Reviewerly/exHarmony huggingface dataset:
SoK: Machine vs. machine – A systematic classification of automated machine learning-based CAPTCHA solvers

Internet services heavily rely on CAPTCHAs for determining whether or not a user is a human being. The recent advances in ML and AI make the efficacy of CAPTCHAs in strengthening Internet services against bots questionable. In this paper, we conduct a systematic analysis and classification of the state-of-the-art ML-based techniques for the automated text-based CAPTCHA breaking problem. The current state and robustness of text-based CAPTCHAs as are utilized by modern Internet applications, against ML-based automated breaking tools, is examined and reported. Our study suggests that ML can be very effective in increasing: (a) accuracy, (b) speed, and (c) abstraction in CAPTCHA solving. Especially, as far as (c) is concerned, ML-based techniques are easier to be applied in different classes of text-based CAPTCHA schemes. To assess the importance of ML in breaking CAPTCHAs, we build our own ML-only classifiers. Surprisingly, an ML-only approach for solving CAPTCHAs is not sufficient. Overall, our study suggests that fundamentally different ways of conducting reverse Turing test, that will be painless for legitimate users (i.e., humans) but at the same time challenging for automated systems (i.e., software), should be considered for ensuring the healthy operation of current Internet services.
Accelerating Super-Resolution and Visual Task Analysis in Medical Images

Medical images are acquired at different resolutions based on clinical goals or available technology. In general, however, high-resolution images with fine structural details are preferred for visual task analysis. Recognizing this significance, several deep learning networks have been proposed to enhance medical images for reliable automated interpretation. These deep networks are often computationally complex and require a massive number of parameters, which restrict them to highly capable computing platforms with large memory banks. In this paper, we propose an efficient deep learning approach, called Hydra, which simultaneously reduces computational complexity and improves performance. The Hydra consists of a trunk and several computing heads. The trunk is a super-resolution model that learns the mapping from low-resolution to high-resolution images. It has a simple architecture that is trained using multiple scales at once to minimize a proposed learning-loss function. We also propose to append multiple task-specific heads to the trained Hydra trunk for simultaneous learning of multiple visual tasks in medical images. The Hydra is evaluated on publicly available chest X-ray image collections to perform image enhancement, lung segmentation, and abnormality classification. Our experimental results support our claims and demonstrate that the proposed approach can improve the performance of super-resolution and visual task analysis in medical images at a remarkably reduced computational cost.
Privacy-preserving certificateless provable data possession scheme for big data storage on cloud, revisited

Certificateless provable data possession (CL-PDP) protocol is an important tool to check the integrity of data outsourced to cloud service provider (CSP) since it is not necessary to consider the certificate management and key escrow problems. In 2017, He et al. proposed an efficient CL-PDP protocol (HKWWC-protocol, for short) with an additional good property: Privacy protection from the verifier [Appl. Math. Comput., vol. 314, pp. 31-43]. However, recently, Liao et al. illustrates that the HKWWC-protocol is completely insecure under their suggested attack (LLON-attack) since CSP can return a forged proof that is able to pass the verification of the verifier, which shows that the HKWWC-protocol will become completely useless. In this paper, we revisit the original HKWWC-protocol and try to rescue it. After our modification, the twisted new protocol can be immune to the LLON-attack. More importantly, we find that the original He et al.’s security model cannot depict the practical scene at all because they viewed CSP and KGC (key generation center) as one entity in their system. Hence, we redefine a new security model and prove the twisted HKWWC-protocol is secure under our new security model. Performance analysis shows that our proposed protocol is still efficient and practical.
Explicit Lower Bounds on Strong Quantum Simulation

We consider the problem of classical strong (amplitude-wise) simulation of n-qubit quantum circuits, and identify a subclass of simulators we call monotone. This subclass encompasses almost all prominent simulation techniques. We prove an unconditional (i.e. without relying on any complexity-theoretic assumptions) and explicit (n - 2)(2n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> - 1) lower bound on the running time of simulators within this subclass. Assuming the Strong Exponential Time Hypothesis (SETH), we further remark that a universal simulator computing any amplitude to precision 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-n</sup> /2 must take at least 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n-o(n)</sup> time. We then compare strong simulators to existing SAT solvers, and identify the time-complexity below which a strong simulator would improve on state-of-the-art general SAT solving. Finally, we investigate Clifford+T quantum circuits with t T-gates. Using the sparsification lemma, we identify a time complexity lower bound of 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.2451×10-8t</sup> below which a strong simulator would improve on state-of-the-art 3-SAT solving. This also yields a conditional exponential lower bound on the growth of the stabilizer rank of magic states.
IFLBC: On the Edge Intelligence Using Federated Learning Blockchain Network

Lately there has been an increase in the number of Machine Learning (ML) and Artificial Intelligence (AI) applications ranging from recommendation systems to face to speech recognition. At the helm of the advent of deep learning is the proliferation of data from diverse data sources ranging from Internet-of-Things (IoT) devices to self-driving automobiles. Tapping into this unlimited reservoir of information presents the problem of finding quality data out of a myriad of irrelevant ones, which to this day, has been a significant issue in data science with a direct ramification of this being the inability to generate quality ML models for useful predictive analysis. Edge computing has been deemed a solution to some of issues such as privacy, security, data silos and latency, as it ventures to bring cloud computing services closer to end-nodes. A new form of edge computing known as edge-AI attempts to bring ML, AI, and predictive analytics services closer to the data source (end devices). In this paper, we investigate an approach to bring edge-AI to end-nodes through a shared machine learning model powered by the blockchain technology and a federated learning framework called iFLBC edge. Our approach addresses the issue of the scarcity of relevant data by devising a mechanism known as the Proof of Common Interest (PoCI) to sieve out relevant data from irrelevant ones. The relevant data is trained on a model, which is then aggregated along with other models to generate a shared model that is stored on the blockchain. The aggregated model is downloaded by members of the network which they can utilize for the provision of edge intelligence to end-users. This way, AI can be more ubiquitous as members of the iFLBC network can provide intelligence services to end-users.
The minimax learning rates of normal and Ising undirected graphical models

Let $G$ be an undirected graph with $m$ edges and $d$ vertices. We show that $d$-dimensional Ising models on $G$ can be learned from $n$ i.i.d. samples within expected total variation distance some constant factor of $\min \{1,\sqrt{(m+d)/n}\}$, and that this rate is optimal. We show that the same rate holds for the class of $d$-dimensional multivariate normal undirected graphical models with respect to $G$. We also identify the optimal rate of $\min \{1,\sqrt{m/n}\}$ for Ising models with no external magnetic field.
An image similarity descriptor for classification tasks

We develop an image similarity descriptor for an image pair, based on deep features. The development consists of two parts - selecting the deep layer whose features are to be included in the descriptor, and a representation of the similarity between the images in the pair. The selection of the deep layer follows a sparse representation of the feature maps followed by multi-output support vector regression. The similarity representation is based on a novel correlation between the histograms of the feature maps of the two images. Experiments to demonstrate the effectiveness of the proposed descriptor are carried out on four applications that can be cast as classification tasks.
Towards a Perceived Audiovisual Quality Model for Immersive Content

This paper studies the quality of multimedia content focusing on 360 video and ambisonic spatial audio reproduced using a head-mounted display and a multichannel loudspeaker setup. Encoding parameters following basic video quality test conditions for 360 videos were selected and a low-bitrate codec was used for the audio encoder. Three subjective experiments were performed for the audio, video, and audiovisual respectively. Peak signal-to-noise ratio (PSNR) and its variants for 360 videos were computed to obtain objective quality metrics and subsequently correlated with the subjective video scores. This study shows that a Cross-Format SPSNR-NN has a slightly higher linear and monotonic correlation over all video sequences. Based on the audiovisual model, a power model shows a highest correlation between test data and predicted scores. We concluded that to enable the development of superior predictive model, a high quality, critical, synchronized audiovisual database is required. Furthermore, comprehensive assessor training may be beneficial prior to the testing to improve the assessors' discrimination ability particularly with respect to multichannel audio reproduction. In order to further improve the performance of audiovisual quality models for immersive content, in addition to developing broader and critical audiovisual databases, the subjective testing methodology needs to be evolved to provide greater resolution and robustness.
Protecting sensitive place visits in privacy-preserving trajectory publishing

The rise of mobile computing has generated huge amount of trajectory data. Since these data are valuable for many people, publishing them while providing adequate individual privacy protection has been a challenging task. In this paper, we present an algorithm for protecting sensitive place visits in privacy-preserving trajectory publishing. By generalizing sensitive places using sensitive zones, and distorting the sub-trajectories within the sensitive zones based on differential privacy, our method not only prevents leakage of sensitive place visits, but also preserves individual movement information. It contains two critical components. First, we generate sensitive zones around sensitive places based on human mobility patterns and the mobility model. The sensitive zones are formed in such a way that the adversary background knowledge does not increase the adversary's belief in whether the trajectory has stopped at a sensitive place or not. Second, to prevent excessive individual movement information loss and sensitive place visit leakage within the sensitive zones, we select reliable segments from the sub-trajectories therein, model the reliable segments as an exploration tree, and synthesize the ɛ– differentially-private sub-trajectories. Our experiments on a real-world dataset show that our method provides good utility, and our sub-trajectory synthesis method preserves detailed information of individual movements.
Blockchain for Video Streaming: Opportunities, Challenges, and Open Issues

While industry has adopted blockchain-based video-streaming platforms, other stakeholders could contribute more to grow the technology and support its implementation. This article reviews current blockchain-based video streaming applications and industrial advancements and identifies today's research activities and future opportunities for investigation.
Following Passive DNS Traces to Detect Stealthy Malicious Domains Via Graph Inference

Malicious domains, including phishing websites, spam servers, and command and control servers, are the reason for many of the cyber attacks nowadays. Thus, detecting them in a timely manner is important to not only identify cyber attacks but also take preventive measures. There has been a plethora of techniques proposed to detect malicious domains by analyzing Domain Name System (DNS) traffic data. Traditionally, DNS acts as an Internet miscreant’s best friend, but we observe that the subtle traces in DNS logs left by such miscreants can be used against them to detect malicious domains. Our approach is to build a set of domain graphs by connecting “related” domains together and injecting known malicious and benign domains into these graphs so that we can make inferences about the other domains in the domain graphs. A key challenge in building these graphs is how to accurately identify related domains so that incorrect associations are minimized and the number of domains connected from the dataset is maximized. Based on our observations, we first train two classifiers and then devise a set of association rules that assist in linking domains together. We perform an in-depth empirical analysis of the graphs built using these association rules on passive DNS data and show that our techniques can detect many more malicious domains than the state-of-the-art.
Recommended System: Attentive Neural Collaborative Filtering

In recent years, neural networks have yielded immense success on speech recognition, computer vision and natural language processing. However, the exploration of neural networks on recommender systems has received relatively less scrutiny. In this work, we strive to develop techniques based on neural networks to tackle the key problem in recommendation — collaborative filtering — on the basis of implicit feedback. Although some recent work has employed deep learning for recommendation, they primarily used it to model auxiliary information, such as textual descriptions of items and acoustic features of music. When it comes to model the key factor in collaborative filtering — the interaction between users and item features, they still resorted to matrix factorization and applied an inner product on the latent features of users and items. And the collaboration signal hidden in the user-item interaction is not encoded during the embedding process. Therefore, the resulting embedding may not be sufficient to capture the collaborative filtering effect. By replacing the inner product with a neural architecture that can learn an arbitrary function from data, we present a general method named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ANCF(Attention Neural network Collaborative Filtering)</i> . ANCF captures collaborative filtering signals and refines the embedding of users and items according to the structure of the graph. By introducing an attention mechanism, the user vector and the item vector are learned on the user-item interaction graph, neighbor interaction information is aggregated to encode, and the embedding is propagated on the user-item interaction graph. This makes it possible to explicitly inject user-item collaboration signals into the embedding process. Extensive experiments conducted on two real world datasets show that ANCF’s recall and ndcg have increased by 30% and 35%, so our proposed ANCF method has been significantly improved over the state-of-the-art method. Empirical evidence shows that using deeper layers of neural networks offers better recommendation performance.
Differentially Private Collaborative Coupling Learning for Recommender Systems

Coupling learning is designed to estimate, discover, and extract the interactions and relationships among learning components. It provides insights into complex interactive data, and has been extensively incorporated into recommender systems to enhance the interpretability of sophisticated relationships between users and items. Coupling learning can be further fostered once the trending collaborative learning can be engaged to take advantage of the cross-platform data. To facilitate this, privacy-preserving solutions are in high demand—it is desired that the collaboration should not expose either the private data of each individual owner or the model parameters trained on their datasets. In this article, we develop a distributed collaborative coupling learning system, which enables differential privacy. The proposed system defends against the adversary who has gained full knowledge of the training mechanism and the access to the model trained collaboratively. It also addresses the privacy-utility tradeoff by a provable tight sensitivity bound. Our experiments demonstrate that the proposed system guarantees favorable privacy gains at a modest cost in recommendation quality, even in scenarios with a large number of training epochs.
Understanding the Sensibility of Social Media Use and Privacy with Bangladeshi Facebook Group Users

Facebook users often join Facebook groups to connect to the people with the same interest regardless of the fact that the other members take the same standing with them. Our study aims to investigate Bangladeshi users' motivation to join and strategies to manage their Facebook groups and identify the relevant challenges. In our ongoing work, we are conducting a survey and interviewing Facebook-group users to understand how Facebook groups are bringing the users of similar interest and agenda together on Facebook and providing the admins with imagined sovereignty. This poster presents some of our crucial findings. This set of findings will be useful in designing better tools for managing Facebook groups for empowering the admins and the users.
Modelling gold potential in the Granites-Tanami Orogen, NT, Australia: A comparative study using continuous and data-driven techniques

The Granites-Tanami Orogen (GTO), a poorly exposed component of the North Australian Craton, is host to significant Paleoproterozoic orogenic gold mineralization amounting to a total endowment of greater 20 Moz Au. This study adopted a multi-technique approach to mineral potential modelling (MPM) of orogenic gold deposits in the GTO, using four different techniques: Continuous fuzzy gamma, geometric average and data-driven index overlay and data-driven random forest methods. The predictive models generated in this study delivered the first public-domain gold prospectivity maps for the Northern Territory (NT)-portion of the GTO. The predictor maps at the core of these models represent mappable proxies of mineralizing processes believed to have been critical in the formation of the orogenic gold deposits, and were generated in the framework of a mineral systems approach. Normalized density, prediction-area and area under the receiver operating characteristic curve methods were used to test the robustness of these predictors and determine the most effective. The performance of the gold potential models generated in this study was assessed using the improved prediction-area plot procedure, with the data-driven random forest approach having delivered the best performing model. The multi-technique approach taken in this study, in which multiple mineral potential models are generated for the same study area and using the same predictor maps, is a relatively new and, as of yet, underutilized approach to MPM that, in contrast to the standard procedure (i.e., generation of a single data- or knowledge-driven model) ensures optimal utilization of the available conceptual and empirical information, helps to better understand and minimize stochastic and systemic uncertainties, facilitates cross-validation of the resulting prospectivity models, and allows the resulting models to be compared and contrasted. From a practical, exploration point of view, the models served as decision-support tools that helped to improve confidence in targeting underexplored, gold-prospective areas in the GTO more effectively and identify new search areas previously not recognized as areas of high gold potential.
Transurbanism

The current trends related to 'smart cities' are bringing the cityscapes of movies such as Blade Runner and Cyberpunk 2077 closer to our immediate reality. The question of what will the cities of the future look like is at the heart of urban studies. In parallel, a similar question is posed by (trans)humanists about the future of humanity and its possible technological enhancements. However, (trans)humanity and future cities are defined in a bi-directional dependency. Therefore, we have to answer the questions of future humans and cities simultaneously. This paper maps several ways of interacting between transhuman communities and smart cities to understand their possible effects on governing, design and society at large resulting in a framework that will work as a cautionary tale and an inspirational blueprint for imagining future urbanity.
StanceVis Prime: visual analysis of sentiment and stance in social media texts

Abstract Text visualization and visual text analytics methods have been successfully applied for various tasks related to the analysis of individual text documents and large document collections such as summarization of main topics or identification of events in discourse. Visualization of sentiments and emotions detected in textual data has also become an important topic of interest, especially with regard to the data originating from social media. Despite the growing interest in this topic, the research problem related to detecting and visualizing various stances, such as rudeness or uncertainty, has not been adequately addressed by the existing approaches. The challenges associated with this problem include the development of the underlying computational methods and visualization of the corresponding multi-label stance classification results. In this paper, we describe our work on a visual analytics platform, called StanceVis Prime, which has been designed for the analysis of sentiment and stance in temporal text data from various social media data sources. The use case scenarios intended for StanceVis Prime include social media monitoring and research in sociolinguistics. The design was motivated by the requirements of collaborating domain experts in linguistics as part of a larger research project on stance analysis. Our approach involves consuming documents from several text stream sources and applying sentiment and stance classification, resulting in multiple data series associated with source texts. StanceVis Prime provides the end users with an overview of similarities between the data series based on dynamic time warping analysis, as well as detailed visualizations of data series values. Users can also retrieve and conduct both distant and close reading of the documents corresponding to the data series. We demonstrate our approach with case studies involving political targets of interest and several social media data sources and report preliminary user feedback received from a domain expert. Graphic abstract