Reviewerly/exHarmony huggingface dataset:
CuRTAIL: ChaRacterizing and Thwarting AdversarIal Deep Learning

Recent advances in adversarial Deep Learning (DL) have opened up a new and largely unexplored surface for malicious attacks jeopardizing the integrity of autonomous DL systems. This article introduces CuRTAIL, a novel end-to-end computing framework to characterize and thwart potential adversarial attacks and significantly improve the reliability (safety) of a victim DL model. We formalize the goal of preventing adversarial attacks as an optimization problem to minimize the rarely observed regions in the latent feature space spanned by a DL network. To solve the aforementioned minimization problem, a set of complementary but disjoint modular redundancies are trained to validate the legitimacy of the input samples. The proposed countermeasure is unsupervised, meaning that no adversarial sample is leveraged to train modular redundancies. This, in turn, ensures the effectiveness of the defense in the face of generic attacks. We evaluate the robustness of our proposed methodology against the state-of-the-art adaptive attacks in a white-box setting considering that the adversary knows everything about the victim model and its defenders. Extensive evaluations for analyzing MNIST, CIFAR10, and ImageNet data corroborate the effectiveness of CuRTAIL framework against adversarial samples. The computations in each modular redundancy can be performed independently of the other redundancy modules. As such, CuRTAIL detection algorithm can be completely parallelized among multiple hardware settings to achieve maximum throughput. We further provide an open-source Application Programming Interface (API) to facilitate the adoption of the proposed framework for various applications.
Computer Vision based Obstacle Identification using Real-Time Illumination Sensor Data

In this study, an IoT based system is developed to monitor obstacles at the indoor surface using mobile sensors in a client-server wireless network. An IR transceiver system gives the positional information and light intensity sensor measures the lux values. An embedded wifi enabled microcontroller is interfaced with the sensors and performs as the client system. The client module is placed over the roof of a car and when it moves through a particular indoor space, it collects the positional illumination data and transmit them to the server unit. The captured sensor values are stored in server laptop as MS-Excel file under the influence of a wifi router. By using offline processing, the real-time sensor data is converted into an image and filtering methods are applied for linear and nonlinear noise removal. Then, edge detection techniques like Canny, Prewitt, Sobel, and Roberts methods are applied to detect the presence of obstacles. The study is repeated for another room to find out the best possible obstacle identification method. Finally, it was concluded that the Canny's algorithm provides the most accurate identification of static obstacles for both the rooms.
Cloud Computing and its Impact on Online Education

Abstract The cloud computing system has a significant role in the distance education field, because it is an important tributary of virtual education, especially mobile and blended education. Perhaps, the only challenge that must be overawed is the comprehensive coverage of rapid access to the Internet. So, the student can benefit from the applications that we will call from now and onward cloud computing services. There is no need to establish evidence to emphasize the importance of employing technological innovations to develop educational practices for developing educational curricula in Iraqi universities, but it is vital to employ technological innovations in the educational process, and this must be related to overcoming the problems facing the educational process. Therefore, employment should not be for technological dazzling that often accompanies technological innovations, but employment must be a function of urgent needs or demands. In this study, the educational process concept, with its multiple cloud inputs and outputs, was discussed how the education crisis could be overcome in all its institutions by resorting to how to employ the electronic cloud in distance education. This study also examined all the specifications of that environment and the possibility of their application in colleges and educational institutions.
Inhibiting Factors for Publishing in Reputable International Journals among Doctoral Students : An Exploratory Mixed Method Study

This study aims to explore the inhibiting factors of Indonesian doctoral students in carrying out reputable international publications. This research is a sequential mixed method exploration with qualitative exploration methods and to determine the relationship between the inhibiting factors of publication performance and quantitative methods. This study used a mixed sequential exploratory method. The first step  was a qualitative analysis that explored the inhibiting factors for doctoral student publication. Primary data collection used open and unstructured interviews in depth to 7 (seven) doctoral program student participants who were selected and determined using purposive sampling method. The second step is to conduct a quantitative analysis to determine the correlation between inhibiting factors and publication performance. Primary data collection used an online questionnaire with 250 respondents selected using the snowball sampling method and then analyzed using the SmartPLS program. The results of this study indicate that the doctoral program student respondents have several obstacles to publishing in internationally reputable journals such as negative results, lack of funding, long response time, low English language skills, insufficient writing time, limited writing skills, limitations. submission capability. , high publication costs, inadequate facilities, limited journal references, limited technology skills. The novelty of this research is the first research on obstacles in the publication of doctoral students in Indonesia using the Exploratory Sequential Mixed Methods with the scope of a country. This research can serve as a reference for similar research, and further research can be carried out in other countries.
Engagement and Usability of Conversational Search – A Study of a Medical Resource Center Chatbot

Due to advances in natural language understanding, chatbots have become popular for assisting users in various tasks, for example, searching. Chatbots allow natural-language queries, which can be useful in case of complex information needs, and they provide a higher level of interactivity by displaying information in a dialog-like format. However, chatbots are often only used as auxiliaries for a graphical search user interface (SUI). Thus, they must be engaging and usable so that users both want to and are able to use them. In this study, we conduct a controlled interactive information retrieval experiment following a within-subject design to compare a chatbot to a graphical SUI in terms of engagement and usability. Our findings point towards the need for flawless usability in order for conversational search interfaces to (1) be able to provide additional value in information retrieval tasks and (2) elicit a higher level of engagement compared to their SUI-based counterparts.
An Adaptive Heterogeneous Online Learning Ensemble Classifier for Nonstationary Environments

In recent years, the prevalence of technological advances has led to an enormous and ever-increasing amount of data that are now commonly available in a streaming fashion. In such nonstationary environments, the underlying process generating the data stream is characterized by an intrinsic nonstationary or evolving or drifting phenomenon known as concept drift. Given the increasingly common applications whose data generation mechanisms are susceptible to change, the need for effective and efficient algorithms for learning from and adapting to evolving or drifting environments can hardly be overstated. In dynamic environments associated with concept drift, learning models are frequently updated to adapt to changes in the underlying probability distribution of the data. A lot of work in the area of learning in nonstationary environments focuses on updating the learning predictive model to optimize recovery from concept drift and convergence to new concepts by adjusting parameters and discarding poorly performing models while little effort has been dedicated to investigate what type of learning model is suitable at any given time for different types of concept drift. In this paper, we investigate the impact of heterogeneous online ensemble learning based on online model selection for predictive modeling in dynamic environments. We propose a novel heterogeneous ensemble approach based on online dynamic ensemble selection that accurately interchanges between different types of base models in an ensemble to enhance its predictive performance in nonstationary environments. The approach is known as Heterogeneous Dynamic Ensemble Selection based on Accuracy and Diversity (HDES-AD) and makes use of models generated by different base learners to increase diversity to circumvent problems associated with existing dynamic ensemble classifiers that may experience loss of diversity due to the exclusion of base learners generated by different base algorithms. The algorithm is evaluated on artificial and real-world datasets with well-known online homogeneous online ensemble approaches such as DDD, AFWE, and OAUE. The results show that HDES-AD performed significantly better than the other three homogeneous online ensemble approaches in nonstationary environments.
Polyth-Net: Classification of Polythene Bags for Garbage Segregation Using Deep Learning

Polythene has always been a threat to the environment since its invention. It is non-biodegradable and very difficult to recycle. Even after many awareness campaigns and practices, Separation of polythene bags from waste has been a challenge for human civilization. The primary method of segregation deployed is manual handpicking, which causes a dangerous health hazards to the workers and is also highly inefficient due to human errors. In this paper I have designed and researched on image-based classification of polythene bags using a deep-learning model and its efficiency. This paper focuses on the architecture and statistical analysis of its performance on the data set as well as problems experienced in the classification. It also suggests a modified loss function to specifically detect polythene irrespective of its individual features. It aims to help the current environment protection endeavours and save countless lives lost to the hazards caused by current methods.
A Sentimental and Semantical Analysis on Facebook Comments to Detect Latent Patterns

Social media posts and their comments are rich in variation of subjects and an interesting pool for opinion mining. Individuals engage in social communications on Facebook through three behaviors: like, share and comment on a Facebook post. Responses to comments are grouped under the respective comment as a conversation thread. Conversation threads become interesting when users have conflicting views with the article posted, or with the opinion of another user. Our research goal is to answer questions such as why some posts in Facebook receive more attention than others? Are conversation threads following a similar pattern between subjects like sport and politics? Is there any harmony between conversation threads of different subjects? We investigated how individuals react to different conversation subjects in the Facebook through a comprehensive analysis. Our aim is to discover semantic and sentimental patterns in conversation threads categories. Finally, we employed Natural Language Processing techniques such as semantic and sentimental analysis and statistical methods like average response time (ART) and average comment length (ACL) of a post and observed that there are interesting patterns exists among different conversation threads.
Integrating AI Techniques in Requirements Elicitation

Requirements elicitation is known as most acute knowledge intensive activity of the procedure of software. The quality of the requirements supports in the success of any software development. The review of literature reveals that scientists have prepared major supports by emerging various tools and methods of requirement elicitation process. However, there are still many challenges faced by requirements engineers in order to carry out requirements elicitation activity in the entire process. Some of the key challenges are poor communication between user and analyst, support tools and stakeholder involvement. These issues may lead to inefficient result and the end of the system development. The previous research history exposes that Artificial Intelligence (AI) methods may help in this regard by providing effective communication between user and analyst by proposing several methods/tools to computerize certain procedures up to a certain extent. The intention of this study is to classify the issues in every phase of the requirements elicitation and explore of AI techniques to solve these recognized challenges. Additionally, the study also discovers the connection between these challenges and their potential AI explanation/s through Venn-Diagram. This study is an addition towards our prior efforts and here, an attempt is made to incorporate and describe AI techniques in many requirements elicitation techniques.
SPGAN: Face Forgery Using Spoofing Generative Adversarial Networks

Current face spoof detection schemes mainly rely on physiological cues such as eye blinking, mouth movements, and micro-expression changes, or textural attributes of the face images [9]. But none of these methods represent a viable mechanism for makeup-induced spoofing, especially since makeup has been widely used. Compared with face alteration techniques such as plastic surgery, makeup is non-permanent and cost efficient, which makes makeup-induced spoofing become a realistic threat to the integrity of a face recognition system. To solve this problem, we propose a generative model to construct spoofing face images (confusing face images) for improving the accuracy and robustness of automatic face recognition. Our network structure is composed of two separate parts, with one using inter-attention mechanism to obtain interested face region, and another using intra-attention to translate imitation style with preserving imitation style-excluding details. These two attention mechanisms can precisely learn imitation style, where inter-attention pays more attention to imitation regions of image and intra-attention learns face attributes with long distance in image. To effectively discriminate generated images, we introduce an imitation style discriminator. Our model (SPGAN) generates face images that transfer the imitation style from target to subject image and preserve the imitation-excluding features. Experimental results demonstrate the performance of our model in improving quality of imitated face images.
An Exploration of Blockchain in Social Networking Applications

Human beings have an innate tendency to socialize and the Internet gives us the opportunity to overcome geographical barriers and communicate with people located even at the opposite side of the world. Computers that started the journey of digital communication with simple emails and bulletin board systems in the eighties of the previous century have manifested their potential of connecting people across the globe with the evolution of social networking. Social networking has emerged as effective tools for bringing people of similar disposition or needs, whether professional or personal, under the same platform enabling interaction and sharing between peers. However, these services operate in a centralized environment. Blockchain, on the other hand, is revolutionizing the process of sharing things digitally between peers in a decentralized and secured fashion. In this paper, we investigate how blockchain can be applied to social networking services and review some of the currently existing social networking applications that utilize blockchain. We then propose our social networking application, e-Mudra, built on the principles of blockchain technology that facilitates peer-to-peer exchange of leftover foreign currency from international travel.
ZH-NER: Chinese Named Entity Recognition with Adversarial Multi-task Learning and Self-Attentions

NER is challenging because of the semantic ambiguities in academic literature, especially for non-Latin languages. Besides, recognizing Chinese named entities needs to consider word boundary information, as words contained in Chinese texts are not separated with spaces. Leveraging word boundary information could help to determine entity boundaries and thus improve entity recognition performance. In this paper, we propose to combine word boundary information and semantic information for named entity recognition based on multi-task adversarial learning. We learn common shared boundary information of entities from multiple kinds of tasks, including Chinese word segmentation (CWS), part-of-speech (POS) tagging and entity recognition, with adversarial learning. We learn task-specific semantic information of words from these tasks, and combine the learned boundary information with the semantic information to improve entity recognition, with multi-task learning. We conduct extensive experiments to demonstrate that our model achieves considerable performance improvements.
Learning Word Representations for Tunisian Sentiment Analysis

Tunisians on social media tend to express themselves in their local dialect using Latin script (TUNIZI). This raises an additional challenge to the process of exploring and recognizing online opinions. To date, very little work has addressed TUNIZI sentiment analysis due to scarce resources for training an automated system. In this paper, we focus on the Tunisian dialect sentiment analysis used on social media. Most of the previous work used machine learning techniques combined with handcrafted features. More recently, Deep Neural Networks were widely used for this task, especially for the English language. In this paper, we explore the importance of various unsupervised word representations (word2vec, BERT) and we investigate the use of Convolutional Neural Networks and Bidirectional Long Short-Term Memory. Without using any kind of handcrafted features, our experimental results on two publicly available datasets [18, 19] showed comparable performances to other languages.
Image Compression Using DWT and Optimization Using Evolutionary Algorithm

The proposed system is based on image compression using Discrete Wavelet Transform (DWT) technique and optimization using evolutionary algorithms like artificial bee colony and particle swarm optimization. The optimization provides the good quality of the image in order to provide better results. As the image quality and its size are constantly increasing. Many products in the market use images for control and display with advanced technology. The I​mage compression is a technique in different types of multi-media services. In this project coding techniques called Discrete Wavelet Transform (DWT) is applied for image compression. These techniques utilize less memory. In order to find the best possible quality of image​ ​we will use evolutionary algorithms for optimization. Evolutionary algorithms are the evolution of the species and it focuses on the mechanism. Since after the compression of the image using DWT the image quality has deteriorated hence we make use of evolutionary algorithms to optimize the compressed image. Here two types of evolutionary algorithms are used: ​artificial bee colony and particle swarm optimization and they are compared in terms of parameters like PSNR, CR, MSE and entropy.
Applying Cross-Modality Data Processing for Infarction Learning in Medical Internet of Things

Cross-modality data processing is critical for the Internet-of-Things (IoT) deployment in healthcare. It can convert the innumerable raw day-to-day medical big data from massive IoT-based medical devices to diagnostic valuable data so that they can be feed to clinical routine. In this article, we propose a novel spatiotemporal two-streams generative adversarial network (SpGAN) as a cross-modality data processing approach to deploy the medical IoT in infarction learning. Our SpGAN remotely converts diagnostic valuable contrast-enhanced images (the “gold standard” for infarction learning, but it requires the injection of contrast agents) directly from raw nonenhanced cine MR images. This converting allows physicians to remotely perform infarction observation and analysis to break through the limitations of time and space by building a cloud computing platform of IoT-based MRI devices. Importantly, this converting offers a low-risk IoT-based manner to eliminate the potential fatal risk caused by contrast agent injection in the current infarction learning workflow. Specifically, SpGAN consists of: 1) a spatiotemporal two-stream framework as an encoding–decoding model to achieve data converting and 2) a spatiotemporal pyramid network enhances those features that are responsible to the infarction learning during encoding to improve decoding performance. Real IoT-based remote diagnosis experiments performed on 230 patients demonstrate that SpGAN provides high-quality converted images for infarction learning and promotes the in-depth application and deployment of IoT in the medical field.
Universal Joy A Data Set and Results for Classifying Emotions Across Languages

While emotions are universal aspects of human psychology, they are expressed differently across different languages and cultures. We introduce a new data set of over 530k anonymized public Facebook posts across 18 languages, labeled with five different emotions. Using multilingual BERT embeddings, we show that emotions can be reliably inferred both within and across languages. Zero-shot learning produces promising results for low-resource languages. Following established theories of basic emotions, we provide a detailed analysis of the possibilities and limits of cross-lingual emotion classification. We find that structural and typological similarity between languages facilitates cross-lingual learning, as well as linguistic diversity of training data. Our results suggest that there are commonalities underlying the expression of emotion in different languages. We publicly release the anonymized data for future research.
Jacobian Regularization for Mitigating Universal Adversarial Perturbations

Universal Adversarial Perturbations (UAPs) are input perturbations that can fool a neural network on large sets of data. They are a class of attacks that represents a significant threat as they facilitate realistic, practical, and low-cost attacks on neural networks. In this work, we derive upper bounds for the effectiveness of UAPs based on norms of data-dependent Jacobians. We empirically verify that Jacobian regularization greatly increases model robustness to UAPs by up to four times whilst maintaining clean performance. Our theoretical analysis also allows us to formulate a metric for the strength of shared adversarial perturbations between pairs of inputs. We apply this metric to benchmark datasets and show that it is highly correlated with the actual observed robustness. This suggests that realistic and practical universal attacks can be reliably mitigated without sacrificing clean accuracy, which shows promise for the robustness of machine learning systems.