Reviewerly/exHarmony huggingface dataset:
R-SigNet: Reduced space writer-independent feature learning for offline writer-dependent signature verification

Handwritten signatures are a widespread biometric trait for person identification and verification. Reliable authentication and authorization of individuals are, however, challenging tasks due to possible skilled forgeries; especially when a forger has access to a given signature and deliberately tries to imitate it. This problem is even more emphasised in offline signature verification, where dynamic signature information is lost, resulting, as a consequence, in an increased difficulty discerning between genuine and forged signatures. To address this issue, solutions based on convolutional neural networks (CNN) are currently being devised to automatically extract features from a signature. Although highly performing, these methods require a high number of learnable parameters to produce meaningful signature representations, ultimately leading to long training times. In this paper, the R-SigNet architecture, a multi-task approach exploiting a relaxed loss to learn a reduced feature space for writer-independent (WI) signature verification, is presented. Compact generic features are automatically extracted by this network, so that a support vector machine (SVM) can be trained and tested in offline writer-dependent (WD) mode. By leveraging a small generic feature space, the proposed system achieves improved performances and reduced training times with respect to the current literature, as shown by the results obtained on several benchmark datasets.
Functional Encryption for Turing Machines with Dynamic Bounded Collusion from LWE

The classic work of Gorbunov, Vaikuntanathan and Wee (CRYPTO 2012) and follow-ups provided constructions of bounded collusion Functional Encryption (FE) for circuits from mild assumptions. In this work, we improve the state of affairs for bounded collusion FE in several ways:
Modeling the development of internal mental models by an adaptive network model

Mental models play a crucial role in individual and organizational learning. The adaptive mental processes involved in the development of mental models are addressed here by integrating psychological and neurological theories on mental models and the learning involved. An adaptive network model has been designed for these processes and used for simulations addressing a case study of learning to drive a car. The developed model may be valuable for different ends, like in improving individual and organizational learning, in designing virtual pedagogical agents, enhancing driver safety, and self-cardriving systems.
An Anonymous Certificateless Signcryption Scheme for Internet of Health Things

Internet of Health Things (IoHT) is a hot topic of research presently, which provides a reliable and intelligent healthcare system for monitoring the physical conditions of the patients over the Internet from anywhere and anytime. The ease of time-independent interaction from geographically remote areas is a core advantage of the IoHT system, which offers preventive or proactive healthcare facilities at a lower cost. IoHT communication, on the other hand, is usually carried out with a range of low-power biomedical sensors, rendering them vulnerable to cyber-attacks and incompatible with traditional cryptographic techniques. The most critical security concern in IoHT is ensuring the authenticity of patients’ health-related messages sent over the internet. Other key concerns include receiver anonymity and forward security, which means that only the sender knows the identities of the recipients. As a result, even if the private key of senders compromised, the adversary will be unable to decrypt the ciphertext. Existing signcryption schemes that employ certificateless cryptography for the healthcare system failed to guarantee both receiver anonymity and forward security simultaneously. Therefore, in this article, we propose an anonymous certificateless signcryption scheme for IoHT applications, which is based on the notion of the Hyperelliptic Curve (HEC) cryptosystem to satisfy these security requirements. The proposed scheme guarantees formal security analysis for confidentiality, unforgeability, and receiver anonymity using the Random Oracle Model (ROM). The results authenticate that the proposed scheme improves security while lowering computation and communication costs.
Implementation of the Australian Computer‐Assisted Theragnostics (AusCAT) network for radiation oncology data extraction, reporting and distributed learning

There is significant potential to analyse and model routinely collected data for radiotherapy patients to provide evidence to support clinical decisions, particularly where clinical trials evidence is limited or non-existent. However, in practice there are administrative, ethical, technical, logistical and legislative barriers to having coordinated data analysis platforms across radiation oncology centres.A distributed learning network of computer systems is presented, with software tools to extract and report on oncology data and to enable statistical model development. A distributed or federated learning approach keeps data in the local centre, but models are developed from the entire cohort.The feasibility of this approach is demonstrated across six Australian oncology centres, using routinely collected lung cancer data from oncology information systems. The infrastructure was used to validate and develop machine learning for model-based clinical decision support and for one centre to assess patient eligibility criteria for two major lung cancer radiotherapy clinical trials (RTOG-9410, RTOG-0617). External validation of a 2-year overall survival model for non-small cell lung cancer (NSCLC) gave an AUC of 0.65 and C-index of 0.62 across the network. For one centre, 65% of Stage III NSCLC patients did not meet eligibility criteria for either of the two practice-changing clinical trials, and these patients had poorer survival than eligible patients (10.6 m vs. 15.8 m, P = 0.024).Population-based studies on routine data are possible using a distributed learning approach. This has the potential for decision support models for patients for whom supporting clinical trial evidence is not applicable.
Tracking towards care: Relational affordances of self‐tracking in gym culture

In the past few years, self-tracking technologies have been celebrated for the possibilities they offer to 'optimize' fitness and wellbeing, yet also criticized for being rigid and isolating. In this article, we identify complex tracking arrangements that consist of a variety of data and multiple modalities of tracking emplaced within arrangements of actors and objects (digital/analog tracking devices and data output). We inquire into how these arrangements afford care. Based on our ethnographic research of gym culture in Denmark, we find that individuals make the technologies 'work' for them in ways that shield them from bodily or emotional distress. Fitness practitioners combine digital tracking technologies with analog methods and enrol other human actors in recording, interpreting, questioning and tinkering with their data; in other words, they perform data work in ways that mend or prevent ruptures and brokenness and thus afford 'care'. We highlight the role of the personal trainer, who often complements or salvages the outputs of digital technologies. We argue that tracking has the capacity to afford care and wellness when it is emplaced within socialities and when actors are able to exercise their capacities and knowledge in ways that mitigate the data outputs.
An Intelligent Arabic Chatbot System Proposed Framework

Recently, several types of research in Chatbot systems have been developed in the literature by their means of having a considerable number of languages such as English, French, and other languages, which serve in various fields of a number of real-life cases. Chatbots are considered unfamiliar besides their little usage. To the best knowledge of the research, no previous research has been conducted in improving an Arabic Chatbot in Jordanian universities. With the outbreak of the COVID-19 pandemic and its global effects, the need to reduce the load on the admissions director and possibly other users became critical. Hence, this paper aims to develop an Intelligent Arabic Chatbot System that can reduce such a load. In particular, this system supports an Arabic spoken by using a Jordanian dialect with students, particularly at the Al-Zaytoonah Private University of Jordan. Therefore, the proposed system represents a primary Jordan chatbot that uses a Jordanian dialect.
Automated Amharic News Categorization Using Deep Learning Models

For decades, machine learning techniques have been used to process Amharic texts. The potential application of deep learning on Amharic document classification has not been exploited due to a lack of language resources. In this paper, we present a deep learning model for Amharic news document classification. The proposed model uses fastText to generate text vectors to represent semantic meaning of texts and solve the problem of traditional methods. The text vectors matrix is then fed into the embedding layer of a convolutional neural network (CNN), which automatically extracts features. We conduct experiments on a data set with six news categories, and our approach produced a classification accuracy of 93.79%. We compared our method to well-known machine learning algorithms such as support vector machine (SVM), multilayer perceptron (MLP), decision tree (DT), XGBoost (XGB), and random forest (RF) and achieved good results.
Contrast and brightness balance in image enhancement using Cuckoo Search-optimized image fusion

Many vision-based systems suffer from poor levels of contrast and brightness, mainly because of inadequate and improper illumination during the image acquisition process. As a result, the required specified information from the acquired image is not available for the particular application. In general, it is hard to achieve a balance between the improvement of contrast and brightness in image enhancement. By introducing nature-inspired optimization in image enhancement, the best features of the image are utilized, and the complexity related to the nonlinearity of images can be solved with various constraints, like a balance between contrast and brightness. In this work, a novel automatic method for image enhancement to find a balance between contrast and brightness is developed by using Cuckoo Search-optimized image fusion. First, the Cuckoo Search-based optimization algorithm generates two sets of optimized parameters. These parameter sets are used to generate a pair of enhanced images, one with a high degree of sharpness and contrast, the other is bright and has been improved without losing the level of detail. Furthermore, the two enhanced images are fused by the fusion process to obtain an output image where the contrast and brightness are in balance. The effectiveness of the proposed method is verified by applying it to standard images (CVG-UGR image database) and lathe tool images. Experimental results demonstrated that the proposed method performs better with regard to both the quality of contrast and brightness, moreover, yields enhanced quality evaluation metrics compared to the other conventional techniques.
Scaling Guarantees for Nearest Counterfactual Explanations

Counterfactual explanations (CFE) are being widely used to explain algorithmic decisions, especially in consequential decision-making contexts (e.g., loan approval or pretrial bail). In this context, CFEs aim to provide individuals affected by an algorithmic decision with the most similar individual (i.e., nearest individual) with a different outcome. However, while an increasing number of works propose algorithms to compute CFEs, such approaches either lack in optimality of distance (i.e., they do not return the nearest individual) and perfect coverage (i.e., they do not provide a CFE for all individuals); or they do not scale to complex models such as neural networks. In this work, we provide a framework based on Mixed-Integer Programming (MIP) to compute nearest counterfactual explanations for the outcomes of neural networks, with both provable guarantees and runtimes comparable to gradient-based approaches. Our experiments on the Adult, COMPAS, and Credit datasets show that, in contrast with previous methods, our approach allows for efficiently computing diverse CFEs with both distance guarantees and perfect coverage.
BERT Based Semi-Supervised Hybrid Approach for Aspect and Sentiment Classification

Aspect-based sentiment analysis (ABSA) includes two sub-tasks namely, aspect extraction and aspect-level sentiment classification. Most existing works address these sub-tasks independently by applying a supervised learning approach using labeled data. However, obtaining such labeled sentences is difficult and extremely expensive. Hence, it is important to solve ABSA without taking a dependency on labeled sentences. In this work, we propose a three-step semi-supervised hybrid approach that jointly detects an aspect and its associated sentiment in a given review sentence. The first step of our approach takes a small set of seed words for each aspect and sentiment class to construct respective semantically coherent class vocabularies. The second step makes use of these constructed vocabularies along with POS tags to label a subset of sentences from the training corpus. As we adopt a semi-automated approach to label the data, this process may induce noise in the labels during the annotation. In the last step, we use such labeled sentences to build a noise-robust deep neural network for aspect and sentiment classification. We conduct experiments on two real data sets to verify the effectiveness of our model ( https://github.com/Raghu150999/UnsupervisedABSA ).
Deep Learning for Ballroom Dance Recognition: A Temporal and Trajectory-Aware Classification Model With Three-Dimensional Pose Estimation and Wearable Sensing

Dance performance recognition methods have been investigated and shown various applications such as picture-pose evaluation and synchronizing foot timing and direction. However, detailed analysis and feedback are still missing. To provide them, understanding the performance by component level is necessary. Specifically, we formulate it as a dance-figure classification problem using three-dimensional body joints and wearable sensors. Our model is based on long short-term memory (LSTM) and includes the temporal and trajectory-wise structure that uses the trajectory information in a timestep and the temporal masking module. As a result, we achieved 93% accuracy with our proposed method, which is highly overwhelming the baseline result (84.7%) and very close to the accuracy of the experienced dancers (93.6%). We have made the dataset of ballroom dance performance dataset open to researchers to develop the activity recognition field further.
Affordances and Constraints of Intelligent Personal Assistants for Second-Language Learning

This paper provides a research synthesis of intelligent personal assistants (IPAs) – that is, cloud-based virtual assistants such as Alexa, Google Assistant, and Siri – for second language (L2) learning. The article also offers a theoretical justification for the use of IPAs in language learning and outlines the affordances and constraints of the technology. Finally, the article proposes directions for future research on the topic of IPAs based on the aforementioned review of L2 literature. While research indicates that IPAs increase opportunities for meaningful communication in the target language, enhance speaking ability, and provide indirect pronunciation feedback, they also present some challenges for L2 learners – namely, IPAs struggle to reliably understand L2 speech, which may limit the usability of virtual assistants among heavily accented learners.
Witnessing negative conditional entropy

Quantum states that possess negative conditional von Neumann entropy provide quantum advantage in several information-theoretic protocols including superdense coding, state merging, distributed private randomness distillation and one-way entanglement distillation. While entanglement is an important resource, only a subset of entangled states have negative conditional von Neumann entropy. Despite this utility, a proper resource theory for conditional von Neumann entropy has not been developed, unlike that of entanglement. We pave the way for such a resource theory by characterizing the class of free states (density matrices having non-negative conditional von Neumann entropy) as convex and compact. This allows us to prove the existence of a Hermitian operator (a witness) for the detection of states having negative conditional entropy for bipartite systems in arbitrary dimensions. We construct a family of such witnesses and prove that the expectation value of any of them in a state is an upper bound to the conditional entropy of the state. We pose the problem of obtaining a tight upper bound to the set of conditional entropies of states in which an operator gives the same expectation value as a convex optimization problem. We solve it numerically for a two qubit case and find that this enhances the usefulness of our witnesses. We also find that for a particular witness, the estimated tight upper bound matches the value of conditional entropy for Werner states. We explicate the utility of our work in the detection of useful states in the above-mentioned protocols.
TAEN: Temporal Aware Embedding Network for Few-Shot Action Recognition

Classification of new class entities requires collecting and annotating hundreds or thousands of samples that is often prohibitively costly. Few-shot learning suggests learning to classify new classes using just a few examples. Only a small number of studies address the challenge of few-shot learning on spatio-temporal patterns such as videos. In this paper, we present the Temporal Aware Embedding Network (TAEN) for few-shot action recognition, that learns to represent actions, in a metric space as a trajectory, conveying both short term semantics and longer term connectivity between action parts. We demonstrate the effectiveness of TAEN on two few shot tasks, video classification and temporal action detection and evaluate our method on the Kinetics-400 and on ActivityNet 1.2 few-shot benchmarks. With training of just a few fully connected layers we reach comparable results to prior art on both few shot video classification and temporal detection tasks, while reaching state-of-the-art in certain scenarios.
Quality-Agnostic Image Recognition via Invertible Decoder

Despite the remarkable performance of deep models on image recognition tasks, they are known to be susceptible to common corruptions such as blur, noise, and low-resolution. Data augmentation is a conventional way to build a robust model by considering these common corruptions during the training. However, a naive data augmentation scheme may result in a non-specialized model for particular corruptions, as the model tends to learn the averaged distribution among corruptions. To mitigate the issue, we propose a new paradigm of training deep image recognition networks that produce clean-like features from any quality image via an invertible neural architecture. The proposed method consists of two stages. In the first stage, we train an invertible network with only clean images under the recognition objective. In the second stage, its inversion, i.e., the invertible decoder, is attached to a new recognition network and we train this encoder-decoder network using both clean and corrupted images by considering recognition and reconstruction objectives. Our two-stage scheme allows the network to produce clean-like and robust features from any quality images, by reconstructing their clean images via the invertible decoder. We demonstrate the effectiveness of our method on image classification and face recognition tasks.
Membership Inference Attack and Defense for Wireless Signal Classifiers With Deep Learning

An over-the-air membership inference attack (MIA) is presented to leak private information from a wireless signal classifier. Machine learning (ML) provides powerful means to classify wireless signals, e.g., for PHY-layer authentication. As an adversarial machine learning attack, the MIA infers whether a signal of interest has been used in the training data of a target classifier. This private information incorporates waveform, channel, and device characteristics, and if leaked, can be exploited by an adversary to identify vulnerabilities of the underlying ML model (e.g., to infiltrate the PHY-layer authentication). One challenge for the over-the-air MIA is that the received signals and consequently the RF fingerprints at the adversary and the intended receiver differ due to the discrepancy in channel conditions. Therefore, the adversary first builds a surrogate classifier by observing the spectrum and then launches the black-box MIA on this classifier. The MIA results (based on both simulations and over-the-air software-defined radio (SDR) experiments) show that the adversary can reliably infer signals (and potentially the radio and channel information) used to build the target classifier. Therefore, a proactive defense is developed against the MIA by building a shadow MIA model and fooling the adversary. This defense can successfully reduce the MIA accuracy and prevent information leakage from the wireless signal classifier. Moreover, this defense does not reduce the accuracy of signal classification.
Access Control for Distributed Ledgers in the Internet of Things: A Networking Approach

In the Internet of Things (IoT) domain, devices need a platform to transact seamlessly without a trusted intermediary. Although distributed ledger technologies (DLTs) could provide such a platform, blockchains, such as Bitcoin, were not designed with IoT networks in mind, hence are often unsuitable for such applications: they offer poor transaction throughput and confirmation times, put stress on constrained computing and storage resources, and require high transaction fees. In this article, we consider a class of IoT-friendly DLTs based on directed acyclic graphs, rather than a blockchain, and with a reputation system in the place of Proof of Work (PoW). However, without PoW, the implementation of these DLTs requires an access control algorithm to manage the rate at which nodes can add new transactions to the ledger. We model the access control problem and present an algorithm that is fair, efficient, and secure. Our algorithm represents a new design paradigm for DLTs in which concepts from networking are applied to the DLT setting for the first time. For example, our algorithm uses distributed rate setting, which is similar in nature to transmission control used in the Internet. However, our solution features novel adaptations to cope with the adversarial environment of DLTs in which no individual agent can be trusted. Our algorithm guarantees utilization of resources, consistency, fairness, and resilience against attackers. All of these are achieved efficiently and with regard for the limitations of IoT devices. We perform extensive simulations to validate these claims.
Augmented reality technology in image-guided therapy: State-of-the-art review

Image-guided therapies have been on the rise in recent years as they can achieve higher accuracy and are less invasive than traditional methods. By combining augmented reality technology with image-guided therapy, more organs, and tissues can be observed by surgeons to improve surgical accuracy. In this review, 233 publications (dated from 2015 to 2020) on the design and application of augmented reality-based systems for image-guided therapy, including both research prototypes and commercial products, were considered for review. Based on their functions and applications. Sixteen studies were selected. The engineering specifications and applications were analyzed and summarized for each study. Finally, future directions and existing challenges in the field were summarized and discussed.