Reviewerly/exHarmony huggingface dataset:
Information and communication technologies role in alleviating poverty in Sub‐Saharan Africa: Impacts and transmission channels

Abstract Research has continued to indicate that the proliferation of information and communication technologies (ICTs) has the potential to alleviate poverty in developing countries. While theoretically, there are several mechanisms through which ICT may reduce poverty, they have not been empirically tested sufficiently. The present study aimed to recognize the mechanisms through which ICT may reduce poverty for a sample of 37 economies in the Sub‐Saharan Africa (SSA) region during the period 2003–2019. Altering the income distribution, improving the ecological system, increasing the employment rate, increasing per capita income, and improving institutional quality were the assumed mechanisms through which ICT may reduce poverty. The results of the two‐step system GMM technique showed that while an increase in the per capita income and employment rate contributed positively to poverty reduction, improvement in the environmental quality increased the poverty rate. The causal mediation analysis findings showed that ICT strengthened the negative impact of worsening the quality of the environment on poverty. However, the strengthened positive impact of ICT on poverty through employment and per capita income was greater than the negative influence through the environmental mechanism. Thus, all national strategies or initiatives aimed at reducing poverty in SSA must incorporate pro‐poor ICT policies.
Trigger-GNN: A Trigger-Based Graph Neural Network for Nested Named Entity Recognition

Nested named entity recognition (NER) aims to identify the entity boundaries and recognize categories of the named entities in a complex hierarchical sentence. Some works have been done using character-level, word-level, or lexicon-level based models. However, such researches ignore the role of the complementary annotations. In this paper, we propose a trigger-based graph neural network (Trigger-GNN) to leverage the nested NER. It obtains the complementary annotation embeddings through entity trigger encoding and semantic matching, and tackle nested entity utilizing an efficient graph message passing architecture, aggregation-update mode. We posit that using entity triggers as external annotations can add in complementary supervision signals on the whole sentences. It helps the model to learn and generalize more efficiently and cost-effectively. Experiments show that the Trigger-GNN consistently outperforms the baselines on four public NER datasets, and it can effectively alleviate the nested NER.
Uncovering hidden opinions: social norms and the expression of xenophobic attitudes

Abstract Social norms against prejudice are widespread and generally supported by society, yet examples of bigotry are often found. I propose that anti-prejudice norms can quickly erode when individuals are exposed to hate content, therefore, facilitating the expression of prejudice. To test this, participants were invited to participate in an experimental online forum discussing immigration. I compare the comments of participants exposed to xenophobic content to those not exposed. The empirical results show that exposure to hateful content erodes norm compliance: the more hateful content participants could observe, the more hateful their subsequent comments were. The effect is primarily driven by those more likely to hold anti-immigrant views. This points to an ‘emboldening effect’ whereby prejudiced individuals refrain from expressing prejudiced opinions in the absence of offensive speech. Otherwise, hidden attitudes are revealed. The results confirm that the expression of prejudice is context dependent and that preserving a ‘norm environment’ requires sustained reinforcement of the norm. Furthermore, the results show that the composition of the population in terms of individual beliefs is paramount for the dynamics of erosion of the social norm. On the whole, these findings can inform effective public strategies against the spread of hate speech and offer a new methodological approach for studying hate speech in online contexts.
You see what I want you to see: poisoning vulnerabilities in neural code search

Searching and reusing code snippets from open-source software repositories based on natural-language queries can greatly improve programming productivity.Recently, deep-learning-based approaches have become increasingly popular for code search. Despite substantial progress in training accurate models of code search, the robustness of these models has received little attention so far.
A Deep Dive into Blockchain-based Smart Contract-specific Security Vulnerabilities

Blockchain smart contracts are prevalent nowadays as numerous applications are developed based on this feature. Though smart contracts are important and widely used, they contain certain vulnerabilities. This paper discusses various security issues that arise in smart contract applications. They are categorized in the smart contract platform, the applications that integrate with the Blockchain, and the vulnerabilities in smart contract code. A detailed study of smart contract-specific vulnerabilities and the defense against those vulnerabilities are presented in this article. Because of certain limitations of platforms or programming language used to write smart contract, there are possibilities of attacks on smart contracts. Hence different security measures or precautions to be taken while writing the smart contract code is discussed in this article. This will prevent the potential attacks happening on Blockchain distributed applications.
Kryvos

Elections are an important corner stone of democratic processes. In addition to publishing the final result (e.g., the overall winner), elections typically publish the full tally consisting of all (aggregated) individual votes. This causes several issues, including loss of privacy for both voters and election candidates as well as so-called Italian attacks that allow for easily coercing voters.
Predicting length of stay in ICU and mortality with temporal dilated separable convolution and context-aware feature fusion

In healthcare, Intensive Care Unit (ICU) bed management is a necessary task because of the limited budget and resources. Predicting the remaining Length of Stay (LoS) in ICU and mortality can assist clinicians in managing ICU beds efficiently. This study proposes a deep learning method based on several successive Temporal Dilated Separable Convolution with Context-Aware Feature Fusion (TDSC-CAFF) modules, and a multi-view and multi-scale feature fusion for predicting the remaining LoS and mortality risk for ICU patients. In each TDSC-CAFF module, temporal dilated separable convolution is used to encode each feature separately, and context-aware feature fusion is proposed to capture comprehensive and context-aware feature representations from the input time-series features, static demographics, and the output of the last TDSC-CAFF module. The CAFF outputs of each module are accumulated to achieve multi-scale representations with different receptive fields. The outputs of TDSC and CAFF are concatenated with skip connection from the output of the last module and the original time-series input. The concatenated features are processed by the proposed Point-Wise convolution-based Attention (PWAtt) that captures the inter-feature context to generate the final temporal features. Finally, the final temporal features, the accumulated multi-scale features, the encoded diagnosis, and static demographic features are fused and then processed by fully connected layers to obtain prediction results. We evaluate our proposed method on two publicly available datasets: eICU and MIMIC-IV v1.0 for LoS and mortality prediction tasks. Experimental results demonstrate that our proposed method achieves a mean squared log error of 0.07 and 0.08 for LoS prediction, and an Area Under the Receiver Operating Characteristic Curve of 0.909 and 0.926 for mortality prediction, on eICU and MIMIC-IV v1.0 datasets, respectively, which outperforms several state-of-the-art methods.
TurboPack

We present a novel approach to honest majority secure multiparty computation in the preprocessing model with information theoretic security that achieves the best online communication complexity. The online phase of our protocol requires 12 elements in total per multiplication gate with circuit-dependent preprocessing, or 20 elements in total with circuit-independent preprocessing. Prior works achieved linear online communication complexity in n, the number of parties, with the best prior existing solution involving 1.5n elements per multiplication gate. Only one recent work packing [28] achieves constant online communication complexity, but the constants are large (108 elements for passive security, and twice that for active security). That said, our protocol offers a very efficient information theoretic online phase for any number of parties.
The evolution of type annotations in python: an empirical study

Type annotations and gradual type checkers attempt to reveal errors and facilitate maintenance in dynamically typed programming languages. Despite the availability of these features and tools, it is currently unclear how quickly developers are adopting them, what strategies they follow when doing so, and whether adding type annotations reveals more type errors. This paper presents the first large-scale empirical study of the evolution of type annotations and type errors in Python. The study is based on an analysis of 1,414,936 type annotation changes, which we extract from 1,123,393 commits among 9,655 projects. Our results show that (i) type annotations are getting more popular, and once added, often remain unchanged in the projects for a long time, (ii) projects follow three evolution patterns for type annotation usage -- regular annotation, type sprints, and occasional uses -- and that the used pattern correlates with the number of contributors, (iii) more type annotations help find more type errors (0.704 correlation), but nevertheless, many commits (78.3%) are committed despite having such errors. Our findings show that better developer training and automated techniques for adding type annotations are needed, as most code still remains unannotated, and they call for a better integration of gradual type checking into the development process.
Pair programming conversations with agents vs. developers: challenges and opportunities for SE community

Recent research has shown feasibility of an interactive pair-programming conversational agent, but implementing such an agent poses three challenges: a lack of benchmark datasets, absence of software engineering specific labels, and the need to understand developer conversations. To address these challenges, we conducted a Wizard of Oz study with 14 participants pair programming with a simulated agent and collected 4,443 developer-agent utterances. Based on this dataset, we created 26 software engineering labels using an open coding process to develop a hierarchical classification scheme. To understand labeled developer-agent conversations, we compared the accuracy of three state-of-the-art transformer-based language models, BERT, GPT-2, and XLNet, which performed interchangeably. In order to begin creating a developer-agent dataset, researchers and practitioners need to conduct resource intensive Wizard of Oz studies. Presently, there exists vast amounts of developer-developer conversations on video hosting websites. To investigate the feasibility of using developer-developer conversations, we labeled a publicly available developer-developer dataset (3,436 utterances) with our hierarchical classification scheme and found that a BERT model trained on developer-developer data performed ~10% worse than the BERT trained on developer-agent data, but when using transfer-learning, accuracy improved. Finally, our qualitative analysis revealed that developer-developer conversations are more implicit, neutral, and opinionated than developer-agent conversations. Our results have implications for software engineering researchers and practitioners developing conversational agents.
Multi-dimensional patient acuity estimation with longitudinal EHR tokenization and flexible transformer networks

Transformer model architectures have revolutionized the natural language processing (NLP) domain and continue to produce state-of-the-art results in text-based applications. Prior to the emergence of transformers, traditional NLP models such as recurrent and convolutional neural networks demonstrated promising utility for patient-level predictions and health forecasting from longitudinal datasets. However, to our knowledge only few studies have explored transformers for predicting clinical outcomes from electronic health record (EHR) data, and in our estimation, none have adequately derived a health-specific tokenization scheme to fully capture the heterogeneity of EHR systems. In this study, we propose a dynamic method for tokenizing both discrete and continuous patient data, and present a transformer-based classifier utilizing a joint embedding space for integrating disparate temporal patient measurements. We demonstrate the feasibility of our clinical AI framework through multi-task ICU patient acuity estimation, where we simultaneously predict six mortality and readmission outcomes. Our longitudinal EHR tokenization and transformer modeling approaches resulted in more accurate predictions compared with baseline machine learning models, which suggest opportunities for future multimodal data integrations and algorithmic support tools using clinical transformer networks.
Hypothesis Tests and Exploratory Analysis Using R Commander and Factoshiny

An understanding of statistical concepts is necessary for a chemist with a complete education. Here, statistical tests were taught using the R Commander and the Factoshiny packages. These packages run on R software and have a graphical user interface (GUI), which allows students to do statistical tests quickly and easily. These packages were presented through 14 case studies. In each case study, a data set was provided in MS Excel format, and a series of questions were answered using these packages. Hypothesis tests and exploratory analysis (principal component analysis; PCA) were taught using R Commander and Factoshiny, respectively. Outlier results were found using boxplots. Data normality was checked using histograms and the Shapiro–Wilk test. Normally distributed data sets were compared using parametric hypothesis tests (t test, paired t test, one-way ANOVA, two-way ANOVA, one-way repeated measure ANOVA). Non-normally distrusted data sets were compared using nonparametric tests (Wilcoxon, Kruskal–Wallis, and Friedman tests). Results provided by these parametric and nonparametric tests were also verified using plots (plot of means and boxplots).
Different structured-light patterns in single-shot 2D-to-3D image conversion using deep learning

Single-shot 3D shape reconstruction integrating structured light and deep learning has drawn considerable attention and achieved significant progress in recent years due to its wide-ranging applications in various fields. The prevailing deep-learning-based 3D reconstruction using structured light generally transforms a single fringe pattern to its corresponding depth map by an end-to-end artificial neural network. At present, it remains unclear which kind of structured-light patterns should be employed to obtain the best accuracy performance. To answer this fundamental and much-asked question, we conduct an experimental investigation of six representative structured-light patterns adopted for single-shot 2D-to-3D image conversion. The assessment results provide a valuable guideline for structured-light pattern selection in practice.
Misogynoir: challenges in detecting intersectional hate

Abstract “Misogynoir” is a term that refers to the anti-Black forms of misogyny that Black women experience. To explore how current automated hate speech detection approaches perform in detecting this type of hate, we evaluated the performance of two state-of-the-art detection tools, HateSonar and Google’s Perspective API, on a balanced dataset of 300 tweets, half of which are examples of misogynoir and half of which are examples of supporting Black women and an imbalanced dataset of 3138 tweets of which 162 tweets are examples of misogynoir and 2976 tweets are examples of allyship tweets. We aim to determine if these tools flag these messages under any of their classifications of hateful speech (e.g. “hate speech”, “offensive language”, “toxicity” etc.). Close analysis of the classifications and errors shows that current hate speech detection tools are ineffective in detecting misogynoir. They lack sensitivity to context, which is an essential component for misogynoir detection. We found that tweets likely to be classified as hate speech explicitly reference racism or sexism or use profane or aggressive words. Subtle tweets without references to these topics are more challenging to classify. We find that the lack of sensitivity to context may make such tools not only ineffective but potentially harmful to Black women.
Color image encryption based on 2D enhanced hyperchaotic logistic-sine map and two-way Josephus traversing

To improve the chaotic behavior of the chaotic map, this paper proposes a new two-dimensional (2D) chaotic map, namely, 2D enhanced hyperchaotic Logistic-sine map (2D-EHLM), which is derived from the idea of the 2D Logistic map and the Sine map. The performance analyses demonstrate that the 2D-EHLM has significantly better hyperchaotic property, unpredictability characteristics in terms of their Lyapunov exponents, trajectories, and 0-1test in comparison to some existing 2D chaotic maps. Such as the 2D-EHLM performs chaotic behavior when their control parameter μ within (−3,4), which indicates a large chaotic range comparing with other 2D dynamical systems. To investigate its application, this paper proposes a new image confusion strategy based on the two-way Josephus traversing (TJT) to efficiently scramble the color image components and pixel positions. Combining 2D-EHLM and TJT operation, a new color image encryption scheme is proposed. The stimulation experiments and security analysis demonstrate that the proposed algorithm is able to protect the image information with a high security as well as to resist common types of attacks, which also verify the superior hyperchaotic behaviors of the 2D-EHLM from the aspect of encryption effect.
Smart Contact Lenses for the New Era of IoT: Integrated Biosensors, Circuits, and Human–Machine Interface Systems

Abstract The rapid development of the Internet of Things (IoT) technology endows some traditional devices with intelligent functions. Compared with traditional contact lenses used for correcting vision or beautifying the eyes, smart contact lenses (SCLs) are developed to monitor some physiological information of the eye. Sculls can be used to continuously monitor eye diseases non‐invasively in real‐time. As a personal electronic device, SCs can aid people in understanding their physical condition better without affecting their personal life. This review mainly discusses the development direction and problems associated with sculls from the perspective of the IoT. SCs consist of three main parts: biosensors, circuits, and human–machine interface (HMI) systems. These three parts correspond to the sensing, connection, and application layer of the IoT. Finally, the problems that may be encountered in the development of SCLs are discussed and potential solutions are proposed.
Fallen person detection for autonomous driving

Fallen person detection (FPD) is a new problem that aims to detect a person who lies or falls down on driving roads. The biggest difficulty in FPD is capturing a sufficient number of training images of people lying on driving roads because of the dangers involved. In this paper, we propose a novel fallen person detection image synthesis framework to address this difficulty. Our framework first embeds a fallen person instance into an image of a driving road, thereby generating a hard-to-acquire image (image of a person who has fallen on a road) from two easy-to-acquire images (driving road image and fallen person image). We then reduce the domain gap between the two images using domain adaptation. Finally, we remove some pixel artifacts from the border between the fallen person and background area in the synthesized image. Our proposed framework addresses the lack of training data, which is a serious problem inherent to FPD. Furthermore, we develop a new dataset named FPD (Fallen Person detection with Driving scenes)-set to train a detection network. FPD-set consists of four subsets: (1) RealFP218, (2) RealD1.8K, (3) RealFPDK1.4K and (4) RealFPDY1.1K. RealFP218 consists of 218 images of real fallen persons and their pixel-level mask annotations; and RealD1.8K consists of 1820 real driving road images. The two sets will be used to synthesize the driving road images including fallen persons. RealFPDK1.4K and RealFPDY1.1K are test sets which are captured at two different places (K-City and Yonsei University). The two test sets consist of 1400 and 1161 images of real fallen persons on the road with bounding box annotations, respectively. Our dataset covers a variety of conditions, including occlusion, lack of lighting, and shadows, thereby facilitating qualitative and quantitative evaluations in the real world. We released this dataset for the benefit of the autonomous driving society. We verify the effectiveness of our training image synthesis method by applying the detector to the RealFPDK1.4K and RealFPDY1.1K datasets. Our approach achieves AP scores of 0.815 and 0.753, and the scores are higher than those of the baseline by +0.287 and +0.210 on RealFPDK1.4K and RealFPDY1.1K, respectively. Experimental results demonstrate that our framework contributes significantly to training an FPD network.
Renew mineral resource-based cities: Assessment of PV potential in coal mining subsidence areas

• The PV potential in coal mining subsidence areas of Yangquan City was evaluated. • Effects of different deformation thresholds on the PV potential has been discussed. • The PV power potential in subsidence area can meet 30% of the electricity demand. • The LCOE considering the land price of the subsidence area is RMB 0.3934/kWh. Accurately assessing the photovoltaic (PV) power generation potential in coal mining subsiding regions is of great significance for the transformation of a resource-based city and the goal of carbon neutrality. In this paper, we proposed an assessment method for the PV power potential in coal mining subsidence areas. Both the environmental factors and ground deformation by interferometric synthetic aperture radar were fully considered. Furthermore, the levelized cost of PV electricity in subsidence areas and the benefits were predicted. Yangquan City, Shanxi province was selected as a study case. Results show that the optimal areas for PV power generation under the three-deformation rate ranges of (−40, −10), (−50, −10), and (−60, −10) mm/year in the Yangquan subsidence area are 32.13, 37.89 and 41.22 km 2 , respectively. The selected optimal PV construction regions agree well with the exiting PV facilities, indicating the effectiveness of the proposed method. The levelized cost of energy is estimated at 0.3934 RMB/kWh. The annual power generation can meet about 33 % of the electricity demand of Yangquan City. This work would guide the construction of PV power generation facilities in the coal mining subsidence areas for the transformation and upgrading of the mineral resource-based cities.