Reviewerly/exHarmony huggingface dataset:
Comparison of computerized and manual versions of the Wisconsin Card Sorting Test on schizophrenia and healthy samples.

This study aims to examine whether there is an equivalence between the two different versions of Wisconsin Card Sorting Test (WCST) regarding psychometric properties on two distinct samples: individuals diagnosed with schizophrenia and those who are healthy. A total of 504 individuals were included in the study, 243 were diagnosed with schizophrenia, and 261 were healthy individuals. Both samples were separated into two subsamples, and the individuals in each separated group were administered either WCST-C (computerized) or WCST-M (manual) version of the test. We compared statistical measures of mean and variance with the data obtained from the two samples. We carried out analyses related to parallel forms reliability and equality of variances of the tests scores produced by parallel forms of the WCST. There was no significant difference between mean values of manual and computerized versions administered for each sample. However, the patterns of variances of the obtained scores were dissimilar. According to these findings, the two versions were found to be inequivalent regarding psychometric properties. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Blockchain-powered Social Media Analytics in Supply Chain Management

Social networking platforms have provided users with incomparable opportunities for the creation of data as well as its analysis. With the increasing use of social media, particularly among teenagers, Social Media Analytics (SMA) has become a critical tool in governing Supply Chain Management (SCM) operations. However, the analysis of data gathered from traditional social media platforms has some serious issues. With the advent of blockchain, some noteworthy social media platforms supported by blockchain are developed. Apropos these issues, this research aims to shed light on how blockchain can be a revolution in overcoming the challenges of traditional social media. Some popular blockchain integrated social media applications are also discussed along with future research directions.
Accurate Multilevel Classification for Wildlife Images

The most common approaches for classification rely on the inference of a specific class. However, every category could be naturally organized within a taxonomic tree, from the most general concept to the specific element, and that is how human knowledge works. This representation avoids the necessity of learning roughly the same features for a range of very similar categories, and it is easier to understand and work with and provides a classification for each abstraction level. In this paper, we carry out an exhaustive study of different methods to perform multilevel classification applied to the task of classifying wild animals and plant species. Different convolutional backbones, data setups, and ensembling techniques are explored to find the model which provides the best performance. As our experimentation remarks, in order to achieve the best performance on the datasets that are arranged in a tree-like structure, the classifier must feature an EfficientNetB5 backbone with an input size of <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mn>300</mn> <mo>×</mo> <mn>300</mn> </math> px, followed by a multilevel classifier. In addition, a Multiscale Crop data augmentation process must be carried out. Finally, the accuracy of this setup is a 62% top-1 accuracy and 88% top-5 accuracy. The architecture could benefit for an accuracy boost if it is involved in an ensemble of cascade classifiers, but the computational demand is unbearable for any real application.
Semiotic Aggregation in Deep Learning

Convolutional neural networks utilize a hierarchy of neural network layers. The statistical aspects of information concentration in successive layers can bring an insight into the feature abstraction process. We analyze the saliency maps of these layers from the perspective of semiotics, also known as the study of signs and sign-using behavior. In computational semiotics, this aggregation operation (known as superization) is accompanied by a decrease of spatial entropy: signs are aggregated into supersign. Using spatial entropy, we compute the information content of the saliency maps and study the superization processes which take place between successive layers of the network. In our experiments, we visualize the superization process and show how the obtained knowledge can be used to explain the neural decision model. In addition, we attempt to optimize the architecture of the neural model employing a semiotic greedy technique. To the extent of our knowledge, this is the first application of computational semiotics in the analysis and interpretation of deep neural networks.
LSOIE: A Large-Scale Dataset for Supervised Open Information Extraction

Open Information Extraction (OIE) systems seek to compress the factual propositions of a sentence into a series of n-ary tuples.These tuples are useful for downstream tasks in natural language processing like knowledge base creation, textual entailment, and natural language understanding.However, current OIE datasets are limited in both size and diversity.We introduce a new dataset by converting the QA-SRL 2.0 dataset to a large-scale OIE dataset (LSOIE).Our LSOIE dataset is 20 times larger than the next largest human-annotated OIE dataset.We construct and evaluate several benchmark OIE models on LSOIE, providing baselines for future improvements on the task.Our LSOIE data, models, and code are made publicly available.
Spons &amp; Shields: practical isolation for trusted execution

Trusted execution environments (TEEs) promise a cost-effective, “lift-and-shift” solution for deploying security-sensitive applications in untrusted clouds. For this, they must support rich, multi-component applications, but a large trusted computing base (TCB) inside the TEE risks that attackers can compromise application security. Fine-grained compartmentalisation can increase security through defense-in-depth, but current solutions either run all software components unprotected in the same TEE, lack efficient shared memory support, or isolate application processes using separate TEEs, impacting performance and compatibility.
Dictionary-based Debiasing of Pre-trained Word Embeddings

Word embeddings trained on large corpora have shown to encode high levels of unfair discriminatory gender, racial, religious and ethnic biases.In contrast, human-written dictionaries describe the meanings of words in a concise, objective and an unbiased manner.We propose a method for debiasing pre-trained word embeddings using dictionaries, without requiring access to the original training resources or any knowledge regarding the word embedding algorithms used.Unlike prior work, our proposed method does not require the types of biases to be pre-defined in the form of word lists, and learns the constraints that must be satisfied by unbiased word embeddings automatically from dictionary definitions of the words.Specifically, we learn an encoder to generate a debiased version of an input word embedding such that it (a) retains the semantics of the pre-trained word embeddings, (b) agrees with the unbiased definition of the word according to the dictionary, and (c) remains orthogonal to the vector space spanned by any biased basis vectors in the pre-trained word embedding space.Experimental results on standard benchmark datasets show that the proposed method can accurately remove unfair biases encoded in pre-trained word embeddings, while preserving useful semantics.
Automatic Qubit Characterization and Gate Optimization with <i>QubiC</i>

As the size and complexity of a quantum computer increases, quantum bit (qubit) characterization and gate optimization become complex and time-consuming tasks. Current calibration techniques require complicated and verbose measurements to tune up qubits and gates, which cannot easily expand to the large-scale quantum systems. We develop a concise and automatic calibration protocol to characterize qubits and optimize gates using QubiC , which is an open source FPGA (field-programmable gate array)-based control and measurement system for superconducting quantum information processors. We propose multi-dimensional loss-based optimization of single-qubit gates and full XY-plane measurement method for the two-qubit CNOT gate calibration. We demonstrate the QubiC automatic calibration protocols are capable of delivering high-fidelity gates on the state-of-the-art transmon-type processor operating at the Advanced Quantum Testbed at Lawrence Berkeley National Laboratory. The single-qubit and two-qubit Clifford gate infidelities measured by randomized benchmarking are of 4.9(1.1) × 10 -4 and 1.4(3) × 10 -2 , respectively.
How resilient is reading to letter rotations? A parafoveal preview investigation.

Skilled readers have developed a certain amount of tolerance to variations in the visual form of words (e.g., CAPTCHAs, handwritten text, etc.). To examine how visual distortion affects the mapping from the visual input onto abstract word representations during normal reading, we focused on a single type of distortion: letter rotation. Importantly, two leading neurally inspired models of word recognition (SERIOL model, Whitney, 2001; LCD model, Dehaene et al., 2005) make distinct predictions: Whereas the SERIOL model postulates that the cost of letter rotation increases gradually, the LCD model assumes an all-or-none boundary at around 40° to 45° rotation angle. To examine these predictions in a normal reading scenario, we conducted a parafoveal preview experiment using the gaze-contingent boundary change paradigm. The parafoveal previews were identical or unrelated to the target word. Critically, each preview's individual letters were rotated 15°, 30°, 45°, or 60°. Apart from the parafoveal previews, all text, including target words, was presented with letters in their canonical upright orientation. Results showed that the advantage of the identity preview condition in eye fixation times on the target word decreased progressively by rotation angle. This pattern of results favors the view that the cost of letter rotation during normal reading increases gradually as a function of the angle of rotation. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Multi-Person Activity Recognition in Continuously Monitored Smart Homes

Activity recognizers are challenging to design for continuous, in-home settings. However, they are notoriously difficult to create when there is more than one resident in the home. Despite recent efforts, there remains a need for an algorithm that can esti-mate the number of residents in the house, split a time series stream into separate substreams, and accurately identify each resident's activities. To address this challenge, we introduce GAMUT. This novel unsupervised method jointly estimates the number of residents and associates sensor readings with those residents, based on a multi-target Gaussian mixture probability hypothesis density filter. We hypothesize that the proposed method will offer robust recognition for homes with two or more residents. In experiments with labeled data collected from 50 single-resident and 11 multi-resident homes, we observe that GAMUT outperforms previous unsupervised and supervised methods, offering a robust strategy to track behavioral routines in complex settings.
Process-Level Representation of Scientific Protocols with Interactive Annotation

We develop Process Execution Graphs (PEG), a document-level representation of real-world wet lab biochemistry protocols, addressing challenges such as cross-sentence relations, long-range coreference, grounding, and implicit arguments.We manually annotate PEGs in a corpus of complex lab protocols with a novel interactive textual simulator that keeps track of entity traits and semantic constraints during annotation.We use this data to develop graph-prediction models, finding them to be good at entity identification and local relation extraction, while our corpus facilitates further exploration of challenging long-range relations. 1
Real time availability and consistency of health-related information across multiple stakeholders: A blockchain based approach

Sensitivity of the health-related data and the focus on compliance and security has traditionally emphasized the need for centralized approach while implementing Electronic Health Records (EHR) systems. These one-institutional architectural designs are leading to fragmented and scattered pieces of valuable data across various data warehouses and silos. Interoperability challenges arise due to the absence of unified data management and exchange mechanisms making the social need for fundamental design changes bigger. The capability of a distributed ledger technology and blockchain to offer immutable, decentralized and cryptographically secured record of transactions throughout a peer-to-peer network can facilitate better collaboration and increased interoperability in the field of health and insurance information exchange processes. The paper examines different approaches and application of blockchain technology and identifies which implementations of components are more suitable and beneficial for the specific eco-system analyzed in the paper. This paper presents alternative way of dealing with information exchange across multiple stakeholders by justifying the use of decentralized approach, distributed access and solution how to comprehensively track and assemble health related data. We propose an architectural design and overview of a specific use case with focus on information exchange processes between health insurance providers and health care organizations, by using blockchain as an underlying technology. The architectural overview and data flows, backed up by sequence diagrams from specific use cases offered in this paper, can serve as a guide to the blockchain technology adoption and initial setup.
Cross-Cultural Similarity Features for Cross-Lingual Transfer Learning of Pragmatically Motivated Tasks

Much work in cross-lingual transfer learning explored how to select better transfer languages for multilingual tasks, primarily focusing on typological and genealogical similarities between languages.We hypothesize that these measures of linguistic proximity are not enough when working with pragmaticallymotivated tasks, such as sentiment analysis.As an alternative, we introduce three linguistic features that capture cross-cultural similarities that manifest in linguistic patterns and quantify distinct aspects of language pragmatics: language context-level, figurative language, and the lexification of emotion concepts.Our analyses show that the proposed pragmatic features do capture cross-cultural similarities and align well with existing work in sociolinguistics and linguistic anthropology.We further corroborate the effectiveness of pragmatically-driven transfer in the downstream task of choosing transfer languages for cross-lingual sentiment analysis.
Rough Neural Network and Adaptive Feedback Linearization Control Based on Lyapunov Function

In this manuscript, rough neural network and adaptive feedback linearization control based on Lyapunov function is studied. The assumed system in this study is a system that contains both linear and nonlinear terms. The considered controller in this paper is adaptive feedback linearization, in which the adaptation laws of the weights are found as a result of the stability proof in the sense of Lyapunov. Although feedback linearization has a strong potential to eliminate the nonlinear term of the system, its weakness is the requirement of an exact model of the process. Due to the fact that the identification of almost all of the nonlinear systems is an arduous task, by using the broad-appeal neural network, this problem has been facilitated. Since the most prominent problem of any identification is uncertainty, the rough strategy is aimed to decrease the uncertainties of the identification. So, they have designed based on rough neurons. A rough neuron is regarded as a pair of neurons called lower boundary neuron and upper boundary neuron. Rough neuron approach, allows the use of interval computing in neural networks. Therefore, it can be considered as a new theory in designing neural networks. Ultimately, by two examples, the effectiveness of the proposed method is illustrated, and a comparison with other papers is made.
Human behavior scoring in credit card fraud detection

&lt;span lang="EN-US"&gt;Now days, the analysis of the behavior of cardholders is one of the important fields in electronic payment. This kind of analysis helps to extract behavioral and transaction profile patterns that can help financial systems to better protect their customers. In this paper, we propose an intelligent machine learning (ML) system for rules generation. It is based on a hybrid approach using rough set theory for feature selection, fuzzy logic and association rules for rules generation. A score function is defined and computed for each transaction based on the number of rules, that make this transaction suspicious. This score is kind of risk factor used to measure the level of awareness of the transaction and to improve a card fraud detection system in general. The behavior analysis level is a part of a whole financial fraud detection system where it is combined to intelligent classification to improve the fraud detection. In this work, we also propose an implementation of this system integrating the behavioral layer. The system results obtained are very convincing and the consumed time by our system, per transaction was 6 ms, which prove that our system is able to handle real time process.&lt;/span&gt;
TeleCOVID-19: A Multifaceted Strategy from a Public Brazilian Telehealth Service During the COVID-19 Pandemic

The COVID-19 pandemic has quickly and radically transformed health systems worldwide. The challenges are imposed by the need for social distance, remote management of less severe cases, and the constant need for updating health care professionals and the population with reliable information. We aim to describe the experiences and developments of a Brazilian telehealth public service during the pandemic. Numerous tools have been developed and made available, to be used in an integrated manner, by both health professionals and the general public. Those included a chatbot for guidance, a teleconsultation platform combined with a telemonitoring system, a teleconsulting service, and a tele-education program. The TNMG services appear to be efficient and robust during the health crisis of COVID-19, through different tools and methodologies focused on both professionals and users of the health systems.
Temporally sorting images from real-world events

As smartphones become ubiquitous in modern life, every major event — from musical concerts to terrorist attempts — is massively captured by multiple devices and instantly uploaded to the Internet. Once shared through social media, the chronological order between available media pieces cannot be reliably recovered, hindering the understanding and reconstruction of that event. In this work, we propose data-driven methods for temporally sorting images originated from heterogeneous sources and captured from distinct angles, viewpoints, and moments. We model the chronological sorting task as an ensemble of binary classifiers whose answers are combined hierarchically to estimate an image’s temporal position within the duration of the event. We evaluate our method on images from the Notre-Dame Catedral fire and the Grenfell Tower fire events and discuss research challenges for analyzing data from real-world forensic events. Finally, we employ visualization techniques to understand what our models have learned, offering additional insights to the problem.
The role of ‘Rich Tasks’ an interdisciplinary and digital approach to learning post COVID-19

The Covid-19 pandemic has thrown into stark relief the importance of students being able to be flexible learners: juggling blended learning opportunities, being independent and creative, and collaborating with staff and students- often in digital virtual environments. This paper reviews alternative interdisciplinary curriculum models that break down artificial barriers between subject disciplines through the use of ‘Rich Tasks’, interdisciplinary tasks which integrate subject knowledge with developing student skills in the 4C’s collaboration, critical thinking, communication, and creativity. The paper suggests that ‘Rich Tasks’ provide a futureproof methodology as they focus learning on the global, emotionally resilient skills that children need to adapt to a changing digital world where they may change jobs many times. Historic and current interdisciplinary learning models are explored. Interviews with key practitioners and learners who were and are involved in these were conducted. Significantly positive responses to interdisciplinary learning from both groups of respondents are found. A development of the form of learning called ‘Rich Tasks’ (See Moulds 2004 for definitions) can be constructed through the enhanced knowledge and utilisation of digital technology which teachers have gained worldwide in order to continue teaching during the pandemic.
Abstract Value Iteration for Hierarchical Reinforcement Learning

We propose a novel hierarchical reinforcement learning framework for control with continuous state and action spaces. In our framework, the user specifies subgoal regions which are subsets of states; then, we (i) learn options that serve as transitions between these subgoal regions, and (ii) construct a high-level plan in the resulting abstract decision process (ADP). A key challenge is that the ADP may not be Markov, which we address by proposing two algorithms for planning in the ADP. Our first algorithm is conservative, allowing us to prove theoretical guarantees on its performance, which help inform the design of subgoal regions. Our second algorithm is a practical one that interweaves planning at the abstract level and learning at the concrete level. In our experiments, we demonstrate that our approach outperforms state-of-the-art hierarchical reinforcement learning algorithms on several challenging benchmarks.
A Blockchain-Based Crypto-Anchor Platform for Interoperable Product Authentication

Counterfeiting is a major problem in many industries, causing hundreds of billions of dollars in damages every year. Digital twins further increase a product's value, making it critical to secure the link between the physical and digital world. An entire industry has sprung up to address this problem with a wide variety of product-authentication technologies, or crypto anchors, offering many different trade-offs between supported product surface, security level, price sensitivity, ease of use, etc. Each crypto-anchor vendor provides their own APIs for commissioning, verification and monitoring which makes it hard for application developers to support many of them. This paper introduces a platform that provides a generic model of an object secured by a crypto anchor, supporting any number of product authentication technologies by means of crypto-anchor plugins. Applications programmed against this generic model can immediately interoperate with any of the plugged-in crypto anchors. We present a blockchain architecture for the platform whose decentralization matches the highly differentiated industry structure. We've implemented the system on Hyperledger Fabric.