Reviewerly/exHarmony huggingface dataset:
A Constructor Theory-based Approach for Computer Code Model Validation: The Crucial Role of an Effort-directing Feedback Mechanism

I argue that human intelligence is effectively utilized when effort-directing feedback processes are taken recourse to, in any validation task, in order go around the human frailty of giving up prematurely in the absence of actionable feedback. The insight comes from application of a principle from Deutsch’s constructor theory when comparing two alternative approaches for validating prior research containing a computer code model. In one approach, an independent researcher undertakes the set of mental processing tasks necessary to convert natural language specifications in a prior publication’s text into fresh computer code instructions. The numerical or graphical outputs from the fresh computer code are compared with the numerical or graphical outputs found in the original publication. In this case, the original computer code must not be consulted. The effort towards obtaining convergence with the numerical or graphical outputs directs intensive and iterative reconsideration of the publication text, for refinement of the fresh computer code. The second approach comprises going through the original computer code and checking conformance with the specifications in the publication text. This approach is inferior, because inconsistencies between the specifications in the publication text and specifications in the original computer code are quite likely to go unchallenged. This principle elucidated here can be put to use to develop better decision-making configurations in business, government and society.
Object Detection for Functional Assessment Applications

This paper presents a proposal for object detection as a first stage for the analysis of Human-Object Interaction (HOI) in the context of automated functional assessment. The proposed system is based in a two-step strategy, thus, in the first stage there are detected the people in the scene, as well as large objects (table, chairs, etc.) using a pre-trained YOLOv8. Then, there is defined a ROI around each person that is processed using a custom YOLO to detect small elements (forks, plates, spoons, etc.). Since there are no large image datasets that include all the objects of interest, there has also been compiled a new dataset including images from different sets, and improving the available labels. The proposal has been evaluated in the novel dataset, and in different images acquired in the area in which the functional assessment is performed, obtaining promising results.
LoockMe: An Ever Evolving Artificial Intelligence Platform for Location Scouting in Greece

LoockMe is an artificial intelligence-powered location scouting platform that combines deep learning image analysis, cutting-edge machine learning natural language processing (NLP) for automated content annotation, and intelligent search. The platform's objective is to label input images of local landscapes, and/or any other assets that regional film offices want to expose to those interested in identifying potential locations for the film production industry. The deep learning-based image analysis achieved high classification performance with an AUC score of 99.4%. Moreover, the state-of-the-art machine learning NLP module enhances the platform's capabilities by analyzing text descriptions of the locations and thus allowing for automated annotation, while the intelligent search engine combines image analysis with NLP to extract relevant context from available data. The proposed artificial intelligence platform has the potential to substantially assist asset publishers and revolutionize the location scouting process for the film production industry in Greece.
Towards Explaining Shortcut Learning Through Attention Visualization and Adversarial Attacks

Since its introduction, the attention-based Transformer architecture has become the de facto standard for building models with state-of-the-art performance on many Natural Language Processing tasks. However, it seems that the success of these models might have to do with their exploitation of dataset artifacts, rendering them unable to generalize to other data and vulnerable to adversarial attacks. On the other hand, the attention mechanism present in all models based on the Transformer, such as BERT-based ones, has been seen by many as a potential way to explain these deep learning models: by visualizing attention weights, it might be possible to gain insights on the reasons behind these opaque models’ decisions. This paper introduces AttentiveBERT, an interactive attention weights visualization tool for diagnosing BERT-based models, focusing on explaining the occurrence of shortcut learning. The distinctive feature of this tool is enabling the visual comparison of attention weights before and after a change to the model’s input, in order to visually analyse adversarial attacks. Some illustrations of this use case are explored in this paper.
Low-Latency Oriented Resource Allocation for MEC-Assisted Adaptive Bitrate Video Streaming

This paper focuses on low-latency transmissions for mobile edge computing (MEC)-assisted adaptive bitrate (ABR) video streaming. With the aim to realize minimum video service latency, an optimization problem is formulated by jointly designing caching, computing and power allocation, subject to diverse video version downloading rate requirements and the limited resources of MEC servers. The formulated optimization problem turns out to be a mixed integer nonlinear programming (MINLP) problem. To solve the problem and satisfy the low-latency service requirement of ABR video streaming, a low-complexity two-step iterative algorithm is proposed by decomposing the MINLP problem into two subproblems, a joint caching and computing subproblem and a power allocation subproblem. Due to the highly coupled nature of the caching and computing, the first subproblem is an integer nonlinear programming (INLP) problem. To solve it efficiently, the INLP problem is equivalently transformed into an integer linear programming (ILP) problem, which is thereafter solved by MATLAB intlinprog function. The second subproblem is a convex problem and is solved by low-complexity power allocation (LCPA) algorithm. Intensive simulation results show that the proposed algorithm converges in a very short time, and can significantly reduce the average latency compared to the state-of-the-art baselines.
Group-Personalized Federated Learning for Human Activity Recognition Through Cluster Eccentricity Analysis

Human Activity Recognition (HAR) plays a significant role in recent years due to its applications in various fields including health care and well-being. Traditional centralized methods reach very high recognition rates, but they incur privacy and scalability issues. Federated learning (FL) is a leading distributed machine learning (ML) paradigm, to train a global model collaboratively on distributed data in a privacy-preserving manner. However, for HAR scenarios, the existing action recognition system mainly focuses on a unified model, i.e. it does not provide users with personalized recognition of activities. Furthermore, the heterogeneity of data across user devices can lead to degraded performance of traditional FL models in the smart applications such as personalized health care. To this end, we propose a novel federated learning model that tries to cope with a statistically heterogeneous federated learning environment by introducing a group-personalized FL (GP-FL) solution. The proposed GP-FL algorithm builds several global ML models, each one trained iteratively on a dynamic group of clients with homogeneous class probability estimations. The performance of the proposed FL scheme is studied and evaluated on real-world HAR data. The evaluation results demonstrate that our approach has advantages in terms of model performance and convergence speed with respect to two baseline FL algorithms used for comparison.
Cryptocurrency Exchange Closure Revisited (Again)

Exchanges serve an essential role in the cryptocurrency ecosystem. It is through exchanges that most people acquire Bitcoin and other cryptocurrencies, often avoiding the blockchain entirely. Because so many customers put their trust and financial resources in exchanges, it is no surprise that they have long been targets of cybercriminal actors. This paper examines 822 cryptocurrency exchanges operational from 2010–2022. We find that 40% of these exchanges subsequently shut down. Using regression and survival analysis, we investigate the factors that could precipitate the closure of exchanges. Consistent with prior work, we find some evidence that experiencing security breaches are associated with closure. However, we find that the strongest effects are connected to how the exchange operates. Exchanges that only trade cryptocurrencies and not fiat face approximately 60% greater odds of shutting down than those that trade both. Trading more coins is negatively associated with failure. Meanwhile, exchanges that permit US customers shut down more quickly, which suggests that the regulatory environment may affect exchange lifetimes.
“I don’t really give them piece of mind”: User Perceptions of Social Engineering Attacks

How do end users understand social engineering attacks, and how do their perceptions of these attacks differ from reality? To investigate, we proposed a new social engineering attack framework, and ran two studies to examine exactly how and when users are misunderstanding social engineering attacks. In our first study, we conducted 30 qualitative interviews asking people about their understanding of, and experiences with social engineering attacks. We found that confidence and accuracy are the two main factors affecting users’ knowledge of social engineering attacks. In our second study, we quantified how confidence and accuracy impact users’ perceptions at different stages of an attack. We found that users tend to be overconfident in their ability to understand social engineering attacks, but hold inaccurate beliefs. Participants had major misconceptions of what constitutes social engineering, and the risks of these attacks. Based on our results, we propose educational and design opportunities to match social engineering mitigation strategies to user perceptions of social engineering.
Leaky Kits: The Increased Risk of Data Exposure from Phishing Kits

Phishing kits allow adversaries with little or no technical experience to launch phishing websites in a short time. Past research has found such phishing kits that contain backdoors (e.g., obfuscated email addresses), which are intentionally added by the kit developers to obtain the phished data. In this work, we augment on prior research by exploring several ways in which security flaws in phishing kits make the victim data accessible to a wider set of adversaries beyond the kit deployers and kit developers. We implement an automated framework for kit collection and analysis, which includes a custom command-line PHP execution tool (for dynamic analysis) along with other open-source tools. Our analysis focuses on finding backdoors (e.g., obfuscated email address, command injection), measuring the extent of disclosure of sensitive information (e.g., via exposed plaintext files, hardcoded Telegram bot tokens, hardcoded admin console passwords) and detecting security vulnerabilities in phishing kits. We analyze 4238 distinct phishing kits (from a set of 26,281 compressed files collected from several sources over a span of 15 months), each having unique SHA-1 hash value. We found that 3.9% of the analyzed kits contained at least one form of backdoor. We also found hardcoded admin console passwords and API keys used to access third party services, in 8.3% and 16% of the analyzed kits, respectively. In addition, 15.8% of the analyzed kits wrote stolen information (PII) of users in plaintext files; 5.6% kits did not restrict external access to these plaintext files, leading to exposure of sensitive phished data (e.g., 178,504 passwords, 133,248 email addresses, 1253 credit card numbers). Furthermore, 11.7% of the analyzed kits contained hardcoded Telegram bots; we obtained invite links to join Telegram chats in 0.5% kits, and found them to expose chat messages containing sensitive PII information of victims (e.g., 73,342 passwords, 141,095 email addresses, 3584 credit card numbers). We also found that 64% of the kits are affected by security vulnerabilities (e.g., insecure file operations, SQL injection), which can be abused to further expose user data. We have open-sourced our framework and other artifacts to benefit future research.
The Challenges of Blockchain-Based Naming Systems for Malware Defenders

Successful malware campaigns often rely on the ability of infected hosts to locate and contact their command-and-control (C2) servers. Malware campaigns often use DNS domains for this purpose, but DNS domains may be taken down by the registrar that sold them. In response to this threat, malware operators have begun using blockchain-based naming systems to store C2 server names. Blockchain naming systems are a threat to malware defenders because they are not subject to a centralized authority, such as a registrar, that can take down abused domains, either voluntarily or under legal pressure. In fact, blockchains are robust against a variety of interventions that work on DNS domains, which is bad news for defenders.We analyze the ecosystem of blockchain naming systems and identify new locations for defenders to stage interventions against malware. In particular, we find that malware is obligated to use centralized or semi-centralized infrastructure to connect to blockchain naming systems and modify the records stored within. In fact, scattered interventions have already been staged against this centralized infrastructure: we present case studies of several such instances. We also present a study of how blockchain naming systems are currently abused by malware operators, and discuss the factors that would cause a blockchain naming system to become an unstoppable threat. We conclude that existing blockchain naming systems still provide opportunities for defenders to prevent malware from contacting its C2 servers.
Quantum Computing and High-Performance Computing: Compilation Stack Similarities

There is a great deal of focus on how quantum computing as an accelerator differs from other traditional high-performance computing (HPC) resources, including accelerators like GPUs and field-programmable gate arrays. In classical computing, how to design the interfaces that connect the different layers of the software stack, from the applications and high-level programming language description, through compilers and schedulers, and down to the hardware and gate level, has been critical. Likewise, quantum computing’s interfaces enable access to quantum technology as a viable accelerator. From the ideation of the quantum application to the manipulation of the quantum chip, each interface has its challenges. In this article, we discuss the structure of this set of quantum interfaces, their many similarities to the traditional HPC compilation stack, and how these interfaces impact the potential of quantum computers as HPC accelerators.
Bilingual Documents Text Lines Extraction Using Conditional GANs

Text line extraction in document recognition is the major step. A number of classical approaches are available like projection profile, bounding box analysis, etc. These classical approaches are unable to segment the text with large variations in individual handwriting. Furthermore, segmentation of documents having data from multiple scripts creates more hurdles due to the presence of different writing styles. The usage of deep networks has been less explored in this domain due to the need of high training time and data. In this research, we have used conditional generative adversarial networks (GANs) for text line extraction in bilingual documents containing Gurumukhi-Latin scripts. It considers text line segmentation problem as image-to-image translation task. Two kinds of encoder–decoder networks are used for comparison, i.e., with skip connections and without skip connections. Dataset for bilingual handwritten documents containing 150 document images has been designed. It includes large variability in writing style and content. Results on the designed dataset for text line extraction are efficient for encoder–decoder network with skip connections.
Span extraction and contrastive learning for coreference resolution

Coreference resolution is a primary task in natural language processing (NLP), designed to automatically identifyand classify noun phrases or pronouns that represent thesame mention. Most of the recent coreference resolution models extraction or cluster mentions. However, these models rely heavily on the word-level representation to find coreference links between words and spans. In this paper, we propose a multi-task learning approach based on the transformers, which integrates three tasks including span extraction, contrastive learning and text classification. Our model pays attention to both word and sentence representation without requiring for the complex annotations and the manual rules. Experiments demonstrate significant performance over previous methods, with an accurate 77.63% in the dev and an accurate 76.90% in the test of the CLUE-WSC.
An Embroidery Touch Sensor With Layered Structure of Conductive and Nonconductive Threads

Textile-based touch sensing methods for ubiquitous and wearable interfaces employ multiple independent or specific shaped electrodes, leading to limitations on three aspects: design freedom of electrode shape, durability, and simple production. We introduce the creation approach of an embroidery touch sensor by adding embroidery using nonconductive threads on top of an electrode made of conductive threads. Changes of stitch densities of the nonconductive thread embroidery lead to differences in electrical properties, which can be useful to distinguish which part of the embroidery is touched. Our method enables the creation of touch-sensitive embroidery with high design freedom of electrode shape and durability in a simple process.
Gaussian process classification for psychophysical detection tasks in multiple populations (wide big data) using transfer learning

Psychophysics is the diagnostic mechanism used to measure neuronal responses to physical or non-physical stimuli, and therefore a means to understand human cognition and behavior. In recent years, Machine Learning (ML) methods have been introduced to speed up and make more efficient very costly psychophysical detection tasks (PDTs) as part of cognitive neurosciences and other similar fields. However, most ML-driven PDT tasks depend on data from single populations despite the growing sets of PDT databases from other populations. This paper therefore offers a brief tutorial on the non-parametric Gaussian process classification (GPC) as a Bayesian machine learning inference method that is growing in appeal for ML-driven PDTs and can be used across multiple populations (wide Big Data) using transfer learning. We then perform a systematic review of GPC in audiogram estimation as an example. GPC reduced expensive diagnostic PDTs in audiometry from as many as a minimum of 400 trials to less than 16 trials, in some instances extending to both ears at the same time. GPC has also significantly been influenced by active learning to maximize PDT experimental data and trials, making them faster and more accessible to wider audiences outside the lab, for example with digital devices such as mobile or web-based apps. GPC with active learning also improved PDTs to measure finer stimulus intensities compared with wider traditional intensity intervals. We conclude by offering suggestions on how GPC combined with active and transfer learning presents an opportunity to serve as an ideal Big Data approach to ML-driven PDTs across multiple populations. We offer some suggestions and areas for further research in other diagnostic assessments.
A Novel Approach for Real-Time Vehicle Re-identification Using Content-Based Image Retrieval with Relevance Feedback

Automated smart traffic surveillance systems constitute a significant part of smart city environments and have attracted significant research attention in recent years. Vehicle re-identification is a major challenge in automated traffic surveillance systems in smart city environments. Vehicle re-identification is the process of retrieving instances of the target vehicle given a gallery of numerous vehicle images. Though multiple models were proposed to perform the task of vehicle re-identification, the models struggle in terms of real-world implementation because of their complexity and computational requirements. This is mainly due to the focus on computation-heavy feature extraction processes, along with complex pre-processing and post-processing steps. To address these issues, an approach incorporating content-based image retrieval techniques with deep neural models that are computationally efficient is proposed. The approach also considers relevance feedback during the post-processing phase. Experimental results reveal that the incorporation of relevance feedback technique as a post-processing technique in vehicle re-identification helps achieve significant improvement in terms of mean average precision and Rank@k.
Steered mixture-of-experts autoencoder design for real-time image modelling and denoising

Research in the past years introduced Steered Mixture-of-Experts (SMoE) as a framework to form sparse, edge-aware models for 2D- and higher dimensional pixel data, applicable to compression, denoising, and beyond, and capable to compete with state-of-the-art compression methods. To circumvent the computationally demanding, iterative optimization method used in prior works an autoencoder design is introduced that reduces the run-time drastically while simultaneously improving reconstruction quality for block-based SMoE approaches. Coupling a deep encoder network with a shallow, parameter-free SMoE decoder enforces an efficent and explainable latent representation. Our initial work on the autoencoder design presented a simple model, with limited applicability to compression and beyond. In this paper, we build on the foundation of the first autoencoder design and improve the reconstruction quality by expanding it to models of higher complexity and different block sizes. Furthermore, we improve the noise robustness of the autoencoder for SMoE denoising applications. Our results reveal that the newly adapted autoencoders allow ultra-fast estimation of parameters for complex SMoE models with excellent reconstruction quality, both for noise free input and under severe noise. This enables the SMoE image model framework for a wide range of image processing applications, including compression, noise reduction, and super-resolution.
Verifiable outsourcing EMRs scheme with attribute-based encryption in cloud-edge environments

With emerging of cloud and edge computing, it is popular to store Electronic Medical Records (EMRs) on a remote cloud. Furthermore, to share the EMRs stored in the cloud, attribute-based encryption (ABE) can provide a fine-grained access control strategy. However, in ABE, the finer the access control is, the bigger the number of required attributes is. Accordingly, it requires the higher computation costs. In this paper, to reduce the burden of patients, we first present a verifiable outsourced ABE scheme with the help of edge servers and fog nodes, in which edge servers are fully responsible for the encryption of ABE while fog nodes assist data users to perform the corresponding decryption. Furthermore, to protect the privacy of data owner, we introduced the proxy re-encryption algorithms, which still can achieve dual access control for EMRs. What's more, to detect the honesty of edge servers and the cloud, we adopt an aggregated digital signature to guarantee the correctness of the EMRs initially encrypted by data owner. The experimental results and security proofs show that our scheme has higher computational efficiency and achieves more fine-grained access control.