Reviewerly/exHarmony huggingface dataset:
Admix: Enhancing the Transferability of Adversarial Attacks

Deep neural networks are known to be extremely vulnerable to adversarial examples under white-box setting. Moreover, the malicious adversaries crafted on the surrogate (source) model often exhibit black-box transferability on other models with the same learning task but having different architectures. Recently, various methods are proposed to boost the adversarial transferability, among which the input transformation is one of the most effective approaches. We investigate in this direction and observe that existing transformations are all applied on a single image, which might limit the adversarial transferability. To this end, we propose a new input transformation based attack method called Admix that considers the input image and a set of images randomly sampled from other categories. Instead of directly calculating the gradient on the original input, Admix calculates the gradient on the input image admixed with a small portion of each add-in image while using the original label of the input to craft more transferable adversaries. Empirical evaluations on standard ImageNet dataset demonstrate that Admix could achieve significantly better transferability than existing input transformation methods under both single model setting and ensemble-model setting. By incorporating with existing input transformations, our method could further improve the transferability and outperforms the state-of-the-art combination of input transformations by a clear margin when attacking nine advanced defense models under ensemble-model setting. Code is available at https://github.com/JHL-HUST/Admix.
Evaluation of students’ attitude toward distance learning during the pandemic (Covid-19): a case study of ELTE university

Purpose Covid-19 has prompted higher institutions around the globe to relocate traditional classes to online classes. Eötvös Loránd University (ELTE) was no exception. It had already drawn up limited strategies regarding distance education, but those Web-based platforms were provided only to students with special needs. Due to the pandemic, all ELTE students were compelled to use online platforms that the university provided, such as Microsoft Teams and Zoom, to resume their studies. This study aims to evaluate the initial experience of students in using these new platforms. It also explores the effects distance learning has on students’ satisfaction and attitudes toward their education. Design/methodology/approach By using a quantitative approach, students’ attitudes toward e-learning and their access to tech-platforms, use of those platforms and satisfaction with online courses are processed and analyzed via a statistical package for the social sciences. Findings The results of this study show that distance learning is still in the development stage, and although traditional classrooms appeared to be indispensable, the positive attitudes and willingness of the majority of students to engage in distance learning classes in the post-COVID19 pandemic indicate that there is an immense potential future for e-learning platforms in higher education institutions. Originality/value The distance learning approach has been the only way for institutions worldwide to resume studies during the pandemic of Covid-19. Students faced mixed feelings of perplexity, loneliness, uncertainty over what is going to happen with classes, exams, graduation and other significant activities impacting their study path; irrespective of their daily struggles with the hard accessibility to means of e-learning and personal potential health problems. This evaluation is considered as a roadmap for institutions to follow-up and to improve the organizational and educational shortcomings they met.
DeepRx: Fully Convolutional Deep Learning Receiver

Deep learning has solved many problems that are out of reach of heuristic algorithms. It has also been successfully applied in wireless communications, even though the current radio systems are well-understood and optimal algorithms exist for many tasks. While some gains have been obtained by learning individual parts of a receiver, a better approach is to jointly learn the whole receiver. This, however, often results in a challenging nonlinear problem, for which the optimal solution is infeasible to implement. To this end, we propose a deep fully convolutional neural network, DeepRx, which executes the whole receiver pipeline from frequency domain signal stream to uncoded bits in a 5G-compliant fashion. We facilitate accurate channel estimation by constructing the input of the convolutional neural network in a very specific manner using both the data and pilot symbols. Also, DeepRx outputs soft bits that are compatible with the channel coding used in 5G systems. Using 3GPP-defined channel models, we demonstrate that DeepRx outperforms traditional methods. We also show that the high performance can likely be attributed to DeepRx learning to utilize the known constellation points of the unknown data symbols, together with the local symbol distribution, for improved detection accuracy.
A comprehensive survey of image segmentation: clustering methods, performance parameters, and benchmark datasets

Image segmentation is an essential phase of computer vision in which useful information is extracted from an image that can range from finding objects while moving across a room to detect abnormalities in a medical image. As image pixels are generally unlabelled, the commonly used approach for the same is clustering. This paper reviews various existing clustering based image segmentation methods. Two main clustering methods have been surveyed, namely hierarchical and partitional based clustering methods. As partitional clustering is computationally better, further study is done in the perspective of methods belonging to this class. Further, literature bifurcates the partitional based clustering methods into three categories, namely K-means based methods, histogram-based methods, and meta-heuristic based methods. The survey of various performance parameters for the quantitative evaluation of segmentation results is also included. Further, the publicly available benchmark datasets for image-segmentation are briefed.
Revolutionizing Medical Data Sharing Using Advanced Privacy-Enhancing Technologies: Technical, Legal, and Ethical Synthesis

Multisite medical data sharing is critical in modern clinical practice and medical research. The challenge is to conduct data sharing that preserves individual privacy and data utility. The shortcomings of traditional privacy-enhancing technologies mean that institutions rely upon bespoke data sharing contracts. The lengthy process and administration induced by these contracts increases the inefficiency of data sharing and may disincentivize important clinical treatment and medical research. This paper provides a synthesis between 2 novel advanced privacy-enhancing technologies—homomorphic encryption and secure multiparty computation (defined together as multiparty homomorphic encryption). These privacy-enhancing technologies provide a mathematical guarantee of privacy, with multiparty homomorphic encryption providing a performance advantage over separately using homomorphic encryption or secure multiparty computation. We argue multiparty homomorphic encryption fulfills legal requirements for medical data sharing under the European Union’s General Data Protection Regulation which has set a global benchmark for data protection. Specifically, the data processed and shared using multiparty homomorphic encryption can be considered anonymized data. We explain how multiparty homomorphic encryption can reduce the reliance upon customized contractual measures between institutions. The proposed approach can accelerate the pace of medical research while offering additional incentives for health care and research institutes to employ common data interoperability standards.
Residual cyclegan for robust domain transformation of histopathological tissue slides

Variation between stains in histopathology is commonplace across different medical centers. This can have a significant effect on the reliability of machine learning algorithms. In this paper, we propose to reduce performance variability by using -consistent generative adversarial (CycleGAN) networks to remove staining variation. We improve upon the regular CycleGAN by incorporating residual learning. We comprehensively evaluate the performance of our stain transformation method and compare its usefulness in addition to extensive data augmentation to enhance the robustness of tissue segmentation algorithms. Our steps are as follows: first, we train a model to perform segmentation on tissue slides from a single source center, while heavily applying augmentations to increase robustness to unseen data. Second, we evaluate and compare the segmentation performance on data from other centers, both with and without applying our CycleGAN stain transformation. We compare segmentation performances in a colon tissue segmentation and kidney tissue segmentation task, covering data from 6 different centers. We show that our transformation method improves the overall Dice coefficient by 9% over the non-normalized target data and by 4% over traditional stain transformation in our colon tissue segmentation task. For kidney segmentation, our residual CycleGAN increases performance by 10% over no transformation and around 2% compared to the non-residual CycleGAN.
The Bodies of TEI – Investigating Norms and Assumptions in the Design of Embodied Interaction

In the few decades since the first mainframe computers, computing technologies have grown smaller, and more pervasive, moving onto and even inside human bodies. Even as those bodies have received increased attention by scholars, designers, and technologists, the bodily expectations and understandings articulated by these technological artefacts have not been a focus of inquiry in the field. I conducted a feminist content analysis on select papers in the proceeding of the ACM International Conference on Tangible, Embedded and Embodied Interaction (TEI) since its inception in 2007. My analysis illustrates how artefacts are implicitly oriented on unmarked bodily norms, while technologies designed for non-normative bodies treat those as deviant and in need of correction. Subsequently, I derive a range of provocations focused on material bodies in embodied interaction which offer a point of reflection and identify potentials for future work in the field.
Blockchain adoption in the fashion sustainable supply chain: Pragmatically addressing barriers

Purpose This paper aims to examine and overcome the barriers to the widespread adoption of blockchain technology, introducing a novel concept of sustainability in the fashion supply chain. Design/methodology/approach This work is an exploratory study of a well-known fashion company operating in the Veneto region (Italy). Data extracted from interviews and focus groups are coded using the (CAQDAS) software AQUAD. The outcome is then organized according to an adapted TOE view. Findings This exploration study's findings support the idea that the blockchain solution could be a valuable add-on in sustainable supply chains. However, a high understanding of technology and extensive communication with clients is required for successful integration. Research limitations/implications Being the outcome of qualitative analysis, the findings require further investigation to be inferable at a broader scale. As the project is still incomplete, some managerial choices are always subject to change. Practical implications Focused on a practitioner approach, this paper should guide managers in the process of successfully implementing blockchain technology. Arguably, similar companies may opt for similar choices. Originality/value To the best of the authors' knowledge, this is the first paper to contextualize and address the blockchain adoption barriers in the fashion supply chain. Furthermore, it offers an overview of how blockchain affects sustainable production.
Deep Contextualized Word Embedding for Text-based Online User Profiling to Detect Social Bots on Twitter

Social media platforms can expose influential trends in many aspects of everyday life. However, the trends they represent can be contaminated by disinformation. Social bots are one of the significant sources of disinformation in social media. Social bots can pose serious cyber threats to society and public opinion. This research aims to develop machine learning models to detect bots based on the extracted user's profile from a Tweet's text. Online user profiles show the user's personal information, such as age, gender, education, and personality. In this work, the user's profile is constructed based on the user's online posts. This work's main contribution is three-fold: First, we aim to improve bot detection through machine learning models based on the user's personal information generated by the user's online comments. The similarity of personal information when comparing two online posts makes it difficult to differentiate a bot from a human user. However, in this research, we leverage personal information similarity among two online posts as an advantage for the new bot detection model. The new proposed model for bot detection creates user profiles based on personal information such as age, personality, gender, education from user's online posts, and introduces a machine learning model to detect social bots with high prediction accuracy based on personal information. Second, we create a new public data set that shows the user's profile for more than 6900 Twitter accounts in the Cresci 2017 [1] data set. All user's profiles are extracted from the online user's posts on Twitter. Third, for the first time, this paper uses a deep contextualized word embedding model, ELMO [2], for a social media bot detection task.
Assessing the regulatory challenges of emerging disruptive technologies

Abstract The past decade has witnessed the emergence of many technologies that have the potential to fundamentally alter our economic, social, and indeed personal lives. The problems they pose are in many ways unprecedented, posing serious challenges for policymakers. How should governments respond to the challenges given that the technologies are still evolving with unclear trajectories? Are there general principles that can be developed to design governance arrangements for these technologies? These are questions confronting policymakers around the world and it is the objective of this special issue to offer insights into answering them both in general and with respect to specific emerging disruptive technologies. Our objectives are to help better understand the regulatory challenges posed by disruptive technologies and to develop generalizable propositions for governments' responses to them.
An Exploration of Embodied Visual Exploration

Embodied computer vision considers perception for robots in novel, unstructured environments. Of particular importance is the embodied visual exploration problem: how might a robot equipped with a camera scope out a new environment? Despite the progress thus far, many basic questions pertinent to this problem remain unanswered: (i) What does it mean for an agent to explore its environment well? (ii) Which methods work well, and under which assumptions and environmental settings? (iii) Where do current approaches fall short, and where might future work seek to improve? Seeking answers to these questions, we first present a taxonomy for existing visual exploration algorithms and create a standard framework for benchmarking them. We then perform a thorough empirical study of the four state-of-the-art paradigms using the proposed framework with two photorealistic simulated 3D environments, a state-of-the-art exploration architecture, and diverse evaluation metrics. Our experimental results offer insights and suggest new performance metrics and baselines for future work in visual exploration. Code, models and data are publicly available.
Image Tampering Localization Using a Dense Fully Convolutional Network

The emergence of powerful image editing software has substantially facilitated digital image tampering, leading to many security issues. Hence, it is urgent to identify tampered images and localize tampered regions. Although much attention has been devoted to image tampering localization in recent years, it is still challenging to perform tampering localization in practical forensic applications. The reasons include the difficulty of learning discriminative representations of tampering traces and the lack of realistic tampered images for training. Since Photoshop is widely used for image tampering in practice, this paper attempts to address the issue of tampering localization by focusing on the detection of commonly used editing tools and operations in Photoshop. In order to well capture tampering traces, a fully convolutional encoder-decoder architecture is designed, where dense connections and dilated convolutions are adopted for achieving better localization performance. In order to effectively train a model in the case of insufficient tampered images, we design a training data generation strategy by resorting to Photoshop scripting, which can imitate human manipulations and generate large-scale training samples. Extensive experimental results show that the proposed approach outperforms state-of-the-art competitors when the model is trained with only generated images or fine-tuned with a small amount of realistic tampered images. The proposed method also has good robustness against some common post-processing operations.
A survey on the Artificial Bee Colony algorithm variants for binary, integer and mixed integer programming problems

Most of the optimization problems encountered in the real world are discrete type which involves decision variables defined in the discrete search space. Binary optimization problems, integer and mixed integer programming problems are of this category, and they require suitable solution representation and search operators to be solved by nature-inspired algorithms. One of the widely-used and well-known nature-inspired algorithms is Artificial Bee Colony (ABC) that has been originally proposed to solve the problems in the continuous domain, and hence, its standard version employs the search operators to exploit the information of the solution vectors encoded in the continuous domain. To be able to cope with the discrete problems, particularly binary, integer and mixed integer programming problems, which are also a group of numeric optimization problems, various encoding types, search operators and selection operators have been integrated into ABC. In this paper, we review the studies proposing new ABC variants to solve discrete numeric optimization problems. To the best of our knowledge, this will be the first comprehensive survey study on this topic. Therefore, we hope that this study would be beneficial to the readers interested in the use of ABC for the binary, integer and mixed integer discrete optimization problems.
Argumentation and explainable artificial intelligence: a survey

Abstract Argumentation and eXplainable Artificial Intelligence (XAI) are closely related, as in the recent years, Argumentation has been used for providing Explainability to AI. Argumentation can show step by step how an AI System reaches a decision; it can provide reasoning over uncertainty and can find solutions when conflicting information is faced. In this survey, we elaborate over the topics of Argumentation and XAI combined, by reviewing all the important methods and studies, as well as implementations that use Argumentation to provide Explainability in AI. More specifically, we show how Argumentation can enable Explainability for solving various types of problems in decision-making, justification of an opinion, and dialogues. Subsequently, we elaborate on how Argumentation can help in constructing explainable systems in various applications domains, such as in Medical Informatics, Law, the Semantic Web, Security, Robotics, and some general purpose systems. Finally, we present approaches that combine Machine Learning and Argumentation Theory, toward more interpretable predictive models.
Deep learning and multilingual sentiment analysis on social media data: An overview

Twenty-four studies on twenty-three distinct languages and eleven social media illustrate the steady interest in deep learning approaches for multilingual sentiment analysis of social media. We improve over previous reviews with wider coverage from 2017 to 2020 as well as a study focused on the underlying ideas and commonalities behind the different solutions to achieve multilingual sentiment analysis. Interesting findings of our research are (i) the shift of research interest to cross-lingual and code-switching approaches, (ii) the apparent stagnation of the less complex architectures derived from a backbone featuring an embedding layer, a feature extractor based on a single CNN or LSTM and a classifier, (iii) the lack of approaches tackling multilingual aspect-based sentiment analysis through deep learning, and, surprisingly, (iv) the lack of more complex architectures such as the transformers-based, despite results suggest the more difficult tasks requires more elaborated architectures.
Fastpitch: Parallel Text-to-Speech with Pitch Prediction

We present FastPitch, a fully-parallel text-to-speech model based on FastSpeech, conditioned on fundamental frequency contours. The model predicts pitch contours during inference. By altering these predictions, the generated speech can be more expressive, better match the semantic of the utterance, and in the end more engaging to the listener. Uniformly increasing or decreasing pitch with FastPitch generates speech that resembles the voluntary modulation of voice. Conditioning on frequency contours improves the overall quality of synthesized speech, making it comparable to state-of-the-art. It does not introduce an overhead, and FastPitch retains the favorable, fully-parallel Transformer architecture, with over 900× real-time factor for mel-spectrogram synthesis of a typical utterance.
Developing a blockchain framework for the automotive supply chain: A systematic review

As world is affected by demand volatility; process uncertainty; supply chain complexity and information ambiguity forming a VUCA world. To manage this scenario, industries are adopting emerging technologies for business excellence and one among them is Blockchain. Blockchain technology (BCT) is a distributed ledger technology (DLT) that stores transactional records in a tamper-proof and immutable way; it is a promising solution for incorporating transparency and traceability in traditional ecosystem. As automotive industries are facing a Volatile environment, Uncertain schedules & information; Complex supply chain networks, and Ambiguous decisions that cripples the automotive supply chain (ASC). Therefore, BCT can be used to address issues related to ASC in VUCA world. Keeping this in mind, study reported a systematic literature review (SLR) of BCT applications in ASC. More than seventy research papers were reviewed based on different BCT characteristics and applications. Through content analysis, study explored how to link supply chain visibility, information transparency with BCT for an efficient ASC in VUCA world. Moreover, a BCT implementation framework is proposed for ASC, to provide a decision-making approach for practitioners in VUCA world.