Reviewerly/exHarmony huggingface dataset:
Car Tracking Technique for DLES Project

Object tracking problem is considered as one of interesting area of research due to the large utilization of video surveillance systems these days. In this paper, a real-time tracking method based on HSV color space is proposed for car location detection in a specific area applied on surveillance videos. This method begins with pre-processing step in which a 3D to 2D transformation on the extracted tracking area is made in order to get the bird's eye view, alongside a bilateral filter or so-called edge preserving filter is used to reduce noises. Then, the following step is extracting the car's movement information by computing Structural Similarity Index between pre-extracted patches from frames side by side with Morphological operation for more noise removing. At the end, the obtained result is sent to a post-processing step in order to detect the location of the car and save their path for further treatment. The main goal of using this approach is to supervise Car Reverse Test (CRT) for driving license exam in Morocco which is the second part of the project called Driving License Exam Supervisor (DLES). The performance of the presented tracking system is evaluated on real surveillance scenes and shown in the experiments results. Experimental result demonstrates that the proposed method can generate a path similar to which is made by the vehicle with considerable level of efficiency for all possible situations compared to the ground truth.
Procedural Level Generation for <i>Sokoban</i> via Deep Learning: An Experimental Study

Deep learning for procedural level generation has been explored in many recent works, however, experimental comparisons with previous works are rare and usually limited to the work they extend upon. The goal of this article is to conduct an experimental study on four recent deep learning procedural level generators for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Sokoban</i> to explore their strengths and weaknesses. The methods will be bootstrapping conditional generative models, controllable and uncontrollable procedural content generation via reinforcement learning (PCGRL), and generative playing networks. We will propose some modifications to either adapt the methods to the task or improve their performance. For the bootstrapping method, we propose using diversity sampling to improve the solution diversity, training with auxiliary targets to enhance the models’ quality and sampling conditions from Gaussian mixture models (GMMs) to improve the sample quality. The results show that the generated solutions are more diverse by at least 16% when diversity sampling is used during training. It also shows that training with auxiliary targets and sampling conditions from GMMs can be used to increase the playability percentage. In our experiments, PCGRL shows superior quality and diversity, while the bootstrapped long-short term memory generators exhibit the least control confusion.
Reversible Data Hiding Based on Stationary Sequence of Pixel Value Ordering

Pixel value ordering (PVO) is an excellent strategy for improving embedding capacity and visual quality of reversible data hiding (RDH) algorithm. PVO utilize strong correlation between pixel values to improve the prediction accuracy. However, for rough texture blocks the pixel value fluctuates greatly, that resulting correlation between the pixel values is weak. This paper proposed an PVO based on stationary pixel-values sequence (SPS-PVO), which eliminates the fluctuating pixel values according to the context of the block, and obtains a stationary pixel-values sequence to enhance the correlation between pixel values. Furthermore, the adaptive two-dimensional mapping (2D-mapping) of existing pairwise PEE has been further extended, increasing the number of adaptive prediction errors. We will original adaptive prediction errors {–2, 1, 0, 1} expanded to {–3, –2, 1, 0, 1, 2}. Improved 2D-mapping increases the solution space, it is more likely to find the optimal 2D mapping. Experimental results show that the marked images by the proposed scheme have higher visual quality, e.g., the average PSNR for the Kodak image database is 64.45 dB after embedding 10,000bits, and the gain is 0.25 dB against the best result in the literature.
A Hybrid Filter/Wrapper Machine Learning Model for Classification Cancer Dataset

Breast cancer is severe disease with high fatality rate in women. As per the National breast cancer foundation of India, one out of 5 casualties occurs due to breast cancer. Data mining techniques play a vital role in the identification of cancer cells in the early stages. In this manuscript, a comparative approach between two popular machine learning algorithms has been analyzed and results are compared in terms of future prediction of the disease. A prefiltering technique along with Support Vector Machine (SVM) and k-Nearest-Neighbor(KNN) techniques are used to enhance the performance of the model. The model is applied on Wisconsin Diagnosis Breast Cancer data set for classfication. The dataset having imbalance classes are difficult to classify because of the high probability associated with a majority of classes and algorithms classify the new data to the majority of classes only. This problem is addressed in the manuscript by introducing the refiltering of data to mitigate the effect that occurred due to class imbalance. The results are evaluated using 10-fold cross-validation. The performance of both techniques is measured based on important key parameters such as accuracy, precision, recall, F-1 score, ROC curve, standard deviation. The results are significant and competitive. The performance of both the algorithm reveals that both algorithms have significant advantages to predict and cure the disease but SVM based filterd/wrapper approach outperforms KNN approach with 98.18% accuracy in detection against the accuracy of 94.66% determined by KNN.
From Translationese to Emergent Irony: A Usage-Based Approach to Chinese Bèi Passive

This study investigates how Chinese bèi passive (B0, default) has developed from a byproduct of translation (B1) between English and Chinese toward two emergent ironic constructions (B2 and B3) in modern Mandarin. Despite their low frequency counts from any existing Chinese corpora, B2 and B3 expressions (notably the former) have worked their way into today’s Chinese popular discourse. We conducted an online survey using a questionnaire to examine the B2 and B3 constructions that reflect a speaker’s cognitive-pragmatic need for communicating an ironic thought to provide the very first systematic study of the development of the bèi passive from B0 (non-irony) and B1 (translationese) to ironic B2 and B3. We also used Pearson’s chi-square (χ2) test and multiple correspondence analysis (MCA) to shed new light on the important claims we have made regarding the bèi constructions.
Assumption-Based Argumentation for Extended Disjunctive Logic Programming

We present the semantic correspondence between generalized assumption-based argumentation and extended disjunctive logic programming. In this paper, we propose an assumption-based framework (ABF) translated from an extended disjunctive logic program (EDLP), which incorporates explicit negation into Heyninck and Arieli’s ABF induced by a disjunctive logic program to resolve problems remained in their approach. In our proposed ABF, we show how an argument is constructed from disjunctive rules. Then we show a 1-1 correspondence between answer sets of an EDLP P and stable argument extensions of the ABF translated from P with trivialization rules. Hereby thanks to introducing explicit negation, the semantic relationship between disjunctive default theories and assumption-based frameworks is obtained in our approach. Finally, after defining rationality postulates and consistency in our ABF, we show answer sets of a consistent EDLP can be captured by consistent stable extensions of the translated ABF with no trivialization rules, that is useful for ABA applications containing explicit negation.
Prototype of a Biomechanical MIDI Controller for Use in Virtual Synthesizers

This paper describes the design, development, and implementation of a prototype for a low-cost, biomechanical device. Using the MIDI protocol, this device is capable of controlling, through body movements, different types of synthesizers or virtual instruments currently available in the music industry. Some aspects are discussed in connection with the design, the choice of components, the manufacture of a printed circuit board, and an ergonomic mounting case designed for this purpose. Finally, metrics and verifications of the prototype’s operation were carried out, by integrating communication and audio software tools.
Information Spread Maximization With Multi-Boosting Stages

Most of the existing works for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Influence Maximization (IM)</i> are defined over a ground set. As the natural extension of set space, in recent years, some works have begun to consider <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IM</i> on lattice space. The lattice boosting problem, however, has not yet been considered. In this paper, we focus on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Lattice <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula>-Boosting Problem</i> . Because each edge has multi-boosting stages, it is an obstacle to estimate the value of boosting influence function by conventional sampling technique. To overcome this obstacle, we adopt the largest boosting probability of each edge to generate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Potentially Reverse-Reachable graph (PRR graph)</i> and propose a boosting parameter of each boosting edge to amend it. Additionally, it is an unbiased estimation of boosting influence. Because <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Lattice <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula>-Boosting Problem</i> is not diminishing returns submodular (DR-submodular), we simplify <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Sandwich Approach</i> to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Semi-Sandwich Approach</i> by finding a tight DR-submodular lower bound, which also keeps a data-driven approximation ratio. Besides, we design a heuristic algorithm, namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Ktop Algorithm</i> , to return a feasible solution of the original problem. Numerical experiments show that, under the same constraint, selecting each node with the stronger boosting power rather than many nodes may have large boosting influences.
Systematic derivation of customized development sprints for an agile development of wind turbines

To account for the increasing volatility and uncertainty of external requirements, mechanical and plant engineering companies are facing growing demands for increased flexibility and adaptability within product development. The wind turbine industry in particular, which is characterized by low unit numbers, long development time and high development costs, is especially affected by this trend. Companies are forced to implement processes that allow for short-term adaptation to changing requirements. Plan-driven approaches typically presume that requirements are defined in the early stages of product development and remain static throughout the process. Companies are increasingly resorting to agile approaches, which rely on an incremental and highly iterative development process divided into short sprints. However, the main challenge for them is moving from plan-driven to agile due to the lack of a comprehensive method of incorporating and transforming their current development activities into an agile approach. This allows for the configuration of agile sprints on a short-term, results-oriented basis from process modules identified in the current development. A five step method for this purpose is being presented. The five steps are as follows: (1) Process analysis to record the status as-is; (2) Identification of recurring process modules throughout the development process; (3) Identification of development objects that are used or generated by the process modules; (4) Bottom-up classification and consolidation of these development objects; (5) Configuration of development sprints as a sequence of process modules linked via development objects. The respective steps were validated in cooperation with a company from the wind turbine industry.
A Movement Adjustment Method for LiDAR Based Mobile Area Decision: Improving Control for AAV Mobility

The Deep Q-Network (DQN) is one of the deep reinforcement learning algorithms, which uses deep neural network structure to estimate the Q-value in Q-learning. In the previous work, we designed and implemented a DQN-based Autonomous Aerial Vehicle (AAV) testbed and proposed a Tabu List Strategy based DQN (TLS-DQN). In this paper, we propose a B-spline curve based movement adjustment method to improve the DQN-based AAV mobility control method. The performance evaluation results show that the proposed method can consider the obstacles and decrease the movement fluctuations.
A Hybrid Fuzzy Rule-Based Polyhedral Separation Approach: Medical Diagnosis Application

Discrimination of two linearly inseparable sets using hyperplane(s) is considered one of the most successful classification methods. There is an expanding body of literature that realize the significance of classification of data accurately. Particularly, medical applications of classification provide meaningful early diagnosis results. However, medical datasets in real-world applications possess some noise and uncertainty. Therefore, dealing with uncertainty is a critical factor for accurate diagnosis. To overcome mentioned drawbacks, this study presents a hybrid fuzzy rule-based robust linear programming (RLP) and h-polyhedral separation (h-PolSep) approaches for breast cancer diagnosis through several consequent stages. In the proposed two-stage model, firstly the data is relabeled according to the fuzzy rule-based system, then the new outputs and original input values are classified using RLP and h-PolSep methods. Input fuzzification, generating membership functions, extracting fuzzy rules, and output defuzzification are examined in detail. A frequently used real-world medical dataset from the UCI Repository: the Wisconsin breast cancer is employed to evaluate the effectiveness of the classification using a number of metrics. Finally, the results demonstrate that the proposed approach effectively handles medical data classification problems.
Age, Gender and Emotion Estimation Using Deep Learning

Age, gender and emotion estimation plays very important role in intelligent applications such as human–computer interaction, access control, healthcare and marketing intelligence. To make computer demonstrate about people age, gender and emotion, lot of research has been conducted. However, it is yet a long way behind the human vision framework. This paper proposes and build an automatic age, gender and emotion estimation towards human faces. This estimation plays a significant part in computer vision and pattern recognition. Non-verbal specialized techniques like facial appearances, eye variation and gestures are utilized in numerous applications of human computer interconnections. This paper proposes a convolutional neural network (CNN)-based engineering architecture for age, gender and emotion classification. The model is trained to categorize input images into eight groups of age, two groups of gender and six groups will be used for the emotion. Basically, our approach shows better accuracy in age, gender and emotion classification compared with different classifier-based methods. In computer modeling the planning is to predict human emotions using CNN and observe changes occurred on emotional intensity. For extracting the features of images preprocessing algorithm that is known as Voila-Jones calculation is done. Experiments conducted using different data-sets: FER13 using our proposed approach provides accuracy of 81% for emotion estimation, for age 79% and gender accuracy is 75%.
Predicting Breast Cancer Diagnosis Delay Using Machine Learning Techniques

The increasing rate of breast cancer incidence and mortality in Iran, has turned this disease into a challenge. Delay in diagnosing leads to more advanced stages of breast cancer and lower chance of survival which makes this cancer even more fatal. The aim of this study is to identify the predictor factors in delayed breast cancer diagnosis in females in Iran. In this paper, four machine learning methods were conducted to analyze the data of 630 women with confirmed breast cancer. The data was collected from 'Motahari Center' in Shiraz. Extreme Gradient Boosting, Random Forest, Neural networks, and Logistic Regression techniques were employed to analyze the data. About 30% of patients in Iran had the delayed breast cancer diagnosis. Age was known as the main important predictor is breast cancer delay, putting age aside the top three important factors Random Forest method was: urban residency (12.04), other breast disease history (11.58) and having other disease (10.72), in Extreme Gradient Boosting was urban residency (17.54), having other diseases (17.14) and age at first childbirth (13.13), in Logistic Regression was having other diseases (49.41), age at first childbirth (>30) (82.570) and having no childbirth (44.19). Finally, in neural networks was being married (50.05), marriage age (18.03) and other breast disease history (15.83).
A BERT-Based Approach for Multilingual Discourse Connective Detection

In this paper, we report on our experiments towards multilingual discourse connective (or DC) identification and show how language specific BERT models seem to be sufficient even with little task-specific training data. While some languages have large corpora with human annotated DCs, most languages are low in such resources. Hence, relying solely on discourse annotated corpora to train a DC identification system for low resourced languages is insufficient. To address this issue, we developed a model based on pretrained BERT and fine-tuned it with discourse annotated data of varying sizes. To measure the effect of larger training data, we induced synthetic training corpora with DC annotations using word-aligned parallel corpora. We evaluated our models on 3 languages: English, Turkish and Mandarin Chinese in the context of the recent DISRPT 2021 Task 2 shared task. Results show that the F-measure achieved by the standard BERT model (92.49%, 93.97%, 87.42% for English, Turkish and Chinese) is hard to improve upon even with larger task specific training corpora
Hate Speech Law and Equality: A Cautionary Tale for Advocates of “Stirring up Gender Hatred” Offences

Abstract Debates about hate speech legislation tend to be dominated by the conflict between freedom of expression on the one hand and freedom from the harms of hatred on the other; it is pervasively claimed that a balance must be struck between these competing interests. This chapter carefully examines this framing of the issue, drawing on parliamentary debates to foreground the classificatory assumptions and power imbalances that are at play in such purportedly neutral balancing exercises. These insights are used to contextualise the question of whether the stirring up hatred offences of England and Wales should be extended to encompass gender hatred. With a focus on the pursuit of intersectional equality, the chapter raises important dynamics that advocates of such offences should consider beyond the headline issue of free speech.
A Multiple Object Recognition Approach via DenseNet-161 Model

In the age of information explosion, image recognition and classification is a great methodology for dealing with and coordinating a huge amount of image data. Here, we present a deep learning–based method for the classification of images. Although earlier deep convolutional neural network models like VGG-19, ResNet, and Inception Net can extricate deep semantic features, they are lagging behind in terms of performance. In this chapter, we propounded a DenseNet-161–based object classification technique that works well in classifying and recognizing dense and highly cluttered images. The experimentations are done on two datasets namely, wild animal camera trap and handheld knife. Experimental results demonstrate that our model can classify the images with severe occlusion with high accuracy of 95.02% and 95.20% on wild animal camera trap and handheld knife datasets, respectively.
Fully Adaptive Radar for Multiple Target Tracking

This work focuses on the development of a fully adaptive radar multiple target tracking (FAR-MTT) model and demonstrates its benefit over static parameter selections. The FAR-MTT framework is an extension of the fully adaptive radar (FAR) framework for single-target tracking (STT). In transitioning FAR-STT concepts to FAR-MTT, the focus was on developing a multiple target Fisher information matrix to capture the effects of multiple targets on the radar measurements, and on developing a robust but straightforward optimization scheme/objective function in the FAR executive processor. Simulation and experimental examples are presented to demonstrate the performance of the FAR-MTT approach. Compared to the fixed parameter case, the FAR-MTT technique is capable of obtaining specified tracking performance for each target and ensuring target separation while reducing sensor resource usage in a variety of multiple target environments. In Monte Carlo testing the method resulted in track merges 28% of the time compared with 50% of the time for a static parameter approach.
Linkable Ring Signature Scheme from NTRU Lattice

There are two serious security problems existing in blockchain: privacy protection problem and quantum attack problem. In order to address them, this paper studied the privacy protection technology of blockchain based on linkable ring signature (LRS) from NTRU lattice, and proposed a new LRS scheme from NTRU lattice whose signature length is $$O(\log M)$$ , where M is the number of members in the ring. Firstly, the GGH standard method is used to construct the homomorphic commitment scheme on NTRU lattice based on the CVP problem of NTRU lattice. Then, based on the homomorphic commitment scheme, the Sigma-protocol on NTRU lattice is constructed. Finally, the protocol is transformed into a LRS scheme through Fiat-Shamir heuristic. The analysis shows that the LRS scheme proposed in this paper not only satisfies the anonymity and unforgeability, but also resists quantum computing attack. In terms of efficiency, since the proposed scheme is constructed based on Sigma-protocol on NTRU lattice, the signature length is $${O}\left( {\log M} \right) $$ .
Impact of Blockchain Technology in the Healthcare Systems

The healthcare industry is one of the most important industries in the world which is in dire need of a restructuring process because of its poor and outdated techniques of data management. Healthcare system has adopted a centralized environment and deals with a lot of intermediaries which makes it prone to issues of single point of failure, lack of traceability of transactions, and privacy issues such as data leakage. Blockchain is a relatively new technology which is able to tackle the obsolete methods and practices existing in the healthcare industry. In this chapter, we analyzed the applications of blockchains in the healthcare industry which can solve the issues prevalent in the healthcare industry. The aim of this chapter is to reveal the potential benefits that comes from using blockchain technology in the healthcare industry and identify the various challenges that this technology has.