Reviewerly/exHarmony huggingface dataset:
Optimal Path and Timetable Planning Method for Multi-Robot Optimal Trajectory

In an environment with limited space and dense goal configuration, the path of robot team is forced to coincide without much adjustment space, which is a challenge for multi-robot collaborative path planning. In this work, a novel Optimal Path and Timetable Planning (OPTP) method is proposed. The OPTP firstly generates the near-shortest paths for each robot by an RRT*-based planner. Then the timetables for each robot in the path-time space are created by the improved Particle Swarm Optimization (PSO) method. A heuristic bias is added to the PSO optimizer to efficiently mediate the conflict near the goal configuration. The OPTP achieves the near-shortest moving distance of the multi-robot team, as well as the near-optimal navigation makespan in face of complex obstacles, narrow channels, and dense goal configurations. The compared simulations and real-world experiments verify the effectiveness of the OPTP method.
The nature and functions of tragedy-farce in a real show (based on the material of the Ukrainian projects “I am changing my wife” and “Hello, I am your mother!”)

The article substantiates the features of the nature and functions of the comical, laughter, farce, and tragedy-farce in Ukrainian’s reality shows “I am changing my wife” and “Hello, I am your mother!” These projects touch upon basic social and everyday problems of the modern family and purport to foster a responsible attitude to family values and the personality of each member. Such tasks involve the stirring up the classical worldview and appeal to its fundamental values. Their circle includes the comical and laughter as public gestures. For this, reality shows use the capabilities of situational models and scenarios. However, the special nature of these projects initially does not allow us to talk only about traditional types and forms of the comical, laughter, and farce as funds for to realize their main goals and objectives. Reality shows, by virtue of their nature, come to tragedy-farce as the leading principle for the realization of goals and objectives. The nature of tragedy-farce in these projects shows the unity of the comical, amusing, kind, humorous, absurd, and scary, vicious, frightening, miserable, dramatic, and destructive, which simultaneously coexist in the life of a person and family. In these reality shows, tragedy-farce is formed by combining heterogeneous realities into one unpredictable and provocative whole: mass media and real life. This not only complicates its nature in relation to theatrical literary and mass media realness, but also leads to a dangerous line beyond which there is no longer a difference between media text and real life, and when they are equally responsible for all the events that happen in the family and with each her member. In this regard, tragedyfarce turns as a strange discourse and threat of the destruction of the surface of meaning.
MACHINE LEARNING APPROACH TO ADDRESS SQL INJECTION ATTACKS IN WEB APPLICATIONS

Ponte Academic JournalJun 2022, Volume 78, Issue 6 MACHINE LEARNING APPROACH TO ADDRESS SQL INJECTION ATTACKS IN WEB APPLICATIONSAuthor(s): Elegbeleye Femi ,Malema Chris, Kesewaa KorantengJ. Ponte - Jun 2022 - Volume 78 - Issue 6 doi: 10.21506/j.ponte.2022.6.3 Abstract:Web applications are easily accessible by cybercriminals and are the main targets for malicious attacks. Web application attacks raise security concerns for individuals and organizations because they may cause financial loss and information leaks that may erode clients’ trust. A type of web application attack, which is very dangerous is SQL injection, it targets databases. Cross-Site Scripting is another web application attack that redirects users to phishing websites. Although tools are available to monitor and detect SQL injection and cross-site scripting, the potential impact of these tools is insufficient. Therefore, a gap in this domain is to develop tools that can adequately monitor and detect web application attacks viz. SQL injection and Cross-Site Scripting. In this study, a security solution is proposed using an artificial neural network (ANN) to protect web applications against attacks. The solution has been experimented on five web applications with weak security and another five with strong security and demonstrated that this solution could eﬀectively identify malicious SQL queries with negligible performance overhead. The solution has also been developed in a prototypic java-based web application firewall called Web Applications Firewall using Artificial Neutral Networks (WAFANN). The security solution system will benefit cloud service providers, website hosting services, website development companies, and individuals that develop websites. Due to the inadequacy of web applications to test this solution, this research concludes based on the ten web applications (used for experiment purposes) to provide eﬃcient security to prevent SQL injection attacks. Download full text:Check if you have access through your login credentials or your institution Username Password
Not just Institutions: what is behind the change in the tra-jectory of the Turkish economy growth in the second decade of the XXI century

The article analyzes the factors of Turkey&amp;apos;s economic growth slowdown since the beginning of the second decade of the XXI century. And although most often unfavorable trends in the development of the institutional environment are focused on, the reduction in the volume of external financing is probably no less significant. The latter circumstance led to a certain decrease in the rate of investment and noticeable reduction in the level of private consumption. The slowdown of growth rate can also be considered as a result of Turkey’s start sliding into the &amp;quot;middle income trap&amp;quot;. The factor that pre-viously provided growth but is much exhausted by today is not cheap labor force as in many other countries, but easy coming external financing. Its decrease caused a reduction in private consumption below the level that until recently prevented sliding into the &amp;quot;trap&amp;quot;. As for the main reasons of external sources shortage investors&amp;apos; distrust of financial stabilization in Turkey due to the lack of the necessary institutions, in particular this one to control the level of the budget deficit and state interference in the operation of market mechanisms, as well as the imprudent, sometimes provocative President Erdogan’s foreign policy should be mentioned. Thus, the slow-down in the Turkish economy is based on a shortage of external capital in-flow, caused in turn by institutional factors. The latter circumstance makes the process of immersion &amp;quot;into the trap&amp;quot; formally reversible. Still there is a question about the productivity of returning to Turkey’s previous model ofeconomic development.
Experimental Evaluation of Mission-Planning Support in Multi-User Manned-Unmanned Teaming Applications with Shared Unmanned Systems

In this study, we evaluate our work towards sharing unmanned systems among multiple distributed users. We propose a hierarchical approach in which the use of unmanned systems can be requested by secondary users. To support the primary user in managing these requests, we developed a prototype of a planning agent that can generate and evaluate potential solutions that incorporate the request into the mission plan. These solutions can then be provided to support the primary user’s decision-making process. We implemented the planning agent in a helicopter research simulator and validated it with military pilots. Within an experiment, we investigated different configurations that supported the participants on different levels of automation. Results indicate that a higher level of automation caused a positive effect on performance but is accompanied with a loss in situational awareness.
Explore New Language: A Quick Introduction to Kotlin

In the first 9 years after Android’s birth, Java was the only language used to develop Android applications. Although Google introduced NDK in Android 1.5 to support the development of Android applications using C and C++, it never challenged the dominant role of Java.
Sparse Autoencoder and Deep Learning Based Framework for Multi-label Classification of Chronic Diseases

The accurate prediction of chronic diseases at an early stage can help the patients to plan the future course of treatments effectively so as to avoid any life threatening situations. Machine learning techniques provide cost and computationally inexpensive solutions for multi-label as well as multi class disease classification problems. In case of multi-label classification problems more intelligent techniques of data analytics are required due to the presence of correlations and label dependencies in the dataset. The accuracy of prediction also depends upon the representation of the data. In this paper a Sparse Autoencoder and Deep learning based Framework has been proposed to predict the presence of multiple diseases in the patients. The framework consists of three phases. In the first phase, Sparse Autoencoder is used to learn features from the original dataset. In the second phase, Label Powerset method is used to transform the problem from multi-label to multiclass classification problem. In third and the final stage, a deep learning architecture is applied. 80.85% of accuracy has been achieved by the proposed framework.
Overview of the Shared Task on Machine Translation in Dravidian Languages

This paper presents an outline of the shared task on translation of under-resourced Dravidian languages at DravidianLangTech-2022 workshop to be held jointly with ACL 2022.A description of the datasets used, approach taken for analysis of submissions and the results have been illustrated in this paper.Five sub-tasks organized as a part of the shared task include the following translation pairs: Kannada to Tamil, Kannada to Telugu, Kannada to Sanskrit, Kannada to Malayalam and Kannada to Tulu.Training, development and test datasets were provided to all participants and results were evaluated on the gold standard datasets.A total of 16 research groups participated in the shared task and a total of 12 submission runs were made for evaluation.Bilingual Evaluation Understudy (BLEU) score was used for evaluation of the translations.
Indian Currency Classification and Counterfeit Detection Using Deep Learning and Image Processing Approach

These days, there is rampant usage of fake currency notes in every country, which is not good for the economy of that country. Further, the other side of the issue is the problem of visually impaired people to recognize bank notes. To address both of these problems, in this work we propose a deep learning model, which not only recognizes the real and fake Indian currency notes but also tells its correct denomination. We have employed various deep learning architectures for this task. In specific, we explored the applications of pre-trained deep learning models, like VGG16, GoogLeNet and MobileNet for currency classification and fake currency detection. The use of pre-trained deep learning models is motivated by the fact that these models are based on transfer learning and hence not data-hungry. As we had limited datasets available for this work, the pre-trained deep learning models were expected to give better performance. After evaluating the proposed approach on around 2572 images belonging to 6 denominations of Rs. 10, 20, 50, 100, 500, 2000, it was found that VGG16 obtained the highest classification as well fake detection accuracy of 98.08% and 97.95%, respectively. We further followed a conventional image processing-based approach and analyzed the discriminative feature differences between a real and a fake note. Various methods of preprocessing of the input image like edge detection, intensity mapping and HSV space conversion are done and the differences between the real and fake notes were analyzed, which can be used further for fake currency detection.
All Work and No Play – Game-Based vs. Text-Based Learning in IT-Security

Game-based learning is a popular tool in IT-security. We study the effects of game-based learning in the context of phishing-emails in comparison with text-based learning by asking participants to classify previously unseen emails after completing their learning exercise. We correlated participants scores with their professional background of learners. Our results indicate that professional background is an important indicator of the success of game-based learning and suggest that learners with a technical background might benefit from text-based learning to a larger degree than from game-based learning.
Neuralfme: A Deep Learning Based Interpolation-Free Technique for Fractionalpixel Motion Estimation in Hevc

High Efficiency Video Coding provides more compression than any of its predecessors. The module with the main contribution to the higher compression rates is the Motion Estimation module, which consists of Integer pixel Motion Estimation, and Fractional-pixel Motion Estimation (FME). The FME process performs computationally demanding interpolations to find sample values at fractional pixel locations. In this paper, we propose fast and flexible deeplearning based interpolation-free methods for FME. Three architectures are proposed in this work, one with Two fully connected (FC) hidden layers, another with three FC hidden layers and the last with one convolutional layer succeeded by two FC layers. Our proposed methods are implemented in HEVC reference software (HM16.9). The two-layered network can perform FME much faster than the standard interpolationbased method, saving up to 95% of the FME time. This comes at the cost of an average increase of 2% in BD-Rate and an average reduction of 0.08 dB in BD-PSNR. The three-layered network trades off some of the speed in favor of encoding gains. Nevertheless, it saves up to 90% of the FME time. The average BD-Rate is increased by 1.6% and the average BD-PSNR is reduced by 0.06 dB in this case. The convolutional network gives better results in terms of BD-Rate and BD-PNSR. Using this architecture, the average BD-Rate is increased by 1.2% and the average BD-PSNR is reduced by 0.05 dB. It also generalizes very well for different quantization parameters (QPs). However, it saves up 38.5% of the FME time on average.
Adaptive Threshold for Anomaly Detection in ATM Radar Data Streams

Intrusion Detection Systems (IDS) are capital instruments for protecting ATM networks against intrusion, and subsequently ensuring the integrity of air traffic. An anomaly detection approach in such systems enables the detection of multiple types of attacks with the aid of a threshold as a criterion for differentiating between normal activity and unusual events in the network. IDS with fixed threshold fail to detect the presence of patterns in the data, thus hampering proper detection ability, and requiring regular human intervention. Detection ability of IDS can be improved by establishing an automated system that recognises pattern shifts in evolving data streams and adjusts the threshold accordingly. Our work focuses on designing an algorithm to recognize the occurrence of new patterns and adjust the threshold consequently for enhanced anomaly detection, whilst offering flexibility for different frameworks and scalability to cope with large data streams. In this article, we present an adaptive threshold approach based on extreme value theory, which aims to automatically detect concept drifts in radar data streams. We evaluate our method in a practical scenario of anomaly detection on time series data collected by air traffic radars across France and show that we can achieve a threefold performance improvement over a standard approach using a fixed threshold.
A Robust ELM Algorithm for Compensating the Effect of Node Fault and Weight Noise

Although the extreme learning machine (ELM) technique is an efficient and effective neural approach, there are still some downsides in the traditional ELM technique. When there are some outlier training samples, the trained neural network is usually with poor performance. Another issue is that when there are some noise and faults in the trained network, the performance of the trained network is also poor. This paper looks into the ELM technique under multiple imperfections, including outlier training samples, weight noise and node faults. This paper first identifies a regularization term for handling weight noise and node faults. To handle outlier training samples, the maximum correntropy criterion (MCC) concept is used in the objective function. A learning algorithm, namely, robust fault aware ELM algorithm (RFAELM), for faulty networks is then proposed. Simulation results show that the performance of the proposed algorithm is much better than that of two state-of-art robust algorithms.
Several new classes of optimal ternary cyclic codes with minimum distance four

<p style='text-indent:20px;'>Let <inline-formula><tex-math id="M1">\begin{document}$ {\mathcal{C}}_{(u, v)} $\end{document}</tex-math></inline-formula> denote the ternary cyclic code with two zeros <inline-formula><tex-math id="M2">\begin{document}$ \alpha^u $\end{document}</tex-math></inline-formula> and <inline-formula><tex-math id="M3">\begin{document}$ \alpha^v $\end{document}</tex-math></inline-formula>, where <inline-formula><tex-math id="M4">\begin{document}$ \alpha $\end{document}</tex-math></inline-formula> is a generator of <inline-formula><tex-math id="M5">\begin{document}$ {\mathbb F}_{3^m}^{*} $\end{document}</tex-math></inline-formula> and <inline-formula><tex-math id="M6">\begin{document}$ 1\leq u, v \leq 3^{m}-1 $\end{document}</tex-math></inline-formula>. In this paper, we first present a necessary and sufficient condition such that <inline-formula><tex-math id="M7">\begin{document}$ {\mathcal{C}}_{(u, v)} $\end{document}</tex-math></inline-formula> is optimal with parameters <inline-formula><tex-math id="M8">\begin{document}$ [3^{m}-1, 3^{m}-1-2m, 4] $\end{document}</tex-math></inline-formula>. Further, based on this condition, we obtain several new classes of optimal ternary cyclic codes <inline-formula><tex-math id="M9">\begin{document}$ {\mathcal{C}}_{(u, v)} $\end{document}</tex-math></inline-formula> with parameters <inline-formula><tex-math id="M10">\begin{document}$ [3^{m}-1, 3^{m}-1-2m, 4] $\end{document}</tex-math></inline-formula>. We show that these codes are new compared with the known results.
Some hematological parameters and enzyme histochemistry of peripheral bloodleukocytes in Kivircik sheep

The aim of the present study was to determine some hematological parameters (RBC, WBC, HCT, HGB, MCH, MCHC, MCV, and PLT), the presence and percentages of ANAE and AcP-ase positivities in leukocytes and the differential leukocyte count in Kivircik sheep. Blood samples were taken from the jugular vein of 40 (rams = 20; ewes= 20) healthy, 1-year-old sheep. MCH and MCHC values were found statistically higher in rams than ewes (p < 0.05). Mean percentages of ANAE-positive PBL were defined as 74.16 ± 11.94% in ewes and 70.33 ± 8.39% in rams. It was not determined any significant changes according to sex. On the other hand, the percentages of ACP were determined as 67.12 ± 6.54% in ewes and 66.71 ± 6.58% in rams. It was not found any significant differences according to sex. For ANAE enzyme activity, lymphocytes, monocytes, neutrophils, and eosinophilic granulocytes showed a positive reaction. For ACP enzyme activity, although lymphocytes presented positivity, monocytes, neutrophils, eosinophils were negative. The ratio of lymphocyte, monocyte, neutrophile, eosinophile, and basophile of Kivircik ewes and rams were found as 64.35 ± 5.74%, 60.64 ± 8.85%; 3.41 ± 1.27%, 3.82 ± 1.42%; 27.58 ± 5.46%, 30.94 ± 8.71%; 2.41 ± 0.93%, 2.88 ± 1.45%; 1.76 ± 0.75%, 1.29 ± 0.58%, respectively. The difference between mean values was not found significant according to sex (p > 0.05). In conclusion, our study showed that enzyme histochemical methods can be used for identifying peripheral blood cells and can provide facilitates for early diagnosis of immune system-related disorders in sheep.
Position and Orientation Registration of Intra-abdominal Point Cloud Generated from Stereo Endoscopic Images and Organ 3D Model Using Open3D

In endoscopic surgery, it is important to search for organs, tumors and blood vessels, recognize their locations, and perform the surgery safely and quickly. Robot-assisted surgery has made it possible to perform precise operations. However, it is still difficult to perform surgery in a narrow surgical field with a narrow view. Several surgical support systems using augmented reality (AR) have been studied, in which three-dimensional computer graphics (3DCG) models of organs, tumors and blood vessels are superimposed on the endoscope camera image. We have developed an AR surgical support system based on SLAM (Simultaneous Localization and Mapping) technology. The SLAM can estimate the camera position and orientation in global coordinates using only the camera image. However, it is difficult to achieve robust AR because of the errors in camera position and orientation estimation and the failure of SLAM due to camera shake, heavy movement and the reflection of surgical instruments. To solve this problem, we have attempted to perform local position and orientation estimation using ICP on the 3DCG model of the organ and the intra-abdominal point cloud.
Multispectral Image Compression Algorithm Based on Sliced Convolutional LSTM

The multispectral imaging which are used for remote sensing imaging has a large amount of data, so this paper proposed a deep learning method which is based on sliced convolutional LSTM for multispectral image compression. Compared with other algorithms, the proposed algorithm further compresses the multispectral images by considering the similarity between the spectra and removing the inter-spectral redundancy. The proposed algorithm is based on end to end framework which is consist of encoder, decoder, entropy coding and quantizer. In experiments, the PSNR of proposed model is compared with that of JPEG2000 to evaluate the performance of our algorithm at several different bit rates.
Stochastic ADMM

For lots of machine learning problems, f 1(x 1) is typically a loss over the data and f 2(x 2) is a regularizer that controls the complexity of the model or provides the prior information of the solution. For such problems, we may assume that f 1 has a structure as follows.
Interactive Usage

Python is often used for exploratory programming. Often, the result is not the program but an answer to a question. For scientists, the question might be about the likelihood of a medical intervention working. For people troubleshooting computers, the question might be which log file has the message I need.
A Text Structural Analysis Model for Address Extraction

Textual data is being generated at an enormous pace in today’s world. Analyzing this data to extract actionable information is one of the biggest challenges faced by researchers. In this paper we tackle the problem of extracting addresses from unstructured texts. Postal address denotes the unique geographical information of a place, person or an organization. Extracting this information automatically with high precision is helpful for public administration & services, location based service companies, geo-spatial mapping, delivery companies, recommendation systems for tourists, OCR based event creation etc. Address formats varies widely based on countries and regions. Even within a same region people can choose to adopt different formats and notations, hence extracting addresses from text becomes a challenging and interesting task in NLP research. In this paper we propose a text structural analysis model, consisting of a novel gazetteer assisted CNN architecture. It uses structural pattern detection capabilities of a CNN to empirically prove that for address extraction task structural analysis is more efficient than pure semantic approach. We further did an ablation study to find the importance of external knowledge for our architecture.