Reviewerly/exHarmony huggingface dataset:
A new climatology reference model to benchmark probabilistic solar forecasts

Probabilistic solar forecasting is becoming a major topic in the solar research community as it provides more information about the uncertainty of the forecast compared to deterministic forecasting. However, to facilitate the adoption of probabilistic forecasts within solar forecasting communities (industry and academic), the definition and the use of standardized best practices are a prerequisite. Among others, there is a need for benchmark models that are able to properly assess the performance of new probabilistic forecasting methods. In this work, we propose a new climatology benchmark model called “CSD-CLIM” (for Clear-Sky Dependent Climatology). This new reference model is evaluated against two other climatology benchmark models namely the naive climatology and a well-referenced model in the literature, the CH-PeEn (for Complete History Persistence Ensemble). The verification of compliance with a set of properties that a climatology benchmark model must follow demonstrates that the new CSD-CLIM model outperforms the naive climatology and that it can be a viable alternative to the CH-PeEn model. It is shown that the better performance of CSD-CLIM is due to a specific binning of the historical irradiance data based on the clear-sky irradiance values.
Proposing a reliable method of securing and verifying the credentials of graduates through blockchain

Abstract Education acts as a soul in the overall societal development, in one way or the other. Aspirants, who gain their degrees genuinely, will help society with their knowledge and skills. But, on the other side of the coin, the problem of fake certificates is alarming and worrying. It has been prevalent in different forms from paper-based dummy certificates to replicas backed with database tampering and has increased to astronomic levels in this digital era. In this regard, an overlay mechanism using blockchain technology is proposed to store the genuine certificates in digital form and verify them firmly whenever needed without delay. The proposed system makes sure that the certificates, once verified, can be present online in an immutable form for further reference and provides a tamper-proof concealment to the existing certification system. To confirm the credibility of the proposed method, a prototype of blockchain-based credential securing and verification system is developed in ethereum test network. The implementation and test results show that it is a secure and feasible solution to online credential management system.
Exacerbating Algorithmic Bias through Fairness Attacks

Algorithmic fairness has attracted significant attention in recent years, with many quantitative measures suggested for characterizing the fairness of different machine learning algorithms. Despite this interest, the robustness of those fairness measures with respect to an intentional adversarial attack has not been properly addressed. Indeed, most adversarial machine learning has focused on the impact of malicious attacks on the accuracy of the system, without any regard to the system's fairness. We propose new types of data poisoning attacks where an adversary intentionally targets the fairness of a system. Specifically, we propose two families of attacks that target fairness measures. In the anchoring attack, we skew the decision boundary by placing poisoned points near specific target points to bias the outcome. In the influence attack on fairness, we aim to maximize the covariance between the sensitive attributes and the decision outcome and affect the fairness of the model. We conduct extensive experiments that indicate the effectiveness of our proposed attacks.
Fully Parallel Implementation of Otsu Automatic Image Thresholding Algorithm on FPGA

This work proposes a high-throughput implementation of the Otsu automatic image thresholding algorithm on Field Programmable Gate Array (FPGA), aiming to process high-resolution images in real-time. The Otsu method is a widely used global thresholding algorithm to define an optimal threshold between two classes. However, this technique has a high computational cost, making it difficult to use in real-time applications. Thus, this paper proposes a hardware design exploiting parallelization to optimize the system’s processing time. The implementation details and an analysis of the synthesis results concerning the hardware area occupation, throughput, and dynamic power consumption, are presented. Results have shown that the proposed hardware achieved a high speedup compared to similar works in the literature.
Deep Verifier Networks: Verification of Deep Discriminative Models with Deep Generative Models

AI Safety is a major concern in many deep learning applications such as autonomous driving. Given a trained deep learning model, an important natural problem is how to reliably verify the model's prediction. In this paper, we propose a novel framework --- deep verifier networks (DVN) to detect unreliable inputs or predictions of deep discriminative models, using separately trained deep generative models. Our proposed model is based on conditional variational auto-encoders with disentanglement constraints to separate the label information from the latent representation. We give both intuitive and theoretical justifications for the model. Our verifier network is trained independently with the prediction model, which eliminates the need of retraining the verifier network for a new model. We test the verifier network on both out-of-distribution detection and adversarial example detection problems, as well as anomaly detection problems in structured prediction tasks such as image caption generation. We achieve state-of-the-art results in all of these problems.
Automated Cross-prompt Scoring of Essay Traits

The majority of current research in Automated Essay Scoring (AES) focuses on prompt-specific scoring of either the overall quality of an essay or the quality with regards to certain traits. In real-world applications obtaining labelled data for a target essay prompt is often expensive or unfeasible, requiring the AES system to be able to perform well when predicting scores for essays from unseen prompts. As a result, some recent research has been dedicated to cross-prompt AES. However, this line of research has thus far only been concerned with holistic, overall scoring, with no exploration into the scoring of different traits. As users of AES systems often require feedback with regards to different aspects of their writing, trait scoring is a necessary component of an effective AES system. Therefore, to address this need, we introduce a new task named Automated Cross-prompt Scoring of Essay Traits, which requires the model to be trained solely on non-target-prompt essays and to predict the holistic, overall score as well as scores for a number of specific traits for target-prompt essays. This task challenges the model's ability to generalize in order to score essays from a novel domain as well as its ability to represent the quality of essays from multiple different aspects. In addition, we introduce a new, innovative approach which builds on top of a state-of-the-art method for cross-prompt AES. Our method utilizes a trait-attention mechanism and a multi-task architecture that leverages the relationships between each trait to simultaneously predict the overall score and the score of each individual trait. We conduct extensive experiments on the widely used ASAP and ASAP++ datasets and demonstrate that our approach is able to outperform leading prompt-specific trait scoring and cross-prompt AES methods.
Searchable Public-Key Encryption With Cryptographic Reverse Firewalls for Cloud Storage

In order to protect data privacy in cloud storage, sensitive data is encrypted before being uploaded to a cloud server. How to retrieve ciphertext safely and effectively has become a problem. Public key encryption with keyword search (PEKS) realizes the retrieval of ciphertexts in clouds without disclosing secret information. However, most PEKS protocols can not resist an keyword guessing attack (KGA) launched by untrusted cloud servers. Meanwhile, these protocols are unable to detect vulnerabilities, resulting in information leakage. In this article, we design a searchable public-key encryption with cryptographic reverse firewalls (SPKE-CRF), and use the JPBC library to implement the protocol. Security analysis shows that the SPKE-CRF protocol can resist a chosen keyword attack (CKA), a KGA, and an algorithm substitution attack (ASA) without secure channels. Performance analysis shows that the SPKE-CRF protocol has a significant communication and computational cost advantage while being resistant to the KGA and ASA from malicious insider attackers in cloud environments. Therefore, our SPKE-CRF protocol is secure and efficient for cloud storage.
ISO Cluster classifier by ArcGIS for unsupervised classification of the Landsat TM image of Reykjavík

The paper presents the use of the Landsat TM image processed by the ArcGIS Spatial Analyst Tool for environmental mapping of southwestern Iceland, region of Reykjavik. Iceland is one of the most special Arctic regions with unique flora and landscapes. Its environment is presented by vulnerable ecosystems of highlands where vegetation is affected by climate, human or geologic factors: overgrazing, volcanism, annual temperature change. Therefore, mapping land cover types in Iceland contribute to the nature conservation, sustainable development and environmental monitoring purposes. This paper starts by review of the current trends in remote sensing, the importance of Landsat TM imagery for environmental mapping in general and Iceland in particular, and the requirements of GIS specifically for satellite image analysis. This is followed by the extended methodological workflow supported by illustrative print screens and technical description of data processing in ArcGIS. The data used in this research include Landsat TM image which was captured using GloVis and processed in ArcGIS. The methodology includes a workflow involving several technical steps of raster data processing in ArcGIS: 1) coordinate projecting, 2) panchromatic sharpening, 3) inspection of raster statistics, 4) spectral bands combination, 5) calculations, 6) unsupervised classification, 7) mapping. The classification was done by clustering technique using ISO Cluster algorithm and Maximum Likelihood Classification. This paper finally presents the results of the ISO Cluster application for Landsat TM image processing and concludes final remarks on the perspectives of environmental mapping based on Landsat TM image processing in ArcGIS.The results of the classification present landscapes divided into eight distinct land cover classes: 1) bare soils; 2) shrubs and smaller trees in the river valleys, urban areas including green spaces; 3) water areas; 4) forests including the Reykjanesfólkvangur National reserve; 5) ice-covered areas, glaciers and cloudy regions; 6) ravine valleys with a sparse type of the vegetation: rowan, alder, heathland, wetland; 7) rocks; 8) mixed areas. The final remarks include the discussion on the development of machine learning methods and opportunities of their technical applications in GIS-based analysis and Earth Observation data processing in ArcGIS, including image analysis and classification, mapping and visualization, machine learning and environmental applications for decision making in forestry and sustainable development.
Survey on the View Planning Problem for Reverse Engineering and Automated Control Applications

At present, optical sensors are being widely used to realize high quality control or reverse engineering of products, systems, buildings, environments or human bodies. Although the intrinsic characteristics of such breakthrough technologies may vary, ensuring complete acquisition relies on the definition of the optimal acquisition planning. To this end, the view planning problem (VPP) must be solved to automatically determine the optimal positions and/or trajectories of the acquisition devices to fully cover the part to be digitized. Such an automatization of the entire acquisition process is of considerably interest in the context of Industry 4.0. The aim of this paper is to review the state of the art works addressing the view planning problem and to identify the future challenges and possible research directions. First, the paper introduces a set of criteria to analyze the available methods, grouped into several macrocategories. The categories are presented and formalized to clearly understand the backbone and similarities of the grouped methods. Second, the paper describes and characterizes the available methods, based on their analysis according to the adopted criteria. The results of this extensive analysis clearly highlight the open issues and future challenges.
Product Sentiment Analysis for Amazon Reviews

Recently, Ecommerce has Witnessed Rapid Development. As A Result, Online Purchasing has grown, and that has led to Growth in Online Customer Reviews of Products. The Implied Opinions in Customer Reviews Have a Massive Influence on Customer's Decision Purchasing, Since the Customer's Opinion About the Product is Influenced by Other Consumers' Recommendations or Complaints. This Research Provides an Analysis of the Amazon Reviews Dataset and Studies Sentiment Classification with Different Machine Learning Approaches. First, the Reviews were Transformed into Vector Representation using different Techniques, I.E., Bag-Of-Words, Tf-Idf, and Glove. Then, we Trained Various Machine Learning Algorithms, I.E., Logistic Regression, Random Forest, Naïve Bayes, Bidirectional Long-Short Term Memory, and Bert. After That, We Evaluated the Models using Accuracy, F1-Score, Precision, Recall, and Cross-Entropy Loss Function. Then, We Analyized The Best Performance Model in Order to Investigate Its Sentiment Classification. The Experiment was Conducted on Multiclass Classifications, Then we Selected the Best Performing Model And Re-Trained It on the Binary Classification.
Time-varying quantum channel models for superconducting qubits

Abstract The decoherence effects experienced by the qubits of a quantum processor are generally characterized using the amplitude damping time ( T 1 ) and the dephasing time ( T 2 ). Quantum channel models that exist at the time of writing assume that these parameters are fixed and invariant. However, recent experimental studies have shown that they exhibit a time-varying (TV) behaviour. These time-dependant fluctuations of T 1 and T 2 , which become even more pronounced in the case of superconducting qubits, imply that conventional static quantum channel models do not capture the noise dynamics experienced by realistic qubits with sufficient precision. In this article, we study how the fluctuations of T 1 and T 2 can be included in quantum channel models. We propose the idea of time-varying quantum channel (TVQC) models, and we show how they provide a more realistic portrayal of decoherence effects than static models in some instances. We also discuss the divergence that exists between TVQCs and their static counterparts by means of a metric known as the diamond norm. In many circumstances this divergence can be significant, which indicates that the time-dependent nature of decoherence must be considered, in order to construct models that capture the real nature of quantum devices.
DEMix Layers: Disentangling Domains for Modular Language Modeling

We introduce a new domain expert mixture (DEMIX) layer that enables conditioning a language model (LM) on the domain of the input text.A DEMIX layer includes a collection of expert feedforward networks, each specialized to a domain, that makes the LM modular: experts can be mixed, added, or removed after initial training.Extensive experiments with autoregressive transformer LMs (up to 1.3B parameters) show that DEMIX layers reduce testtime perplexity (especially for out-of-domain data), increase training efficiency, and enable rapid adaptation.Mixing experts during inference, using a parameter-free weighted ensemble, enables better generalization to heterogeneous or unseen domains.We also show it is possible to add experts to adapt to new domains without forgetting older ones, and remove experts to restrict access to unwanted domains.Overall, these results demonstrate benefits of domain modularity in language models.
Hierarchical Social Similarity-guided Model with Dual-mode Attention for session-based recommendation

Session-based recommendation models users’ interests in sessions to make recommendations. Many previous studies have shown that users usually have similar interests to their friends, and are easily influenced by friends. However, these studies also tend to ignore the fact that users’ interests may merely be similar to certain friends’ interests in certain aspects. To address the above issues, we propose a novel Hierarchical Social Similarity-guided Model with Dual-mode Attention (HMDA) for Session-based Recommendation. Specifically, we first calculate the item-level similarity between users and their friends to select influential friends. We then compute the aspect-level similarity to explore the aspect difference between users’ interests and friends’ interests. Under the guidance of the item-level and aspect-level similarity, HMDA is capable of effectively and accurately aggregating the social influence exerted by friends on users, and further combining users’ individual interests to enhance recommendation performance. In addition, we design a dual-mode attention mechanism to capture the internal dependence and mutual dependence between the long-term and short-term interests of users. The proposed model is extensively evaluated on three real-world datasets. Experimental results demonstrate that our model outperforms the state-of-the-art baseline methods.
Assessing the credibility of COVID-19 vaccine mis/disinformation in online discussion

This study examines how the credibility of the content of mis- or disinformation, as well as the believability of authors creating such information is assessed in online discussion. More specifically, the investigation was focused on the credibility of mis- or disinformation about COVID-19 vaccines. To this end, a sample of 1887 messages posted to a Reddit discussion group was scrutinised by means of qualitative content analysis. The findings indicate that in the assessment of the author’s credibility, the most important criteria are his or her reputation, expertise and honesty in argumentation. In the judgement of the credibility of the content of mis/disinformation, objectivity of information and plausibility of arguments are highly important. The findings highlight that in the assessment of the credibility of mis/disinformation, the author’s qualities such as poor reputation, incompetency and dishonesty are particularly significant because they trigger expectancies about how the information content created by the author is judged.
Multiple Pairwise Ranking Networks for Personalized Video Summarization

In this paper, we investigate video summarization in the supervised setting. Since video summarization is subjective to the preference of the end-user, the design of a unique model is limited. In this work, we propose a model that provides personalized video summaries by conditioning the summarization process with predefined categorical user labels referred to as preferences. The underlying method is based on multiple pairwise rankers (called Multi-ranker), where the rankers are trained jointly to provide local summaries as well as a global summarization of a given video. In order to demonstrate the relevance and applications of our method in contrast with a classical global summarizer, we conduct experiments on multiple benchmark datasets, notably through a user study and comparisons with the state-of-art methods in the global video summarization task.
Blockchain Enabled Automatic Reward System in Solid Waste Management

Solid waste management (SWM) is a key administrative unit for managing the urban waste to deliver an eco-friendly environment to the citizens residing in urban cities. Generally, many technologies are implemented and developed by researchers for enhancing the mechanism of SWM and minimizing the waste generation. Yet, the management of waste generation is still a concern. So, here, there is requirement of technology that can involve the individuals for achieving the target reducing the waste. At present, the blockchain technology is an appropriate technology for SWM, as it provides the applications of time tracing activities, secure data transactions, and automatic reward system. In this study, a blockchain-based reward system is proposed to generate the rewards based on real-time series data such as quantity of garbage and level of waste. Furthermore, LoRa-range-based customized sensors are developed for bins to obtain real time information. Moreover, the generated information further transferred to cloud by utilizing LoRa wireless enabled gateway. By the use of flask server, a technique is proposed for integrating real-time data with blockchain via a local network application programming interface (API). A real-time implementation is evaluated on the data to the check the performance efficiency of the proposed approach, where the procedure of automatic reward system is presented in detail.
<i>Where Can We Help</i>? A Visual Analytics Approach to Diagnosing and Improving Semantic Segmentation of Movable Objects

Semantic segmentation is a critical component in autonomous driving and has to be thoroughly evaluated due to safety concerns. Deep neural network (DNN) based semantic segmentation models are widely used in autonomous driving. However, it is challenging to evaluate DNN-based models due to their black-box-like nature, and it is even more difficult to assess model performance for crucial objects, such as lost cargos and pedestrians, in autonomous driving applications. In this work, we propose VASS, a Visual Analytics approach to diagnosing and improving the accuracy and robustness of Semantic Segmentation models, especially for critical objects moving in various driving scenes. The key component of our approach is a context-aware spatial representation learning that extracts important spatial information of objects, such as position, size, and aspect ratio, with respect to given scene contexts. Based on this spatial representation, we first use it to create visual summarization to analyze models' performance. We then use it to guide the generation of adversarial examples to evaluate models' spatial robustness and obtain actionable insights. We demonstrate the effectiveness of VASS via two case studies of lost cargo detection and pedestrian detection in autonomous driving. For both cases, we show quantitative evaluation on the improvement of models' performance with actionable insights obtained from VASS.