Reviewerly/exHarmony huggingface dataset:
Trading Accuracy for Enjoyment? Data Quality and Player Experience in Data Collection Games

Games have become a popular way of collecting human subject data, based on the premise that they are more engaging than surveys or experiments, but generate equally valid data. However, this premise has not been empirically tested. In response, we designed a game for eliciting linguistic data following Intrinsic Elicitation – a design approach aiming to minimise validity threats in data collection games – and compared it to an equivalent linguistics experiment as control. In a preregistered study and replication (n=96 and n=136), using two different ways of operationalising accuracy, the game generated substantially more enjoyment (d=.70,.73) and substantially less accurate data (d=-.68, -.40) – though still more accurate than random responding. We conclude that for certain data types data collection games may present a serious trade-off between participant enjoyment and data quality, identify possible causes of lower data quality for future research, reflect on our design approach, and urge games HCI researchers to use careful controls where appropriate.
Industrial equipment detection algorithm under complex working conditions based on ROMS R-CNN

In the paper, we proposed a deep learning-based industrial equipment detection algorithm ROMS R-CNN (Rotation Occlusion Multi-Scale Region-CNN). It can solve the problem of inaccurate detection of industrial equipment under complex working conditions such as multi-scale ratio, rotation tilt, occlusion and overlap. The method proposed in this paper first is to construct the MobileNetV2 as the feature pyramid network, and then to combine high semantic information with high resolution information solved industrial equipment detection of different scales. Secondly, a specific rotation anchor scheme is proposed, and the data set is clustered through the k-means algorithm to obtain a specific aspect ratio. Combined with the rotation angle, a rotation anchor of any direction and size is generated to solve the problem of easy tilting of industrial equipment. Finally, a Non-Maximum Suppression algorithm with penalty factors is introduced to solve the overlapping in industrial equipment detection. The experimental results in common industrial equipment detection show that this method is better than other algorithms, significantly improves the missed detection and false detection, and the mAP reaches 0.939.
Blockchain for Smart Transport Applications

Smart transportation or Internet of Vehicles (IoV)-enabled transportation extends the vehicle-to-vehicle (V2V) communication network. Smart transportation is a real-time application that helps enhance driving aids with the help of vehicle’s Artificial Intelligence (AI), awareness of other vehicles, and their actions giving out the best on-road experience. Smart transportation also protects life and saves cost by avoiding life-threatening instances like collisions, accidents, and thefts on the road. Since the communication among various entities involved in the IoV environment is via an open channel (e.g., vehicles, pedestrians, fleet management systems, and roadside infrastructure), it allows a passive/active adversary to intercept, modify, delete, or even insert fake information during communication. It is then a severe concern for the vehicles users to determine whether the received information is genuine. Many security protocols have addressed this issue by adding an authentication mechanism. Authentication schemes face a lot of limitations like central registration authority, privacy issues, long certificate mechanism, fabricated hardware problems, excess storage overheads, and large computation and communication costs. To mitigate these issues, several researchers have proposed blockchain technology-based authentication protocols in smart transportation. This chapter focuses on the security aspects of smart transportation and how blockchain can be used as a security solution for smart transportation systems. We briefly explain blockchain, its types and its advantages. We outline few schemes that provide security solutions for the blockchain-based smart transportation system. A comparative analysis is also provided to study the effectiveness of the schemes.
Weakly Supervised Regional and Temporal Learning for Facial Action Unit Recognition

Automatic facial action unit (AU) recognition is a challenging task due to the scarcity of manual annotations. To alleviate this problem, a large amount of efforts has been dedicated to exploiting various weakly supervised methods which leverage numerous unlabeled data. However, many aspects with regard to some unique properties of AUs, such as the regional and relational characteristics, are not sufficiently explored in previous works. Motivated by this, we take the AU properties into consideration and propose two auxiliary AU related tasks to bridge the gap between limited annotations and the model performance in a self-supervised manner via the unlabeled data. Specifically, to enhance the discrimination of regional features with AU relation embedding, we design a task of RoI inpainting to recover the randomly cropped AU patches. Meanwhile, a single image based optical flow estimation task is proposed to leverage the dynamic change of facial muscles and encode the motion information into the global feature representation. Based on these two self-supervised auxiliary tasks, local features, mutual relation and motion cues of AUs are better captured in the backbone network. Furthermore, by incorporating semi-supervised learning, we propose an end-to-end trainable framework named weakly supervised regional and temporal learning (WSRTL) for AU recognition. Extensive experiments on BP4D and DISFA demonstrate the superiority of our method and new state-of-the-art performances are achieved.
Text Summarization using Extractive Techniques

Text Summarization is an indirect way of contracting the given content into more simple words while saving its information and importance. It is hard for individuals to sum up huge reports physically. Text summarization strategy mainly consists of removal of unwanted repeated matter and making it as precise as possible without losing its core concept. In this venture, we have applied three extractive outline strategies to be specific: term frequency inward document frequency (TF-IDF), text rank calculation, and latent semantic analysis (LSA). In this work, an exhaustive survey of extractive book synopsis measure strategies has been determined. Rouge-N metric has been utilized to break down and assess the came-about synopsis of the first report. Text Summarization reduces study time. It can be used in question answering systems as it provides personalized information. For certain reports, summarization makes the procedure simpler. Summarizing by the text summarizer is less biased than the human summarizer. It also empowers business theoretical administrations to build the number of content archives they can process. In this paper, the different methods and difficulties of extractive outline have been surveyed. This paper deciphers extractive content synopsis methods with a less excess outline, profoundly cement, intelligent, and profundity data. We have found a moderate methodology for the component extraction of sentences.
An Online Reversed Bangla Sign Language Learning System

Hearing and speech impaired people around the world express their thoughts through sign language, which is incomprehensible to most people. As a result, interacting with them is quite challenging. Specially in Bangladesh, where people are unable to learn the Bangla sign language (BdSL) due to a lack of resources. We have proposed and implemented an online reversed Bangla sign language learning system using unsupervised rule-based method, which can be utilized to interpret Bangla natural language (speech and/or text) into BdSL. The system takes input of Bangla speech via a Bangla voice recognition module and then convert it into Bangla text. The system can also take Bangla text directly from the text editor module of the system. The system parse Bangla text using a designed set of rules from Bangla semantic analysis. After semantic analysis the system is trained based on unsupervised learning using the parsed Bangla text for each corresponding BdSL. We have used an efficient animation algorithm instead of video clips to display the BdSL for corresponding Bangla speech or text. We have considered only starting and ending frame of each gesture to make the animation. As a result, by using limited number of postures, the system can dynamically make any gestures based on the proposed animation algorithm. The system can animate sentences of various lengths and numerals ranging from 0 to 9 in BdSL. It can generate many phrases with a small quantity of data and has been trained and and tested with over 500 examples with lower computation cost rather than existing systems. The proposed system achieves 99.60% accuracy for speech/voice inputs and 100% accuracy for text inputs where as the average accuracy is 99.8% with the mean computational cost of 120 milliseconds per gesture animation.
Transformer Based Bengali Chatbot Using General Knowledge Dataset

An AI chatbot provides an impressive response after learning from the trained dataset. In this decade, most of the research work demonstrates that deep neural models superior to any other model. RNN model regularly used for determining the sequence-related problem like a question and it answers. This approach acquainted with everyone as seq2seq learning. In a seq2seq model mechanism, it has encoder and decoder. The encoder embedded any input sequence, and the decoder embedded output sequence. For reinforcing the seq2seq model performance, attention mechanism added into the encoder and decoder. After that, the transformer model has introduced itself as a high-performance model with multiple attention mechanism for solving the sequence-related dilemma. This model reduces training time compared with RNN based model and also achieved state-of-the-art performance for sequence transduction. In this research, we applied the transformer model for Bengali general knowledge chatbot based on the Bengali general knowledge Question Answer (QA) dataset. It scores 85.0 BLEU on the applied QA data. To check the comparison of the transformer model performance, we trained the seq2seq model with attention on our dataset that scores 23.5 BLEU.
Implementation of Android Based Text to Image Steganography Using 512-Bit Algorithm with LSB Technique

Steganography security is the main concern in today’s informative world. The fact is that communication takes place to hide information secretly. Steganography is the technique of hiding secret data within an ordinary, non-secret, file, text message and images. This technique avoids detection of the secret data then extracted at its destination. The main reason for using steganography is, we can hide any secret message behind its ordinary file. This work presents a unique technique for image steganography based on a 512-bit algorithm. The secure stego image is a very challenging task to give protection. Therefore we used the least significant bit (LSB) techniques for implementing stego and cover image. However, data encryption and decryption are used to embedded text and replace data into the least significant bit (LSB) for better approaches. Android-based interface used in encryption-decryption techniques that evaluated in this process.Contribution—this research work with 512-bit data simultaneously in a block cipher to reduce the time complexity of a system, android platform used for data encryption decryption process. Steganography model works with stego image that interacts with LSB techniques for data hiding.
Towards a Continuous Solution of the $d$-Visibility Watchman Route Problem in a Polygon With Holes

A new heuristic solution framework is proposed to address the challenging <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">watchman route problem</i> (WRP) in a polygonal domain, which can be viewed as an offline version of the robot exploration task. The solution is the shortest route from which the robot can visually inspect a known 2D environment. Our framework considers a circular robot with radius <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$r$</tex-math></inline-formula> equipped with an omnidirectional sensor with limited visibility range <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$d$</tex-math></inline-formula> . Instead of a standard <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">decoupled</i> solution, the framework generates a set of specifically constrained regions covering the domain and then solves the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">traveling salesman problem with continuous neighborhoods</i> (TSPN) to obtain the solution route. The TSPN is solved by another proposed heuristic algorithm that finds a discretized solution first and then improves it back in the continuous domain. The whole framework is evaluated experimentally, compared to two approaches from the literature, and shown to provide the highest-quality solutions. The current version of the framework is one step from a fully continuous approach to the WRP that we will address in the future.
Theoretical approaches to the definition of the concept of "Digital Education"

The relevance of the research is due to active introduction of digital tools into the education system in the context of Industry 4.0. Today digital service in education is assigned the role of an effective and comfortable mechanism for presenting information and communication services, digital resources to the subjects of education. Educational practices of using digital tools in education, as well as theoretical studies of the phenomenon of "digital education" need to be analyzed and systematized, which confirms the relevance of the research. The research problem lies in the theoretical substantiation of the category of "digital education". The purpose of the research is to clarify the essence of the concept of "digital education", to determine its meaningful content. The research methods. To achieve the goal theoretical methods have been used - analysis of materials and scientific publications on the problem under study, generalization, visual presentation of the results. The research results and conclusions. The author carried out a theoretical analysis of the current definitions of the content of "digital education", highlighted its most significant characteristics. The attention is focused on the general content aspect of digital education as an innovative model of educational practice, taking into account modern trends in the modernization of the social and economic sphere in the era of Industry 4.0. The study demonstrates the importance of digital education in the context of the digital revolution in the direction of expanding the opportunities for the provision of educational services
IoT-Based Wearable Sensors

Recognition of gestures has gained a lot of popularity in recent years and in the world-wide community as it has played an important role in many of the practical applications. A few of the popular practical applications are reading sign language, authentication of user, assistance provided in day-to-day life. Just to take an example, humans interact with small computers like mobile, wearable smart devices, popularly known as internet of things (IoT), which have a limitation to be embedded with in screens or keyboards. As per statistics, 26% of the population is under the age of 15 whereas some 9% of the population is above the age of 65 where they have problems performing a few activities on a daily basis, and hand gesture recognition can prove to be a good solution.
The Methods of Online and Offline Blended Teaching in College English on the Basis of Internet

With the rapid development of modern information technology, College English teaching mode is constantly reforming and innovating. In order to adapt to the changes of the times, online and offline hybrid teaching mode is more and more common. It is particularly important to apply online and offline blended teaching in College English. This article introduces the characteristics of online and offline mixed teaching mode, compares online and offline mixed teaching with traditional teaching, and analyzes the problems in them. At last, this paper puts forwards the teaching modes of online and offline mixed teaching in college English curriculum so as to improve students' English ability.
KERMITviz: Visualizing Neural Network Activations on Syntactic Trees

The study of symbolic syntactic interpretations has been the cornerstone of natural language understanding for many years. Today, modern artificial neural networks are widely searched to assess their syntactic ability, through several probing tasks. In this paper, we propose a neural network system that explicitly includes syntactic interpretations: Kernel-inspired Encoder with Recursive Mechanism for Interpretable Trees Visualizer (KERMITviz). The most important result is that KERMITviz allows to visualize how syntax is used in inference. This system can be used in combination with transformer architectures like BERT, XLNet and clarifies the use of symbolic syntactic interpretations in specific neural networks making the black-box neural network neural networks explainable, interpretable and clear.
Photonic Lanterns as Wavefront Sensors

Photonic lanterns are low-loss mode convertors easily integrated with optical fiber technologies. We present the proof of concept of a focal plane low-order wavefront sensor based on a 19-core multicore photonic lantern and deep learning.
Reducing the Range of Cancer Risk on BI-RADS 4 Subcategories via Mathematical Modelling

Breast Imaging Reporting and Data System, also known as BI-RADS is a universal system used by radiologists and doctors.It constructs a comprehensive language for the diagnosis of breast cancer.BI-RADS 4 category has a wide range of cancer risk since it is divided into 3 categories.Mathematical models play an important role in the diagnosis and treatment of cancer.In this study, data of 42 BI-RADS 4 patients taken from the Center for Breast Health, Near East University Hospital is utilized.Regarding the analysis, a mathematical model is constructed by dividing the population into 4 compartments.Sensitivity analysis is applied to the parameters with the desired outcome of a reduced range of cancer risk.Numerical simulations of the parameters are demonstrated.The results of the model have revealed that an increase in the lactation rate and early menopause have a negative correlation with the chance of being diagnosed with BI-RADS 4 whereas a positive correlation increase in age, the palpable mass, and family history is distinctive.Furthermore, the negative effects of smoking and late menopause on BI-RADS 4C diagnosis are vehemently outlined.Consequently, the model showed that the percentages of parameters play an important role in the diagnosis of BI-RADS 4 subcategories.All things considered, with the assistance of the most effective parameters, the range of cancer risks in BI-RADS 4 subcategories will decrease.
A Multi-granularity Fusion Neural Network Model for Medical Question Classification

Knowledge-Based Question Answering (KBQA) is a novel method for Question Answering. To construct the semantic parser for a given question, it is vital to effectively encode the existing question for question classification. In this work, we propose a novel Multi-granularity fusion deep learning architecture that consists of sequence encoding, phrase vector recombination and feature extraction for the given question strings. We adopt Bi-GRU to learn features by different forms for question classification. In addition, attention mechanism is incorporated in the proposed model. We construct the local question answering base on clinical neurologic. We deploy plenty of comparision experiments among our proposed multi-granularity fusion model and other well-known methods. Experiments show that our proposed method achieves the highest accuracy.
Trust management and data protection for online social networks

Online social networks (OSNs) have been an integral aspect of daily life. Most people use OSNs such as Facebook and Twitter to communicate and exchange information. Numerous analytical methods have been applied to OSNs by active exploration of their graph structure, like clustering analyses for automatic online group detection and node impact analyses of awareness of influential nodes within social networks. On OSN, a large-scale attack was launched by creating a bogus profile and using it to propagate spam, malware, and phishing attacks. To overcome the above issues, we designed a trust management and data protection model (TM-A-DPM), in which the trust management identifies the trust factor accuracy to achieve confidentiality, integrity, and privacy. Experimental results determine the performance and computational efficiency as well as resolve the privacy threats such as data leakage and data forgery.
Generating Adversarial Source Programs Using Important Tokens-based Structural Transformations

Deep learning models have been widely used in source code processing tasks, such as code captioning, code summarization, code completion, and code classification. Recent studies have shown that deep learning-based source code processing models are vulnerable. Attackers can generate adversarial examples by adding perturbations to source programs. Existing attack methods perturb a source program by renaming one or multiple variables in the program. These attack methods do not take into account the perturbation of the equivalent structural transformations of the source code. We propose a set of program transformations involving identifier renaming and structural transformations, which can ensure that the perturbed program retains the original semantics but can fool the source code processing model to change the original prediction result. We propose a novel method of applying semantics-preserving structural transformations to attack the source program pro-cessing model in the white-box setting. This is the first time that semantics-preserving structural transformations are applied to generate adversarial examples of source code processing models. We first find the important tokens in the program by calculating the contribution values of each part of the program, then select the best transformation for each important token to generate semantic adversarial examples. The experimental results show that the attack success rate of our attack method can improve 8.29 % on average compared with the state-of-the-art attack method; adversarial training using the adversarial examples generated by our attack method can reduce the attack success rates of source code processing models by 21.79% on average.
<scp>SDK4ED</scp>: A platform for technical debt management

Abstract Technical debt management is of paramount importance for the software industry, since maintenance is the costlier activity in the software development lifecycle. In this article, we present the SDK4ED platform that enables efficient technical debt management (i.e., measurement, evolution analysis, prevention, etc.) at the code level, and evaluate its capabilities in an industrial setting. The SDK4ED platform is the outcome of a 3‐year project, including several software industries. Since, the research rigor of the approaches that reside in SDK4ED have already been validated, in this work we focus: (a) on the presentation of the platform per se; (b) the evaluation of its industrial relevance; (c) the usability of the platform; as well as (d) the financial implications of its usage.