Reviewerly/exHarmony huggingface dataset:
Fairness-aware News Recommendation with Decomposed Adversarial Learning

News recommendation is important for online news services. Existing news recommendation models are usually learned from users' news click behaviors. Usually the behaviors of users with the same sensitive attributes (e.g., genders) have similar patterns and news recommendation models can easily capture these patterns. It may lead to some biases related to sensitive user attributes in the recommendation results, e.g., always recommending sports news to male users, which is unfair since users may not receive diverse news information. In this paper, we propose a fairness-aware news recommendation approach with decomposed adversarial learning and orthogonality regularization, which can alleviate unfairness in news recommendation brought by the biases of sensitive user attributes. In our approach, we propose to decompose the user interest model into two components. One component aims to learn a bias-aware user embedding that captures the bias information on sensitive user attributes, and the other aims to learn a bias-free user embedding that only encodes attribute-independent user interest information for fairness-aware news recommendation. In addition, we propose to apply an attribute prediction task to the bias-aware user embedding to enhance its ability on bias modeling, and we apply adversarial learning to the bias-free user embedding to remove the bias information from it. Moreover, we propose an orthogonality regularization method to encourage the bias-free user embeddings to be orthogonal to the bias-aware one to better distinguish the bias-free user embedding from the bias-aware one. For fairness-aware news ranking, we only use the bias-free user embedding. Extensive experiments on benchmark dataset show that our approach can effectively improve fairness in news recommendation with minor performance loss.
Latent Hatred: A Benchmark for Understanding Implicit Hate Speech

Hate speech has grown significantly on social media, causing serious consequences for victims of all demographics.Despite much attention being paid to characterize and detect discriminatory speech, most work has focused on explicit or overt hate speech, failing to address a more pervasive form based on coded or indirect language.To fill this gap, this work introduces a theoretically-justified taxonomy of implicit hate speech and a benchmark corpus with fine-grained labels for each message and its implication.We present systematic analyses of our dataset using contemporary baselines to detect and explain implicit hate speech, and we discuss key features that challenge existing models.This dataset will continue to serve as a useful benchmark for understanding this multifaceted issue.To download the data, see https://github.com/GT-SALT/implicit-hate
The Growth of Internet of Things (IoT) In The Management of Healthcare Issues and Healthcare Policy Development

The internet of things is becoming a new sensation in the medical and healthcare field.&#x0D; Healthcare is one of the sustainable development areas across different regions. This study aims at developing an analysis of Internet of Things (IoT) in the healthcare sector for the purposes of achieving sustainable development. The study took a direction of applied descriptive research. The methodology applied was Fuzzy Analytical Hierarchy Process (FAHP). This is a single cross- section survey research. After data gathering, the agreed paired comparison matrices, allocated to weighted criteria and the priority of internet of things usage were determined. The two criteria, quality of life and economic prosperity had the highest priority for internet of things sustainable development in the healthcare system. Fall detection, dental health, and ultraviolet radiation wereamong the areas where IoT was found to be applied prominently.
The Difference Between Emergency Remote Teaching and e-Learning

OPINION article Front. Educ., 02 June 2022Sec. Higher Education https://doi.org/10.3389/feduc.2022.921332
Learning To Retrieve Prompts for In-Context Learning

In-context learning is a recent paradigm in natural language understanding, where a large pretrained language model (LM) observes a test instance and a few training examples as its input, and directly decodes the output without any update to its parameters.However, performance has been shown to strongly depend on the selected training examples (termed prompts).In this work, we propose an efficient method for retrieving prompts for in-context learning using annotated data and an LM.Given an inputoutput pair, we estimate the probability of the output given the input and a candidate training example as the prompt, and label training examples as positive or negative based on this probability.We then train an efficient dense retriever from this data, which is used to retrieve training examples as prompts at test time.We evaluate our approach on three sequence-tosequence tasks where language utterances are mapped to meaning representations, and find that it substantially outperforms prior work and multiple baselines across the board.
Deep learning for image inpainting: A survey

Image inpainting has been widely exploited in the field of computer vision and image processing. The main purpose of image inpainting is to produce visually plausible structure and texture for the missing regions of damaged images. In the past decade, the success of deep learning has brought new opportunities to many vision tasks, which promoted the development of a large number of deep learning-based image inpainting methods. Although these methods have many similarities, they also have their own characteristics due to the differences in data types, application scenarios, computing platforms, etc. It is necessary to classify and summarize these methods to provide a reference for the research community. In this survey, we present a comprehensive overview of recent advances in deep learning-based image inpainting. First, we categorize the deep learning-based techniques from multiple perspectives: inpainting strategies, network structures, and loss functions. Second, we summarize the open source codes and representative public datasets, and introduce the evaluation metrics for quantitative comparisons. Third, we summarize the real-world applications of image inpainting in different scenarios, and give a detailed analysis on the performance of different inpainting algorithms. At last, we conclude the survey and discuss about the future directions.
Mitosis domain generalization in histopathology images — The MIDOG challenge

The density of mitotic figures (MF) within tumor tissue is known to be highly correlated with tumor proliferation and thus is an important marker in tumor grading. Recognition of MF by pathologists is subject to a strong inter-rater bias, limiting its prognostic value. State-of-the-art deep learning methods can support experts but have been observed to strongly deteriorate when applied in a different clinical environment. The variability caused by using different whole slide scanners has been identified as one decisive component in the underlying domain shift. The goal of the MICCAI MIDOG 2021 challenge was the creation of scanner-agnostic MF detection algorithms. The challenge used a training set of 200 cases, split across four scanning systems. As test set, an additional 100 cases split across four scanning systems, including two previously unseen scanners, were provided. In this paper, we evaluate and compare the approaches that were submitted to the challenge and identify methodological factors contributing to better performance. The winning algorithm yielded an F1 score of 0.748 (CI95: 0.704-0.781), exceeding the performance of six experts on the same task.
Do You Feel What I Feel? Social Aspects of Emotions in Twitter Conversations

We present a computational framework for understanding the social aspects of emotions in Twitter conversations. Using unannotated data and semisupervised machine learning, we look at emotional transitions, emotional influences among the conversation partners, and patterns in the overall emotional exchanges. We find that conversational partners usually express the same emotion, which we name Emotion accommodation, but when they do not, one of the conversational partners tends to respond with a positive emotion. We also show that tweets containing sympathy, apology, and complaint are significant emotion influencers. We verify the emotion classification part of our framework by a human-annotated corpus.
TIMBER-V: Tag-Isolated Memory Bringing Fine-grained Enclaves to RISC-V

Embedded computing devices are used on a large scale in the emerging internet of things (IoT).However, their wide deployment raises the incentive for attackers to target these devices, as demonstrated by several recent attacks.As IoT devices are built for long service life, means are required to protect sensitive code in the presence of potential vulnerabilities, which might be discovered long after deployment.Tagged memory has been proposed as a mechanism to enforce various fine-grained security policies at runtime.However, none of the existing tagged memory schemes provides efficient and flexible compartmentalization in terms of isolated execution environments.We present TIMBER-V, a new tagged memory architecture featuring flexible and efficient isolation of code and data on small embedded systems.We overcome several limitations of previous schemes.We augment tag isolation with a memory protection unit to isolate individual processes, while maintaining low memory overhead.TIMBER-V significantly reduces the problem of memory fragmentation, and improves dynamic reuse of untrusted memory across security boundaries.TIMBER-V enables novel sharing of execution stacks across different security domains, in addition to interleaved heaps.TIMBER-V is compatible to existing code, supports real-time constraints and is open source.We show the efficiency of TIMBER-V by evaluating our proofof-concept implementation on the RISC-V simulator.
Real-Time Interference Identification via Supervised Learning: Embedding Coexistence Awareness in IoT Devices

Energy sampling-based interference detection and identification (IDI) methods collide with the limitations of commercial off-the-shelf (COTS) IoT hardware. Moreover, long sensing times, complexity, and inability to track concurrent interference strongly inhibit their applicability in most IoT deployments. Motivated by the increasing need for on-device IDI for wireless coexistence, we develop a lightweight and efficient method targeting interference identification already at the level of single interference bursts. Our method exploits real-time extraction of envelope and model-aided spectral features, specifically designed considering the physical properties of the signals captured with COTS hardware. We adopt manifold supervised-learning (SL) classifiers ensuring suitable performance and complexity tradeoff for the IoT platforms with different computational capabilities. The proposed IDI method is capable of real-time identification of IEEE 802.11b/g/n, 802.15.4, 802.15.1, and Bluetooth Low Energy wireless standards, enabling isolation and extraction of standard-specific traffic statistics even in the case of heavy concurrent interference. We perform an experimental study in real environments with heterogeneous interference scenarios, showing 90%–97% burst identification accuracy. Meanwhile, the lightweight SL methods, running online on wireless sensor networks-COTS hardware, ensure sub-ms identification time and limited performance gap from machine-learning approaches.
Offshore Underwater Image Restoration Using Reflection-Decomposition-Based Transmission Map Estimation

A new restoration model for underwater images is presented, based on the dark channel reflection-illumination decomposition and local backscattering lighting estimation, to promote the clarity of edge detail and the colorfulness of the output image. For turbid offshore environments, a novel underwater image restoration method is further introduced by applying a statistical prior to the offshore attenuation coefficients. It is shown that the estimated transmission map with lighting-reflection decomposition, rather than dividing the dark channel by the maximal backscattering light as the other methods do, provides better clarity and color restoration. Detailed qualitative and quantitative analysis on hundreds of underwater images is performed, which demonstrates that the proposed method outperforms the state-of-the-art algorithms on the images taken in offshore water characterized by a heavy concentration of colored dissolved organic matter and total suspended matter, and is suitable for fast underwater processing.
An Insider Threat Detection Approach Based on Mouse Dynamics and Deep Learning

In the current intranet environment, information is becoming more readily accessed and replicated across a wide range of interconnected systems. Anyone using the intranet computer may access content that he does not have permission to access. For an insider attacker, it is relatively easy to steal a colleague’s password or use an unattended computer to launch an attack. A common one-time user authentication method may not work in this situation. In this paper, we propose a user authentication method based on mouse biobehavioral characteristics and deep learning, which can accurately and efficiently perform continuous identity authentication on current computer users, thus to address insider threats. We used an open-source dataset with ten users to carry out experiments, and the experimental results demonstrated the effectiveness of the approach. This approach can complete a user authentication task approximately every 7 seconds, with a false acceptance rate of 2.94% and a false rejection rate of 2.28%.
Motivasi belajar sebagai determinan hasil belajar siswa

This study aimed to determine the influence of learning motivation on student learning outcome. The research method used explanatory survey. Data collection technique used questionnaire rating scale models. Respondents are 106 students of vocational high school in Bandung. Data were analyzed using regression. The results of the study revealed that learning motivation a positive and significant influence on student learning outcome . Therefore, the student learning outcomes can be improved through improving the student learning motivation.ABSTRAKPenelitian ini bertujuan untuk menganalisis pengaruh motivasi belajar terhadap hasil belajar siswa. Metode penelitian menggunakan explanatory survey. Teknik pengumpulan data menggunakan angket model rating scale. Responden adalah 106 siswa di salah satu Sekolah Menengah Kejuruan swasta di Kota Bandung. Teknik analisis data menggunakan regresi. Hasil penelitian menunjukan bahwa motivasi belajar memiliki pengaruh yang positif dan signifikan terhadap hasil belajar siswa. Oleh karena itu, hasil belajar siswa dapat ditingkatkan melalui peningkatan motivasi belajar siswa.
Cyber security in the quantum era

Quantum systems will significantly affect the field of cyber security research.
Investigating Graph Embedding Neural Networks with Unsupervised Features Extraction for Binary Analysis

In this paper we investigate the use of graph embedding networks, with unsupervised features learning, as neural architecture to learn over binary functions.We propose several ways of automatically extract features from the control flow graph (CFG) and we use the structure2vec graph embedding techniques to translate a CFG to a vectors of real numbers.We train and test our proposed architectures on two different binary analysis tasks: binary similarity, and, compiler provenance.We show that the unsupervised extraction of features improves the accuracy on the above tasks, when compared with embedding vectors obtained from a CFG annotated with manually engineered features (i.e., ACFG proposed in [39]).We additionally compare the results of graph embedding networks based techniques with a recent architecture that do not make use of the structural information given by the CFG, and we observe similar performances.We formulate a possible explanation of this phenomenon and we conclude identifying important open challenges.
Machine Teaching for Inverse Reinforcement Learning: Algorithms and Applications

Inverse reinforcement learning (IRL) infers a reward function from demonstrations, allowing for policy improvement and generalization. However, despite much recent interest in IRL, little work has been done to understand the minimum set of demonstrations needed to teach a specific sequential decisionmaking task. We formalize the problem of finding maximally informative demonstrations for IRL as a machine teaching problem where the goal is to find the minimum number of demonstrations needed to specify the reward equivalence class of the demonstrator. We extend previous work on algorithmic teaching for sequential decision-making tasks by showing a reduction to the set cover problem which enables an efficient approximation algorithm for determining the set of maximallyinformative demonstrations. We apply our proposed machine teaching algorithm to two novel applications: providing a lower bound on the number of queries needed to learn a policy using active IRL and developing a novel IRL algorithm that can learn more efficiently from informative demonstrations than a standard IRL approach.
Latent Distribution Preserving Deep Subspace Clustering

Subspace clustering is a useful technique for many computer vision applications in which the intrinsic dimension of high-dimensional data is smaller than the ambient dimension. Traditional subspace clustering methods often rely on the self-expressiveness property, which has proven effective for linear subspace clustering. However, they perform unsatisfactorily on real data with complex nonlinear subspaces. More recently, deep autoencoder based subspace clustering methods have achieved success owning to the more powerful representation extracted by the autoencoder network. Unfortunately, these methods only considering the reconstruction of original input data can hardly guarantee the latent representation for the data distributed in subspaces, which inevitably limits the performance in practice. In this paper, we propose a novel deep subspace clustering method based on a latent distribution-preserving autoencoder, which introduces a distribution consistency loss to guide the learning of distribution-preserving latent representation, and consequently enables strong capacity of characterizing the real-world data for subspace clustering. Experimental results on several public databases show that our method achieves significant improvement compared with the state-of-the-art subspace clustering methods.
Forecasting Hourly Solar Irradiance Using Hybrid Wavelet Transformation and Elman Model in Smart Grid

With the integration of photovoltaic (PV) power into an electrical network, the complexity of the grid management is increasing because of intermittent and fluctuation nature of solar energy.Solar irradiance forecasting is essential to facilitate planning and managing electricity generation and distribution in smart grid cyber-physical system (CPS).The performance of existing short-term forecasting methods is far from satisfactory due to a lack of reliable and fast time-frequency model for continuous-time solar irradiance data.To address this problem, this paper proposes a new method, Elman Neural Network (ENN) driven Wavelet Transform (WT-ENN), for hourly solar irradiance forecasting.Firstly, the solar irradiance series was decomposed into a set of constitutive series using wavelet transform.Secondly, the new wavelet coefficients were predicted by ENNs in every sub-series with the best network structure and parameters.Thirdly, Wavelet reconstruction will predict next hour solar irradiance through the aggregation of outputs of the ensemble of ENNs.Finally, the forecasting performance was evaluated using two large real-world solar irradiance datasets.Experiment results show that the new WT-ENN model outperforms a large number of alternative methods and an average forecast skill of 0.7590 over the persistence model.Thus, it is concluded that the proposed approach can significantly improve the forecasting accuracy and reliability.
Medical Image Super Resolution Using Improved Generative Adversarial Networks

Details of small anatomical landmarks and pathologies, such as small changes of the microvasculature and soft exudates, are critical to accurate disease analysis. However, actual medical images always suffer from limited spatial resolution, due to imaging equipment and imaging parameters (e.g. scanning time of CT images). Recently, machine learning, especially deep learning techniques, have brought revolution to image super resolution reconstruction. Motivated by these achievements, in this paper, we propose a novel super resolution method for medical images based on an improved generative adversarial networks. To obtain useful image details as much as possible while avoiding the fake information in high frequency, the original squeeze and excitation block is improved by strengthening important features while weakening non-important ones. Then, by embedding the improved squeeze and excitation block in a simplified EDSR model, we build a new image super resolution network. Finally, a new fusion loss that can further strengthen the constraints on low-level features is designed for training our model. The proposed image super resolution model has been validated on the public medical images, and the results show that visual effects of the reconstructed images by our method, especially in the case of high upscaling factors, outperform state-of-the-art deep learning-based methods such as SRGAN, EDSR, VDSR and D-DBPN.
Peers matter: The moderating role of social influence on information security policy compliance

Abstract Information security in an organization largely depends on employee compliance with information security policy (ISP). Previous studies have mainly explored the effects of command‐and‐control and self‐regulatory approaches on employee ISP compliance. However, how social influence at both individual and organizational levels impacts the effectiveness of these two approaches has not been adequately explored. This study proposes a social contingency model in which a rules‐oriented ethical climate (employee perception of a rules‐adherence environment) at the organizational level and susceptibility to interpersonal influence (employees observing common practices via peer interactions) at the individual level interact with both command‐and‐control and self‐regulatory approaches to affect ISP compliance. Using employee survey data, we found that these two social influence factors weaken the effects of both command‐and‐control and self‐regulatory approaches on ISP compliance. Theoretical and practical implications are also discussed.