Reviewerly/exHarmony huggingface dataset:
Evidence Inference 2.0: More Data, Better Models

How do we most effectively treat a disease or condition?Ideally, we could consult a database of evidence gleaned from clinical trials to answer such questions.Unfortunately, no such database exists; clinical trial results are instead disseminated primarily via lengthy natural language articles.Perusing all such articles would be prohibitively time-consuming for healthcare practitioners; they instead tend to depend on manually compiled systematic reviews of medical literature to inform care.NLP may speed this process up, and eventually facilitate immediate consult of published evidence.The Evidence Inference dataset (Lehman et al., 2019) was recently released to facilitate research toward this end.This task entails inferring the comparative performance of two treatments, with respect to a given outcome, from a particular article (describing a clinical trial) and identifying supporting evidence.For instance: Does this article report that chemotherapy performed better than surgery for five-year survival rates of operable cancers?In this paper, we collect additional annotations to expand the Evidence Inference dataset by 25%, provide stronger baseline models, systematically inspect the errors that these make, and probe dataset quality.We also release an abstract only (as opposed to full-texts) version of the task for rapid model prototyping.The updated corpus, documentation, and code for new baselines and evaluations are available at http
Potassium and Calcium <i>K</i>‐Edge XANES in Chemical Compounds and Minerals: Implications for Geological Phase Identification

Potassium (K) and calcium (Ca) K ‐edge X‐ray adsorption near‐edge (XANES) spectroscopy were performed on thirty‐three chemical compounds and geological materials, including chemical reagents, organometallic compounds, silicates, carbonates and igneous rock reference materials. The results confirm that the fine structure of the K ‐edges for specimens is unique and distinguishable. The results suggest that compositional and local atomic variations strongly regulate spectral characteristics. Acquired XANES spectra with the library of distinctive spectral features of model references approve the fingerprint identification of different phases of K and Ca involved in geological materials. Moreover, this reveals that typical compositional changes in geological samples could strongly affect spectral features. As an example, we quantitatively determined the silicate species of K and Ca in two igneous rock reference materials by linear combination fitting. The dominant hosts and molecular environments of K and Ca can be interpreted based on pre‐edge/post‐edge peak position, intensity, shifts and resonance features, thus improving the understanding of the (bio)geochemical cycling, partitioning and isotopic fractionation of K and Ca. The outcomes serve as a complementary database for a vast number of scientific contexts, including aspects of geological and environment sciences.
Influence of solar tracking error on the performance of a small-scale linear Fresnel reflector

Solar tracking systems are an indispensable requirement for optimal efficiency in small-scale linear Fresnel reflector systems. A thorough study of the influence of solar tracking errors on energy production is, therefore, required. The power produced by each mirror at any moment is also explicitly computed, as well as its variation under error. We study the large effect that the distance between the mirror and the absorber tube, solar time, and the day of the year have on the variation of power. Our results provide the foundations for the analysis of small-scale solar tracking systems. Solar tracking errors of less than 0.09∘ are acceptable (they cause power losses less than 1%), whereas errors larger than 0.36∘ start being noticeable (power losses greater than 3%).
Multi-modal cyberbullying detection on social networks

Because social networks have become a vital part of people's lives, cyberbullying becomes the most common risk encountered by young people on social networking platforms and raised serious concerns in society. Over the past few decades, most existing work on cyberbullying has focused on text analysis. Yet, the cyberbullying develops into multi-objective, multi-channel, and multi-form. Traditional text analysis methods cannot satisfy the diversity of bullying data in social networks. To deal with the new type of cyberbullying, we propose a multi-modal detection framework that takes into multi-modal information(e.g., image, video, comments, time) on social networks. Specifically, we not only extract textual features but also use the hierarchical attention networks to capture the session feature in social networks and encode several media information(e.g., video, image). Based on these features, we model the multi-modal cyberbullying detection framework to solve the new form of cyberbullying. Experimental analysis on two real-world datasets shows that our framework outperforms several existing state-of-the-art models.
A Machine Learning Based Fall Detection for Elderly People with Neurodegenerative Disorders

Fall is one of the most serious clinical problems faced by the elderly people. Elder people with neurodegenerative disorders like Parkinson disease often fall. This leads to the damage of physical condition and also mental condition. Therefore, elderly people should be taken care of all the time. However, it is not possible to take care of them every moment. Therefore, an automatic fall detection system is required to track elderly at any time. An automated fall detection system will provide timely assistance and hence, it will reduce medical care costs significantly. The recent developments in motion- sensor technologies have allowed the efficient use of wearable sensors in the overall treatment of the elderly. The paper presents a machine learning framework consisting of data collection, preprocessing of data, feature extraction and machine learning classifiers. They comprise C4.5, Random Forest, RepTree, and LMT (Logistic Model Tree). Dataset used in this research has been collected by using 3-axis accelerometer sensors which are mounted on a person’s waist. Features have been extracted from this dataset which are used by these classifiers. C4.5 gives the highest accuracy which is 97.36% in comparison to other classifiers.
Circuit-QED with phase-biased Josephson weak links

By coupling a superconducting weak link to a microwave resonator, recent experiments probed the spectrum and achieved the quantum manipulation of Andreev states in various systems. However, the quantitative understanding of the response of the resonator to changes in the occupancy of the Andreev levels, which are of fermionic nature, is missing. Here, using Bogoliubov–de Gennes formalism to describe the weak link and a general formulation of the coupling to the resonator, we calculate the shift of the resonator frequency as a function of the levels occupancy and describe how transitions are induced by phase or electric field microwave drives. We apply this formalism to analyze recent experimental results obtained using circuit-QED techniques on superconducting atomic contacts and semiconducting nanowire Josephson junctions.7 MoreReceived 29 September 2020Accepted 4 December 2020DOI:https://doi.org/10.1103/PhysRevResearch.3.013036Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasAndreev reflectionJosephson effectSuperconductivityPhysical SystemsWeak linksTechniquesMicrowave techniquesCondensed Matter, Materials & Applied Physics
A metric for Filter Bubble measurement in recommender algorithms considering the news domain

Recommender systems have been constantly refined to improve the accuracy of rating prediction and ranking generation. However, when a recommender system is too accurate in predicting the users’ interests, negative impacts can arise. One of the most critical is the filter bubbles creation, a situation where a user receives less content diversity. In the news domain, such effect is critical once they are ways of opinion formation. In this paper, we aim to assess the role that a specific set of recommender algorithms has in the creation of filter bubbles and if diversification approaches can decrease such effect. We also verify the effects of such an environment in the users’ exposition and interaction to fake news in the Brazilian presidential election of 2018. To perform such a study, we developed a prototype that recommends news stories and presents these recommendations in a feed. To measure the filter bubble, we introduce a new metric based on the homogenization of a recommended items’ set. Our results show KNN item-based recommendation with the MMR diversification algorithm performs slightly better in putting the user in contact with less homogeneous content while presenting a lower index of likes in fake news.
A novel community answer matching approach based on phrase fusion heterogeneous information network

Community Question Answering (CQA) allows users to ask or answer questions in a social way, so it is becoming the primary means for people acquiring knowledge. However, the asker must wait until a satisfactory answer appears, which reduces user activity. In this paper, we propose an innovative answering method that matches the most relevant answers for the new issue automatically. Firstly, we utilize phrases to represent the semantic of the posts (answers/questions) and construct a Phrase Fusion Heterogeneous Information Network, called PFHIN, to represent complex entity relationships in CQA. So, the answer selection is regarded as the related entity retrieval task. Then, we define the distance between entities in PFHIN, which is independent of the meta path. Finally, the Type-constrained Top-k Similarity Entity Finding Algorithm (TTSEF) is proposed for finding the nearest entities according to the known start entity and end-entity type, which can match the most relevant answers automatically.To the best of our knowledge, it is the first work to define the phrase information network for answer selection and provide a novel idea for the heterogeneous information network fusion. Experimental results on three large-scale datasets (Stack Overflow, Super User, and Mathematics) from Stack Exchange demonstrate that our proposed approaches significantly outperform the state-of-the-art answer retrieval methods. Moreover, we conduct an in-depth analysis of the meta path to the optimal answer and reveal the critical role of phrases in community answer matching.
Two-Round Trip Schnorr Multi-signatures via Delinearized Witnesses

We construct a two-round Schnorr-based signature scheme (DWMS) by delinearizing two pre-commitments supplied by each signer. DWMS is a secure signature scheme in the algebraic group model (AGM) and the random oracle model (ROM) under the assumption of the hardness of the one-more discrete logarithm problem and the 2-entwined sum problem that we introduce in this paper. Our new m-entwined sum problem tweaks the k-sum problem in a scalar field using the associated group. We prove the hardness of our new problem in the AGM assuming the hardness of the discrete logarithm problem in the associated group. We believe that our new problem simplifies the security proofs of multi-signature schemes that use the delinearization of commitments.
Composed Variational Natural Language Generation for Few-shot Intents

In this paper, we focus on generating training examples for few-shot intents in the realistic imbalanced scenario.To build connections between existing many-shot intents and few-shot intents, we consider an intent as a combination of a domain and an action, and propose a composed variational natural language generator (CLANG), a transformer-based conditional variational autoencoder.CLANG utilizes two latent variables to represent the utterances corresponding to two different independent parts (domain and action) in the intent, and the latent variables are composed together to generate natural examples.Additionally, to improve the generator learning, we adopt the contrastive regularization loss that contrasts the in-class with the out-of-class utterance generation given the intent.To evaluate the quality of the generated utterances, experiments are conducted on the generalized few-shot intent detection task.Empirical results show that our proposed model achieves state-of-the-art performances on two real-world intent detection datasets.
A note on tools for prediction under uncertainty and identifiability of SIR-like dynamical systems for epidemiology

We provide an overview of the methods that can be used for prediction under uncertainty and data fitting of dynamical systems, and of the fundamental challenges that arise in this context. The focus is on SIR-like models, that are being commonly used when attempting to predict the trend of the COVID-19 pandemic. In particular, we raise a warning flag about identifiability of the parameters of SIR-like models; often, it might be hard to infer the correct values of the parameters from data, even for very simple models, making it non-trivial to use these models for meaningful predictions. Most of the points that we touch upon are actually generally valid for inverse problems in more general setups.
Mutual authentication with multi-factor in IoT-Fog-Cloud environment

Authentication of restricted memory devices presents significant problems since memory consumption is high in mutual authentication using cryptographic protocols in IoT environments. According to previous studies, the development of a multi-factor mutual authentication method that can be used in fog and cloud computing remains a challenge. The present work aims to improve a method of mutual authentication with multi-factor using an adjustable variable response time, challenge-response function, and nonce. With these factors, the same method can be regulated for both the Fog and Cloud Computing contexts. We compared the present method with the evaluations carried out in related works, achieving a satisfactory result regarding the cost of computing and communication. The code developed in Java showed a better average processing time, low energy consumption than other studies, and a linear complexity metric. Finally, using the Proverif tool and an informal analysis to provide the security assessment, it was demonstrated that it is impossible to derive the discovery of keys, keeping the proposed method safe.
Building Trust for Smart Connected Devices: The Challenges and Pitfalls of TrustZone

TrustZone-based Trusted Execution Environments (TEEs) have been utilized extensively for the implementation of security-oriented solutions for several smart intra and inter-connected devices. Although TEEs have been promoted as the starting point for establishing a device root of trust, a number of published attacks against the most broadly utilized TEE implementations request a second view on their security. The aim of this research is to provide an analytical and educational exploration of TrustZone-based TEE vulnerabilities with the goal of pinpointing design and implementation flaws. To this end, we provide a taxonomy of TrustZone attacks, analyze them, and more importantly derive a set of critical observations regarding their nature. We perform a critical appraisal of the vulnerabilities to shed light on their underlying causes and we deduce that their manifestation is the joint effect of several parameters that lead to this situation. The most important ones are the closed implementations, the lack of security mechanisms, the shared resource architecture, and the absence of tools to audit trusted applications. Finally, given the severity of the identified issues, we propose possible improvements that could be adopted by TEE implementers to remedy and improve the security posture of TrustZone and future research directions.
Rethinking Positional Encoding in Language Pre-training

In this work, we investigate the positional encoding methods used in language pre-training (e.g., BERT) and identify several problems in the existing formulations. First, we show that in the absolute positional encoding, the addition operation applied on positional embeddings and word embeddings brings mixed correlations between the two heterogeneous information resources. It may bring unnecessary randomness in the attention and further limit the expressiveness of the model. Second, we question whether treating the position of the symbol \texttt{[CLS]} the same as other words is a reasonable design, considering its special role (the representation of the entire sentence) in the downstream tasks. Motivated from above analysis, we propose a new positional encoding method called \textbf{T}ransformer with \textbf{U}ntied \textbf{P}ositional \textbf{E}ncoding (TUPE). In the self-attention module, TUPE computes the word contextual correlation and positional correlation separately with different parameterizations and then adds them together. This design removes the mixed and noisy correlations over heterogeneous embeddings and offers more expressiveness by using different projection matrices. Furthermore, TUPE unties the \texttt{[CLS]} symbol from other positions, making it easier to capture information from all positions. Extensive experiments and ablation studies on GLUE benchmark demonstrate the effectiveness of the proposed method. Codes and models will be released.
Toward a Blockchain-Based Technology in Dealing with Emergencies in Patient-Centered Healthcare Systems

Nowadays, medical healthcare always plays a vital role for humans in society, especially problems related to personal health records due to its security and sensitivity. For each patient, personal health records are critical and vital assets, so how to manage them effectively is becoming exciting research to solve. Many types of research in aspects of managing and operating personal health records have been introduced; however, dealing with patients’ data in emergency cases remains an uncertain issue. When emergencies happen in reality, using a traditional access system is very hard for patients to give consent to staff to access their data. Besides, there is no secured record management of patient’ data, which reveals highly confidential personal information, such as what happened, when, and who has access to such information. Therefore, in this paper, an emergency access control management system is proposed to protect the patients’ data. This system is built based on permissioned Blockchain Hyperledger fabric. The proposed system will define several rules and regulations by using smart contracts and time duration to deal with emergencies. The patients also restrict the time to access the data in such urgent cases. Several algorithms that represent how the system works are also provided to make readers understand about the proposed management system.
Cross-modal image sentiment analysis via deep correlation of textual semantic

Social media has become indispensable to people’s lives, where they can share their views and emotion with images and texts. Analyzing social images for sentiment prediction can help understand human social behavior and provide better recommendation results. Most current researches on image sentiment analysis have achieved quite good progress, which ignores the semantic correlation between an image and its corresponding descriptive sentences (caption). To capture the complementary multimodal information for joint sentiment classification, in this paper, we propose a novel cross-modal Semantic Content Correlation (SCC) method based on deep matching and hierarchical networks, which bridges the correlation between images and captions. Specifically, pre-trained convolutional neural networks (CNNs) are leveraged to encode the visual sub-regions contents, and a GloVe is employed to embed the textual semantic. Relying on visual contents and textual semantic, a joint attention network is proposed to learn the content correlation of the image and its caption, which is then exported as an image–text pair. To exploit the dependence of visual contents on textual semantic in caption effectively, the caption is processed by a Class-Aware Sentence Representation (CASR) network with a class dictionary, and a fully connected layer concatenates the outputs of CASR into a class-aware vector. Finally, the class-aware distributed vector is fed into an Inner-class Dependency Long Short-Term Memory network (IDLSTM) with the image–text pair as a query to further capture the cross-modal non-linear correlations for sentiment prediction. The performance of extensive experiments conducted on three datasets verifies the effectiveness of the model SCC.
AEchain: A Lightweight Blockchain for IoT Applications

IoT nodes comprise of sensors and embedded resource-constrained systems. On the other hand, blockchain is regarded as computationally expensive due to the consensus algorithms. Therefore, it is challenging to apply blockchain to an IoT system. This work presents a unique concept that integrates blockchain with lightweight cryptographic solutions targeting resource-constrained IoT sensor nodes. In particular, proof-of-authentication utilizing a lightweight authenticated encryption (AE) scheme to achieve consensus is proposed. At sensor nodes, a tag is generated based on sensor data, which is then broadcast to the network. Upon authentication from the cluster head node (e.g., a gateway), the block is hashed using the lightweight hash function and added to the blockchain. The proposed solution can be implemented in software (e.g., microcontroller) or hardware platform (e.g., FPGA, ASIC). Experimental results show that lightweight authentication can perform 1.34 M authentications per-second with only 6.55 k lookup tables (LUTs) on the Spartan-6 FPGA platform. This high-throughput authentication can speed up the consensus in blockchain, utilizing few resources and making it very suitable for applications in IoT sensor nodes.
Machine Learning for Anomaly Assessment in Sensor Networks for NDT in Aerospace

We investigated and compared various algorithms in machine learning for anomaly assessment with different feature analyses on ultrasonic signals recorded by sensor networks. The following methods were used and compared in anomaly detection modeling: hidden Markov models (HMM), support vector machines (SVM), isolation forest (IF), and reconstruction autoencoders (AEC). They were trained exclusively on sensor signals of the intact state of structures commonly used in various industries, like aerospace and automotive. The signals obtained on artificially introduced damage states were used for performance evaluation. Anomaly assessment was evaluated and compared using various classifiers and feature analysis methods. We introduced novel methodologies for two processes. The first was the dataset preparation with anomalies. The second was the detection and damage severity assessment utilizing the intact object state exclusively. The experiments proved that robust anomaly detection is practically feasible. We were able to train accurate classifiers which had a considerable safety margin. Precise quantitative analysis of damage severity will also be possible when calibration data become available during exploitation or by using expert knowledge.