Reviewerly/exHarmony huggingface dataset:
Fine-Tuning the Ant Colony System Algorithm Through Harris’s Hawk Optimizer for Travelling Salesman Problem

The ant colony system (ACS) algorithm is one of the metaheuristics in solving combinatorial optimization problems.The control parameters namely, pheromone coefficient, heuristic coefficient, decision rule, and evaporation rate of the pheromone in ACS are important in determining the performance of the algorithm.However, the initialized values of these parameters stay constant during the search process which leads to performance degradation of the algorithm.This paper aims to tune the parameters of the ACS algorithm by using Harris's hawk optimization (HHO) algorithm in solving the travelling salesman problem (TSP).The proposed hybrid algorithm is called Harris's hawk optimizer ant colony system (HHO-ACS).The final process of HHO-ACS will output the path distance.The performance of the HHO-ACS has been evaluated by using different symmetric TSP instances of various scale size.The results showed the HHO-ACS provided superior performance compared to other well-known metaheuristics namely black hole, particle swarm optimization, dragonfly, genetic and ant colony optimization algorithms.Thus, it can attain a better solution with higher accuracy.The proposed algorithm was able to achieve best known optimal solution in solving bayg29, att48 and berlin52 instances and near optimal solution in solving bays29, eil51, st70, eil76 and eil101 instances.Compared to other algorithms, the HHO-ACS algorithm showed superior performance in solving the TSP instances which indicates its effectiveness.This is possible because, in the HHO-ACS algorithm, the main control parameters of ACS algorithm namely, the pheromone coefficient, heuristic coefficient, evaporation rate of the pheromone, and decision rule were tuned according to the problem instance at hand.Thus, the HHO-ACS algorithm can be used to solve problems of travelling salesman nature with minimum customization.
Scalable First-Order Methods for Robust MDPs

Robust Markov Decision Processes (MDPs) are a powerful framework for modeling sequential decision making problems with model uncertainty. This paper proposes the first first-order framework for solving robust MDPs. Our algorithm interleaves primal-dual first-order updates with approximate Value Iteration updates. By carefully controlling the tradeoff between the accuracy and cost of Value Iteration updates, we achieve an ergodic convergence rate that is significantly better than classical Value Iteration algorithms in terms of the number of states S and the number of actions A on ellipsoidal and Kullback-Leibler s-rectangular uncertainty sets. In numerical experiments on ellipsoidal uncertainty sets we show that our algorithm is significantly more scalable than state-of-the-art approaches. Our framework is also the first one to solve robust MDPs with s-rectangular KL uncertainty sets.
Methods of Constructing Time Series for Predicting Local Time Scales by Means of a GMDH-Type Neural Network

Ensuring the best possible stability of UTC(k) (local time scale) and its compliance with the UTC scale (Universal Coordinated Time) forces predicting the [UTC-UTC(k)] deviations, the article presents the results of work on two methods of constructing time series (TS) for a neural network (NN), increasing the accuracy of UTC(k) prediction. In the first method, two prepared TSs are based on the deviations determined according to the UTC scale with a 5-day interval. In order to improve the accuracy of predicting the deviations, the PCHIP interpolating function is used in subsequent TSs, obtaining TS elements with a 1-day interval. A limitation in the improvement of prediction accuracy for these TS has been a too large prediction horizon. The introduction in 2012 of the additional UTC Rapid scale by BIPM makes it possible to shorten the prediction horizon, and the building of two TSs has been proposed according to the second method. Each of them consists of two subsets. The first subset is based on deviations determined according to the UTC scale, the second on the UTC Rapid scale. The research of the proposed TS in the field of predicting deviations for the Polish Timescale by means of GMDH-type NN shows that the best accuracy of predicting the deviations has been achieved for TS built according to the second method.
Detecting Emotion from Natural Language Text Using Hybrid and NLP Pre-trained Models

In current days emotion analysis has been attracting many users to continue their research towards detecting various emotions from natural text. Almost a lot of study is being done in the area of Artificial Intelligence (AI), which mainly focuses on identifying the human state rather than digging the inner reason behind those state or emotions, like why those states are not recognized properly. There were many failures in the primitive methods for emotion detection because of improper correlation among emotions. Hence this motivated me to design a new model, which can fill the gap between emotion recognition and emotion correlation mining through natural language text from text conversations. In this proposed model, we try to mine emotion correlation from emotion recognition through natural text by using various kinds of features and by applying machine learning and deep learning pre-trained models. The main features are anger, disgust, fear, guilt, joy, sadness, and shame. By conducting various experiments on proposed models by taking a sample dataset collected from the Kaggle website, we try to observe the performance metrics of each and every individual algorithm in order to detect the emotions very accurately from natural language text.
Content-Based Video Retrieval Using Integration of Curvelet Transform and Simple Linear Iterative Clustering

Video is a rich information source containing both audio and visual information along with motion information embedded in it. Applications such as e-learning, live TV, video on demand, traffic monitoring, etc. need an efficient video retrieval strategy. Content-based video retrieval and superpixel segmentation are two diverse application areas of computer vision. In this work, we are presenting an algorithm for content-based video retrieval with help of Integration of Curvelet transform and Simple Linear Iterative Clustering (ICTSLIC) algorithm. Proposed algorithm consists of two steps: off line processing and online processing. In offline processing, keyframes of the database videos are extracted by employing features: Pearson Correlation Coefficient (PCC) and color moments (CM) and on the extracted keyframes superpixel generation algorithm ICTSLIC is applied. The superpixels generated by applying ICTSLIC on keyframes are used to represent database videos. On other side, in online processing, ICTSLIC superpixel segmentation is applied on query frame and the superpixels generated by segmentation are used to represent query frame. Then videos similar to query frame are retrieved through matching done by calculation of Euclidean distance between superpixels of query frame and database keyframes. Results of the proposed method are irrespective of query frame features such as camera motion, object’s pose, orientation and motion due to the incorporation of ICTSLIC superpixels as base feature for matching and retrieval purpose. The proposed method is tested on the dataset comprising of different categories of video clips such as animations, serials, personal interviews, news, movies and songs which is publicly available. For evaluation, the proposed method randomly picks frames from database videos, instead of selecting keyframes as query frames. Experiments were conducted on the developed dataset and the performance is assessed with different parameters Precision, Recall, Jaccard Index, Accuracy and Specificity. The experimental results shown that the proposed method is performing better than the other state-of-art methods.
Dataset segmentation considering the information about impact factors

Introduction: The application of machine learning methods involves the collection and processing of data which comes from the recording elements in the offline mode. Most models are trained on historical data and then used in forecasting, classification, search for influencing factors or impacts, and state analysis. In the long run, the data value ranges can change, affecting the quality of the classification algorithms and leading to the situation when the models should be constantly trained or readjusted taking into account the input data. Purpose: Development of a technique to improve the quality of machine learning algorithms in a dynamically changing and non-stationary environment where the data distribution can change over time. Methods: Splitting (segmentation) of multiple data based on the information about factors affecting the ranges of target variables. Results: A data segmentation technique has been proposed, based on taking into account the factors which affect the change in the data value ranges. Impact detection makes it possible to form samples based on the current and alleged situations. Using PowerSupply dataset as an example, the mass of data is split into subsets considering the effects of factors on the value ranges. The external factors and impacts are formalized based on production rules. The processing of the factors using the membership function (indicator function) is shown. The data sample is divided into a finite number of non-intersecting measurable subsets. Experimental values of the neural network loss function are shown for the proposed technique on the selected dataset. Qualitative indicators (Accuracy, AUC, F-measure) of the classification for various classifiers are presented. Practical relevance: The results can be used in the development of classification models of machine learning methods. The proposed technique can improve the classification quality in dynamically changing conditions of the functioning.
Differentially Private Link Prediction with Protected Connections

Link prediction (LP) algorithms propose to each node a ranked list of nodes that are currently non-neighbors, as the most likely candidates for future linkage. Owing to increasing concerns about privacy, users (nodes) may prefer to keep some of their connections protected or private. Motivated by this observation, our goal is to design a differentially private LP algorithm, which trades off between privacy of the protected node-pairs and the link prediction accuracy. More specifically, we first propose a form of differential privacy on graphs, which models the privacy loss only of those node-pairs which are marked as protected. Next, we develop DPLP, a learning to rank algorithm, which applies a monotone transform to base scores from a non-private LP system, and then adds noise. DPLP is trained with a privacy induced ranking loss, which optimizes the ranking utility for a given maximum allowed level of privacy leakage of the protected node-pairs. Under a recently introduced latent node embedding model, we present a formal trade-off between privacy and LP utility. Extensive experiments with several real-life graphs and several LP heuristics show that DPLP can trade off between privacy and predictive performance more effectively than several alternatives.
Implicit Gradient Alignment in Distributed and Federated Learning

A major obstacle to achieving global convergence in distributed and federated learning is the misalignment of gradients across clients or mini-batches due to heterogeneity and stochasticity of the distributed data. In this work, we show that data heterogeneity can in fact be exploited to improve generalization performance through implicit regularization. One way to alleviate the effects of heterogeneity is to encourage the alignment of gradients across different clients throughout training. Our analysis reveals that this goal can be accomplished by utilizing the right optimization method that replicates the implicit regularization effect of SGD, leading to gradient alignment as well as improvements in test accuracies. Since the existence of this regularization in SGD completely relies on the sequential use of different mini-batches during training, it is inherently absent when training with large mini-batches. To obtain the generalization benefits of this regularization while increasing parallelism, we propose a novel GradAlign algorithm that induces the same implicit regularization while allowing the use of arbitrarily large batches in each update. We experimentally validate the benefits of our algorithm in different distributed and federated learning settings.
SkeletonVis: Interactive Visualization for Understanding Adversarial Attacks on Human Action Recognition Models

Skeleton-based human action recognition technologies are increasingly used in video-based applications, such as home robotics, healthcare on the aging population, and surveillance. However, such models are vulnerable to adversarial attacks, raising serious concerns for their use in safety-critical applications. To develop an effective defense against attacks, it is essential to understand how such attacks mislead the pose detection models into making incorrect predictions. We present SkeletonVis, the first interactive system that visualizes how the attacks work on the models to enhance human understanding of attacks.
Ways to Improve the Teaching Ability of College Teachers in the New Era

The teaching ability of college teachers is analyzed and there is a realization of the necessity to improve the teaching ability in colleges and universities in view of the new era. The core purpose of this paper is to summarize the factors that affect the teachers’ teaching ability, construct targeted strategies to improve the comprehensive quality of college teachers, as well as to provide reference for educational innovation and the education system reform.
Robustifying Multi-hop QA through Pseudo-Evidentiality Training

This paper studies the bias problem of multihop question answering models, of answering correctly without correct reasoning.One way to robustify these models is by supervising to not only answer right, but also with right reasoning chains.An existing direction is to annotate reasoning chains to train models, requiring expensive additional annotations.In contrast, we propose a new approach to learn evidentiality, deciding whether the answer prediction is supported by correct evidences, without such annotations.Instead, we compare counterfactual changes in answer confidence with and without evidence sentences, to generate "pseudo-evidentiality" annotations.We validate our proposed model on an original set and challenge set in HotpotQA, showing that our method is accurate and robust in multi-hop reasoning.
Dynamic ICT Modeling for Handling Student Data Using Big Data Technology and Hybrid Cloud Computing

The Indian State of Punjab is a prosperous state. It has three-tier technical education systems consisting of industrial training institutes, polytechnics and engineering colleges. Due to faster Internet technology available at cheaper costs and the students using IoT like mobile phones and other devices, they are searching for courses and colleges of their choice along with options of scholarships to fund their education. Also, a large number of underprivileged students apply for scholarships every intake and generate a large amount of student data consisting of enrollments, scholarships, academic content, literature search, testing results and finally placement tracking. Such a high volume of data requires integrity security and scalability. Therefore, dynamic modeling for handling high volumes of student data is needed. In this paper, a new ICT model for providing need-based scholarships and fee waivers to large number of students has been suggested using high-end database technology and hybrid cloud computing. A case study for scholarships to under-represented students is discussed. This model is useful for other states looking for similar interests in a cost-effective and reliable manner.
Entrepreneurship ecosystem in the United Arab Emirates: An empirical comparison with Qatar and Saudi Arabia

Objective: This paper aims to investigate and assess the UAE’s overall entrepreneurship performance (ecosystem) by applying the Global Entrepreneurship Index (GEI).Research Design &amp; Methods: This paper assesses the United Arab Emirates’ entrepreneurial ecosystem and compares it with Qatar and Saudi Arabia by using the Global Entrepreneurship Index (GEI) data collected between 2011 and 2016. The GEI approach is applied to measure the entrepreneurship ecosystem at the country level, and it constitutes an appropriate method to encourage a higher level of entrepreneurship by considering its quality aspects.Findings: Outcomes show an entrepreneurial ecosystem with a GEI index of 44.6. Technology absorption is a critical bottleneck that suppresses entrepreneurial goals. Despite the low score of this pillar, the UAE has one of the highest GDP per capita among all participating countries. As a result, the GEI score of the United Arab Emirates and its sub-indices are above the global average trend and count to the most innovation-driven economies worldwide. However, the UAE is not performing well compared to other average performers in its category.Implications &amp; Recommendations: Crucial policy interferences are required for the improvement of technology absorption. UAE calls for 4% additional resources to expand the GEI score by 10%.Contribution &amp; Value Added: Using a novel approach incorporating individual and institutional variables into a single model, this paper identifies the weak aspects of UAE’s entrepreneurial performance. Also, the utilisation of (PFB) is used to identify which entrepreneurial factors should be tackled.
Aspect-Level Sentiment-Controllable Review Generation with Mutual Learning Framework

Review generation, aiming to automatically generate review text according to the given information, is proposed to assist in the unappealing review writing. However, most of existing methods only consider the overall sentiments of reviews and cannot achieve aspect-level sentiment control. Even though some previous studies attempt to generate aspect-level sentiment-controllable reviews, they usually require large-scale human annotations which are unavailable in the real world. To address this issue, we propose a mutual learning framework to take advantage of unlabeled data to assist the aspect-level sentiment-controllable review generation. The framework consists of a generator and a classifier which utilize confidence mechanism and reconstruction reward to enhance each other. Experimental results show our model can achieve aspect-sentiment control accuracy up to 88% without losing generation quality.
H-Transformer-1D: Fast One-Dimensional Hierarchical Attention for Sequences

We describe an efficient hierarchical method to compute attention in the Transformer architecture.The proposed attention mechanism exploits a matrix structure similar to the Hierarchical Matrix (H-Matrix) developed by the numerical analysis community, and has linear run time and memory complexity.We perform extensive experiments to show that the inductive bias embodied by our hierarchical attention is effective in capturing the hierarchical structure in the sequences typical for natural language and vision tasks.Our method is superior to alternative sub-quadratic proposals by over +6 points on average on the Long Range Arena benchmark.It also sets a new SOTA test perplexity on One-Billion Word dataset with 5x fewer model parameters than that of the previous-best Transformer-based models.Computing the similarity matrix S in Eq. ( 4) and the attention matrix A in Eq. ( 3) takes O(L 2 d) time and O(L 2 ) memory.Similarly, computing AV in Eq. (2) takes O(L 2 d) time, and computing A • 1 L in Eq. (5) takes O(L 2 ) time.The O(L 2 d) and O(L 2 ) complexities are the bottlenecks for applying the attention mechanism over very long sequences.
Learning to Cascade: Confidence Calibration for Improving the Accuracy and Computational Cost of Cascade Inference Systems

Recently, deep neural networks have become to be used in a variety of applications. While the accuracy of deep neural networks is increasing, the confidence score, which indicates the reliability of the prediction results, is becoming more important. Deep neural networks are seen as highly accurate but known to be overconfident, making it important to calibrate the confidence score. Many studies have been conducted on confidence calibration. They calibrate the confidence score of the model to match its accuracy, but it is not clear whether these confidence scores can improve the performance of systems that use confidence scores. This paper focuses on cascade inference systems, one kind of systems using confidence scores, and discusses the desired confidence score to improve system performance in terms of inference accuracy and computational cost. Based on the discussion, we propose a new confidence calibration method, Learning to Cascade. Learning to Cascade is a simple but novel method that optimizes the loss term for confidence calibration simultaneously with the original loss term. Experiments are conducted using two datasets, CIFAR-100 and ImageNet, in two system settings, and show that naive application of existing calibration methods to cascade inference systems sometimes performs worse. However, Learning to Cascade always achieves a better trade-off between inference accuracy and computational cost. The simplicity of Learning to Cascade allows it to be easily applied to improve the performance of existing systems.
A State of the Art Survey of Machine Learning Algorithms for IoT Security

The Internet of Things (IoT) is a paradigm shift that enables billions of devices to connect to the Internet. The IoT's diverse application domains, including smart cities, smart homes, and e-health, have created new challenges, chief among them security threats. To accommodate the current networking model, traditional security measures such as firewalls and Intrusion Detection Systems (IDS) must be modified. Additionally, the Internet of Things and Cloud Computing complement one another, frequently used interchangeably when discussing technical services and collaborating to provide a more comprehensive IoT service. In this review, we focus on recent Machine Learning (ML) and Deep Learning (DL) algorithms proposed in IoT security, which can be used to address various security issues. This paper systematically reviews the architecture of IoT applications, the security aspect of IoT, service models of cloud computing, and cloud deployment models. Finally, we discuss the latest ML and DL strategies for solving various security issues in IoT networks.