Reviewerly/exHarmony huggingface dataset:
EQUAL: Improving the Fidelity of Quantum Annealers by Injecting Controlled Perturbations

Quantum computing is an information processing paradigm that uses quantum-mechanical properties to speedup computationally hard problems. Gate-based quantum computers and Quantum Annealers (QAs) are two commercially available hardware platforms that are accessible to users today. Although promising, existing gate-based quantum computers consist of only a few dozen qubits and are not large enough for most applications. On the other hand, existing QAs with few thousand qubits have the potential to solve some domain-specific optimization problems. QAs are single instruction machines and to execute a program, the problem is cast to a Hamiltonian, embedded in the hardware, and a single quantum machine instruction (QMI) is run. Unfortunately, noise and imperfections in hardware result in sub-optimal solutions on QAs even if the QMI is run for thousands of trials.Due to the limited programmability of QAs users execute the same QMI for all trials. This subjects all trials to a similar noise profile throughout the execution, resulting in a systematic bias. We observe that systematic bias leads to sub-optimal solutions and cannot be alleviated by executing more trials or using existing error-mitigation schemes. To address this challenge, we propose EQUAL (Ensemble QUantum AnneaLing). EQUAL generates an ensemble of QMIs by adding controlled perturbations to the program QMI. When executed on the QA, the ensemble of QMIs steers the program away from encountering the same bias during all trials and thus, improves the quality of solutions. Our evaluations using the 2041-qubit D-Wave QA show that EQUAL bridges the difference between the baseline and the ideal by an average of 14% (and up to 26%) without requiring any additional trials. EQUAL can be combined with existing error mitigation schemes to further bridge the difference between the baseline and ideal by an average of 55% (and up to 68%).
Towards Robust Detection and Segmentation Using Vertical and Horizontal Adversarial Training

Adversarial training (AT) commonly serves as an advanced regularization to establish enhanced robust models. However, it usually scarifies performance on clean inputs, especially in complicated object detection and semantic segmentation tasks. However, how to fully unleash the power of adversarial training regularization to improve the trade-off between standard performance and adversarial robustness of detection and segmentation models, has not been explored. In this paper, we present the Vertical and Horizontal Adversarial Training (VHAT) regularization on both input and intermediate features, which consists of two major components: i) Vertical Adversarial Training (VAT) by utilizing adversarial features with a wide range of attack strengths; ii) Horizontal Adversarial Training (HAT) by injecting layer-wise adversarial feature perturbations together with adversarial samples. Extensive experiment results demonstrate that VHAT achieves the standard performance and adversarial robustness double-win for Faster-RCNN on PASCAL VOC and DeepLabv3+ on PASCAL VOC and Cityscapes datasets, respectively. Comprehensive ablation studies and visualizations are provided to reveal the insights and working mechanisms.
Evaluating Tip Selection Algorithms for IOTA Blockchain

IOTA blockchain is an alternative to blockchain, which is utilized for millions of devices connected to an Internet of Things network. These devices can communicate data with other devices in the form of transactions using IOTA. The IOTA tangle is the network of such devices. To complete a new transaction, tips of the tangle must be discovered, which is accomplished through the use of several tip selection methods. The process of picking two tips in the IOTA tangle to which the incoming transaction connects is known as tip selection. The study of various tip selection methods used in IOTA blockchain is described in this research, and tip selection strategies are evaluated with respect to time and varied number of node configurations using JGraphT library. Finally, a comparison of all tip selection algorithms is shown, with the adaptive random walk algorithm outperforming the others.
Reclassify and Readjust Software Risk Taxonomy in Software Development Activities Context

A lot of research on software project risk associated with the software development lifecycle. To accommodate the many risk factors in software development, the Software Engineering Institute (SEI) then launched a risk taxonomy model that consist of 3 classes, 13 elements, and 63 attributes. This study aims to map the risk factors obtained from the results of a systematic literature review, then adjust the risk weights for 12 software development activities. Of the 197 risk factors that have been mapped into 12 activities, then adjusted weight for each activity is calculated next. The results of this study indicate that the Implementation activity in the software development phase is the activity with the greatest potential risk, which is 9.57 percent. Meanwhile, in the on-going lifecycle phase, the greatest potential risk is Project Management activity as much as 19.14 percent. That's why an experienced project manager can reduce impact of software development project risk failure.
Methodology to determine the age of the text’s author based on readability and lexical diversity metrics

The article describes the approaches to determining the age of the author of an anonymous text written in Russian. The fundamental works of the subject area are considered, both proven approaches (support vector machine, naive Bayes classifier, convolutional and recurrent neural networks) and modern methods (fastText, BERT) are implemented. The study used its own data set containing 1,5 million comments from social media users. A separate experiment is devoted to assessing the impact on the classification accuracy of various text vectorization methods. As a result of a series of experiments aimed at evaluating the efficiency of the methods used and selecting informative features, a model was obtained that can predict the age of the author of an anonymous text with an accuracy of 83.2%.
Who benefits from Visualization Adaptations? Towards a better Understanding of the Influence of Visualization Literacy

The ability to read, understand, and comprehend visual information representations is subsumed under the term visualization literacy (VL). One possibility to improve the use of information visualizations is to introduce adaptations. However, it is yet unclear whether people with different VL benefit from adaptations to the same degree. We conducted an online experiment (n = 42) to investigate whether the effect of an adaptation (here: De-Emphasis) of visualizations (bar charts, scatter plots) on performance (accuracy, time) and user experiences depends on users' VL level. Using linear mixed models for the analyses, we found a positive impact of the De-Emphasis adaptation across all conditions, as well as an interaction effect of adaptation and VL on the task completion time for bar charts. This work contributes to a better understanding of the intertwined relationship of VL and visual adaptations and motivates future research.
Modeling Access Environment and Behavior Sequence for Financial Identity Theft Detection in E-Commerce Services

Online-to-Offline (O2O) e-commerce service platforms and their users are faced with various fraud risks. Among them, financial identity theft is a widely existing challenge. However, existing methods are insufficient to detect this type of fraud. In this paper, we address the financial identity theft detection problem in e-commerce services by leveraging access environment and behavior sequence. To explore the fraud patterns, we first make a detailed analysis using real cases of identity theft from Meituan, a leading O2O e-commerce platform in China. Our findings are twofold. First, fraudulent accounts sharing the same personal ID would have different access environments, such as devices and IP addresses. Second, a group of fraudulent accounts may have aggregations of devices, IP addresses, and delivery addresses. Based on these observations, we propose a hybrid method termed EnvIT to detect financial identity theft based on the heterogeneous graph and the behavior sequence. EnvIT is able to characterize the access environment and the historical behavior of the accounts. Furthermore, an attentive module is adopted to assign weights to different features automatically. We further evaluate EnvIT via extensive experiments using a real-world dataset from Meituan. Our experimental results demonstrate that EnvIt outperforms several baseline methods in fraudulent account detection and achieves an AUC of 0.9210.
Interpretability of Knowledge Graph-based Explainable Process Analysis

The last decade produced rapid developments and powerful new technologies that are creating a huge upsurge in artificial intelligence research. However, for critical operational decisions (e.g., consulting services), the need for explanations and interpretable results are becoming a necessity. The integration of knowledge graphs that provide relevant background knowledge in machine-readable form, and machine learning methods represents a new form of hybrid intelligent systems that benefit from each other's strengths. Our research aims at an explainable system with a specific knowledge graph architecture that can generate human-understandable results even when no suitable domain experts are available. Against this background, the interpretability of a knowledge graph-based explainable artificial intelligence approach for business process analysis is focused. We design a framework of interpretation, and show how interpretable models are generated. Result paths on weaknesses and improvement measures related to a business process are used to produce stochastic decision trees, which improve the interpretability of results. This can lead to interesting consulting self-services for clients or be applied as a device for accelerating classical consulting projects.
More than words: A Framework for Describing Human-Robot Dialog Designs

This paper presents a novel framework for describing human-robot interaction dialog, developed from a survey and analysis of existing systems and research. We collected data from 75 published systems and conducted an iterative thematic analysis to distill the broad range of work into key underlying factors de-fining them. Our framework provides a language to describe hu-man-robot dialog systems and a new way of classifying and under-standing human-robot dialog, in terms of both high-level design aspects and relevant implementation details. Our quantitative sur-vey summary further provides a detailed, contemporary snapshot of predominant approaches in the field, highlighting opportunities for further exploration.
Collusion Resistant Copy-Protection for Watermarkable Functionalities

Copy-protection is the task of encoding a program into a quantum state to prevent illegal duplications. A line of recent works studied copy-protection schemes under “ $$1\rightarrow 2$$ attacks”: the adversary receiving one program copy can not produce two valid copies. However, under most circumstances, vendors need to sell more than one copy of a program and still ensure that no duplicates can be generated. In this work, we initiate the study of collusion resistant copy-protection in the plain model. Our results are twofold:
Research on an Intelligent Computing Offloading Model for the Internet of Vehicles Based on Blockchain

Aiming at the problems of computing power, reliability and cost when intelligent vehicles deal with computationally intensive and delay-sensitive emerging applications in multiple business scenarios in the Internet of Vehicles, an intelligent computing offloading model is proposed. This can minimize the total system cost under the constraint of time delay and energy consumption. Considering the cost of blockchain and the cost of intelligent vehicles, the DDPG algorithm is used to solve the proposed model. Simulation results show that the method proposed in this paper can effectively reduce the total cost of computing offload and further improve the success rate of computational offloading under the premise of computational offloading safety.
UformPose: A U-Shaped Hierarchical Multi-Scale Keypoint-Aware Framework for Human Pose Estimation

Human pose estimation is a fundamental yet challenging task in computer vision. However, difficult scenarios such as invisible keypoints, occlusions and small-scale persons are still not well-handed. In this paper, we present a novel pose estimation framework named UformPose which targets to relieve these issues. UformPose has two core designs: Shared Feature Pyramid Stem (SFPS) and U-shaped hierarchical Multi-scale Keypoint-aware Attention Module (U-MKAM). SFPS is a feature pyramid stem with shared mechanism to learn stronger low-level features at the initial stage, and the shared mechanism can facilitate cross-resolution commonality learning. Our U-MKAM attempts to generate high-quality high-resolution representations by integrating all levels of feature representation of the backbone layer by layer. More importantly, we utilize the flexibility of attention operations for keypoint-aware modeling, which explicitly captures and trades-offs the dependencies between keypoints. We empirically demonstrate the effectiveness of our framework through the competitive pose estimation results on the COCO dataset. Extensive experiments and visual analysis on CrowdPose demonstrate the robustness of our model in crowd scenes.
Facilitating Safety and Security Co-design and Formal Analysis in Multi-layered System Modeling

The engineering process of systems deployed in critical domains (e.g., automotive) advocates for early-stage integrated analysis of safety and security concerns, given their mutual influence. Specifically, in the design phase, safety and security requirements undergo a transition to the system architectural design across different granular and conceptual representations. However, such an enrichment process is often complex and lacks preliminary guidance to consistently break down high-level system specifications and requirements into intricate architecture and deployment. In particular, engineers require further support to interpret diverse system, safety, and security expertise and facilitate the consistent passage of knowledge pertaining to these disciplines for automated analysis. To this end, we propose an approach to facilitate the joint design and formal analysis of system safety and security concerns. Notably, the approach aims for a three-layered system modeling, integrating mission, functional and component views, and also, reusable libraries of pre-defined safety and security properties, specialize-able across them. We couple the Model-Driven Engineering (MDE) paradigm and Formal Methods (FM) for the hierarchical-precise modeling, formal interpretation, and verification of model views w.r.t. the desired properties. The accompanying tool-chain support for approach instantiation builds upon Papyrus as a modeling framework and Rodin as a formal-based tool for verification. The proposed approach is illustrated via a Connected-Driving Vehicles (CDVs) use case.
Metamorphic Testing-based Adversarial Attack to Fool Deepfake Detectors

Deepfakes utilise Artificial Intelligence (AI) techniques to create synthetic media where the likeness of one person is replaced with another. There are growing concerns that deepfakes can be maliciously used to create misleading and harmful digital contents. As deepfakes become more common, there is a dire need for deepfake detection technology to help spot deepfake media. Present deepfake detection models are able to achieve outstanding accuracy (>90%). However, most of them are limited to within-dataset scenario. Most models do not generalise well enough in cross-dataset scenario. Furthermore, state-of-the-art deepfake detection models rely on neural network-based classification models that are known to be vulnerable to adversarial attacks. Motivated by the need for a robust deepfake detection model, this study adapts metamorphic testing (MT) principles to help identify potential factors that could influence the robustness of the examined model, while overcoming the test oracle problem in this domain. Metamorphic testing is specifically chosen as the testing technique as it fits our demand to address learning-based system testing with probabilistic outcomes from largely black-box components, based on potentially large input domains. We performed our evaluations on MesoInception-4 and TwoStreamNet models, which are the state-of-the-art deepfake detection models. This study identified makeup application as an adversarial attack that could fool deepfake detectors. Our experimental results demonstrate that both the MesoInception-4 and TwoStreamNet models degrade in their performance by up to 30% when the input data is perturbed with makeup.
Design of a Self-Tuning PID Controller for a Temperature Control System Using Fuzzy Logic

Temperature plays a significant role in industrial realm, especially in metallurgy and chemical industries. As maintaining optimal conditions in manufacturing can make a huge difference to its profitability and efficiency. Without providing a proper temperature control, the entire production lines can fall apart. Every product needs to be manufactured in an individual and specific conditions. If the temperature is too high or too low then it can affect the output product, potentially it may ruin completely and leads to wasting of raw materials. To maintain the stability of temperature requiring in any situation a temperature control system is needed. Typically classical conventional PID controllers are being used but these controllers have some lack of efficiency in controlling. These controllers exhibit higher overshoot, long rise time and settling time. Self- tuning Fuzzy PID controllers are more efficient than those classical PID control- lers. In this paper we proposed a design of Fuzzy Logic based self-tuning PID controller to reduce overshoot and conquer long rise time and settling time. This design provides more suitable and effective control to the temperature control system in industries.
Relationship Oriented Semantic Scene Understanding for Daily Manipulation Tasks

Assistive robot systems have been developed to help people accomplish daily manipulation tasks especially for those with disabilities, where scene understanding plays a crucial role in enabling robots to interpret the surroundings and behave accordingly. Most of the current systems approach scene understanding without considering the functional dependencies between objects. However, it is only valuable to interact with some objects when their function-relevant counterparts are considered. In this paper, we augment an assistive robotic arm system with an end-to-end semantic relationship reasoning model. It incorporates functional relationships between pairs of objects for semantic scene understanding. To ensure good generalization to unseen objects and relationships, the model works in a category-agnostic manner. We evaluate our design and three baseline methods on a self-collected benchmark with two levels of difficulty. To further demonstrate the effectiveness, the model is integrated with a symbolic planner for goal-oriented, multi-step manipulation task on a real-world assistive robotic arm platform.
Feature Extraction of Tweet data Characteristics to Determine Community Habits

Social media is used as a place of interaction between users. On Twitter social media there is a lot of data that can be used, especially community data that will be used to explore the characteristics of the data. In this study, there are several characteristics of community data such as tweet data, user data, slang words, standard words and non-standard words. all of the data is used to determine user habits, this study creates a model that can determine user habits. This research uses a regression tree whose process will check the frequency of the most words for each user in the community tweet, then check the highest frequency and looping to get a pattern, the test carried out by the model from the Regression Tree can determine habits with test data as many as 4 users then it is found that the habit in the Indonesian standup community is “Tonton”.
Non-profiling based Correlation Optimization Deep Learning Analysis

Differential Deep Learning Analysis (DDLA) is a deep learning-based non-profiling side-channel attack leveraging neural networks to classify Physical Leakage Information with labels. To avoid the Class Imbalance Problem (CIP) of significantly different data sizes in different data groups, DDLA employs bit labels. However, applying bit labels will be less effective for exploiting leakage. In this paper, we propose to employ Correlation optimization Deep Learning Analysis (CO-DLA) to circumvent the CIP in DDLA by converting the classification in DDLA into a correlation optimization. Bus labels can then be used to exploit stronger leakage information. To validate the attack efficacy improvement, we perform experiments on ASCAD synchronized and de-synchronized masked AES-128 datasets. For the synchronized masked dataset, our proposed CO-DLA requires only 5k traces, which is 75% lesser than the 20k traces required by the reported DDLA, to reveal the key-byte. For the 2 de-synchronized masked datasets, our proposed CO-DLA requires only 10k traces to reveal the key-byte from both of them while the reported DDLA fails to reveal the key-byte.
Temporally Consistent Semantic Video Editing

Generative adversarial networks (GANs) have demonstrated impressive image generation quality and semantic editing capability of real images, e.g., changing object classes, modifying attributes, or transferring styles. However, applying these GAN-based editing to a video independently for each frame inevitably results in temporal flickering artifacts. We present a simple yet effective method to facilitate temporally coherent video editing. Our core idea is to minimize the temporal photometric inconsistency by optimizing both the latent code and the pre-trained generator. We evaluate the quality of our editing on different domains and GAN inversion techniques and show favorable results against the baselines.
Federated Learning for Tabular Data: Exploring Potential Risk to Privacy

Federated Learning (FL) has emerged as a potentially powerful privacy-preserving machine learning method-ology, since it avoids exchanging data between participants, but instead exchanges model parameters. FL has traditionally been applied to image, voice and similar data, but recently it has started to draw attention from domains including financial services where the data is predominantly tabular. However, the work on tabular data has not yet considered potential attacks, in particular attacks using Generative Adversarial Networks (GANs), which have been successfully applied to FL for non-tabular data. This paper is the first to explore leakage of private data in Federated Learning systems that process tabular data. We design a Generative Adversarial Networks (GANs)-based attack model which can be deployed on a malicious client to reconstruct data and its properties from other participants. As a side-effect of considering tabular data, we are able to statistically assess the efficacy of the attack (without relying on human observation such as done for FL for images). We implement our attack model in a recently developed generic FL software framework for tabular data processing. The experimental results demonstrate the effectiveness of the proposed attack model, thus suggesting that further research is required to counter GAN-based privacy attacks.