Reviewerly/exHarmony huggingface dataset:
Trademark Similarity Evaluation Using a Combination of ViT and Local Features

The origin of the trademark similarity analysis problem lies within the legal area, specifically the protection of intellectual property. One of the possible technical solutions for this issue is the trademark similarity evaluation pipeline based on the content-based image retrieval approach. CNN-based off-the-shelf features have shown themselves as a good baseline for trademark retrieval. However, in recent years, the computer vision area has been transitioning from CNNs to a new architecture, namely, Vision Transformer. In this paper, we investigate the performance of off-the-shelf features extracted with vision transformers and explore the effects of pre-, post-processing, and pre-training on big datasets. We propose the enhancement of the trademark similarity evaluation pipeline by joint usage of global and local features, which leverages the best aspects of both approaches. Experimental results on the METU Trademark Dataset show that off-the-shelf features extracted with ViT-based models outperform off-the-shelf features from CNN-based models. The proposed method achieves a mAP value of 31.23, surpassing previous state-of-the-art results. We assume that the usage of an enhanced trademark similarity evaluation pipeline allows for the improvement of the protection of intellectual property with the help of artificial intelligence methods. Moreover, this approach enables one to identify cases of unfair use of such data and form an evidence base for litigation.
Decoding Decentralized Autonomous Organizations: A Content Analysis Approach to Understanding Scoring Platforms

This paper evaluates the scoring platforms for decentralized autonomous organization (DAO), examining their methodologies and highlighting their strengths and limitations. Using content analysis, we scrutinize the scoring methodologies of the Prime Rating, DAO Meter, and DeFi Safety platforms, evaluating code, documentation, security, team composition, governance, and regulatory compliance. We analyze the underlying assumptions and data sources relied upon by these platforms, using a content analysis approach. Our investigation furnishes valuable information for stakeholders, aiming to evaluate or enhance DAO scoring methodologies used by scholars and practitioners in the finance and blockchain fields. By contributing to a more rigorous understanding of DAO performance assessment, this paper supports informed decision making and promotes the development of a dependable and efficient scoring system for the decentralized financial ecosystem.
<scp>PassivePy</scp>: A tool to automatically identify passive voice in big text data

Abstract The academic study of grammatical voice (e.g., active and passive voice) has a long history in the social sciences. It has been examined in relation to psychological distance, attribution, credibility, and deception. Most evaluations of passive voice are experimental or small‐scale field studies, however, and perhaps one reason for its lack of adoption is the difficulty associated with obtaining valid, reliable, and replicable results through automated means. We introduce an automated tool to identify passive voice from large‐scale text data, PassivePy, a Python package (readymade website: https://passivepy.streamlit.app/ ). This package achieves 98% agreement with human‐coded data for grammatical voice as revealed in two large validation studies. In this paper, we discuss how PassivePy works, and present preliminary empirical evidence of how passive voice connects to various behavioral outcomes across three contexts relevant to consumer psychology: product complaints, online reviews, and charitable giving. Future research can build on this work and further explore the potential relevance of passive voice to consumer psychology and beyond.
Literary quiz as a form of involving technical university students in extracurricular activities

The aim of the study is to reveal the features of organizing and holding a literary quiz as a form of involving students in extracurricular activities at a technical university. The paper presents various types of tasks that were created by the authors for the described events aimed at the development of students’ reading activity. Furthermore, the results of surveys of participants in the quizzes are analyzed in order to obtain feedback, which allows the researchers to determine the degree of effectiveness of these events and compare the results obtained with the tasks set by the organizers. The scientific novelty of the study consists in identifying the criteria for the elaboration of tasks and the organization of a literary quiz at a technical university, as well as in describing the experience of holding a literary quiz. As a result, it has been determined that this form of extracurricular activities is productive in terms of developing skills of working with the text.
Complexity-based drift detection for nonstationary data streams

This publication presents the Complexity Drift Detector (C2D) – the method for detecting a concept shift in the data stream based on the classification task complexity measures. The method belongs to the group of detectors agnostic to the recognition quality of the base classifier. The possibility of selecting a set of difficulty measures taken into account during the data stream processing allows applying the method to many tasks in which the detection of a classification task complexity change is expected. The publication includes experiments analyzing the hyperparameters’ influence on the operation of the method and a broad comparative experiment comparing the proposed algorithm with state-of-the-art solutions. The experiments were carried out on synthetic data streams of different dimensions and with different concept drift characteristics, also presenting the effects of processing real-world data streams. The results of the conducted research confirm the high efficiency of the method in detecting concept changes, sensitive not only to the fact of drift occurrence but also to its dynamics.
Sexual Crimes Committed Through The Internet

If sexual offenses to take into account are only the types of crimes that are prescribed in criminal codes, the concept of sexual offenses would be perceived no more than sexual harassment, molestation, simple sexual assault and qualified sexual assault. However, the concept of sexual offenses is broader than and beyond these in terms of the effects. Especially after the informatics revolution with the internet becoming an indispensable part of our lives and with the transfer of all kinds of personal data including our private lives to social media, the frequency and the impact area of these offenses are increased unforeseeably. In the pre-internet era, being close enough to a person to have physical contact with him in order to commit a sexual crime is at least a letter, etc. Today, it is extremely easy for the perpetrator to remain anonymous and sexually assault digitally. While it makes difficult to find the perpetrator on the other hand it is an environment that facilitates the commission of the crime. Lately “revenge porn” is the most common example of that ex-partner or spouse shares anonymously the other one's naked images or videos during sexual affair. Another method of committing sexual crime that digital era brought out, is to force the victim to have sexual intercourse with perpetrator himself or with third parties by blackmailing with the victims’ private images or information obtained somehow from the digital environment. Another sexual offense is providing an easy access to the contents including child pornography, animal pornography or violence. Especially these are the methods of committing crime against which the whole world has been fighting. In this article, I will try to make a general assessment about the sexual crimes which is exactly a very old type of crime, transformation of sexual crime in today’s digital world, opportunities that created in a negative way by perpetrators and methods of dealing with them.
Level Line Guided Interest Point Detection

Detection of interest points, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , corners and blobs, lays the foundation for many vision tasks. Numerous methods have been proposed to improve the detection performance, and the gradient-based ones are the most investigated. However, existing gradient-based methods lack an adequate utilization of gradient orientations. In this letter, we show that the level line, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , the unit vector orthogonal to the gradient orientation of a specific point, is particularly important to interest point detection. The support level lines of an interest point, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , the level lines used to identify corners/blobs, exhibit a significantly different pattern from those of other points. Based on this observation, this letter proposes two robust interest point detectors for finding corners and blobs, respectively. For each detector, a specific type of level line difference is defined, and the corresponding differences are leveraged with different weights. Numerical experiments show the superior performance of the proposed detectors. The code will be publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/roylin1229/LLD-IP</uri> .
A blurred star image restoration method based on gyroscope data and enhanced sparse model

Abstract Star sensors usually have a fixed exposure time to guarantee detection of adequate navigation stars. In a high dynamic environment, star images suffer from degradation due to spacecraft movement, which will severely affects both centroid extraction and attitude accuracy. This paper presents an algorithm for the restoration of motion-blurred star images. The algorithm employs gyroscope assistance and consists of two steps: preprocessing and motion-blurred image restoration. In the preprocessing step, the angular velocity of the gyroscope predicts the motion trajectory, position, and shape of each star point during exposure. This step ensures a good initial estimate of the blur kernel for image restoration. The image restoration step employs an enhanced sparse model inspired by blind deblurring method to solve blur kernel and latent image alternately. Simulations and experiments have verified the effectiveness of the proposed algorithm.
CONCORD: Clone-Aware Contrastive Learning for Source Code

Deep Learning (DL) models to analyze source code have shown immense promise during the past few years. More recently, self-supervised pre-training has gained traction for learning generic code representations valuable for many downstream SE tasks, such as clone and bug detection.
SmartState: Detecting State-Reverting Vulnerabilities in Smart Contracts via Fine-Grained State-Dependency Analysis

Smart contracts written in Solidity are widely used in different blockchain platforms such as Ethereum, TRON and BNB Chain. One of the unique designs in Solidity smart contracts is its statereverting mechanism for error handling and access control. Unfortunately, a number of recent security incidents showed that adversaries also utilize this mechanism to manipulate critical states of smart contracts, and hence, bring security consequences such as illegal profit-gain and Deny-of-Service (DoS). In this paper, we call such vulnerabilities as the State-reverting Vulnerability (SRV). Automatically identifying SRVs poses unique challenges, as it requires an in-depth analysis and understanding of the state-dependency relations in smart contracts.
Improving Binary Code Similarity Transformer Models by Semantics-Driven Instruction Deemphasis

Given a function in the binary executable form, binary code similarity analysis determines a set of similar functions from a large pool of candidate functions. These similar functions are usually compiled from the same source code with different compilation setups. Such analysis has a large number of applications, such as malware detection, code clone detection, and automatic software patching. The state-of-the art methods utilize complex Deep Learning models such as Transformer models. We observe that these models suffer from undesirable instruction distribution biases caused by specific compiler conventions. We develop a novel technique to detect such biases and repair them by removing the corresponding instructions from the dataset and finetuning the models. This entails synergy between Deep Learning model analysis and program analysis. Our results show that we can substantially improve the state-of-the-art models’ performance by up to 14.4% in the most challenging cases where test data may be out of the distributions of training data.
Systematic Testing of the Data-Poisoning Robustness of KNN

Data poisoning aims to compromise a machine learning based software component by contaminating its training set to change its prediction results for test inputs. Existing methods for deciding data-poisoning robustness have either poor accuracy or long running time and, more importantly, they can only certify some of the truly-robust cases, but remain inconclusive when certification fails. In other words, they cannot falsify the truly-non-robust cases. To overcome this limitation, we propose a systematic testing based method, which can falsify as well as certify data-poisoning robustness for a widely used supervised-learning technique named k-nearest neighbors (KNN). Our method is faster and more accurate than the baseline enumeration method, due to a novel over-approximate analysis in the abstract domain, to quickly narrow down the search space, and systematic testing in the concrete domain, to find the actual violations. We have evaluated our method on a set of supervised-learning datasets. Our results show that the method significantly outperforms state-of-the-art techniques, and can decide data-poisoning robustness of KNN prediction results for most of the test inputs.
Detecting State Inconsistency Bugs in DApps via On-Chain Transaction Replay and Fuzzing

Decentralized applications (DApps) consist of multiple smart contracts running on Blockchain. With the increasing popularity of the DApp ecosystem, vulnerabilities in DApps could bring significant impacts such as financial losses. Identifying vulnerabilities in DApps is by no means trivial, as modern DApps consist of complex interactions across multiple contracts. Previous research suffers from either high false positives or false negatives, due to the lack of precise contextual information which is mandatory for confirming smart contract vulnerabilities when analyzing smart contracts.
RefBERT: A Two-Stage Pre-trained Framework for Automatic Rename Refactoring

Refactoring is an indispensable practice of improving the quality and maintainability of source code in software evolution. Rename refactoring is the most frequently performed refactoring that suggests a new name for an identifier to enhance readability when the identifier is poorly named. However, most existing works only identify renaming activities between two versions of source code, while few works express concern about how to suggest a new name. In this paper, we study automatic rename refactoring on variable names, which is considered more challenging than other rename refactoring activities. We first point out the connections between rename refactoring and various prevalent learning paradigms and the difference between rename refactoring and general text generation in natural language processing. Based on our observations, we propose RefBERT, a two-stage pre-trained framework for rename refactoring on variable names. RefBERT first predicts the number of sub-tokens in the new name and then generates sub-tokens accordingly. Several techniques, including constrained masked language modeling, contrastive learning, and the bag-of-tokens loss, are incorporated into RefBERT to tailor it for automatic rename refactoring on variable names. Through extensive experiments on our constructed refactoring datasets, we show that the generated variable names of RefBERT are more accurate and meaningful than those produced by the existing method. Our implementation and data are available at https://github.com/KDEGroup/RefBERT.
Validating Multimedia Content Moderation Software via Semantic Fusion

The exponential growth of social media platforms, such as Facebook, Instagram, Youtube, and TikTok, has revolutionized communication and content publication in human society. Users on these platforms can publish multimedia content that delivers information via the combination of text, audio, images, and video. Meanwhile, the multimedia content release facility has been increasingly exploited to propagate toxic content, such as hate speech, malicious advertisement, and pornography. To this end, content moderation software has been widely deployed on these platforms to detect and blocks toxic content. However, due to the complexity of content moderation models and the difficulty of understanding information across multiple modalities, existing content moderation software can fail to detect toxic content, which often leads to extremely negative impacts (e.g., harmful effects on teen mental health). We introduce Semantic Fusion, a general, effective methodology for validating multimedia content moderation software. Our key idea is to fuse two or more existing single-modal inputs (e.g., a textual sentence and an image) into a new input that combines the semantics of its ancestors in a novel manner and has toxic nature by construction. This fused input is then used for validating multimedia content moderation software. We realized Semantic Fusion as DUO, a practical content moderation software testing tool. In our evaluation, we employ DUO to test five commercial content moderation software and two state-of-the-art models against three kinds of toxic contents. The results show that DUO achieves up to 100% error finding rate (EFR) when testing moderation software and it obtains up to 94.1% EFR when testing the state-of-the-art models. In addition, we leverage the test cases generated by DUO to retrain the two models we explored, which largely improves model robustness (2.5%∼5.7% EFR) while maintaining the accuracy on the original test set.
Cross-domain fashion cloth retrieval via novel attention-guided cascade neural network and clothing parsing

Cross-domain fashion retrieval is a feasible method for finding the required clothing among a large number of products. Computer vision-based retrieval algorithms have been developed by many researchers. However, the precise retrieval process still faces challenges such as corrupted query images, ambiguous patterns, and an array of attributes. In this work, a novel attention-guided cascaded network has been proposed for efficient cloth retrieval by overcoming the aforementioned challenges. Initially, the input images are pre-processed with Wavelet integrated Retinex algorithm (WRA) to enhance the query cloth image without diminishing the important information. The attention-guided cascaded network is designed with global feature descriptor (GFD) and local feature descriptor (LFD) modules for extracting the domain-enriched features. These two modules are integrated with an attention block to reduce complexity and improve performance using selected features. In the LFD module, the Clothing Parsing Encoder-Decoder network (CPED-Net) is used to separate the bottom and top regions and sends them into the respective network for fine-grained feature analysis. Finally, global and local features from the modules are combined in similarity analysis for cloth retrieval. The proposed model achieves an overall accuracy of 98.54% based on the fashion retrieval benchmarks dataset. From the experimental results, the proposed model is superior to the state-of-the-art models.
Diversity-enhanced modified sine cosine algorithm and its application in solving engineering design problems

The sine cosine algorithm (SCA) is a recently developed metaheuristic algorithm that is inspired by the characteristics of sine and cosine trigonometric functions. Although it has successfully solved several benchmark and real-life problems, it experiences the issues of slow convergence rate, premature convergence and local optima stagnation. To overcome these shortcomings, this paper introduces an improved version of the SCA named ISCA, in which four different search strategies namely, population division, modification of the original SCA search scheme, hybridization of the modified SCA with differential evolution (DE), and additional mutation phase are integrated. In the first stage, the population division is performed to pass candidate solutions through different levels of exploration and exploitation. The second stage modifies the original SCA to exploit the main features of the SCA related to exploring the elite area of the search space. In the third stage, the proposed modified SCA is hybridized with DE to maintain the diversity of the population. The fourth and last stage embeds the Cauchy mutation-based search scheme for failure candidates to prevent them from stagnation at local optima. To validate the performance of the ISCA, it has been tested on 30 standard benchmark problems including unimodal, multimodal and composite problems and its comparison is performed with the original SCA and other metaheuristic algorithms. Its performance on the scalability of optimization problems is also verified by increasing the dimensions of test problems from 30 to 1000. Various performance measures such as average and standard deviation of optimization results, statistical analysis, and convergence analysis conclude the better search efficiency of the ISCA. Furthermore, six engineering application problems are solved using ISCA to demonstrate its real-world applicability. The experimental results attest that the proposed ISCA is highly competitive with the other metaheuristics.
Transfer Learning-Based QoE Estimation For Different Cloud Gaming Contexts

Cloud Gaming renders game data in the cloud and forwards it to players over the network. While this reduces hardware costs for players, it introduces challenges in network management and delivering a good gaming experience. In this context, network providers are encouraged to implement QoE-aware management systems to guarantee a desired Quality of Experience (QoE), in which Machine Learning (ML) models achieve the state-of-the-art on QoE estimation/monitoring. However, it is hard to create ML models that generalize to different contexts, especially since QoE perception is subjective and varies among games and players. This paper employs transfer learning and fine-tuning to adjust a source model to different target domains. First, we performed a subjective QoE assessment with real users playing on a realistic testbed. Based on this, we derived four datasets, one being the source dataset (to create the source model) and three distinct target datasets. Experiments show that transfer learning can decrease the average MSE error by at least 41.6% compared to the source model performance on the target datasets while decreasing the demand for labeled data by at least 81.1%. Furthermore, the improvement is greater when compared to models trained from scratch for each target dataset.