Reviewerly/exHarmony huggingface dataset:
Bridging Differential Privacy and Byzantine-Robustness via Model Aggregation

This paper aims at jointly addressing two seemly conflicting issues in federated learning: differential privacy (DP) and Byzantine-robustness, which are particularly challenging when the distributed data are non-i.i.d. (independent and identically distributed). The standard DP mechanisms add noise to the transmitted messages, and entangles with robust stochastic gradient aggregation to defend against Byzantine attacks. In this paper, we decouple the two issues via robust stochastic model aggregation, in the sense that our proposed DP mechanisms and the defense against Byzantine attacks have separated influence on the learning performance. Leveraging robust stochastic model aggregation, at each iteration, each worker calculates the difference between the local model and the global one, followed by sending the element-wise signs to the master node, which enables robustness to Byzantine attacks. Further, we design two DP mechanisms to perturb the uploaded signs for the purpose of privacy preservation, and prove that they are (epsilon,0)-DP by exploiting the properties of noise distributions. With the tools of Moreau envelop and proximal point projection, we establish the convergence of the proposed algorithm when the cost function is nonconvex. We analyze the trade-off between privacy preservation and learning performance, and show that the influence of our proposed DP mechanisms is decoupled with that of robust stochastic model aggregation. Numerical experiments demonstrate the effectiveness of the proposed algorithm.
Research trends in disruptive technologies for accounting of the future – A bibliometric analysis

Research Question: Is there a gap in research preferences on disruptive technologies between academia and practitioners? Motivation: Disruptive technologies in accounting represent a new evolutionary phase of accounting impacted by emerging technologies that are part of industrial revolution 4.0. The relevance of emerging technologies, their potential, and the opportunities they offer for the accounting profession attract both academia and professionals with accelerated research efforts. Academia and scientific researchers must research and provide an appropriate theoretical basis to help practitioners better adapt and increase their awareness and trust in technology. Idea: This paper provides early quantitative research data on publication trends related to most disruptive technologies in accounting such as big data, data analytics, cloud, artificial intelligence, and blockchain. Data: The data were collected through detailed analysis and research of websites and publishing platforms on Big Four accounting firms, professional accounting associations, and institutes, and selected high-ranking journals for the period from 2016 to 2020. Tools: The research was conducted using bibliometric analysis to identify how many articles were published by individual parties. Findings: The results highlight that there are no significant discrepancies or different views of academia and practitioners. It is a positive result indicating that academia and scientific researchers exploit in the same direction as practitioners, thus providing support for adaptation and alignment to technology trends. Contribution: The significance of the results of such research is important for obtaining directions in which the academy should research in the future but also for the regulators as well.
Image Forensics Using Non-Reducing Convolutional Neural Network for Consecutive Dual Operators

Digital image forensics has become necessary as an emerging technology. Images can be adulterated effortlessly using image tools. The latest techniques are available to detect whether an image is adulterated by a particular operator. Most of the existing techniques are suitable for high resolution and manipulated images by a single operator. In a real scenario, multiple operators are applied to manipulate the image many times. In this paper, a robust moderate-sized convolutional neural network is proposed to identify manipulation operators and also the operator’s sequence for two operators in particular. The proposed bottleneck approach is used to make the network deeper and reduce the computational cost. Only one pooling layer, called a global averaging pooling layer, is utilized to retain the maximum flow of information and to avoid the overfitting issue between the layers. The proposed network is also robust against low resolution and JPEG compressed images. Even though the detection of the operator is challenging due to the limited availability of statistical information in low resolution and JPEG compressed images, the proposed model can also detect an operator with different parameters and compression quality factors that are not considered in training.
From Spoken Thoughts to Automated Driving Commentary: Predicting and Explaining Intelligent Vehicles’ Actions

In commentary driving, drivers verbalise their observations, assessments and intentions. By speaking out their thoughts, both learning and expert drivers are able to create a better understanding and awareness of their surroundings. In the intelligent vehicle context, automated driving commentary can provide intelligible explanations about driving actions, thereby assisting a driver or an end-user during driving operations in challenging and safety-critical scenarios. In this paper, we conducted a field study in which we deployed a research vehicle in an urban environment to obtain data. While collecting sensor data of the vehicle’s surroundings, we obtained driving commentary from a driving instructor using the think-aloud protocol. We analysed the driving commentary and uncovered an explanation style; the driver first announces his observations, announces his plans, and then makes general remarks. He also makes counterfactual comments. We successfully demonstrated how factual and counterfactual natural language explanations that follow this style could be automatically generated using a transparent tree-based approach. Generated explanations for longitudinal actions (e.g., stop and move) were deemed more intelligible and plausible by human judges compared to lateral actions, such as lane changes. We discussed how our approach can be built on in the future to realise more robust and effective explainability for driver assistance as well as partial and conditional automation of driving functions.
Sentiment Classification: Review of Text Vectorization Methods: Bag of Words, Tf-Idf, Word2vec and Doc2vec

In Sentiment Analysis, there are three (3) approaches namely, machine learning, lexicon-based and ruled based approaches. This study investigates on machine learning approaches which involves text vectorization or word embedding- an essential step in natural language processing tasks since most machine learning algorithms work with numerical input. Text vectorization involves the representation or mapping of words or documents of a corpus to numerical vectors of numbers or real numbers. There are several approaches in the literatures on document/text representation, however this study will focus on three (3) commonly used ones viz; Bag of words, TF-IDF, word2vec and doc2vec, and try to identify the reason behind that for review and recommendation to the researchers in hurry. Review of this study shows that TF-IDF feature vector representations generally outperforms other two (2) vectorization methods word2vec and doc2vec, specifically in book review sentiment classification. And therefore recommended for future studies in book review data set
THINGSplus: New Norms and Metadata for the THINGS Database of 1,854 Object Concepts and 26,107 Natural Object Images

To study visual object processing, the need for well-curated object concepts and images has grown significantly over the past years. To address this we have previously developed THINGS (Hebart et al., 2019), a large-scale database of 1,854 systematically sampled object concepts with 26,107 high-quality naturalistic images of these concepts. With THINGSplus we aim to extend THINGS by adding concept-specific and image-specific norms and metadata. Concept-specific norms were collected for all 1,854 object concepts for the object properties real-world size, manmadeness, preciousness, liveliness, heaviness, naturalness, ability to move, graspability, holdability, ability to be moved, pleasantness, and arousal. Further, we extended high-level categorization to 53 superordinate categories and collected typicality ratings for members of all 53 categories. Image-specific metadata includes measures of nameability for objects in all 26,107 images. To this end, we asked participants to provide labels for prominent objects depicted in each of the 26,107 images and measured the alignment with the original object concept. Finally, to present example images in publications without copyright restrictions, we identified one new public domain image per object concept. In this study we demonstrate a high consistency of property (r = 0.92-0.99, M = 0.98, SD = 0.34) and typicality ratings (r = 0.88-0.98; M = 0.96, SD = 0.19), with arousal ratings as the only exception (r = 0.69). Correlations of our data with external norms were moderate to high for object properties (r = 0.44-0.95; M = 0.85, SD = 0.32) and typicality scores (r = 0.72-0.88; M = 0.79, SD = 0.18), again with the lowest validity for arousal (r = 0.30 - 0.52). To summarize, THINGSplus provides a broad, externally-validated extension to existing object norms and an important extension to THINGS as a general resource of object concepts, images, and category memberships. Our norms, metadata, and images provide a detailed selection of stimuli and control variables for a wide range of research interested in object processing and semantic memory.
An Urban Population Health Observatory for Disease Causal Pathway Analysis and Decision Support: Underlying Explainable Artificial Intelligence Model

Background Many researchers have aimed to develop chronic health surveillance systems to assist in public health decision-making. Several digital health solutions created lack the ability to explain their decisions and actions to human users. Objective This study sought to (1) expand our existing Urban Population Health Observatory (UPHO) system by incorporating a semantics layer; (2) cohesively employ machine learning and semantic/logical inference to provide measurable evidence and detect pathways leading to undesirable health outcomes; (3) provide clinical use case scenarios and design case studies to identify socioenvironmental determinants of health associated with the prevalence of obesity, and (4) design a dashboard that demonstrates the use of UPHO in the context of obesity surveillance using the provided scenarios. Methods The system design includes a knowledge graph generation component that provides contextual knowledge from relevant domains of interest. This system leverages semantics using concepts, properties, and axioms from existing ontologies. In addition, we used the publicly available US Centers for Disease Control and Prevention 500 Cities data set to perform multivariate analysis. A cohesive approach that employs machine learning and semantic/logical inference reveals pathways leading to diseases. Results In this study, we present 2 clinical case scenarios and a proof-of-concept prototype design of a dashboard that provides warnings, recommendations, and explanations and demonstrates the use of UPHO in the context of obesity surveillance, treatment, and prevention. While exploring the case scenarios using a support vector regression machine learning model, we found that poverty, lack of physical activity, education, and unemployment were the most important predictive variables that contribute to obesity in Memphis, TN. Conclusions The application of UPHO could help reduce health disparities and improve urban population health. The expanded UPHO feature incorporates an additional level of interpretable knowledge to enhance physicians, researchers, and health officials' informed decision-making at both patient and community levels. International Registered Report Identifier (IRRID) RR2-10.2196/28269
Threat Model and Defense Scheme for Side-Channel Attacks in Client-Side Deduplication

In cloud storage, client-side deduplication is widely used to reduce storage and communication costs. In client-side deduplication, if the cloud server detects that the user's outsourced data have been stored, then clients will not need to reupload the data. However, the information on whether data need to be uploaded can be used as a side-channel, which can consequently be exploited by adversaries to compromise data privacy. In this paper, we propose a new threat model against side-channel attacks. Different from existing schemes, the adversary could learn the approximate ratio of stored chunks to unstored chunks in outsourced files, and this ratio will affect the probability that the adversary compromises the data privacy through side-channel attacks. Under this threat model, we design two defense schemes to minimize privacy leakage, both of which design interaction protocols between clients and the server during deduplication checks to reduce the probability that the adversary compromises data privacy. We analyze the security of our schemes, and evaluate their performances based on a real-world dataset. Compared with existing schemes, our schemes can better mitigate data privacy leakage and have a slightly lower communication cost.
PV Power Forecasting Based on Relevance Vector Machine with Sparrow Search Algorithm Considering Seasonal Distribution and Weather Type

Accurate photovoltaic (PV) power forecasting is indispensable to enhancing the stability of the power grid and expanding the absorptive photoelectric capacity of the power grid. As an excellent nonlinear regression model, the relevance vector machine (RVM) can be employed to forecast PV power. However, the optimization of the free parameters is still a key problem for improving the performance of the RVM. Taking advantage of the strong global search capability, good stability, and fast convergence rate of the sparrow search algorithm (SSA), this paper optimizes the parameters of the RVM by using the SSA to develop an excellent RVM (called SSA-RVM). Consequently, a novel hybrid PV power forecasting model via the SSA-RVM is proposed to perform ultra-short-term (4 h ahead) prediction. In addition, the effects of seasonal distribution and weather type on PV power are fully considered, and different seasonal prediction models are established separately to improve the prediction capability. The benchmark is used to verify the accuracy of the SSA-RVM-based forecasting model under various conditions, and the experiment results demonstrate that the proposed SSA-RMV method outperforms the traditional RVM and support vector machine models, and it even shows a better prediction effect than the RVM models with other optimization approaches.
A Simplistic and Cost-Effective Design for Real-World Development of an Ambient Assisted Living System for Fall Detection and Indoor Localization: Proof of Concept

Falls, highly common in the constantly increasing global aging population, can have a variety of negative effects on their health, well-being, and quality of life, including restricting their capabilities to conduct Activities of Daily Living (ADLs), which are crucial for one&amp;rsquo;s sustenance. Timely assistance during falls is highly necessary, which involves tracking the indoor location of the elderly during their diverse navigational patterns associated with ADLs to detect the precise location of a fall. With the decreasing caregiver population on a global scale, it is important that the future of intelligent living environments can detect falls during ADL.s while being able to track the indoor location of the elderly in the real world. Prior works in these fields have several limitations, such as &amp;ndash; the lack of functionalities to detect both falls and indoor locations, high cost of implementation, complicated design, the requirement of multiple hardware components for deployment, and the necessity to develop new hardware for implementation, which make the wide-scale deployment of such technologies challenging. To address these challenges, this work proposes a cost-effective and simplistic design paradigm for an Ambient Assisted Living system that can capture multimodal components of user behaviors during ADLs that are necessary for performing fall detection and indoor localization in a simultaneous manner in the real world. Proof of concept results from real-world experiments are presented to uphold the effective working of the system. The findings from two comparison studies with prior works in this field are also presented to uphold the novelty of this work. The first comparison study shows how the proposed system outperforms prior works in the areas of indoor localization and fall detection in terms of the effectiveness of its software design and hardware design. The second comparison study shows that the cost for the development of this system is the least as compared to prior works in these fields, which involved real-world development of the underlining systems, thereby upholding its cost-effective nature.
Technology and Security Analysis of Cryptocurrency Based on Blockchain

Blockchain technology applied to cryptocurrencies is the dominant factor in maintaining the security of cryptocurrencies. This article reviews the technological implementation of cryptocurrency and the security and stability of cryptocurrency and analyzes the security support from blockchain technology and its platforms based on empirical case studies. Our results show that the security support from blockchain technology platforms is signiﬁcantly insufﬁcient and immature. In addition, we further Zyskind and Nathan (2015) and Choi (2019) and find that the top ten platforms play critical roles in security support and have signiﬁcant advantages in terms of funds, duration, and human resources. Moreover, these platforms provide computational resources and beneﬁts to the consensus algorithm selection for blockchain practitioners. Second, encryption ensures the security of cryptocurrencies. On the one hand, the digital signatures identify the identity of the signatory and the transaction. However, the principle of the hash algorithm (SHA256) conﬁrms ownership. Meanwhile, SHA256 is infeasible to compute in the reverse direction and is difficult to attack. Furthermore, the records in the blockchain can be queried by every participant, making the system information transparent and open reliable. Third, compared to the study of Fu and Fang 2016, we find that the blockchain structure is composed of security components and basic components of six layers that are independent and cannot be extended completely and have a certain coupling among them. Fourth, the underlying ledger structures of Bitcoin and DAG are highly correlated to their security. Specifically, we follow Sompolinsky et al. (2016) and detect that the structure of SPECTRE ensures network security and robustness from its block production, conflict resolution, and generated trusted transaction sets. Meanwhile, the voting algorithm of SPECTRE makes resolving conflicting transactions by calculating votes and ensuring the transaction information that is virtually unable to be tampered with possible. In particular, the security calculation power of SPECTRE can reach 51% and resist “double-spend attacks” and “censorship attacks” effectively. In addition, the RDL framework of SPECTRE achieves security conﬁrmation of transferring funds. Moreover, PHANTOM identifies evil blocks by employing block connectivity analysis and ensures its security. Eventually, we also expand the studies of (Sompolinsky et al., 2016 and Sompolinsky et al., 2017) and compare the basic characteristics of the protocols of Bitcoin, SPECTRE, and PHANTOM and find that protocols play imperative roles throughout the implementation process of cryptocurrency. In addition, the underlying ledger structure and consensus mechanism make up a blockchain while the confirmation time, throughput limit, and ordering are prerequisites for smart contracts.
How Do Injected Bugs Affect Deep Learning?

In recent years, deep learning obtains amazing achievements in various fields, and has been used in safety-critical scenarios. In such scenarios, bugs in deep learning software can introduce disastrous consequences. To deepen the understanding on bugs in deep learning software, researchers have conducted several empirical studies on their bug characteristics. In the prior studies, researchers analyzed the source code, bug reports, pull requests, and fixes of deep learning bugs. Although these studies provide meaningful findings, to the best of our knowledge, no prior studies have explored the runtime behaviors of deep learning bugs, because it is rather expensive to collect runtime impacts of deep learning bugs. As a result, some fundamental questions along with deep learning bugs are still open. For example, do most such bugs introduce significant impacts on prediction accuracy? The answers to these open questions are useful to a wide range of audience. In this paper, we conducted the first empirical study to analyze the runtime impacts of deep learning bugs. Our basic idea is to inject deliberately designed bugs into a typical deep learning application and its libraries with a mutation tool, and to compare the runtime differences between clean and buggy versions. In this way, we constructed 1,832 buggy versions, and compared their execution results with corresponding clean versions. Based on our comparison, we summarize 9 findings, and present our answers to 3 research questions. For example, we find that more than half of buggy versions do not lead to any observable errors, and most of them introduce only insignificant differences on the accuracy of their trained models. We interpret the significance of our findings from the perspectives of application programmers, API developers, and researchers. For example, based on our findings, better results alone are insufficient to prove better parameters nor better treatments, and researchers shall build strong theories to explain their improvements.
Optimal frequency selection for accuracy improvement in binary defocusing fringe projection profilometry

The binary defocusing fringe projection profilometry (FPP) technique has demonstrated various advantages for high-speed and high-accuracy three-dimensional (3D) surface measurement. However, higher fringe frequency does not necessarily give better measurements in binary defocusing FPP. To improve the 3D geometry measurement accuracy, this paper proposes an optimal frequency selection approach by analyzing the phase error distribution under different defocusing degrees. The phase error is analyzed theoretically based on the multi-frequency temporal phase unwrapping process, and the associated relationship with fringe frequency, system defocusing degree, noise, and other influencing factors is established. Meanwhile, optimal fringe frequency in a specific system is selected by the theoretical model combined with the validation of simulation experiments. Finally, the measurement accuracy could be effectively enhanced by the generated binary fringe patterns of optimal frequency. Both simulations and experiments verify the effectiveness and robustness of the proposed method.