Reviewerly/exHarmony huggingface dataset:
To Secure Crime Data Alert and User Alert System using Blockchain

In the current scenario, the demand for computerization and data security has been increasing to combat with the increasing rate of crimes in the society. In the existing solution for prison management system there exist substantive data symmetry among prisons. Thus, during access of any prisoner detail, system may provide inappropriate details. Our objective is to implement a novel approach that stores the prisoner’s credentials and efficiently verifies the prisoner’s credentials via the blockchain technology. Our proposed solution is implements in the Hyperledger framework and the experimental result shows that our proposed solution is beneficial for the existing and upcoming investigation department.
Three-dimensional Human Tracking of a Mobile Robot by Skeleton Extraction Using Two Cameras

In this paper, we propose a new human tracking method equipped with a function to return tracking a person. Under the stereo framework of fusing the processing results of two cameras, there is a three-dimensional tracking system for a specific person by a mobile robot using OpenPose that can acquire skeletal information using deep learning. We propose a new human tracking system equipped with the function to return tracking a person by template matching that limits the search range by epipolar constraint. The function is to return tracking a person who failed to be tracked by using the image information that was successfully tracked. With this function, even if one camera fails to track a person, the robot can continue to track the person. The effectiveness of the proposed human tracking system is verified by experiments.
Genetic discrimination and protection of personal genetic data: Adapting legal standards to advances in genetics

Abstract Genetics has advanced to the point where genetic data on an individual could mark them as predisposed to hereditary illness or unsuitable for certain kinds of jobs. There is widespread apprehension that workers with ‘problematic’ genetics will be singled out by employers and insurance companies for treatment as second-class citizens with restrictions placed on their rights. The article takes up the legal issues involved in defining the concept of genetic data, in regulating genetic information as a type of personal information, and in applying genetic antidiscrimination laws in various countries. Legal restraints on genetic information must be more extensive than on other personal information about a worker because genetic data has implications for their biological relatives. Protection against genetic discrimination must therefore begin with barring employers from collecting genetic information on workers unless it is necessary to prevent hazards to people’s lives or health.
Proposal and Evaluation of Deep Profit Sharing Method in a Mixed Reward and Penalty Environment

Deep reinforcement learning, which combines deep learning and reinforcement learning, is attracting attention. In many cases, Q-learning (QL) is used as a reinforcement learning method. On the other hand, the authors are paying attention to exploitation-oriented learning such as Profit Sharing (PS) from the standpoint of strongly enhancing experience. Though many methods such as DQNwithPS have been proposed as a method that combines exploitation-oriented Learning and deep learning, complete independence from QL has not been realized in an environment where rewards and penalties are mixed. In this paper, we propose a deep exploitation-oriented learning method called deep profit sharing that does not use QL in an environment where rewards and penalties coexist. We confirm the effectiveness by numerical experiments.
USER’S IDENTIFICATION BY BIOSIGNALS FOR NETWORK ACCESS TO TELEMEDICAL SYSTEM

Применена реконструированная математическая модель, позволяющая генерировать искусственные временные ряды, которые диагностически эквивалентны зарегистрированным биосигналам. Приведены результаты моделирования. In the paper, it is proposed to use human biosignals for his identification in a telemedicine system for remote state monitoring. A reconstructed mathematical model is applied that allows generating artificial time series that are diagnostically equivalent to registered biosignals. Simulation results are presented.
A Formalism for Defeasible Inheritance

In this paper, we present a formalism for defeasible inheritance reasoning which we can show that it is computationally tractable. Links are interpreted as sentences in the language, L⇒, of a conditional logic D which is suitable for such type of reasoning.Defeasible inheritance reasoning is simply captur-able by employing simple and intuitive principle and strategies. We compare our approach to other approaches such as the default based-based approach by [POO 85, POO 88 and the path-based approach by [TOU 86] and show that it performs better representationally and/or in terms of capturing the required reasoning.
DIDACTIC ASPECTS OF CONCEPT MAPS IN THE EDUCATION BY THE ELECTRONIC EDUCATIONAL MODULES

The theoretical starting points of this discussion are based on the requirements of real practice of improving electronic education of students in the Slovak republic. Nowadays, there is a lack of electronic modules aimed at the measurement of work environment factors. Electronic education is putting increased demands on the self-education of students. The results of students’ education are dependent on their ability to identify significant information/notions acquired by self-education and to understand the connection between them. We putted down the research question - are students able to use concept maps to present their knowledge acquired by self-education? We were searching for the answers for this question and other questions in the research realized in the project KEGA Electronic learning modules for measuring working environment factors. The main goal of this paper is to present the results of the research aimed at verifying the level of students’ ability to create concept maps in certain topics of online education. The results of this research are pointing to differences in students’ perception of their ability to summarize their knowledge in the form of concept maps.
EXPLORING THE USE OF VOICE NOTES AS AN EFFICIENT ANSWER TO STUDENT EMAILS

C. Ferrer-Pérez1, M.C. Blanco-Gandía 1, S. Montagud-Romero2, N. Sánchez-Pérez1 1University of Zaragoza (SPAIN) 2University of Valencia (SPAIN)
A Framework of Meta Functional Learning for Regularising Knowledge Transfer

Machine learning classifiers' capability is largely dependent on the scale of available training data and limited by the model overfitting in data-scarce learning tasks. To address this problem, this work proposes a novel framework of Meta Functional Learning (MFL) by meta-learning a generalisable functional model from data-rich tasks whilst simultaneously regularising knowledge transfer to data-scarce tasks. The MFL computes meta-knowledge on functional regularisation generalisable to different learning tasks by which functional training on limited labelled data promotes more discriminative functions to be learned. Based on this framework, we formulate three variants of MFL: MFL with Prototypes (MFL-P) which learns a functional by auxiliary prototypes, Composite MFL (ComMFL) that transfers knowledge from both functional space and representational space, and MFL with Iterative Updates (MFL-IU) which improves knowledge transfer regularisation from MFL by progressively learning the functional regularisation in knowledge transfer. Moreover, we generalise these variants for knowledge transfer regularisation from binary classifiers to multi-class classifiers. Extensive experiments on two few-shot learning scenarios, Few-Shot Learning (FSL) and Cross-Domain Few-Shot Learning (CD-FSL), show that meta functional learning for knowledge transfer regularisation can improve FSL classifiers.
An Attention-based ConvLSTM Autoencoder with Dynamic Thresholding for Unsupervised Anomaly Detection in Multivariate Time Series

As a substantial amount of multivariate time series data is being produced by the complex systems in Smart Manufacturing, improved anomaly detection frameworks are needed to reduce the operational risks and the monitoring burden placed on the system operators. However, building such frameworks is challenging, as a sufficiently large amount of defective training data is often not available and frameworks are required to capture both the temporal and contextual dependencies across different time steps while being robust to noise. In this paper, we propose an unsupervised Attention-based Convolutional Long Short-Term Memory (ConvLSTM) Autoencoder with Dynamic Thresholding (ACLAE-DT) framework for anomaly detection and diagnosis in multivariate time series. The framework starts by pre-processing and enriching the data, before constructing feature images to characterize the system statuses across different time steps by capturing the inter-correlations between pairs of time series. Afterwards, the constructed feature images are fed into an attention-based ConvLSTM autoencoder, which aims to encode the constructed feature images and capture the temporal behavior, followed by decoding the compressed knowledge representation to reconstruct the feature images input. The reconstruction errors are then computed and subjected to a statistical-based, dynamic thresholding mechanism to detect and diagnose the anomalies. Evaluation results conducted on real-life manufacturing data demonstrate the performance strengths of the proposed approach over state-of-the-art methods under different experimental settings.
A note on the factorization of iterated quadratics over finite fields

Let $f$ be a monic quadratic polynomial over a finite field of odd characteristic. In 2012, Boston and Jones constructed a Markov process based on the post-critical orbit of $f$, and conjectured that its limiting distribution explains the factorization of large iterates of $f$. Later on, Xia, Boston, and the author did extensive Magma computations and found some exceptional families of quadratics that do not seem to follow the original Markov model conjectured by Boston and Jones. They did this by empirically observing that certain factorization patterns predicted by the Boston-Jones model never seem to occur for these polynomials, and suggested a multi-step Markov model which takes these missing factorization patterns into account. In this note, we provide proofs for all these missing factorization patterns. These are the first provable results that explain why the original conjecture of Boston and Jones does not hold for all monic quadratic polynomials.
Colonoscopy polyp detection with massive endoscopic images

We improved an existing end-to-end polyp detection model with better average precision validated by different data sets with trivial cost on detection speed. Our previous work on detecting polyps within colonoscopy provided an efficient end-to-end solution to alleviate doctor's examination overhead. However, our later experiments found this framework is not as robust as before as the condition of polyp capturing varies. In this work, we conducted several studies on data set, identifying main issues that causes low precision rate in the task of polyp detection. We used an optimized anchor generation methods to get better anchor box shape and more boxes are used for detection as we believe this is necessary for small object detection. A alternative backbone is used to compensate the heavy time cost introduced by dense anchor box regression. With use of the attention gate module, our model can achieve state-of-the-art polyp detection performance while still maintain real-time detection speed.
Computation offloading to hardware accelerators in Intel SGX and Gramine Library OS

The Intel Software Guard Extensions (SGX) technology enables applications to run in an isolated SGX enclave environment, with elevated confidentiality and integrity guarantees. Gramine Library OS facilitates execution of existing unmodified applications in SGX enclaves, requiring only an accompanying manifest file that describes the application's security posture and configuration. However, Intel SGX is a CPU-only technology, thus Gramine currently supports CPU-only workloads. To enable a broader class of applications that offload computations to hardware accelerators - GPU offload, NIC offload, FPGA offload, TPM communications - Gramine must be augmented with device-backed mmap support and generic ioctl support. In this paper, we describe the design and implementation of this newly added support, the corresponding changes to the manifest-file syntax and the requisite deep copy algorithm. We evaluate our implementation on Intel Media SDK workloads and discuss the encountered caveats and limitations. Finally, we outline a use case for the presented mmap/ioctl support beyond mere device communication, namely the mechanism to slice the application into the trusted enclave part (where the core application executes) and the untrusted shared-memory part (where insecure shared libraries execute).
ConTrip: Consensus Sentiment review Analysis and Platform ratings in a single score

People unequivocally employ reviews to decide on purchasing an item or an experience on the internet. In that regard, the growing significance and number of opinions have led to the development of methods to assess their sentiment content automatically. However, it is not straightforward for the models to create a consensus value that embodies the agreement of the different reviews and differentiates across equal ratings for an item. Based on the approach proposed by Nguyen et al. in 2020, we derive a novel consensus value named ConTrip that merges their consensus score and the overall rating of a platform for an item. ConTrip lies in the rating range values, which makes it more interpretable while maintaining the ability to differentiate across equally rated experiences. ConTrip is implemented and freely available under MIT license at https://github.com/pepebonet/contripscore
Coordinate Invariant User-Guided Constrained Path Planning with Reactive Rapidly Expanding Plane-Oriented Escaping Trees

As collaborative robots move closer to human environments, motion generation and reactive planning strategies that allow for elaborate task execution with minimal easy-to-implement guidance whilst coping with changes in the environment is of paramount importance. In this paper, we present a novel approach for generating real-time motion plans for point-to-point tasks using a single successful human demonstration. Our approach is based on screw linear interpolation,which allows us to respect the underlying geometric constraints that characterize the task and are implicitly present in the demonstration. We also integrate an original reactive collision avoidance approach with our planner. We present extensive experimental results to demonstrate that with our approach,by using a single demonstration of moving one block, we can generate motion plans for complex tasks like stacking multiple blocks (in a dynamic environment). Analogous generalization abilities are also shown for tasks like pouring and loading shelves. For the pouring task, we also show that a demonstration given for one-armed pouring can be used for planning pouring with a dual-armed manipulator of different kinematic structure.
Efficiency of photonic state tomography affected by fiber attenuation

In this article, we investigate the efficiency of photonic state tomography in the presence of fiber attenuation. The theoretical formalism of the photon loss is provided by implementing methods from the theory of open quantum systems. The quantum state is reconstructed from photon counts obtained for symmetric informationally complete POVMs. The number of photons that reach the detectors is numerically modeled by the binomial distribution, which describes the loss of light caused by the medium. This approach allows us to study the quality of state tomography versus the length of the fiber. In particular, we focus on entangled qubits and qutrits, which are sent through fibers of different lengths. The amount of entanglement detected by the measurement scheme is quantified and presented on graphs. The results demonstrate how the quality of photonic tomography depends on the distance between the source and the receiver.
Efficient Reinforcement Learning in Block MDPs: A Model-free Representation Learning Approach

We present BRIEE (Block-structured Representation learning with Interleaved Explore Exploit), an algorithm for efficient reinforcement learning in Markov Decision Processes with block-structured dynamics (i.e., Block MDPs), where rich observations are generated from a set of unknown latent states. BRIEE interleaves latent states discovery, exploration, and exploitation together, and can provably learn a near-optimal policy with sample complexity scaling polynomially in the number of latent states, actions, and the time horizon, with no dependence on the size of the potentially infinite observation space. Empirically, we show that BRIEE is more sample efficient than the state-of-art Block MDP algorithm HOMER and other empirical RL baselines on challenging rich-observation combination lock problems that require deep exploration.
Exploring the acceptability of digital contact tracing for UK students

Contact tracing systems control the spread of disease by discovering the set of people an infectious individual has come into contact with. Students are often mobile and sociable and therefore can contribute to the spread of disease. Controls on the movement of students studying in the UK were put in place during the Covid-19 pandemic, and some restrictions may be necessary over several years. App based digital contact tracing may help ease restrictions by enabling students to make informed decisions and take precautions. However, designing for the end user acceptability of these apps remains under-explored. This study with 22 students from UK Universities (inc. 11 international students) uses a fictional user interface to prompt in-depth interviews on the acceptability of contact tracing tools. We explore intended uptake, usage and compliance with contact tracing apps, finding students are positive, although concerned about privacy, security, and burden of participating.
Fine-Tuning Approach for Arabic Offensive Language Detection System: BERT-Based Model

The problem of online offensive language limits the health and security of online users. It is essential to apply the latest state-of-the-art techniques in developing a system to detect online offensive language and to ensure social justice to the online communities. Our study investigates the effects of fine-tuning across several Arabic offensive language datasets. We develop multiple classifiers that use four datasets individually and in combination in order to gain knowledge about online Arabic offensive content and classify users comments accordingly. Our results demonstrate the limited effects of transfer learning on the classifiers performance, particularly for highly dialectal comments.
Minimax rate of consistency for linear models with missing values

Missing values arise in most real-world data sets due to the aggregation of multiple sources and intrinsically missing information (sensor failure, unanswered questions in surveys...). In fact, the very nature of missing values usually prevents us from running standard learning algorithms. In this paper, we focus on the extensively-studied linear models, but in presence of missing values, which turns out to be quite a challenging task. Indeed, the Bayes rule can be decomposed as a sum of predictors corresponding to each missing pattern. This eventually requires to solve a number of learning tasks, exponential in the number of input features, which makes predictions impossible for current real-world datasets. First, we propose a rigorous setting to analyze a least-square type estimator and establish a bound on the excess risk which increases exponentially in the dimension. Consequently, we leverage the missing data distribution to propose a new algorithm, andderive associated adaptive risk bounds that turn out to be minimax optimal. Numerical experiments highlight the benefits of our method compared to state-of-the-art algorithms used for predictions with missing values.
Model-Agnostic Augmentation for Accurate Graph Classification

Given a graph dataset, how can we augment it for accurate graph classification? Graph augmentation is an essential strategy to improve the performance of graph-based tasks, and has been widely utilized for analyzing web and social graphs. However, previous works for graph augmentation either a) involve the target model in the process of augmentation, losing the generalizability to other tasks, or b) rely on simple heuristics that lead to unreliable results. In this work, we introduce five desired properties for effective augmentation. Then, we propose NodeSam (Node Split and Merge) and SubMix (Subgraph Mix), two model-agnostic algorithms for graph augmentation that satisfy all desired properties with different motivations. NodeSam makes a balanced change of the graph structure to minimize the risk of semantic change, while SubMix mixes random subgraphs of multiple graphs to create rich soft labels combining the evidence for different classes. Our experiments on social networks and molecular graphs show that NodeSam and SubMix outperform existing approaches in graph classification.
NIMBLE: A Non-rigid Hand Model with Bones and Muscles

Emerging Metaverse applications demand reliable, accurate, and photorealistic reproductions of human hands to perform sophisticated operations as if in the physical world. While real human hand represents one of the most intricate coordination between bones, muscle, tendon, and skin, state-of-the-art techniques unanimously focus on modeling only the skeleton of the hand. In this paper, we present NIMBLE, a novel parametric hand model that includes the missing key components, bringing 3D hand model to a new level of realism. We first annotate muscles, bones and skins on the recent Magnetic Resonance Imaging hand (MRI-Hand) dataset and then register a volumetric template hand onto individual poses and subjects within the dataset. NIMBLE consists of 20 bones as triangular meshes, 7 muscle groups as tetrahedral meshes, and a skin mesh. Via iterative shape registration and parameter learning, it further produces shape blend shapes, pose blend shapes, and a joint regressor. We demonstrate applying NIMBLE to modeling, rendering, and visual inference tasks. By enforcing the inner bones and muscles to match anatomic and kinematic rules, NIMBLE can animate 3D hands to new poses at unprecedented realism. To model the appearance of skin, we further construct a photometric HandStage to acquire high-quality textures and normal maps to model wrinkles and palm print. Finally, NIMBLE also benefits learning-based hand pose and shape estimation by either synthesizing rich data or acting directly as a differentiable layer in the inference network.
OWL (Observe, Watch, Listen): Audiovisual Temporal Context for Localizing Actions in Egocentric Videos

Egocentric videos capture sequences of human activities from a first-person perspective and can provide rich multimodal signals. However, most current localization methods use third-person videos and only incorporate visual information. In this work, we take a deep look into the effectiveness of audiovisual context in detecting actions in egocentric videos and introduce a simple-yet-effective approach via Observing, Watching, and Listening (OWL). OWL leverages audiovisual information and context for egocentric temporal action localization (TAL). We validate our approach in two large-scale datasets, EPIC-Kitchens, and HOMAGE. Extensive experiments demonstrate the relevance of the audiovisual temporal context. Namely, we boost the localization performance (mAP) over visual-only models by +2.23% and +3.35% in the above datasets.