Reviewerly/exHarmony huggingface dataset:
Social Influence Prediction with Train and Test Time Augmentation for Graph Neural Networks

Data augmentation has been widely used in machine learning for natural language processing and computer vision tasks to improve model performance. However, little research has studied data augmentation on graph neural networks, particularly using augmentation at both train- and test-time. Inspired by the success of augmentation in other domains, we have designed a method for social influence prediction using graph neural networks with train- and test-time augmentation, which can effectively generate multiple augmented graphs for social networks by utilising a variational graph autoencoder in both scenarios. We have evaluated the performance of our method on predicting user influence on multiple social network datasets. Our experimental results show that our end-to-end approach, which jointly trains a graph autoencoder and social influence behaviour classification network, can outperform state-of-the-art approaches, demonstrating the effectiveness of train-and test-time augmentation on graph neural networks for social influence prediction. We observe that this is particularly effective on smaller graphs.
Measuring feature importance of symbolic regression models using partial effects

In explainable AI, one aspect of a prediction's explanation is to measure each predictor's importance to the decision process. The importance can measure how much variation a predictor promotes locally or how much the predictor contributes to the deviation from a reference point (Shapley value). If we have the ground truth analytical model, we can calculate the former using the Partial Effect, calculated as the predictor's partial derivative. Also, we can estimate the latter by calculating the average partial effect multiplied by the difference between the predictor and the reference value. Symbolic Regression is a gray-box model for regression problems that returns an analytical model approximating the input data. Although it is often associated with interpretability, few works explore this property. This paper will investigate the use of Partial Effect with the analytical models generated by the Interaction-Transformation Evolutionary Algorithm symbolic regressor (ITEA). We show that the regression models returned by ITEA coupled with Partial Effect provide the closest explanations to the ground-truth and a close approximation to Shapley values. These results open up new opportunities to explain symbolic regression models compared to the approximations provided by model-agnostic approaches.
QScored: A Large Dataset of Code Smells and Quality Metrics

Code quality aspects such as code smells and code quality metrics are widely used in exploratory and empirical software engineering research. In such studies, researchers spend a substantial amount of time and effort to not only select the appropriate subject systems but also to analyze them to collect the required code quality information. In this paper, we present QScored dataset; the dataset contains code quality information of more than 86 thousand C# and Java GitHub repositories containing more than 1.1 billion lines of code. The code quality information contains seven kinds of detected architecture smells, 20 kinds of design smells, eleven kinds of implementation smells, and 27 commonly used code quality metrics computed at project, package, class, and method levels. Availability of the dataset will facilitate empirical studies involving code quality aspects by making the information readily available for a large number of active GitHub repositories.
Architectural Design Decisions for Blockchain-Based Applications

Designing blockchain-based applications is a challenging task and requires a number of coordinated architecture decisions. To guide decision making in this regard, we systematically explore this architectural design space and possible solution strategies. More precisely, we provide architectural design decisions and decision options in terms of patterns and practices. Our research shows that most design decisions are influenced by the need to offset current blockchain drawbacks such as scalability, privacy, and usability by using centralized elements. This suggests that a hybrid architecture is beneficial in many design situations.
An experimental study measuring human annotator categorization agreement on commonsense sentences

Abstract Developing agents capable of commonsense reasoning is an important goal in Artificial Intelligence (AI) research. Because commonsense is broadly defined, a computational theory that can formally categorize the various kinds of commonsense knowledge is critical for enabling fundamental research in this area. In a recent book, Gordon and Hobbs described such a categorization, argued to be reasonably complete. However, the theory’s reliability has not been independently evaluated through human annotator judgments. This paper describes such an experimental study, whereby annotations were elicited across a subset of eight foundational categories proposed in the original Gordon-Hobbs theory. We avoid bias by eliciting annotations on 200 sentences from a commonsense benchmark dataset independently developed by an external organization. The results show that, while humans agree on relatively concrete categories like time and space, they disagree on more abstract concepts. The implications of these findings are briefly discussed.
Jointly Learning the Attributes and Composition of Shots for Boundary Detection in Videos

In film making, shot has a profound influence on how the movie content is delivered and how the audiences are echoed, where different emotions and contents can be delivered through well-designed camera movements or shot editing. Therefore, in pursuit of a high-level understanding of long videos, accurate shot detection from untrimmed videos should be considered as the first and the most fundamental step. Existing approaches address this problem based on the visual difference and content transitions between consecutive frames, while ignoring intrinsic shot attributes, viz., camera movements, scales and viewing angles, which essentially reveals how each shot is created. In this work, we propose a new learning framework (SCTSNet) for shot boundary detection by jointly recognizing the attributes and composition of shots in videos. To facilitate the analysis of shots and the evaluation of shot detection models, we collect a large-scale shot boundary dataset MovieShots2, which contains 15K shots from 282 movie clips. It is richly annotated with the temporal boundary between consecutive shots and its shot attributes, including camera movements, scales and viewing angles, which are the three most distinct shot attributes. Our experiments show that the joint learning framework can significantly boost the boundary detection performance, surpassing the previous scores by a large margin. SCTSNet improves shot boundary detection AP from 0.65 to 0.77, pushing the performance to a new level.
Performance Characterization of HTAP Workloads

Hybrid Transactional and Analytical Processing (HTAP) systems have become popular in the past decade. HTAP systems allow running transactional and analytical processing workloads on the same data and hardware. As a result, they suffer from workload interference. Despite the large body of existing work in HTAP systems and architectures, none of the existing work has systematically analyzed workload interference for HTAP systems.In this work, we characterize workload interference for HTAP systems. We show that the OLTP throughput drops by up to 42% due to sharing the hardware resources. Partitioning the last-level cache (LLC) among the OLTP and OLAP workloads can significantly improve the OLTP throughput without hurting the OLAP throughput. The OLAP throughput is significantly reduced due to sharing the data. The OLAP execution time is exponentially increased if the OLTP workload generates fresh tuples faster than the HTAP system propagates them. Therefore, in order to minimize the workload interference, HTAP systems should isolate the OLTP and OLAP workloads in the shared hardware resources and should allocate enough resources to fresh tuple propagation to propagate the fresh tuples faster than they are generated.
End-to-end High Dynamic Range Camera Pipeline Optimization

The real world is a 280 dB High Dynamic Range (HDR) world which imaging sensors cannot record in a single shot. HDR cameras acquire multiple measurements with different exposures, gains and photodiodes, from which an Image Signal Processor (ISP) reconstructs an HDR image. Dynamic scene HDR image recovery is an open challenge because of motion and because stitched captures have different noise characteristics, resulting in artifacts that ISPs must resolve in real time at double-digit megapixel resolutions. Traditionally, ISP settings used by downstream vision modules are chosen by domain experts; such frozen camera designs are then used for training data acquisition and supervised learning of downstream vision modules. We depart from this paradigm and formulate HDR ISP hyperparameter search as an end-to-end optimization problem, proposing a mixed 0 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> and 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> -order block coordinate descent optimizer that jointly learns sensor, ISP and detector network weights using RAW image data augmented with emulated SNR transition region artifacts. We assess the proposed method for human vision and image understanding. For automotive object detection, the method improves mAP and mAR by 33% over expert-tuning and 22% over state-of-the-art optimization methods, outperforming expert-tuned HDR imaging and vision pipelines in all HDR laboratory rig and field experiments.
QoE Assessment of FPV Drone Control in a Cloud Gaming Based Simulation

The technology of Unmanned Aerial Vehicles (UAVs), or simply drones, has gained much attention over the past two decades, with a rapid decrease in cost and increase in potential applications such as surveillance, journalism, agriculture, film-making, entertainment, etc. With the growth in the popularity of First-person view (FPV) drone piloting comes the challenge of transmitting control information and streaming video at very low latencies to enable proper real-time control. Due to strict latency requirements, FPV technology stagnated, with analog video transmission currently representing the most common implementation. In this paper, we present our preliminary research directed towards developing a digital alternative to this technology. We report on a user study designed to assess the Quality of Experience (QoE) of an FPV system coupled with a cloud-based drone flight simulator, with the focus being on video quality in ideal network conditions. Obtained results show that video encoding parameters have a significant impact on user perceived QoE. Furthermore, our findings highlight the relevance of simulator sickness in this kind of system.
Intersection Type Distributors

We study a family of distributors-induced bicategorical models of λ-calculus, proving that they can be syntactically presented via intersection type systems. We first introduce a class of 2-monads whose algebras are monoidal categories modelling resource management. We lift these monads to distributors and define a parametric Kleisli bicategory, giving a sufficient condition for its cartesian closure. In this framework we define a proof-relevant semantics: the interpretation of a term associates to it the set of its typing derivations in appropriate systems. We prove that our model characterize solvability, adapting reducibility techniques to our setting. We conclude by describing two examples of our construction.
Makeup Presentation Attack Potential Revisited: Skills Pay the Bills

Facial appearance can be substantially altered through the application of facial cosmetics. In addition to the widespread, socially acceptable, and in some cases even expected use for the purpose of beautification, facial cosmetics can be abused to launch so-called makeup presentation attacks. Thus far, the potential of such attack instruments has generally been claimed to be relatively low based on experimental evaluations on available datasets. This paper presents a new dataset of such attacks with the purpose of impersonation and identity concealment. The images have been collected from online sources, concentrating on seemingly highly skilled makeup artists. A vulnerability assessment of face recognition with respect to probe images contained in the collected dataset is conducted on state-of-the-art open source and commercial off-the-shelf facial recognition systems with a standardised methodology and metrics. The obtained results are especially striking for the impersonation attacks: the obtained attack success chance of almost 70% at a fixed decision threshold corresponding to 0.1% false match rate is significantly higher than results previously reported in the scientific literature.
Automatic Optimization of Matrix Implementations for Distributed Machine Learning and Linear Algebra

Machine learning (ML) computations are often expressed using vectors, matrices, or higher-dimensional tensors. Such data structures can have many different implementations, especially in a distributed environment: a matrix could be stored as row or column vectors, tiles of different sizes, or relationally, as a set of (rowIndex, colIndex, value) triples. Many other storage formats are possible. The choice of format can have a profound impact on the performance of a ML computation. In this paper, we propose a framework for automatic optimization of the physical implementation of a complex ML or linear algebra (LA) computation in a distributed environment, develop algorithms for solving this problem, and show, through a prototype on top of a distributed relational database system, that our ideas can radically speed up common ML and LA computations.
Blockchain and IPFS Based Service Model for the Internet of Things

In this paper, blockchain and InterPlanetary File System (IPFS) based service model is proposed for Internet of Things (IoT). In the IoT, nodes’ credentials and generated data are stored on the IPFS in a hashed format. In order to ensure the security of data, encrypted hash is stored on the blockchain. However, blockchain is very expensive for storing the large amount of data. While, in the case of centralized database, there is a possibility of data tampering and information leakage. Moreover, a service model is proposed for sharing the services from the service providers to the consumers. In addition, a product consensus mechanism is performed between admin and user which is replaced with a blockchain service model. Due to this, consumers send the request to the service provider through blockchain for the required services. Though, peer and minor nodes’ involvement in consensus mechanisms, hinder in finding the service provider. Moreover, existing scheme is computationaly expensive and delay occurs due to the lengthy procedure of verification and consensus. Here, blockchain is used to record the evidence of the services. Also, a service verification scheme is designed using the Secure Hash Algorithm-256 (SHA-256). Furthermore, the smart contract is utilized to settle disputes between consumers and service providers. The simulation results show that Proof of Authority consumes less gas and has low latency as compared to Proof of Work, which represents the efficiency and effectiveness of the proposed solution.
The “Quantum Annoying” Property of Password-Authenticated Key Exchange Protocols

During the Crypto Forum Research Group (CFRG)'s standardization of password-authenticated key exchange (PAKE) protocols, a novel property emerged: a PAKE scheme is said to be "quantum-annoying" if a quantum computer can compromise the security of the scheme, but only by solving one discrete logarithm for each guess of a password. Considering that early quantum computers will likely take quite long to solve even a single discrete logarithm, a quantum-annoying PAKE, combined with a large password space, could delay the need for a post-quantum replacement by years, or even decades. In this paper, we make the first steps towards formalizing the quantum-annoying property. We consider a classical adversary in an extension of the generic group model in which the adversary has access to an oracle that solves discrete logarithms. While this idealized model does not fully capture the range of operations available to an adversary with a general-purpose quantum computer, this model does allow us to quantify security in terms of the number of discrete logarithms solved. We apply this approach to the CPace protocol, a balanced PAKE advancing through the CFRG standardization process, and show that the $$\text {CPace}_{\text {base}}$$ variant is secure in the generic group model with a discrete logarithm oracle.
Geometric resistant polar quaternion discrete Fourier transform and its application in color image zero-hiding

As a typical frequency-domain analysis method, quaternion discrete Fourier transform (QDFT) has been widely used in information hiding in color images. However, due to the sensitivity of QDFT to geometric attacks, existing QDFT-based information hiding schemes have limited ability in resisting geometric attacks. In this study, a kind of novel geometrically resilient polar QDFT (PQDFT) is constructed and the properties of the proposed PQDFT are analyzed. Subsequently, a PQDFT-based color image zero-hiding scheme robust to geometric attacks is proposed for lossless copyright protection of color images, which experimentally shows reasonable resistance against geometric and common attacks, indicating better robustness compared with the existing QDFT-based information hiding schemes and other leading-edge zero-hiding schemes.
FederatedReverse: A Detection and Defense Method Against Backdoor Attacks in Federated Learning

Federated learning is a secure machine learning technology proposed to protect data privacy and security in machine learning model training. However, recent studies show that federated learning is vulnerable to backdoor attacks, such as model replacement attacks and distributed backdoor attacks. Most backdoor defense techniques are not appropriate for federated learning since they are based on entire data samples that cannot be hold in federated learning scenarios. The newly proposed methods for federated learning sacrifice the accuracy of models and still fail once attacks persist in many training rounds. In this paper, we propose a novel and effective detection and defense technique called FederatedReverse for federated learning. We conduct extensive experimental evaluation of our solution. The experimental results show that, compared with the existing techniques, our solution can effectively detect and defend against various backdoor attacks in federated learning, where the success rate and duration of backdoor attacks can be greatly reduced and the accuracies of trained models are almost not reduced.
Investigating Preferred Food Description Practices in Digital Food Journaling

Journaling of consumed foods through digital devices is a popular self-tracking strategy for weight loss and eating mindfulness. Research has explored modalities, like photos and open-ended text and voice descriptions, to make journaling less burdensome and more descriptive than traditional barcode and database searches. However, less is known about how people prefer to journal foods when less constrained by limitations of databases, natural language processing, and image recognition. We deployed a food journal prototype supporting varied devices and input modalities, which 15 participants used to journal 1008 food logs over two weeks. Participants had diverse strategies for indicating what and how much they ate, varying from ambiguous foods to specifying varieties and using different measurements for clarifying amount. Some strategies were interpretable by natural language food identification and image classification services, while others point to open research questions. We finally discuss opportunities for accounting for variance in food journaling.
Experience in teaching analytical chemistry in a joint English-language educational project of Chinese and Ukrainian universities

Abstract The paper was aimed to study the problems that may arise when Chinese students learn an analytical chemistry course in English, read by teachers from Ukraine. In particular, the reasons for the possible excessive increase in cognitive load were investigated. The comparative analysis of the existing learning styles was carried out to achieve the goal of the study. For this purpose, the indicators were compared for respondents studying in similar chemical specialities at the Kyiv College of the Qilu University of Technology in China and Kyiv National University of Technologies and Design in Ukraine. Some students from China demonstrate more pronounced reflective, verbal and intuitive learning styles. In contrast, a decisive advantage towards active, visual and sensing styles is characteristic of Ukraine students. The structure of the lecture course was analysed from the viewpoint of e-resources used. The optimal application of different electronic resources for students with varying learning preferences was established based on the results of experiments by the method of dual-task. The difference in educational priorities should be reflected in the various forms and methods used in the teaching of chemical disciplines. Recommendations for the development of appropriate learning resources are given.
DIV

Zero-knowledge set membership (ZKSM) proof is widely used in blockchain to enable private membership attestation. However, existing mechanisms do not fully consider dynamic issues in the blockchain scenario. Particularly, frequent addition/removal of set elements, not only brings the significant cost to keep public parameters up to date to provers and verifiers but also affects mechanism efficiency (e.g., generation time of the proof and verification, etc.). In this paper, we propose DIV to shard elements on blockchain into independent subsets with the same cardinality to reduce the effect of dynamic issues. However, due to the diverse proof frequency, an improper element-set assignment can result in frequently used elements being easily inferred and corrupted. Thus, we formalize the assignment problem under both element addition and removal cases as two optimization problems and prove their NP-hardness. For each problem, we consider two cases if each element proof frequency is known in advance by the set maintainer or not, and propose solutions with theoretical guarantees. We implement DIV on both Merkle tree and RSA-based ZKSM mechanisms to evaluate its efficiency and effectiveness and apply DIV on a ZKSMbased application named zkSync to demonstrate its applicability. Results show that DIV can achieve O(1) time/space cost on ZKSM under dynamic situations while protecting the information about frequently used elements. It also notably reduces the system latency of zkSync.