Reviewerly/exHarmony huggingface dataset:
Why Smart Contracts Reported as Vulnerable were not Exploited?

&lt;p&gt;Smart contract security is essential for blockchain applications. Studies show that few of the reported vulnerabilities are exploited. However, no follow-up study is performed to why the reported vulnerabilities are not exploited. We aim to understand the reasons for the low exploitation rate to help improve vulnerability detection practices. We first collect 136,969 unique real-world smart contracts and analyze them using seven vulnerability detectors. Then, we apply Strauss’ grounded theory approach to understand if they are exploitable. In addition, we analyze the transaction logs of the exploitable vulnerabilities to understand their exploitations in history. Among the 4,364 smart contracts reported as vulnerable by the vulnerability detectors, 75.27% of them are unexploitable, and only 66 (0.015%) have been exploited. We uncover 11 reasons for making the detectors misidentify unexploitable vulnerabilities and six reasons for demotivating and preventing the attackers from exploiting the exploitable ones. We illustrate that: beyond treating the smart contracts as yet another Object Oriented (OO) application, it is essential to consider the Solidity programming language’s design principle, smart contracts' application scenarios, and their execution environments. Our results can help differentiate exploitable smart contracts to help allocate efforts to exploitable ones to mitigate emergent risks. &lt;/p&gt;
DraG4Rec: A Symmetrical View of User Preference and Item Popularity

&lt;p&gt;Utilizing user-item interaction dynamics is crucial for providing efficient and precise sequential recommendations (SR). Besides earlier works based on Matrix factorization, Markov Chains, and RNNs, GCNs taking advantage of attention mechanism and Knowledge-enhanced NNs digging heterogenous auxiliary information recently made improvements. However, there still exist some problems: 1) existing works mainly focus on capturing user preference without considering the importance of item popularity; 2) introducing too much auxiliary information denies the independence of interaction behavior; 3) favoring high-order chronological history with heavy memory while ranking score referring different bias is unappreciated. To tackle these problems, we proposed DraG4Rec (Dual rating enhanced attention-based GCN for Recommendation), which makes symmetry of user preference and item popularity manifest great value. Specifically, we first merge ranking score, time, and position encoding for user/item representation in a bipartite graph. Then, an edge-view message-passing mechanism is imported into the attention-based GCN to use implicit 2-order historical information with less memory. Finally, user preference and item popularity are learned jointly. Extensive experiments on dense and sparse real-world datasets demonstrate the superior performance of our framework over state-of-the-art baselines regarding two commonly-used metrics: i.e., Recall and NDCG. The ablation study illustrates the boosting effect of different components. &lt;/p&gt;
Hybrid Recommendation System for Tourism using Deep Learning

Recommendation systems have become increasingly important in the tourism industry as they assist travelers in making informed decisions about their trips. Traditional recommendation systems use either collaborative filtering or content-based approaches to provide recommendations, which have certain limitations in terms of accuracy and personalization. In this poster, we present a hybrid recommendation system that combines both approaches using deep learning algorithms. Our system uses convolutional neural networks (CNN) to analyze and classify images of tourist destinations, and recurrent neural networks (RNN) to analyze user-generated content such as reviews and ratings. The system then combines the results of both approaches using a hybrid model that considers both item and user similarities. The model is designed to learn from user feedback and adapt to new trends and changes in the tourism industry. We evaluate our system using real-world data and compare it to traditional recommendation systems. Our results demonstrate that the proposed system provides more accurate and personalized recommendations compared to traditional approaches. We show that our system improves user satisfaction and engagement in the tourism industry. Overall, our hybrid recommendation system provides an innovative approach to solving the limitations of traditional approaches in the tourism industry. The system has the potential to improve user experience and engagement, leading to increased revenue for tourism businesses.
The Influence of War and Global Economy on Article Publication (Bibliometric Analysis using Biblioshiny-R)

Abstract This descriptive qualitative research uses Bibliometric Analysis by utilizing the Biblioshiny-R tool. This research is to determine the influence of an author, document, affiliation, source, and country in article publication with war and global economy themes. The source of the analysis data with the theme of war and global economy comes from the Scopus database without any restrictions. Weiss MA is the most influential author, at the same time Liu Y has the highest h index. The University of California is the most influential affiliate, and at the same time, the Cambridge Journal of Economics has the highest h index. The economy is the most influential keyword in the publication, at the same time, the war keyword also appears the most. Gössling S, 2020, J Sustainable Tour and USA are the most influential documents and country publications. Until now, no bibliometric research has been found that discusses the Scopus database on war and the global economy. What is new from this research is the use of Biblioshiny-R in the Scopus database analysis on war and the global economy which has never been done before. It is hoped that in the future more scientists, students, lecturers, and researchers will be interested in researching the effects of war on the global economy, especially wars that use nuclear and chemical weapons. As a result, the Government can develop policies to anticipate/avoid wars using nuclear weapons and chemical weapons.
Раннемеловые гранитно-метаморфические ассоциации и оруденение Золотого хребта (Восточная Чукотка)

The geological characteristic of the Golden Ridge, one of the oldest gold-bearing areas in the North-East, is presented. The important role of the Early Cretaceous metamorphism and the associated granitoid plutonism controlling the orogenic gold mineralization and the placer gold content, which is typical for the granite-metamorphic domes of Chukotka, is demonstrated. The area prospects for the Late Cretaceous copper-molybdenum-porphyry mineralization are characterized. The potential of ore and placer gold in the area discussed is assessed.
High Frequency Rule Synthesis in a Large Scale Multiple Database with MapReduce

Increasing development in information and communication technology leads to the generation of large amount of data from various sources.These collected data from multiple sources grows exponentially and may not be structurally uniform.In general, these are heterogeneous and distributed in multiple databases.Because of large volume, high velocity and variety of data mining knowledge in this environment becomes a big data challenge.Distributed Association Rule Mining(DARM) in these circumstances becomes a tedious task for an effective global Decision Support System(DSS).The DARM algorithms generate a large number of association rules and frequent itemset in the big data environment.In this situation synthesizing highfrequency rules from the big database becomes more challenging.Many algorithms for synthesizing association rule have been proposed in multiple database mining environments.These are facing enormous challenges in terms of high availability, scalability, efficiency, high cost for the storage and processing of large intermediate results and multiple redundant rules.In this paper, we have proposed a model to collect data from multiple sources into a big data storage framework based on HDFS.Secondly, a weighted multi-partitioned method for synthesizing high-frequency rules using MapReduce programming paradigm has been proposed.Experiments have been conducted in a parallel and distributed environment by using commodity hardware.We ensure the efficiency, scalability, high availability and costeffectiveness of our proposed method.
Molecular Electronic Structure Calculation via a Quantum Computer

Quantum computers can be used to calculate the electronic structure and estimate the ground state energy of many-electron molecular systems. In the present study, we implement the Variational Quantum Eigensolver (VQE) algorithm, as a hybrid quantum-classical algorithm to calculate the ground state energy of the molecules such as H3+, OH-, HF and BH3 in which the number of qubits has an increasing trend. We use the parity transformation for Fermion to qubit encoding and the Unitary Coupled Cluster for Single and Double excitations (UCCSD) to construct an ansatz. We compare our quantum simulation results with the computational chemistry approaches including Full Configuration Interaction (FCI), as benchmark energy and Unrestricted Hartree-Fock (UHF), as a common computational method. Our results show that there is a good agreement between molecular ground state energy obtained from VQE and FCI. Moreover, the accuracy of the ground state energies obtained from VQE in our work is higher than the previously reported values.
Hematoxylin and eosin stained oral squamous cell carcinoma histological images dataset

Computer-aided diagnosis (CAD) can be used as an important tool to aid and enhance pathologists' diagnostic decision-making. Deep learning techniques, such as convolutional neural networks (CNN) and fully convolutional networks (FCN), have been successfully applied in medical and biological research. Unfortunately, histological image segmentation is often constrained by the availability of labeled training data once labeling histological images for segmentation purposes is a highly-skilled, complex, and time-consuming task. This paper presents the hematoxylin and eosin (H&E) stained oral cavity-derived cancer (OCDC) dataset, a labeled dataset containing H&E-stained histological images of oral squamous cell carcinoma (OSCC) cases. The tumor regions in our dataset are labeled manually by a specialist and validated by a pathologist. The OCDC dataset presents 1,020 histological images of size 640x640 pixels containing tumor regions fully annotated for segmentation purposes. All the histological images are digitized at 20x magnification.
SFE: A Simple, Fast and Efficient Feature Selection Algorithm for High-Dimensional Data

In this paper, a new feature selection algorithm, called SFE (Simple, Fast, and Efficient), is proposed for high-dimensional datasets. The SFE algorithm performs its search process using a search agent and two operators: non-selection and selection. It comprises two phases: exploration and exploitation. In the exploration phase, the non-selection operator performs a global search in the entire problem search space for the irrelevant, redundant, trivial, and noisy features, and changes the status of the features from selected mode to non-selected mode. In the exploitation phase, the selection operator searches the problem search space for the features with a high impact on the classification results, and changes the status of the features from non-selected mode to selected mode. The proposed SFE is successful in feature selection from high-dimensional datasets. However, after reducing the dimensionality of a dataset, its performance cannot be increased significantly. In these situations, an evolutionary computational method could be used to find a more efficient subset of features in the new and reduced search space. To overcome this issue, this paper proposes a hybrid algorithm, SFE-PSO (particle swarm optimization) to find an optimal feature subset. The efficiency and effectiveness of the SFE and the SFE-PSO for feature selection are compared on 40 high-dimensional datasets. Their performances were compared with six recently proposed feature selection algorithms. The results obtained indicate that the two proposed algorithms significantly outperform the other algorithms, and can be used as efficient and effective algorithms in selecting features from high-dimensional datasets.
Operating critical machine learning models in resource constrained regimes

The accelerated development of machine learning methods, primarily deep learning, are causal to the recent breakthroughs in medical image analysis and computer aided intervention. The resource consumption of deep learning models in terms of amount of training data, compute and energy costs are known to be massive. These large resource costs can be barriers in deploying these models in clinics, globally. To address this, there are cogent efforts within the machine learning community to introduce notions of resource efficiency. For instance, using quantisation to alleviate memory consumption. While most of these methods are shown to reduce the resource utilisation, they could come at a cost in performance. In this work, we probe into the trade-off between resource consumption and performance, specifically, when dealing with models that are used in critical settings such as in clinics.
Prevalence of Code Smells in Reinforcement Learning Projects

Reinforcement Learning (RL) is being increasingly used to learn and adapt application behavior in many domains, including large-scale and safety critical systems, as for example, autonomous driving. With the advent of plug-n-play RL libraries, its applicability has further increased, enabling integration of RL algorithms by users. We note, however, that the majority of such code is not developed by RL engineers, which as a consequence, may lead to poor program quality yielding bugs, suboptimal performance, maintainability, and evolution problems for RL-based projects. In this paper we begin the exploration of this hypothesis, specific to code utilizing RL, analyzing different projects found in the wild, to assess their quality from a software engineering perspective. Our study includes 24 popular RL-based Python projects, analyzed with standard software engineering metrics. Our results, aligned with similar analyses for ML code in general, show that popular and widely reused RL repositories contain many code smells (3.95% of the code base on average), significantly affecting the projects' maintainability. The most common code smells detected are long method and long method chain, highlighting problems in the definition and interaction of agents. Detected code smells suggest problems in responsibility separation, and the appropriateness of current abstractions for the definition of RL algorithms.
3D Data Augmentation for Driving Scenes on Camera

Driving scenes are extremely diverse and complicated that it is impossible to collect all cases with human effort alone. While data augmentation is an effective technique to enrich the training data, existing methods for camera data in autonomous driving applications are confined to the 2D image plane, which may not optimally increase data diversity in 3D real-world scenarios. To this end, we propose a 3D data augmentation approach termed Drive-3DAug, aiming at augmenting the driving scenes on camera in the 3D space. We first utilize Neural Radiance Field (NeRF) to reconstruct the 3D models of background and foreground objects. Then, augmented driving scenes can be obtained by placing the 3D objects with adapted location and orientation at the pre-defined valid region of backgrounds. As such, the training database could be effectively scaled up. However, the 3D object modeling is constrained to the image quality and the limited viewpoints. To overcome these problems, we modify the original NeRF by introducing a geometric rectified loss and a symmetric-aware training strategy. We evaluate our method for the camera-only monocular 3D detection task on the Waymo and nuScences datasets. The proposed data augmentation approach contributes to a gain of 1.7% and 1.4% in terms of detection accuracy, on Waymo and nuScences respectively. Furthermore, the constructed 3D models serve as digital driving assets and could be recycled for different detectors or other 3D perception tasks.
SOCS: Semantically-aware Object Coordinate Space for Category-Level 6D Object Pose Estimation under Large Shape Variations

Most learning-based approaches to category-level 6D pose estimation are design around normalized object coordinate space (NOCS). While being successful, NOCS-based methods become inaccurate and less robust when handling objects of a category containing significant intra-category shape variations. This is because the object coordinates induced by global and rigid alignment of objects are semantically incoherent, making the coordinate regression hard to learn and generalize. We propose Semantically-aware Object Coordinate Space (SOCS) built by warping-and-aligning the objects guided by a sparse set of keypoints with semantically meaningful correspondence. SOCS is semantically coherent: Any point on the surface of a object can be mapped to a semantically meaningful location in SOCS, allowing for accurate pose and size estimation under large shape variations. To learn effective coordinate regression to SOCS, we propose a novel multi-scale coordinate-based attention network. Evaluations demonstrate that our method is easy to train, well-generalizing for large intra-category shape variations and robust to inter-object occlusions.
Blind Multimodal Quality Assessment of Low-light Images

Blind image quality assessment (BIQA) aims at automatically and accurately forecasting objective scores for visual signals, which has been widely used to monitor product and service quality in low-light applications, covering smartphone photography, video surveillance, autonomous driving, etc. Recent developments in this field are dominated by unimodal solutions inconsistent with human subjective rating patterns, where human visual perception is simultaneously reflected by multiple sensory information. In this article, we present a unique blind multimodal quality assessment (BMQA) of low-light images from subjective evaluation to objective score. To investigate the multimodal mechanism, we first establish a multimodal low-light image quality (MLIQ) database with authentic low-light distortions, containing image-text modality pairs. Further, we specially design the key modules of BMQA, considering multimodal quality representation, latent feature alignment and fusion, and hybrid self-supervised and supervised learning. Extensive experiments show that our BMQA yields state-of-the-art accuracy on the proposed MLIQ benchmark database. In particular, we also build an independent single-image modality Dark-4K database, which is used to verify its applicability and generalization performance in mainstream unimodal applications. Qualitative and quantitative results on Dark-4K show that BMQA achieves superior performance to existing BIQA approaches as long as a pre-trained model is provided to generate text description. The proposed framework and two databases as well as the collected BIQA methods and evaluation metrics are made publicly available on here.
An Empirical Study of Pre-trained Language Models in Simple Knowledge Graph Question Answering

Large-scale pre-trained language models (PLMs) such as BERT have recently achieved great success and become a milestone in natural language processing (NLP). It is now the consensus of the NLP community to adopt PLMs as the backbone for downstream tasks. In recent works on knowledge graph question answering (KGQA), BERT or its variants have become necessary in their KGQA models. However, there is still a lack of comprehensive research and comparison of the performance of different PLMs in KGQA. To this end, we summarize two basic KGQA frameworks based on PLMs without additional neural network modules to compare the performance of nine PLMs in terms of accuracy and efficiency. In addition, we present three benchmarks for larger-scale KGs based on the popular SimpleQuestions benchmark to investigate the scalability of PLMs. We carefully analyze the results of all PLMs-based KGQA basic frameworks on these benchmarks and two other popular datasets, WebQuestionSP and FreebaseQA, and find that knowledge distillation techniques and knowledge enhancement methods in PLMs are promising for KGQA. Furthermore, we test ChatGPT, which has drawn a great deal of attention in the NLP community, demonstrating its impressive capabilities and limitations in zero-shot KGQA. We have released the code and benchmarks to promote the use of PLMs on KGQA.
A Graph-Guided Reasoning Approach for Open-ended Commonsense Question Answering

Recently, end-to-end trained models for multiple-choice commonsense question answering (QA) have delivered promising results. However, such question-answering systems cannot be directly applied in real-world scenarios where answer candidates are not provided. Hence, a new benchmark challenge set for open-ended commonsense reasoning (OpenCSR) has been recently released, which contains natural science questions without any predefined choices. On the OpenCSR challenge set, many questions require implicit multi-hop reasoning and have a large decision space, reflecting the difficult nature of this task. Existing work on OpenCSR sorely focuses on improving the retrieval process, which extracts relevant factual sentences from a textual knowledge base, leaving the important and non-trivial reasoning task outside the scope. In this work, we extend the scope to include a reasoner that constructs a question-dependent open knowledge graph based on retrieved supporting facts and employs a sequential subgraph reasoning process to predict the answer. The subgraph can be seen as a concise and compact graphical explanation of the prediction. Experiments on two OpenCSR datasets show that the proposed model achieves great performance on benchmark OpenCSR datasets.
Entanglement as the cross-symmetric part of quantum discord

In this paper, we show that the minimal quantum discord over "cross-symmetric" state extensions is an entanglement monotone. In particular, we show that the minimal Bures distance of discord over cross-symmetric extensions is equivalent to the Bures distance of entanglement. At last, we refute a long-held but unstated convention that only contractive distances can be used to construct entanglement monotones by showing that the entanglement quantifier induced by the Hilbert-Schmidt distance, which is not contractive under quantum operations, is also an entanglement monotone.