Reviewerly/exHarmony huggingface dataset:
End to End System for Handwritten Text Recognition and Plagiarism Detection using CNN &amp; BLSTM

In recent times, plagiarism of handwritten assignments has been rampant. Deep Learning models such as Convolutional Neural Networks have proven to be resourceful for recognition tasks in computer vision. Additionally, sequential models like LSTMs have been useful for handling cursive handwriting and variations in styles of writing. In this paper, we propose an end-to-end system that performs recognition of handwritten text assignment documents and gives the similarity scores between them. This system is a conjunction of a page-to-word segmentation algorithm, a convolutional neural network (CNN) and bi-directional long short-term memory (BLSTM) network for recognition, and a plagiarism detection module.
Application of Voiceprint Recognition Based on Improved ECAPA-TDNN

In speech signals, the characteristics of the same type of voiceprint in time usually have similar trends, and the trends of different voiceprints in time are quite different, which makes the time context information have a great influence on the results of speech recognition. Although the ECAPA-TDNN model can obtain time context information through dilated convolution to a certain extent, the time context information obtained by the model is not sufficient. Based on the ECAPA-TDNN model, this paper adds a bidirectional long-term and short-term memory network (Bi-directional LSTM, BLSTM) that can fully mine the time context, thus proposing the ECAPA-TDNN-LSTM model. The proposed model can not only emphasize channel attention, but also have the characteristics of information dissemination and aggregation, and can obtain rich time context information. Experimental results show that the recognition accuracy of the proposed algorithm reaches 89.17%, which improves the recognition accuracy of voiceprint.
Hybrid Model Convolutional Stage-Positive Definite Metric Operator-Infinity Laplacian Applied to Depth Completion

Depth completion is a fundamental task for many applications such as autonomous vehicles, 3D cinema, 3D reconstruction, and others. Many approaches have been proposed to tackle this problem, from classical models (variational models, morphological models, etc.) to convolutional networks. Hybrid models consider the advantages of the convolutional networks to select features and the generalization capacities of the classical models to extrapolate data. A hybrid model that considers convolutional stages and an interpolator has been proposed in the literature. This model assumes an anisotropic metric gij and the image domain (Ω), embedding the data in a Riemannian manifold <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$M$</tex> = (gij, Ω). The interpolation task is performed by solving a degenerated partial differential equation in this manifold M. The proposed metric gij used in this manifold is fundamental to correctly estimate distances between points in the sparse depth data. In this paper, our contributions are two-fold: first, an empirical evaluation of a metric based on a Positive Definite Operator Metric to compare color pixels applied to the depth completion task, and second, a variation of the infinity Laplacian (also the biased infinity Laplacian), namely unbalanced infinity Laplacian (unbalanced biased infinity Laplacian). Both interpolators include a weight map that balances the contribution of different models in the interpolation process. Experimental results in the KITTI Depth Completion Suite dataset, which is publicly available, show that the use of the Positive Definite Metric Operator performs better than the other two models and also performs better than similar models in this dataset.
Optimized Software Implementations of Ascon, Grain-128AEAD, and TinyJambu on ARM Cortex-M0

Implementations of lightweight cryptographic algorithms are needed in resource-constrained environments. The National Institute of Standards and Technology is evaluating the standardization of lightweight authenticated encryption with associated data and has selected ten finalists. This study conducts software evaluations of the finalists on ARM Cortex-M0, focusing on three performance metrics; latency, throughput, and memory usage. We also discuss the impact of input data length and parameters on implementation performance of the ten candidates. Based on the evaluation results of the reference code, we focus on three cryptographic algorithms, Ascon, Grain-128AEAD, and TinyJambu, and optimize the software codes. Our measurement results reveal that the finalists consume less memory than AES Galois counter mode.
A Multidimensional Cyber Defense Exercise: Emphasis on Emotional, Social, and Cognitive Aspects

Hands-on and practical learning has been key to cybersecurity education and training success. Cyber Defense Exercises (CDX) are a common approach to training, testing, and verifying technical and soft skills. However, full-scale CDX implementation is also an expensive training event. In order to advance such exercises to the next level, CDX organizers should further focus on educational, psychological, and cross-domain relationships. The paper discusses and proposes a multidimensional approach for CDX that balances cognitive, emotional, and social aspects critical for successful interdisciplinary learning. We share our experience incorporating knowledge from well-known psychology theories to CDX. We derive and describe seven elementary ingredients if a CDX is to meet the interdisciplinary and critical thinking needs of defensive cyberspace operations.
Practitioners’ Perceptions on the Adoption of Low Code Development Platforms

Organizations are under increasing pressure to develop applications within budget and time at high quality.Therefore, multiple organizations adopt Low Code Development Platforms (LCDP) to develop applications faster and cheaper compared to traditional application development.However, current research on LCDP adoption lacks empirical grounding as well as a deeper understanding of the importance of adoption drivers and inhibitors.We conducted semi-structured interviews and a Delphi study with seventeen experts to address these gaps.As a result, we identified twelve drivers and nineteen inhibitors for adopting LCDPs.We show that the experts have a consensus on the most and the least important drivers and inhibitors for LCDP adoption.Yet, the ranking of the drivers and inhibitors between the most and least important is highly context dependent.For some drivers and inhibitors, the experts' ranking is similar to academic literature, whereas, for others, it differs.In conclusion, the study at hand empirically validates drivers and inhibitors for LCDP adoption, adds six new drivers and six new inhibitors to the body of knowledge, and analyses the importance of these factors.
Ultralow Power On-the-Eye Vergence and Distance Sensing Through Differential Magnetometry

We demonstrate the realization of a very low energy, on-the-eye vergence-type distance ranger based on sensing of a locally-uniform vector field, specifically the earth's magnetic field.This ranging method is passive, only requiring measurement of the magnetic field vector at both eyeballs utilizing magnetometer chips placed on the eye scleral regions.The eye vergence angle and range distance are calculated from these two vector quantities.The method can obtain a range reading with as little as 118 nJ of energy consumed per eye for 3.3V and 50 nJ when operated at 1.9V.This method is thus suitable for applications where energy storage is very limited such as in smart contacts vision correcting microsystems.
Nonparametric failure time: Time‐to‐event machine learning with heteroskedastic Bayesian additive regression trees and low information omnibus Dirichlet process mixtures

Many popular survival models rely on restrictive parametric, or semiparametric, assumptions that could provide erroneous predictions when the effects of covariates are complex. Modern advances in computational hardware have led to an increasing interest in flexible Bayesian nonparametric methods for time-to-event data such as Bayesian additive regression trees (BART). We propose a novel approach that we call nonparametric failure time (NFT) BART in order to increase the flexibility beyond accelerated failure time (AFT) and proportional hazard models. NFT BART has three key features: (1) a BART prior for the mean function of the event time logarithm; (2) a heteroskedastic BART prior to deduce a covariate-dependent variance function; and (3) a flexible nonparametric error distribution using Dirichlet process mixtures (DPM). Our proposed approach widens the scope of hazard shapes including nonproportional hazards, can be scaled up to large sample sizes, naturally provides estimates of uncertainty via the posterior and can be seamlessly employed for variable selection. We provide convenient, user-friendly, computer software that is freely available as a reference implementation. Simulations demonstrate that NFT BART maintains excellent performance for survival prediction especially when AFT assumptions are violated by heteroskedasticity. We illustrate the proposed approach on a study examining predictors for mortality risk in patients undergoing hematopoietic stem cell transplant (HSCT) for blood-borne cancer, where heteroskedasticity and nonproportional hazards are likely present.
Lightweight saliency detection method for real-time localization of livestock meat bones

Existing salient object detection networks are large, have many parameters, are bulky and take up a lot of computational resources. Seriously hinder its application and promotion in boning robot. To solve this problem, this paper proposes a lightweight saliency detection algorithm for real-time localization of livestock meat bones. First, a lightweight feature extraction network based on multi-scale attention is constructed in the encoding stage. To ensure that more adequate salient object features are extracted with fewer parameters. Second, the fusion of jump connections is introduced in the decoding phase. Used to capture fine-grained semantics and coarse-grained semantics at full scale. Finally, we added a residual refinement module at the end of the backbone network. For optimizing salient target regions and boundaries. Experimental results on both publicly available datasets and self-made Pig leg X-ray (PLX) datasets show that. The proposed method is capable of ensuring first-class detection accuracy with 40 times less parameters than the conventional model. In the most challenging SOD dataset. The proposed algorithm in this paper achieves a value of Fωβ of 0.699. And the segmentation of livestock bones can be effectively performed on the homemade PLX dataset. Our model has a detection speed of 5fps on industrial control equipment.
Cyber range design framework for cyber security education and training

Abstract The need for effective training of cyber security personnel working in critical infrastructures and in the corporate has brought attention to the evolution of Cyber Ranges (CRs) as learning and training tools. Although CRs have been organized for many years, there is a lack of standards and common methodologies that facilitate their development and optimize their effectiveness. Aiming at strengthening cyber security education and research that utilize well designed CRs, we first analyze the CRs domain to identify key characteristics, strengths and fundamental weaknesses, and based on these outcomes we propose the Cyber Range Design Framework (CRDF), which includes the CR Architecture and the CR Life-Cycle. The CR Architecture presents the main components of CRDF compliant CRs, whereas the CR Life-Cycle presents the development phases of such approaches and the activities these phases embrace. CRDF builds on the Conceptual Framework for eLearning and Training (COFELET) and on the Exercise Life-Cycle. COFELET is particularly elaborated for the development of cyber security educational approaches, by adopting its design considerations that were based on widely adopted educational theories and approaches (e.g., scenario-based, reuse of elements). CRDF envisages the elaboration of CRs which optimize their impact, mitigate their weaknesses, and minimize their preparation and running costs. Under this prism, a preliminary appreciation of the CRDF approaches effectiveness is presented along with the expected outcomes of such approaches.
How to Improve Smart Contracts in the European Union Data Act

Abstract The article analyses the role of smart contracts in the architecture of the European Union’s Data Act proposal. It identifies five difficulties: lack of flexibility in terms of both content and operation; dependence on oracles which could lead to errors; vulnerability to bugs and changes in architecture; immutability and privacy; and problems of enforcement. It then offers some recommendations about how to address them to improve the Data Act.
Uses of epistemic uncertainties in the USGS National Seismic Hazard Models

The need for US Geological Survey (USGS) National Seismic Hazard Models (NSHMs) to report estimates of epistemic uncertainties in the hazard (e.g. fractile hazard curves) in all forthcoming releases is increasing. With fractile hazard curves as potential new outputs from the USGS 2023 NSHM, a simultaneous need is to help end-users better understand these epistemic uncertainties and clarify their potential uses. In this article, we address the latter need by (1) characterizing epistemic uncertainties in two updates of the USGS NSHM (2014 for California and 2021 for Hawaii), (2) illustrating a variety of downstream applications of fractile hazard curves in both hazard and risk contexts, and (3) discussing implications from the various types of uncertainties. We found that the epistemic uncertainty in hazard is generally larger for Hawaii than for California, the epistemic uncertainty in hazard can be reasonably approximated with a lognormal distribution for most of the cases considered, and the correlation between epistemic uncertainty in hazard at two different intensity measure levels generally varies with both location and type of intensity measure. Furthermore, we developed models for readily generating approximate fractile hazard curves in California and Hawaii. Finally, given the complexities involved in the hazard modeling process, we developed an open-source interactive tool to enable a broad range of users to independently examine and potentially start using such epistemic uncertainties for their respective applications.
There are no minimal essentially undecidable theories

Abstract We show that there is no theory that is minimal with respect to interpretability among recursively enumerable essentially undecidable theories.
Sensor Data Integration Using Ontologies for Event Detection

Nowadays, computing applications operate in environments with multiple and heterogeneous data sources, such as data generated from IoT devices. Without contextual information, the information derived from these isolated data sources may cause bias, error, or a lack of correct comprehension. Data integration can help to promote a holistic view of data and support getting the most trustful meaning from the information. This work proposes an architecture in which ontologies help to provide context for data integration. Furthermore, ontologies and complex network concepts enrich context awareness and derive relations among data to identify events of interest. The approach is evaluated in a controlled experiment using real data from hydrological and hydrometric sensors. The results indicate it is possible to detect context and relate events from different data sources to new significant events through ontology and graph network analysis.
Developing a Virtual Reality Educational Tool to Stimulate Emotions for Learning: Focus Group Study

By watching 360° videos in virtual reality headsets, students may experience being immersed in the portrayed situation. There is a paucity of empirical studies on the application of immersive 360° videos watched in virtual reality headsets for students in health care and social work education and the pedagogical theory guiding the development of such educational tools. This led to our interest in exploring how a virtual reality educational tool involving 360° videos can stimulate emotions and how this can be used as a pedagogical tool in these educational programs.The aim of this study was to explore the experiences of faculty members and students regarding a prototype 360° video watched in virtual reality headsets during the development phase of an educational project. We addressed the following research questions: How does the virtual reality prototype stimulate emotions? How can virtual reality be used in higher education for health care and social work students?We used a qualitative design and collected data through focus group interviews with project participants. The data were analyzed using qualitative content analysis.Our analysis identified 2 main themes in participants' experiences with the virtual reality prototype. The first theme highlights that when participants experienced watching the 360° video in a virtual reality headset, it stimulated their emotions as an authentic professional experience would. The second theme, contextualization of virtual reality, highlights participants' perceptions of how the virtual reality experience should be incorporated into a safe educational context.Our findings suggest that 360° videos with human actors who use eye contact with the camera can trigger emotions in the viewer and therefore serve as a pedagogic tool that can create authentic professional experiences for students. The participants expressed the view that the virtual reality educational tool could be used to prepare students for real-life practice in health care and social work. However, they underlined that 360° videos in virtual reality need to be contextualized in educational programs to create a safe environment for learning and to ensure follow-up on the emotions such experiences can trigger in students. Our results highlight the perceived importance of allowing students to reflect on the virtual reality experience in a safe setting and of follow-up by faculty members. In-person follow-up with students can be resource intensive for programs with large numbers of students and makes it challenging to offer repeated training, something that has been identified as one of the benefits of virtual reality.
An Instrument for measuring users’ meta-intents

We propose the concept of meta-intents which represent high-level user preferences related to the interaction and decision-making in conversational recommender systems (CRS) and present a questionnaire instrument for measuring meta-intents. We conducted a two-stage user study, an exploratory study with 212 participants on Prolific, and a confirmatory study with 394 participants on Prolific. We obtained a reliable and stable meta-intents questionnaire with 22 question items, corresponding to seven latent factors (concepts). These seven factors cover important interaction preferences and are closely related to users' decision-making process. For example, the factor dialog-initiative reflects whether users prefer to follow the system's guidance or ask their own questions in a CRS. We conducted statistical analyses of meta-intents in two domains (smartphones and hotels), and a general chatbot scenario. We also investigated the influence of additional factors (demography, decision-making style) on meta-intents through Structural Equation Modeling (SEM). Our results provide preliminary evidence that the proposed meta-intents are domain and demography (gender, age) independent. They can be linked to the general decision-making style and can thus be instrumental in translating general decision-making factors into more concrete design guidance for CRS and their potential personalization. Meta-intents also provide a basis for future analyses of interaction behavior in CRS and the development of a cognitively founded theoretical framework.
Guiding Oral Conversations: How to Nudge Users Towards Asking Questions?

How could an envisioned voice-based conversational information system assist the information seeker when the seeker does not know how to continue the conversation? The system could explicitly suggest a question to ask after each of its responses, but this approach quickly feels restrictive, repetitive, and interrupts immersion in the conversation. In this paper, we explore, for the first time, unobtrusive syntactic and auditive modifications of oral system responses to nudge information seekers towards asking about specific topics. We report the results of a crowdsourcing study with 965 participations that investigated the effectiveness and drawbacks of different modifications in three information scenarios.
Weakly Supervised Turn-level Engagingness Evaluator for Dialogues

Engagingness is an important measurement for evaluating open-domain conversational systems. The standard approach to evaluating dialogue engagingness is by measuring conversation turns per session (CTPS), which implies that the dialogue length is the main predictor of the user engagement with a dialogue system. The main limitation of CTPS is that it can only be measured at the session level, i.e., once the dialogue is over. But a dialogue system has to continuously monitor user engagement throughout the dialogue session as well. Existing approaches to measuring turn-level engagingness require human annotations for training. We pioneer an alternative approach, Weakly Supervised Engagingness Evaluator (WeSEE), which uses the remaining depth for each turn as a heuristic weak label for engagingness. Weakly Supervised Engagingness Evaluator (WeSEE) does not require human annotations and also relates closely to CTPS, thus serving as a good learning proxy for this metric. We show that WeSEE achieves the new state-of-the-art results on the Fine-grained Evaluation of Dialog dataset (0.38 Spearman correlation coefficient) and the DailyDialog dataset (0.62 Spearman correlation coefficient).
DisETrac: Distributed Eye-Tracking for Online Collaboration

Coordinating viewpoints with another person during a collaborative task can provide informative cues on human behavior. Despite the massive shift of collaborative spaces into virtual environments, versatile setups that enable eye-tracking in an online collaborative environment (distributed eye-tracking) remain unexplored. In this study, we present DisETrac- a versatile setup for eye-tracking in online collaborations. Further, we demonstrate and evaluate the utility of DisETrac through a user study. Finally, we discuss the implications of our results for future improvements. Our results indicate promising avenue for developing versatile setups for distributed eye-tracking.
RULKNE: Representing User Knowledge State in Search-as-Learning with Named Entities

A reliable representation of the user's knowledge state during a learning search session is crucial to understand their real information needs. When a search system is aware of such a state, it can adapt the search results and provide greater support for the user's learning objectives. A common practice to track the user's knowledge state is to consider the content of the documents they read during their search session(s). However, most current work ignores entity mentions in the documents, which, when linked to knowledge graphs, can be a source of valuable information regarding the user's knowledge. To fill this gap, we extend RULK—Representing User Knowledge in Search-as-Learning—with entity linking capabilities. The extended framework RULK represents and tracks user knowledge as a collection of such entities. It eventually estimates the user knowledge gain—learning outcome—by measuring the similarity between the represented knowledge and the learning objective. We show that our methods allow for up to 10% improvements when estimating user knowledge gains.