Reviewerly/exHarmony huggingface dataset:
Detecting Malicious DNS Queries Over Encrypted Tunnels Using Statistical Analysis and Bi-Directional Recurrent Neural Networks

The exponential rise in the number of malicious threats targeting computer networks and digital services puts network infrastructure in jeopardy. Domain name protocol attacks are one of the most pervasive network attacks posing a threat to networks, whereby attackers send harmful information to the network; this type of threat is identified as DNS tunneling. The DNS protocol has recently gained increased attention from cyber-attackers, targeting organizations with a web presence or reliance on e-commerce businesses. Cyber-attackers can subtly exploit the contents of encrypted DNS packets that are sent across covert network tunnels, which are difficult for firewalls and blacklist detection methods to detect. Therefore, efficient methods for detecting DNS intrusions in the network are required. Machine learning (ML), deep learning (DL), and computational intelligence models have proved to be increasingly effective in dealing with these cyber-attacks, especially when using an appropriate dataset. This paper proposes an intrusion detection model to detect malicious DNS over HTTPS (DoH) queries among network covert tunnels, using statistical analysis and Bi-directional Recurrent Neural Network (BRNN) techniques, based on the flow level of the network traffic. The proposed approach was tested and evaluated based on a realistic dataset called CIRA-CIC-DoHBrw-2020, provided by the Canadian Institute for Cybersecurity. Experiments have shown that the robustness of the model is strong, with a detection rate of 100%. Furthermore, the proposed model achieved high performance in terms of the accuracy rate in detecting malicious DoH queries, with low false-negative and false-positive rates. Furthermore, the number of features used is fewer than other approaches, making it perform faster in the training and testing phases.
Detection of Hot Topic in Tweets Using Modified Density Peak Clustering

Tweets based micro blogging is the most widely used social media to share the opinions in terms of short messages.Tweets facilitate business men to release the products based on the user interest which thereby produces more profits to their business.It also helps the government to monitor the public opinion which leads to better policies and standards.The large number of tweets on different topics are shared daily so, there is a need to identify trending topics.This paper proposes a method for automatic detection of hot topics discussed predominantly in social media by aggregating tweets of similar topics into manageable clusters.This produces hot topic detection irrespective of the current user location.A Modified Density Peak Clustering (MDPC) algorithm based hot topic detection is proposed.Local density of traditional Density Peak Clustering (DPC) is redefined by using the gaussian function in the calculation of dc (threshold distance).The traditional DPC considering some random value as dc (threshold distance) this gives a negative impact on the cluster formation thereby return inappropriate clusters.This can be solved by using the MDPC.The MDPC algorithm works by taking the cosine similarity between the tweets as the input and produces clusters of similar tweets.The cluster having a greater number of tweets is considered as hot topic which is frequently discussed by most of the users on twitter.Events 2012 dataset is collected with streaming API.This contains tweets from 2012 to 2016.The dataset consists of 149 target events and 30 million tweets.Experimental result shows that the proposed algorithm performs better than the traditional algorithms such as density peak clustering, K-means clustering, and Spectral clustering.It has produced the accuracy of 97%.
What the #®¥§≠$@ is Creativity?

Creativity is The Holy Grail of the Cognitive Sciences and it is very important for researchers in the Computer Sciences and AI fields. Although all attempts to explain and replicate intelligence have so far failed, the quest remains a key part of their research. This paper takes two innovative approaches. First, we see cognitive processes as involving rule-followingand as flexible, even chaotic, heuristics. This first concept uses a multi-heuristic concept without any complexes as mixed-cognition. Second, we propose abduction which, though seldom employed in this specific debate, is nonetheless a good way to explore creativity. Using both strategies, along with analysis of specific human creativity cases, we suggesta new cognitive paradigm that is both more realistic and truthful than hitherto. The idea is to offer a new way to achieve more powerful, complex artificial reasoning systems.
Face Recognition System Against Adversarial Attack Using Convolutional Neural Network

Face recognition is the technology that verifies or recognizes faces from images, videos, or real-time streams. It can be used in security or employee attendance systems. Face recognition systems may encounter some attacks that reduce their ability to recognize faces properly. So, many noisy images mixed with original ones lead to confusion in the results. Various attacks that exploit this weakness affect the face recognition systems such as Fast Gradient Sign Method (FGSM), Deep Fool, and Projected Gradient Descent (PGD). This paper proposes a method to protect the face recognition system against these attacks by distorting images through different attacks, then training the recognition deep network model, specifically Convolutional Neural Network (CNN), using the original and distorted images. Diverse experiments have been conducted using combinations of original and distorted images to test the effectiveness of the system. The system showed an accuracy of 93% using FGSM attack, 97% using deep fool, and 95% using PGD.
Cartesian Constrained Stochastic Trajectory Optimization for Motion Planning

This paper presents novel extensions of the Stochastic Optimization Motion Planning (STOMP), which considers cartesian path constraints. It potentially has high usage in many autonomous applications with robotic arms, where preservation or minimization of tool-point rotation is required. The original STOMP algorithm is unable to use the cartesian path constraints in a trajectory generation because it works only in robot joint space. Therefore, the designed solution, described in this paper, extends the most important parts of the algorithm to take into account cartesian constraints. The new sampling noise generator generates trajectory samples in cartesian space, while the new cost function evaluates them and minimizes traversed distance and rotation change of the tool-point in the resulting trajectory. These improvements are verified with simple experiments and the solution is compared with the original STOMP. Results of the experiments show that the implementation satisfies the cartesian constraints requirements.
Mr. Dr. Health-Assistant Chatbot

Research says 60% of visits to a doctor are for simple small-scale diseases, 80% of which can be diagnosed at home using simple check-up. These diseases mostly include common cold and cough, headache, abdominal pains etc. Whereas, chat-bots in healthcare are highly in demand, which functioning can offer various services from symptom checking and appointment scheduling. Therefore, the purpose of the research aims to design, develop and evaluate a health-assistant Chat-bot Application entitled “MR.Dr.” that helps users to ask any personal query related to healthcare without physically available to the hospital. MR.Dr. is evaluated in term of usability. 30 respondents attended the survey of usability evaluation. In the system usability scale MR.Dr. achieved 87.6 % rating which means Grade A (excellent). User's feedback level was pretty satisfying where 24/7 service is the highest one.
Comparison of Control Algorithms by Simulating Power Consumption of Differential Drive Mobile Robot Motion Control in Vineyard Row

Abstract The paper deals with comparing electricity power consumption of various control algorithms by simulating differential mobile robot motion control in a vineyard row. In field of autonomous mobile robotics, the quality of control is a crucial aspect. Besides the precision of control, the energy consumption for motion is becoming an increasingly demanding characteristic of a controller due to the increasing costs of fossil fuels and electricity. A simulation model of a differential drive mobile robot motion in a vineyard row was created, including robot dynamics for evaluating motion consumption, and there were implemented commonly used PID, Fuzzy, and LQ control algorithms, the task of which was to navigate the robot through the centre of vineyard row section by measuring distances from trellises on both robot sides. The comparison was carried out using Matlab software and the best results in terms of both power consumption and control accuracy were achieved by LQI controller. The designed model for navigating the robot through the vineyard row centre and optimized controllers were implemented in a real robot and tested under real conditions.
Multi Distance and Angle Models of the Gray Level Co-occurrence Matrix (GLCM) to Extract the Acute Lymphoblastic Leukemia (ALL) Images

Acute Lymphoblastic Leukemia (ALL) is one of the most dangerous cancer diseases.Therefore, it is necessary to classify this disease accurately.We proposed a method to classify ALL using multi-distance models of the Gray Level Co-occurrence Matrix (GLCM).We employed sixteen distance models to obtain several features of the main object.We applied three channels of the image's enhancement results, where each channel has been extracted using second-order statistic models, which are homogeneity, entropy, energy, and contrast.We have obtained one hundred and ninety-two features for each object.Moreover, the feature extraction performances have been classified using Canberra and Chebyshev techniques.We have evaluated our proposed method using the ALL Image database and produced accuracy at 96.97%, where 2.27% false positive and 0.75% false negative.Our developed method has been compared to other approaches, such as Support Vector Machine (SVM)-Linear Binary Pattern (LBP), SVM Shape, Naïve Bayes Deterministic, Fuzzy based Leukemia detection, Color Correlation, Hausdorff SVM-based Leukemia detection, Automated Differential Learning Vector Quantization (LVQ), and Multi-Level Perceptron (MLP).The results show that our proposed method outperformed the others.
Improvement of a mineral discrimination method using multispectral image and surrounding hyperspectral image

Hyperspectral (HS) images are highly accurate for mineral discrimination, but available areas are limited. For this reason, several methods have been proposed to extend the mineral map of the overlap region between HS and multispectral (MS) images to the surrounding area with no HS image. One such method, proposed by Hirai and Tonooka, discriminates minerals using MS images by obtaining the endmembers of MS images from the positions of the endmember pixels of HS images in the overlap region. While this method (referred to as HT method) has the advantage of being less susceptible to the spectral distortions of HS and MS images, it also has the problem of reduced accuracy due to misalignment between HS and MS images. We proposed an improved HT method that reduces the effects of the above problems by incorporating a process that improves the robustness against misalignment by searching for the best MS endmember pixel around the position of the HS endmember pixel and a process that determines more optimum threshold value of each mineral in the spectral angle mapper method used in the HT method. As a result of evaluation using an AVIRIS image as an HS image and a World View-3 image as an MS image at Cuprite, Nevada, the improved method improved the overall accuracy by 2.6% compared with the original HT method, and in the case that the HS and the MS images were misaligned, the overall accuracy of the original method decreased by 7.0%, while the improved method decreased by only 1.5%. These results indicate that the improved method can perform as expected.
Faster Provable Sieving Algorithms for the Shortest Vector Problem and the Closest Vector Problem on Lattices in ℓp Norm

In this work, we give provable sieving algorithms for the Shortest Vector Problem (SVP) and the Closest Vector Problem (CVP) on lattices in ℓp norm (1≤p≤∞). The running time we obtain is better than existing provable sieving algorithms. We give a new linear sieving procedure that works for all ℓp norm (1≤p≤∞). The main idea is to divide the space into hypercubes such that each vector can be mapped efficiently to a sub-region. We achieve a time complexity of 22.751n+o(n), which is much less than the 23.849n+o(n) complexity of the previous best algorithm. We also introduce a mixed sieving procedure, where a point is mapped to a hypercube within a ball and then a quadratic sieve is performed within each hypercube. This improves the running time, especially in the ℓ2 norm, where we achieve a time complexity of 22.25n+o(n), while the List Sieve Birthday algorithm has a running time of 22.465n+o(n). We adopt our sieving techniques to approximation algorithms for SVP and CVP in ℓp norm (1≤p≤∞) and show that our algorithm has a running time of 22.001n+o(n), while previous algorithms have a time complexity of 23.169n+o(n).
On the radar: Predicting near-future surges in skills’ hiring demand to provide early warning to educators

The AI-driven Fourth Industrial Revolution and the COVID-19 pandemic have one important thing in common: they both have caused significant and rapid changes to the skill set landscape of various industries. These disruptive forces mean that the early identification of the newly rising skills in a labour market — which we call its "emerging skills" — is crucial to its workforce. It is also crucial to the educators who, in order to provide lifelong training to the workforce, need to quickly adapt their curricula to the new skills. We propose a classification methodology that uses the past job ad trends of skills to predict the emerging skills of a future period, defined as the skills that have experienced a surge in hiring demand in said period. This general definition allows for freedom in specifying the criteria for a skill being emerging (through thresholds on hiring demand and its growth), which could be important to educators. Applying our methodology to the Information and Communication Technologies (ICT) labour market in Singapore, we show that we are able to predict future emerging skills with good precision and recall and beat two baseline classifiers for multiple threshold sets. Our methodology also allows us to see where job ads fail to provide sufficient predictive signals, pointing to auxiliary data sources (such as Stack Overflow for ICT) and skill ontologies as potential remedies. The success of our method shows how AI can be used to empower learners and educators in the ICT domain (and potentially other domains) with useful and well-curated insights at a moment's notice, thus helping speed up the process of curricular change.
High Efficient Virtual Machine Migration Using Glow Worm Swarm Optimization Method for Cloud Computing

In this paper, high efficient Virtual Machine (VM) migration using GSO algorithm for cloud computing is proposed. This algorithm contains 3 phases: (i) VM selection, (ii) optimum number of VMs selection, (iii) VM placement. In VM selection phase, VMs to be migrated are selected based on their resource utilization and fault probability. In phase-2, optimum number of VMs to be migrated are determined based on the total power consumption. In VM placement phase, Glowworm Swarm Optimization (GSO) is used for finding the target VMs to place the migrated VMs. The fitness function is derived in terms of distance between the main server and the other server, VM capacity and memory size. Then the VMs with best fitness functions are selected as target VMs for placing the migrated VMs. The proposed algorithms are implemented in Cloudsim and performance results show that PEVM-GSO algorithm attains reduced power consumption and response delay with improved CPU utilization.
An Automatic Image Stitching Method for Infrared Image Series

Infrared images have a limited field of view in general, and it is difficult to monitor the entire scene with only a single frame. To generate a wide field of view, image stitching is the necessary tactic. However, in real infrared images stitching tasks, high overlap ratio between the adjacent frames in the image series often leads to high computation cost and accumulative errors. Therefore, manually selecting the subset frames is sometimes inevitable. In order to tackle the drawbacks, an automatic method for infrared images series is proposed. Firstly, dominant frames subset is selected based on cosine similarity between images, which could be used as the substitute for all the image series. Secondly, feature extraction and image registration is performed on the image subset. And finally, several image blending methods are utilized to obtain the seamless panoramic result. Experiments carried out on real infrared dataset demonstrated that the proposed method could effectively select the dominant frames of the images series and significantly reduce the time cost to automatically generate the panoramic result.
Vietnamese Legal Question Answering with combined features and deep learning

Legal Question Answering is an arduous problem that is divided into certain phases, each with its own set of challenges. In this work, we have accomplished three tasks given by the ALQAC 2021 competition, which are aimed at addressing the aforementioned problem, by proposing the combined features (cosine similarity of TF-IDF, an average of word embedding; and Jaccard distance) accompanied by a classification model for task 1; ensemble learning multiple deep learning models for task 2. Finally, we employed a specifically modified mechanism for long documents to undertake task 3. All three methods perform satisfactory results and have profuse potential improvements.
Multi-robot Task Assignment for Aerial Tracking with Viewpoint Constraints

We address the problem of assigning a team of drones to autonomously capture a set desired shots of a dynamic target in the presence of obstacles.We present a two-stage planning pipeline that generates offline an assignment of drone to shots and locally optimizes online the viewpoint.Given desired shot parameters, the high-level planner uses a visibility heuristic to predict good times for capturing each shot and uses an Integer Linear Program to compute drone assignments.An online Model Predictive Control algorithm uses the assignments as reference to capture the shots.The algorithm is validated in hardware with a pair of drones and a remote controlled car.
Experimental self-testing for photonic graph states

Graph states-one of the most representative families of multipartite entangled states-are important resources for multiparty quantum communication, quantum error correction, and quantum computation. Device-independent certification of highly entangled graph states plays a prominent role in quantum information processing tasks. Here we have experimentally demonstrated device-independent certification for multipartite graph states by adopting the robust self-testing scheme based on scalable Bell inequalities. Specifically, the prepared multi-qubit Greenberger-Horne-Zeilinger (GHZ) states and linear cluster states achieve a high degree of Bell violation, which are beyond the nontrivial bounds of the robust self-testing scheme. Furthermore, our work paves the way to the device-independent certification of complex multipartite quantum states.
A Study on Characteristics Analysis and Countermeasures of Digital Sex Crimes in Korea

In the modern knowledge-information age, various information and communication technologies provide us with many benefits and at the same time, bring various side effects such as cybercrime. The number of cybercrimes is increasing gradually, and in particular, the number of digital sex crimes has been increasing recently. In addition, digital sex crimes are becoming increasingly violent, so national measures are needed. In this study, statistical data at the national level were used to investigate the overall characteristics of digital sex crimes in Korea. First, statistical analysis shows that the victims are mainly women in their teens and 20s. Typical types of digital sex crimes are distribution of illegal contents and illegal filming, the perpetrators are mainly unknown, and digital sex crimes were less often recognized by others and more often by the victims themselves. Based on these results, countermeasures against various digital sex crimes are suggested.
Cross-Browser Incompatibilities Classification Layout: A comparative study between different models

When the same web application is rendered in different browsers, inconsistencies detected in the layout or behavior of pages are known as (XBIs Cross Browser Incompatibilities). Currently, there are different classification models in the literature for the identification and automatic correction of XBIs. These models have evolved with the aim of reducing false positives and negatives. This paper proposes to compare these different models, focusing on those that use the classification of layout XBIs, through machine learning algorithms. There is still no paper in the literature to compare them, identifying their main advantages and disadvantages. This paper consists of an experiment that compares the results of models and presents metrics that allow to affirm how effective they are, aiming also to bring important information as contributions to propose future works regarding the evolution of the explored models. The result of the experiment is the metric of F-Score. For this metric, the higher values imply greater efficiency in detecting incompatibilities between browsers, and the C5.0 10 iterations - X configuration obtained the best result in the experiment.
Pedagogy of Spirituality as a Factor of Sustainable Development of Humanity

Education today is an area around which heated discussions are unfolding, both at the state level and in society. The processes of integration and globalization are especially active today, but they should not replace the content of national cultures and educational systems on which they occur. It should combine an openness of education with its general availability at all levels, in the interaction of interested parties and, above all, the state, in all components of this process in order to preserve society’s intellectual potential. Scientific analysis of any phenomenon involves considering it from several positions. Nowadays, modern society is going through complex processes of revaluation of basic values in all spheres of life - material, spiritual, socio-political, social (in the narrow sense) against the background of various activation of the human factor in the above areas. The analysis of global problems should consider the principal objects of influence of these problems and the subjects whose activities cause their aggravation. In other words, it is necessary to distinguish between the spheres of interaction between the individual, nature, society (in this case, the world community), which comprises states with different forms of social structure, economy, national-state organisation, different mentality, legal systems, and the like.