Reviewerly/exHarmony huggingface dataset:
Cyberbullying Identification and Classification using Hybrid Techniques

Now a days in digital society, cyberbullying is a vigorous and widespread issue affecting huge number of online users, mostly teenagers. However, increased usage of social media lead to increase in the rise of cyberbullying. Cyberbullying is an aggressive action usually carried out by some persons using any electronic items, against people who cannot protect themselves. The existing works distinguishes the bullies from the usual Instagram users by considering the user, network and text related attributes using classifiers. Most of the available cyberbullying detection methods are supervised but they have mainly two drawbacks such as labelling the data which often take more time and labour and Current guidelines for labelling may not useful for future instances because of evolving social networks and different language usage. In order to overcome those limitations, the proposed work is introduced for the unsupervised cyberbullying detection method.Our proposed detection method will extract linguistic attributes such as idioms, sarcasm, irony and active or passive voice. In addition, the representation learning network which learns the multimodal session representation and the multitask learning network which will simultaneously understand the bullying energy and then models the comments arriving times from Instagram dataset.
UETfishes at MEDIQA 2021: Standing-on-the-Shoulders-of-Giants Model for Abstractive Multi-answer Summarization

This paper describes a system developed to summarize multiple answers challenge in the MEDIQA 2021 shared task collocated with the BioNLP 2021 Workshop.We present an abstractive summarization model based on BART, a denoising auto-encoder for pretraining sequence-to-sequence models.As focusing on the summarization of answers to consumer health questions, we propose a query-driven filtering phase to choose useful information from the input document automatically.Our approach achieves potential results, rank no.2 (evaluated on extractive references) and no.3 (evaluated on abstractive references) in the final evaluation.
SCAFFY

Denial of service (DoS) attack is one of the common threats to the availability of critical infrastructure and services. As more and more services are online enabled, the attack on the availability of these services may have a catastrophic impact on our day-to-day lives. Unlike the traditional volumetric DoS, the slow DoS attacks use legitimate connections with lesser bandwidth. Hence, it is difficult to detect slow DoS by monitoring bandwidth usage and traffic volume. In this paper, a novel machine learning model called ‘SCAFFY' to classify slow DoS on HTTP traffic using flow level parameters is explained. SCAFFY uses a multistage approach for the feature section and classification. Comparison of the classification performance of decision tree, random forest, XGBoost, and KNN algorithms are carried out using the flow parameters derived from the CICIDS2017 and SUEE datasets. A comparison of the result obtained from SCAFFY with two recent works available in the literature shows that the SCAFFY model outperforms the state-of-the-art approaches in classification accuracy.
Recognizing the formations of CVBG based on shape context using electronic reconnaissance data

To recognize the formation of the carrier battle group (CVBG) from passive electronic reconnaissance and location data, a shape context-based method is proposed. The proposed method treats the location data as a graph model, and a graph matching method is used for formation recognition. The shape context and background context for each unit in CVBG is calculated and used for multi-viewpoint shape feature, which is rotation invariance and distinctive. The experimental results show that the proposed method can recognize the formation with a higher accuracy and locate the ship with electromagnetic silence.
Detection and Recognition of Abnormal Behaviour Patterns in Surveillance Videos using SVM Classifier

In recent day’s video surveillance is important to identify the abnormal activities of human in order to maintain serene environment. This paper proposes an automatic detection of abnormal events in the recorded video. This work mainly focuses on detection of human (Student) and their abnormal activities in academic zone. The proposed system consists of three phases 1. Motion Segmentation 2. Feature Extraction 3. Action Classification. Background subtraction method primarily used to segment the moving object. Feature extraction is carried out by Hu moments. Classification of normal and abnormal activities is done by support vector machine (SVM) classifier algorithm. The proposed method evaluated with the recorded video dataset “SAIAZ” (Student Activities In Academic Zone) of our Institution and “Sarkar” tamil movie dataset. The SAIAZ and Sarkar” tamil movie dataset consists of activities like slapping, kicking, punching, hitting, and fighting. The performance metrics such as TPR, TNR, FPR, FNR are used to evaluate the proposed system. The accuracy of action detection rate in SAIAZ is 72% and “Sarkar” tamil movie dataset is 68%.
Improved, Deterministic Smoothing for L_1 Certified Robustness

Randomized is a general technique for computing sample-dependent robustness guarantees against adversarial attacks for deep classifiers. Prior works on against L_1 adversarial attacks use additive noise and provide probabilistic robustness guarantees. In this work, we propose a non-additive and deterministic method, Deterministic Smoothing with Splitting Noise (DSSN). To develop DSSN, we first develop SSN, a method which involves generating each noisy sample by first randomly splitting the input space and then returning a representation of the center of the subdivision occupied by the input sample. In contrast to uniform additive smoothing, the SSN certification does not require the random noise components used to be independent. Thus, can be done effectively in just one dimension and can therefore be efficiently derandomized for quantized data (e.g., images). To the best of our knowledge, this is the first work to provide deterministic randomized smoothing for a norm-based adversarial threat model while allowing for an arbitrary classifier (i.e., a deep model) to be used as a base classifier and without requiring an exponential number of samples. On CIFAR-10 and ImageNet datasets, we provide substantially larger L_1 robustness certificates compared to prior works, establishing a new state-of-the-art. The determinism of our method also leads to significantly faster certificate computation. Code is available at: this https URL
Multi-target Attachment for Surgical Instrument Tracking

The pose estimation of a surgical instrument is a common problem in the new needs of medical science. Many instrument tracking methods use markers with a known geometry that allows for solving the instrument pose as detected by a camera. However, marker occlusion happens, and it hinders correct pose estimation. In this work, we propose an adaptable multi-target attachment with ArUco markers to solve occlusion problems on tracking a medical instrument like an ultrasound probe or a scalpel. Our multi-target system allows for precise and redundant real-time pose estimation implemented in OpenCV. Encouraging results show that the multi-target device may prove useful in the clinical setting.
PROGRAMMING DATA STRUCTURE ALGORITHMS IN PYTHON

The article discusses ways of programming algorithms for complex data structures in Python. Knowledge of these structures and the corresponding algorithms is necessary when choosing the best methods for developing various software. When studying the subject "Algorithms and Data Structures", it is important to understand the essence of data structures. This is due to the fact that manipulating a data structure to fit a specific problem requires an understanding of the essence and algorithms of this data structure. Examples of programming algorithms related to dynamic lists and binary search trees in the currently widely used Python language are given. The algorithms for traversing the graph in depth and breadth are optimally and clearly implemented using the Python dictionary.
CONVEXITY CONDITION OF THE GENERALIZED BERNATSKY INTEGRAL FOR ONE SUBCLASS OF STAR-LIKE FUNCTIONS

The article carried out a study on the convexity of the Bernatsky integral in the proposition that the function in question belongs to a subclass of star-shaped functions that satisfy certain conditions. For this, the condition of convexity of univalent functions was considered. The geometrical interpretation of the conditions is given, the radius of the convexity of the star-shaped functions is established. The intervals for the parameter are found for which the Bernatsky integral is a convex function in the whole unit circle, in cases where the parameter does not belong to the given interval, the Bernatsky integral will be a convex function in a circle of smaller radius. Three consequences are given in which various cases of convexity of the Bernatsky integral for analytic functions that belong to classes of functions with certain conditions are analyzed. For the considered classes of analytic functions, the radius of convexity of the Bernatsky integral is determined.
Towards Grounded Theory Perspectives of Cognitive Load in Software Engineering

Background: The socio-technical characteristic of software engineering is acknowledged by many, while the technical side still dominates research. As software engineering is a human-intensive activity, the cognitive side of software engineering needs more exploration when trying to improve its efficiency.Aim: The aim of this study is to increase the understanding of the impact of cognitive load in software engineering. Our ultimate goal is to theorize the knowledge and thereby reveal opportunities to make software engineering more efficient for companies and compelling for the developers to engage in.Method: We synthesize knowledge, using a grounded theory approach, from our empirical observations and literature on cognitive load in software engineering, using cognitive load theory as a stepping stone and theoretical filter.Results: We present a grounded theory of cognitive load in software engineering, emerging from the analysis, which classifies cognitive load drivers into eight perspectives – task, environment, information, tool, communication, interruption, structure and temporal – each of which is further detailed.Conclusion: We intend to use this theory as a starting point for further generation of theory to be used in the design of software engineering tools, methods and organizational structures to improve efficiency and developer satisfaction by reducing the cognitive load. (Less)
Stretch-VST: Getting Flexible With Visual Stories

In visual storytelling, a short story is generated based on a given image sequence.Despite years of work, most visual storytelling models remain limited in terms of the generated stories' fixed length: most models produce stories with exactly five sentences because five sentence stories dominate the training data.The fix-length stories carry limited details and provide ambiguous textual information to the readers.Therefore, we propose to "stretch" the stories, which create the potential to present in-depth visual details.This paper presents Stretch-VST, a visual storytelling framework that enables the generation of prolonged stories by adding appropriate knowledge, which is selected by the proposed scoring function.We propose a length-controlled Transformer to generate long stories.This model introduces novel positional encoding methods to maintain story quality with lengthy inputs.Experiments confirm that long stories are generated without deteriorating the quality.The human evaluation further shows that Stretch-VST can provide better focus and detail when stories are prolonged compared to the state of the art.The demo video is available on Youtube 1 , and the live demo can be found on website 2 .
Analyzing Effects of Architectural Alternatives on Performance of Blockchain

The first idea that arises in mind after having read the title of this paper would be the heading of this section. This is most certainly because until now we have only heard about how amazing the blockchain technology has turned out to be in terms of cyber security. Almost all the previous methods of implementing cyber security are directly or indirectly implemented in the blockchain technology and it can, therefore, be treated like an epitome of cyber security implementations. This, with the fact that all the variations possible in those individual sub-technologies also become a variation in blockchain, gives us a wide array of possible configurations to suit the requirements of different shapes and sizes of organizations. Moreover, the blockchain is still in the emerging phase, meaning that it still has many undiscovered use cases in the current and future world. In such a situation, tweaking of the implementational mechanisms as per the requirements of the organization have led to the branching in the way in which blockchains are created. This is because different implementations cost differently. This can be considered as a step further in the implementation where all the tools that we are using have been replaced with optimized and efficient use of those tools. This paper tries to satiate the emerging hunger for utilizing the blockchain technology into more and more spheres of life than just currency.
Augmented Reality Peripheral Performance: Light Intensity, Distance, Occlusion and Marker Testing

Abstract Augmented Reality peripherals (ARP) that use marker detection objects have developed rapidly and are implemented in various forms to support the activities of everyday human life. The challenge that is currently being faced is the increasingly widespread application of ARP technology to the reliability of software product performance in detecting objects. This research has developed and implemented ARP in the academic space. The application development method uses the Multimedia Development Life Cycle (MDLC) approach, developed based on the Android operating system, using the Unity 3D Engine and Vuforia SDK. 3D object modelling with Sweet Home 3D. Whereas for testing the reliability of ARP performance in object detection using testing of light intensity, distance, occlusion and marker with each scenario. The research produces augmented reality technology products that are able to offer visualization in the form of interactive 2D and 3D objects in academic rooms that provide information to users about the peripheral layout and division of academic spatial layouts and the good reliability performance.
Penerapan Metode K-Means Dalam Mengelompokkan Banyaknya Desa/ Kelurahan Menurut Keberadaan Permukiman Di Bantaran Sungai Berdasarkan Provinsi

The research aims to classify the settlements along the river banks by province. To solve this problem, the researchers applied the K-Means Algorithm method. Where the source of research data was collected based on documents explaining the number of villages / sub-districts according to the existence of settlements on the river banks produced by the Central Statistics Agency (BPS). The data used in the study are data from 2014 - 2018 which consists of 34 provinces. The data will be processed by clustering in 2 clusters, namely the settlement level cluster on the high riverbank and the settlement level cluster on the low riverbank. The high cluster consists of 11 data, namely the provinces of Aceh, North Sumatra, Jambi, South Sumatra, West Java, Central Java, East Java, West Kalimantan, Central Kalimantan, South Kalimantan, and South Sulawesi. By conducting the research, it can provide input and as a solution to related parties in charge of dealing with settlement problems along the river banks, especially for the government, in order to get more attention in provinces with high riverbank settlement rates.
Proposed Copyright Protection Systems for 3D Video Based on Key Frames

Recently, three Dimension (3D) multimedia technology has grown over a wide range, where the 3D used in many applications such as medical, television, cinema, game, education, etc. The developments also include the computer network, these evaluations make the store and distribute the media very easy and at the same time very critical, where any person can access these data and manipulate it or redistribute it with illegal copyrights. Therefore, it is necessary to find a watermarking technique to keep the security of these media. 3D multimedia, especially 3D Video contains a huge amount of data, trying achievement copyright protection for all these data consumes the computation, and slow down securing process, thus 3D video key frame extraction process is necessary. In this paper, the histogram-based key frame extraction technique is applied to extract essential and important data of the 3D video. A modified invisible, and blind watermark system is suggested in this research for 3D Video copyrights and authentication protection using Dual-Tree Complex Wavelet Transform (DTCWT). The experiment results show that 3D video key extraction system has given high compression ratio ( from 4:1 to 5:1) and the protection system could put an invisible watermark while keeping the quality of the 3D video, as well as the proposed watermark system, is robust where the watermark extracted successfully after applying a different types of attack.
Mobile-based Clock Drawing Test for Detecting Early Signs of Dementia

Dementia is one of the major causes of disability and dependency among older people. Early detection is the key for preserving the quality of life of the patients and reducing caring costs. The Clock Drawing Test (CDT) is commonly used by clinicians to screen for early signs of dementia. We build an automated CDT that runs on mobile platforms, enabling convenient and frequent self-monitoring and testing at minimal costs. Our system combines both a spatial-temporal approach and a purely image-based deep learning approach to analyze and evaluate the hand-drawn clocks based on established clinical criteria. Our system produces scores that are highly correlated with expert human raters.
Improvement of projection-based LiDAR data segmentation algorithms using object-contextual representations

Abstract Nowadays self-driving cars and such unmanned aerial vehicles as drones are one of the most actively developed technologies, where machine learning algorithms play an irreplaceable role especially in the perception problem, which is the context of this research. To be applicable in self-driving cars and especially drones such algorithms should not only have good output quality, but also be real-time. For this reason, in the case of LiDAR data segmentation problem we pay special attention to algorithms that are based on point cloud projections because of their speed superiority over other heavy algorithms that process input point cloud directly. The main drawback of projection-based algorithms is their lower segmentation accuracy, so in this paper we show that it can be improved by integrating contextual representation module inside segmentation algorithm architecture. In our work we consider SalsaNext as a segmentation algorithm and OCR as a context representation module because these methods are among the highest in the corresponding datasets’ leaderboards. We provide results from quantitative evaluation on the Semantic-KITTI dataset, which demonstrate that the proposed SalsaNext modification gives 6.2% mean intersection over union metric improvement with no speed reduction.
Fiscal Dominance in Sub-Saharan Africa Revisited

This paper explores the causes and consequences of fiscal dominance over monetary policy in Sub-Saharan Africa (SSA). Fiscal dominance has always been a pressing problem as it can contribute to inflation and macroeconomic instability, and increasingly so as fiscal deficits and public debt are rising in many SSA countries. We find that legal limits and availability of alternative financing options play an important role in determining the extent to which government deficits tend to be financed by the central bank. We also find economically significant effects of central bank lending to government on the exchange rate and inflation.
POSSIBILITIES OF ARTIFICIAL INTELLIGENCE IN TEACHING HIGHER MATHEMATICS

The example of application of methods of computer representation of knowledge to the «Matrix» of the higher mathematics course, the authors examine the possibility of using elements of artificial intelligence, but do not propose an exhaustive algorithm for the construction of relevant systems, and based on the known thesis: «intellectual tasks no formal solution algorithm». Moreover, we implicitly proceed from the concept that when solving problems that require the participation of human intelligence, artificial intelligence can be only an auxiliary tool. Analysis of Russian and English sources has led to the conclusion that automated the process of proving mathematical theorems with the use of artificial intelligence (including neural networks) leads first-year students does not lead first-year students to the natural goal of teaching higher mathematics – » to teach logical reasoning. Usually the result comes down to «programmed» using the keyboard that, in certain circumstances, of course, is a very useful skill. But, unfortunately, the task «to teach the student to think, to reason logically and think outside the box», based on modern level of development of artificial intelligence (machine learning), according to the authors, is not yet resolved. Using the same knowledge base in an adequate format, and semantic networks allow at least allow you to at least get closer to the possibility of using artificial intelligence in the proof of theorems of higher mathematics. Because properly constructed semantic network will greatly assist as a teacher is in presenting the proof of the theorem, and demonstrate to the students a well-known logical connections and, we hope, will interest them in creating a new one. The above arguments determine the purpose of the proposed study is to build semantic networks for proof of mathematical theorems, and to solve the so-called «proof-based» tasks. In our opinion, the results of this study will not only contribute to streamlining the structure by the teacher and better understanding by students of the relevant sections of mathematics, but few will bring the solution of the problem of formation of artificial intelligence, based on the decision of «evidence-based» tasks of higher mathematics.
Quantum Amplitude Amplification for Reinforcement Learning

Reinforcement learning models require a choice rule for assigning probabilities to actions during learning. This chapter reviews work on a new choice rule based on an amplitude amplification algorithm originally developed in quantum computing. The basic theoretical ideas for amplitude amplification are reviewed, as well as four different simulation applications to a task with a predator learning to catch a prey in a grid world, and one application to human learning in a 4 arm bandit choice task. The applications reviewed in this chapter demonstrate that QRL can improve speed and robustness of learning compared to standard choice rules such as epsilon-greedy and softmax choice rules.
When does gradient descent with logistic loss interpolate using deep networks with smoothed ReLU activations

We establish conditions under which gradient descent applied to fixed-width deep networks drives the logistic loss to zero, and prove bounds on the rate of convergence. Our analysis applies for smoothed approximations to the ReLU, such as Swish and the Huberized ReLU, proposed in previous applied work. We provide two sufficient conditions for convergence. The first is simply a bound on the loss at initialization. The second is a data separation condition used in prior analyses.