokens in the input (i.e.post-hoc explanations) or by designing inherently faithful models that first select the most important tokens and then use them to predict the correct label (i.e.select-then-predict models).Currently, these approaches are largely evaluated on in-domain settings.Yet, little is known about how post-hoc explanations and inherently faithful models perform in out-ofdomain settings.In this paper, we conduct an extensive empirical study that examines: (1) the out-of-domain faithfulness of post-hoc explanations, generated by five feature attribution methods; and (2) the out-of-domain performance of two inherently faithful models over six datasets.Contrary to our expectations, results show that in many cases out-of-domain post-hoc explanation faithfulness measured by sufficiency and comprehensiveness is higher compared to in-domain.We find this misleading and suggest using a random baseline as a yardstick for evaluating post-hoc explanation faithfulness.Our findings also show that selectthen predict models demonstrate comparable predictive performance in out-of-domain settings to full-text trained models. 1
ASSESSMENT OF COUNTRY READINESS FOR DISTANCE LEARNING

The article highlights the advantages and disadvantages of using e-learning in the educational process, systematizes modern distance technologies, ranked and grouped universities in countries by level of e-learning, gave a rating assessment of the readiness of states for distance learning.The author examines the integrated indicators of the latest international indices such as the RemoteLearningReadinessIndex and the E-LearningIndex.In the wake of the Covid-19 epidemic, the closure of higher education institutions in 223 countries resulted in 1.57 billion students (90% of the world's population) being forced to study remotely.Higher education institutions had to quickly and efficiently organize distance or blended learning, modernize its material and technical base. The threat of future pandemics and economic unrest increases the importance of a reliable distance education system to prevent the next global socio-economic crisis, avoid health emergencies and bridge educational gaps at the national and international levels.In order to assess the readiness of countries to implement distance education, the UNESCO Institute for Statistics conducted a survey in 53 countries.The UNESCO report on the results of monitoring global education in 2021 noted that only 3 countries in the world are fully ready to move to distance learning, 19 countries have partially used online methods, and 28 countries have used a hybrid approach – a combination of distance and full-time learning. Middle-income countries from Colombia to Egypt and from China to the Russian Federation have developed online platforms for blended and distance learning. The survey found positive the fact that 39% of students from sub-Saharan Africa enrolled in institutions that offer different options for distance learning. In the EU, on the other hand, 41% of online students lost their jobs (29% temporarily with the right to return to work and 12% permanently without a job). These results prove the effectiveness of distance education only with the development of an effective model of online learning and its support by the state.
Measuring the Impact of (Psycho-)Linguistic and Readability Features and Their Spill Over Effects on the Prediction of Eye Movement Patterns

There is a growing interest in the combined use of NLP and machine learning methods to predict gaze patterns during naturalistic reading.While promising results have been obtained through the use of transformer-based language models, little work has been undertaken to relate the performance of such models to general text characteristics.In this paper we report on experiments with two eye-tracking corpora of naturalistic reading and two language models (BERT and GPT-2).In all experiments, we test effects of a broad spectrum of features for predicting human reading behavior that fall into five categories (syntactic complexity, lexical richness, register-based multiword combinations, readability and psycholinguistic word properties).Our experiments show that both the features included and the architecture of the transformer-based language models play a role in predicting multiple eye-tracking measures during naturalistic reading.We also report the results of experiments aimed at determining the relative importance of features from different groups using SP-LIME.
SSNCSE NLP@TamilNLP-ACL2022: Transformer based approach for detection of abusive comment for Tamil language

Social media platforms along with many other public forums on the Internet have shown a significant rise in the cases of abusive behavior such as Misogynism, Misandry, Homophobia, and Cyberbullying.To tackle these concerns, technologies are being developed and applied, as it is a tedious and time-consuming task to identify, report and block these offenders.Our task was to automate the process of identifying abusive comments and classify them into appropriate categories.The datasets provided by the DravidianLangTech@ACL2022 organizers were a code-mixed form of Tamil text.We trained the datasets using pre-trained transformer models such as BERT,m-BERT, and XLNET and achieved a weighted average of F1 scores of 0.96 for Tamil-English code mixed text and 0.59 for Tamil text.
Learning from Missing Relations: Contrastive Learning with Commonsense Knowledge Graphs for Commonsense Inference

Commonsense inference poses a unique challenge to reason and generate the physical, social, and causal conditions of a given event.Existing approaches to commonsense inference utilize commonsense transformers, which are large-scale language models that learn commonsense knowledge graphs.However, they suffer from a lack of coverage and expressive diversity of the graphs, resulting in a degradation of the representation quality.In this paper, we focus on addressing missing relations in commonsense knowledge graphs, and propose a novel contrastive learning framework called SOLAR 1 .Our framework contrasts sets of semantically similar and dissimilar events, learning richer inferential knowledge compared to existing approaches.Empirical results demonstrate the efficacy of SOLAR in commonsense inference of diverse commonsense knowledge graphs.Specifically, SOLAR outperforms the state-of-the-art commonsense transformer on commonsense inference with ConceptNet by 1.84% on average among 8 automatic evaluation metrics.In-depth analysis of SOLAR sheds light on the effects of the missing relations utilized in learning commonsense knowledge graphs.
A Gender Recognition System from Human Face Images Using VGG16 with SVM

Human beings have many distinct attributes, and facial features are a significant part of them. Facial features help in distinguishing people. An automatic gender recognition (AGR) system recognizes a person’s gender based on these distinct features by highly advanced human cognitive skills built through sufficient training. This paper proposes a system which combines convolutional neural network and SVM to classify the gender of human. It aims in achieving efficiency on a larger dataset which includes face images of all human stages. Initially, human face images are trained using the pre-trained model of CNN, i.e., VGG16. In addition, the extracted features are loaded into the SVM classifier for classification, which identifies the gender class and labels it to which class the input image belongs to, i.e., male or female. The system performance is evaluated on a larger dataset. The proposed method achieves good classification results on the larger dataset.
Round-Optimal Black-Box Protocol Compilers

We give black-box, round-optimal protocol compilers from semi-honest security to malicious security in the Random Oracle Model (ROM) and in the 1-out-of-2 OT correlations model. We use our compilers to obtain the following results: As a contribution of independent interest, we provide a new variant of the IPS compiler (Ishai, Prabhakaran and Sahai, Crypto 2008) in the two-round setting, where we relax requirements on the IPS “inner protocol” by strengthening the “outer protocol”.
Fuzzy C-Means Clustering Based Deep Patch Learning With Improved Interpretability for Classification Problems

Grid partitioning for input space results in the exponential rise in the number of rules in adaptive network-based fuzzy inference system (ANFIS) and patch learning (PL) as the number of features increases, thus resulting in the huge computational load and deteriorating its interpretability. An improved PL (iPL) is put forward for the training of each sub-fuzzy system to overcome the rule-explosion problem. In the iPL, input partitioning is done using fuzzy c-means (FCM) clustering to avoid the heavy computational complexity arising due to the large number of rules generated from high dimensionality. In this paper, two novel classifiers, called FCM clustering based deep patch learning with improved high-level interpretability for classification problems, are presented, named as HI-FCMDPL-CP1 and HI-FCMDPL-CP2. The proposed classifiers have two characteristics: One is a stacked deep structure of component iPL fuzzy classifiers for high accuracy, and the other is the use of maximal information coefficient (MIC) and the maximum misclassification threshold (MMT) to optimize the deep structures. High interpretability is achieved at each layer by using the FCM clustering, concise structure and large input dimensionality. The MMT, random input (RI) and parameter sharing (PS) are integrated to improve their classification accuracy without losing their interpretability. Experiments on several real-word datasets demonstrated that MIC, RI and PS in HI-FCMDPL-CP1 and HI-FCMDPL-CP2 are effective individually, and integrating them all three can further improve the classification performance. A more concise deep fuzzy system is obtained with the number of features and fuzzy rules reduced simultaneously. Furthermore, MIC, RI and PS are used to determine the advantages and disadvantages of using serial versus parallel structures to avoid subjective selection of these two categories.
Increasing Cyber Resilience of SCADA and WAMS in the Event of Cyber Attacks on the Information and Communication Subsystem of the Electric Power System

Research objective: development of measures to ensure cyber resilience of SCADA and WAMS under realized threats, the consequence of which is a decrease in the quality of information required in the control of the electric power system (EPS). Research methods: probabilistic methods, methods of power system reliability analysis, Markov methods. Research result. A comparative analysis of possible states of information collection, transmission, and processing systems (SCADA, WAMS) during cyberattacks on the information and communication system was carried out. SCADA and WAMS cyber resilience models were developed. On the basis of the models proposed, measures to ensure cyber resilience of information collection, transmission, and processing system were put forward. Keywords: cyber-physical power system; resilience; information collection, processing, and transmission system; false data injection attack; DoS-attack; state estimation.
Lesion Segmentation in Paediatric Epilepsy Utilizing Deep Learning Approaches

Focal cortical dysplasia (FCD) is one of the most common lesions responsible for drug-resistant epilepsy, and is frequently missed by visual inspection. FCD may be amenable to surgical resection to achieve seizure freedom. By improving lesion detection the surgical outcome of these patients can be improved. Image processing techniques are a potential tool to improve the detection of FCD prior to epilepsy surgery. In this research, we propose and compare the performance of two type of models, Fully Convolutional Network (FCN) and a multi-sequence FCN to classify and segment FCD in children with drug-resistant epilepsy. This experiment utilized the volumetric T1-weighted, T2 weighted and FLAIR sequences. The whole slice FCN models were applied to each sequence separately while the multi-sequence model leverages combined information of all three sequences simultaneously. A leave-one-subject-out technique was utilized to train and evaluate the models. We evaluated subjectwise sensitivity and specificity, which corresponds to the ability of the model to classify those with or without a lesion. We also evaluated lesional sensitivity and specificity, which expresses the ability of the model to segment the lesion and the dice coefficient to evaluate lesion coverage. Our data consisted of 80 FCD subjects (56 MR-positive and 24 MR-negative) and 15 healthy controls. Performance of whole slice FCN was best on T1-weighted, followed by T2-weighted and lowest with FLAIR sequences. Multi-sequence model performed better than the T1 whole slice FCN, and detected 98% vs. 93% respectively MR-positive cases, and 92% vs. 88% respectively MR-negative cases, as well as achieved lesion coverage of 74% vs. 67% respectively for MR-positive cases and 68% vs. 64% for MR negative cases. The dice coefficient for the multi-sequence model was 57% and for whole slice FCN was 56% for MR-positive cases. In the test cohort of six new cases, the multi-sequence model detected 4 out of 6 cases where the predicted lesion had 56% overlap with the actual lesion. This work showed that deep learning methods in particular fully convolutional networks are a promising tool for classification and segmentation of FCD. Additional work is required to further improve lesion classification and segmentation, particularly for small lesions, as well as to train and test optimal algorithms on a larger multi-center dataset.
Enhanced Lightweight End-to-End Semantic Segmentation for High-Resolution Remote Sensing Images

Deep learning based methods have shown promising performance in semantic segmentation of high-resolution remote sensing (HRRS) images.However, due to the multi-scale property and complexity of HRRS images, it still faces many challenges in tackling the scale variance problem and obtaining global context information.In this paper, we propose an enhanced lightweight end-to-end semantic segmentation (ELES2) framework for HRRS images, where a superpixel segmentation pooling (SSP) module is embedded with the framework for result refinement, leading to a more accurate end-to-end semantic segmentation.Besides, compensation connections (CC) are applied between encoder blocks to establish long-range dependencies.In addition, a dense dilated convolutional pyramid (DDCP) module is proposed to generate dense features under different scales and capture global context information.Experiments conducted showed that our ELES2 respectively achieves mIoU values of 80.16% and 73.20% on the ISPRS Potsdam and Vaihingen benchmark datasets using only 12.62M parameters and 13.09G FLOPs.Experimental results prove that our method achieves a promising balance between segmentation accuracy and computational efficiency compared with the state-of-the-art semantic segmentation models.
A geometric approach of gradient descent algorithms in linear neural networks

In this paper, we propose a geometric framework to analyze the convergence properties of gradient descent trajectories in the context of linear neural networks. We translate a well-known empirical observation of linear neural nets into a conjecture that we call the \emph{overfitting conjecture} which states that, for almost all training data and initial conditions, the trajectory of the corresponding gradient descent system converges to a global minimum. This would imply that the solution achieved by vanilla gradient descent algorithms is equivalent to that of the least-squares estimation, for linear neural networks of an arbitrary number of hidden layers. Built upon a key invariance property induced by the network structure, we first establish convergence of gradient descent trajectories to critical points of the square loss function in the case of linear networks of arbitrary depth. Our second result is the proof of the \emph{overfitting conjecture} in the case of single-hidden-layer linear networks with an argument based on the notion of normal hyperbolicity and under a generic property on the training data (i.e., holding for almost all training data).