Reviewerly/exHarmony huggingface dataset:
Efficient and Secure Outsourcing of Differentially Private Data Publishing With Multiple Evaluators

Since big data becomes a main impetus to the next generation of IT industry, data privacy has received considerable attention in recent years. To deal with the privacy challenges, differential privacy has been widely discussed and related private mechanisms are proposed as privacy-enhancing techniques. However, with today&#x2019;s differential privacy techniques, it is difficult to generate a sanitized dataset that can suit every machine learning task. In order to adapt to various tasks and budgets, different kinds of privacy mechanisms have to be implemented, which inevitably incur enormous costs for computation and interaction. To this end, in this article, we propose two novel schemes for outsourcing differential privacy. The first scheme efficiently achieves outsourcing differential privacy by using our preprocessing method and secure building blocks. To support the queries from multiple evaluators, we give the second scheme that employs a trusted execution environment to aggregately implement privacy mechanisms on multiple queries. During data publishing, our proposed schemes allow providers to go off-line after uploading their datasets, so that they achieve a low communication cost which is one of the critical requirements for a practical system. Finally, we report an experimental evaluation on UCI datasets, which confirms the effectiveness of our schemes.
Beetle antenna strategy based grey wolf optimization

Finding feasible solutions to real-world problems is a crucial task. Metaheuristic algorithms are widely used in many fields due to the variety of solutions they can produce. The grey wolf optimizer (GWO) is a relatively novel population-based metaheuristic algorithm that has been shown to have good optimization performance. However, due to the insufficient diversity of wolves in some cases, this approach can lead to locally optimal situations. Therefore, this paper proposes a grey wolf optimization method based on a beetle antenna strategy (BGWO) that gives the leader wolf a sense of hearing to improve the global search ability and reduce unnecessary searches. In addition, to balance exploration and exploitation, a nonlinear dynamic control parameter update strategy based on the cosine function is proposed. To evaluate the performance of the proposed BGWO, this paper uses 23 standard benchmark functions to test the method in different dimensions. Moreover, four well-known engineering problems are used to evaluate the ability of the proposed algorithm to obtain real-world problem solutions. The experimental results show that BGWO has superior performance and is competitive with many state-of-the-art algorithms in terms of solution accuracy, convergence rate, and stability.
A New Consensus Protocol for Blockchain Interoperability Architecture

Blockchain is widely recognized as a potential disruptive technology that has gained much popularity recently.Despite many promising results, the current blockchain landscape is fragmented, in which many blockchain systems exist in silos.Interoperability becomes a critical functionality to facilitate broad blockchain adoption and starts to attract attention in both industry and academia research.In this paper, we propose a new consensus protocol, Multi-tokens Proof of Stake (MPoS), for blockchain interoperability architecture.The MPoS protocol is able to strengthen the token network effects in a cross-chain ecosystem and grow the user base of blockchain systems dramatically.We also provide an analytical model to analyze and prove that the MPoS protocol can offer better security than traditional single-token PoS consensus protocols.
Spatula: Efficient cross-camera video analytics on large camera networks

Cameras are deployed at scale with the purpose of searching and tracking objects of interest (e.g., a suspected person) through the camera network on live videos. Such cross-camera analytics is data and compute intensive, whose costs grow with the number of cameras and time. We present Spatula, a cost-efficient system that enables scaling cross-camera analytics on edge compute boxes to large camera networks by leveraging the spatial and temporal cross-camera correlations. While such correlations have been used in computer vision community, Spatula uses them to drastically reduce the communication and computation costs by pruning search space of a query identity (e.g., ignoring frames not correlated with the query identity’s current position). Spatula provides the first system substrate on which cross-camera analytics applications can be built to efficiently harness the cross-camera correlations that are abundant in large camera deployments. Spatula reduces compute load by $8.3\times$ on an 8-camera dataset, and by $23\times-86\times$ on two datasets with hundreds of cameras (simulated from real vehicle/pedestrian traces). We have also implemented Spatula on a testbed of 5 AWS DeepLens cameras.
What in the world is educational technology? Rethinking the field from the perspective of the philosophy of technology

Technology has been an important influence on the development of education. However, very little research in Educational Technology considers this history, and even less questions it. In this paper, we argue that our understanding of educational technology should be informed by the philosophy of technology – a field that has attempted to explore what these historical developments mean. This paper focuses on the ontological turn in philosophy and the technological thought of Marx and Heidegger. This is used to propose a foundation that can help researchers rethink educational technology, expanding research to account for human-education, human-technology and education-technology relations. It is proposed that human-education relationships should make students 'become what they are', highlighting their subjectivity rather than concentrating on information. Human-technology relationships can change the focus of design practice, so that technology is not merely seen as an efficient tool, but something 'handy' for peoples' educational needs. Education-technology relationships explain how technology can shape education, and re-introduces the idea of 'techne' as something to be valued in education.
Pairwise dependence-based unsupervised feature selection

Many research topics present very high dimensional data. Because of the heavy execution times and large memory requirements, many machine learning methods have difficulty in processing these data. In this paper, we propose a new unsupervised feature selection method considering the pairwise dependence of features (feature dependency-based unsupervised feature selection, or DUFS). To avoid selecting redundant features, the proposed method calculates the dependence among features and applies this information to a regression-based unsupervised feature selection process. We can select small feature set with the dependence among features by eliminating redundant features. To consider the dependence among features, we used mutual information widely used in supervised feature selection area. To our best knowledge, it is the first study to consider the pairwise dependence of features in the unsupervised feature selection method. Experimental results for six data sets demonstrate that the proposed method outperforms existing state-of-the-art unsupervised feature selection methods in most cases.
A survey on continuous authentication methods in Internet of Things environment

IoT environment merges the digital and physical universes and enabling them to communicate real-time data It is crucial to constantly ensure that the user is not impersonated which brings a particular type of authentication known as continuous authentication. Therefore, this survey is primary attempt to present an overview about continuous authentication methods in IoT environment, where Blockchain-related solutions provided in this context is also discussed. Subsequently, we overview IoT environment and classify IoT attacks based on its layers with the corresponding proposed countermeasures. Next, we identify a number of open issues that must be taken into account when developing authentication schemes for IoT. We further contribute to this survey by providing a comprehensive analysis of designing continuous authentication process. Besides, we highlight the primary advantages of continuous authentication over the static authentication scheme. In this context, we review relevant works that have proposed Blockchain-based solution to strength the security of IoT systems. We further contribute to present, analyze, and compare user-related IoT-based continuous authentication solutions. Also, we present the solutions that integrate Blockchain. Finally, several research directions and open challenges are identified.
Adiabatic evolution on a spatial-photonic Ising machine

Combinatorial optimization problems are crucial for widespread applications but remain difficult to solve on a large scale with conventional hardware. Novel optical platforms, known as coherent or photonic Ising machines, are attracting considerable attention as accelerators on optimization tasks formulable as Ising models. Annealing is a well-known technique based on adiabatic evolution for finding optimal solutions in classical and quantum systems made by atoms, electrons, or photons. Although various Ising machines employ annealing in some form, adiabatic computing on optical settings has been only partially investigated. Here, we realize the adiabatic evolution of frustrated Ising models with 100 spins programmed by spatial light modulation. We use holographic and optical control to change the spin couplings adiabatically, and exploit experimental noise to explore the energy landscape. Annealing enhances the convergence to the Ising ground state and allows to find the problem solution with probability close to unity. Our results demonstrate a photonic scheme for combinatorial optimization in analogy with adiabatic quantum algorithms and classical annealing methods but enforced by optical vector-matrix multiplications and scalable photonic technology.
Stress and burnout in open source

Developers from open-source communities have reported high stress levels from frequent demands for features and bug fixes and from the sometimes aggressive tone of these demands. Toxic conversations may demotivate and burn out developers, creating challenges for sustaining open source. We outline a path toward finding, understanding, and possibly mitigating such unhealthy interactions. We take a first step toward finding them, by developing and demonstrating a measurement instrument (an SVM classifier tailored for software engineering) to detect toxic discussions in GitHub issues. We used our classifier to analyze trends over time and in different GitHub communities, finding that toxicity varies by community and that toxicity decreased between 2012 and 2018.
Attacks on blockchain

The blockchain network is generally to be considered as secure and scalable but their security level is directly proportional to the amount of hash computing power that supports the blockchain. As the miners increase in the mining process the more difficult for an attacker to attack the blockchain. Several ways a blockchain can be attacked. Performing these attacks such as Finney attack, race attack, 51% attack, eclipse attack, Sybil attack, DDoS, routing attack, DAO attack, parity multisig parity attack on a blockchain becomes more difficult as more computing power is added to the network. The attacks described in this chapter are not endemic to blockchains except 51% attack and there are many measures and solutions to mitigate the risk of these attacks.
Detecting Phishing Scams on Ethereum Based on Transaction Records

With the increasing popularity of blockchain technology, it has also become a hotbed of various cybercrimes. As a traditional way of scam, the phishing scam has new means of scam in the blockchain scenario and swindles a lot of money from users. In order to create a safe environment for investors, an efficient method for phishing detection is urgently needed. In this paper, we propose a three steps framework to detect phishing scams on Ethereum by mining Ethereum transaction records. First, we obtain the labeled phishing accounts and corresponding transaction records from two authorized websites. According to the collected transaction records we build an Ethereum transaction network. Then, a network embedding method node2vec which can extract the latent features of accounts is used for subsequent phishing classification. Finally, to distinguish whether the account is a phishing account, we adopt the one-class support vector machine (SVM) to classify. The experimental result demonstrates that F-score of our phishing detection method can achieve 0.846, which verifies the validity of our model. To the best of our knowledge, this is the first work that investigates the phishing scams on Ethereum based on transaction records.
KT-GAN: Knowledge-Transfer Generative Adversarial Network for Text-to-Image Synthesis

This paper presents a new framework, Knowledge-Transfer Generative Adversarial Network (KT-GAN), for fine-grained text-to-image generation. We introduce two novel mechanisms: an Alternate Attention-Transfer Mechanism (AATM) and a Semantic Distillation Mechanism (SDM), to help generator better bridge the cross-domain gap between text and image. The AATM updates word attention weights and attention weights of image sub-regions alternately, to progressively highlight important word information and enrich details of synthesized images. The SDM uses the image encoder trained in the Image-to-Image task to guide training of the text encoder in the Text-to-Image task, for generating better text features and higher-quality images. With extensive experimental validation on two public datasets, our KT-GAN outperforms the baseline method significantly, and also achieves the competive results over different evaluation metrics.
Internal Factors Affect Knowledge Management and Firm Performance: A Systematic Review

The research paper sets out to prepare a systematic review of one of the essential terms in any organization, which is knowledge management (KM) and firm performance (FP). The research aimed to learn more about the relationship between the KM and FP. This systematic review coved 41 articles published between 2010–2020 and come from different databases like ProQuest, Emerald, ScienceDirect, Taylor & Francis, and Google Scholar. The results filtered by a set of inclusion and exclusion criteria. The more frequency factors will be highlighted as the main findings for future study. A proposed framework can be defined in that stage for future research. After that, a comprehensive analysis will determine the 41 articles and answers the five research questions. The main finding in this research knowledge management positively related to firm performance. Many factors affect this relation like organization learning, intellectual capital, human resources management, information technology, soft total quality management, knowledge management practice, strategy, structure.
Quantum simulation of electronic structure with a transcorrelated Hamiltonian: improved accuracy with a smaller footprint on the quantum computer

Quantum simulations of electronic structure with a transformed Hamiltonian that includes some electron correlation effects are demonstrated. The transcorrelated Hamiltonian used in this work is efficiently constructed classically, at polynomial cost, by an approximate similarity transformation with an explicitly correlated two-body unitary operator. This Hamiltonian is Hermitian, includes no more than two-particle interactions, and is free of electron-electron singularities. We investigate the effect of such a transformed Hamiltonian on the accuracy and computational cost of quantum simulations by focusing on a widely used solver for the Schrodinger equation, namely the variational quantum eigensolver method, based on the unitary coupled cluster with singles and doubles (q-UCCSD) Ansatz. Nevertheless, the formalism presented here translates straightforwardly to other quantum algorithms for chemistry. Our results demonstrate that a transcorrelated Hamiltonian, paired with extremely compact bases, produces explicitly correlated energies comparable to those from much larger bases. For the chemical species studied here, explicitly correlated energies based on an underlying 6-31G basis had cc-pVTZ quality. The use of the very compact transcorrelated Hamiltonian reduces the number of CNOT gates required to achieve cc-pVTZ quality by up to two orders of magnitude, and the number of qubits by a factor of three.
Scribble2Label: Scribble-Supervised Cell Segmentation via Self-generating Pseudo-Labels with Consistency

Segmentation is a fundamental process in microscopic cell image analysis. With the advent of recent advances in deep learning, more accurate and high-throughput cell segmentation has become feasible. However, most existing deep learning-based cell segmentation algorithms require fully annotated ground-truth cell labels, which are time-consuming and labor-intensive to generate. In this paper, we introduce Scribble2Label, a novel weakly-supervised cell segmentation framework that exploits only a handful of scribble annotations without full segmentation labels. The core idea is to combine pseudo-labeling and label filtering to generate reliable labels from weak supervision. For this, we leverage the consistency of predictions by iteratively averaging the predictions to improve pseudo labels. We demonstrate the performance of Scribble2Label by comparing it to several state-of-the-art cell segmentation methods with various cell image modalities, including bright-field, fluorescence, and electron microscopy. We also show that our method performs robustly across different levels of scribble details, which confirms that only a few scribble annotations are required in real-use cases.
Deep Multimodal Neural Architecture Search

Designing effective neural networks is fundamentally important in deep multimodal learning. Most existing works focus on a single task and design neural architectures manually, which are highly task-specific and hard to generalize to different tasks. In this paper, we devise a generalized deep multimodal neural architecture search (MMnas) framework for various multimodal learning tasks. Given multimodal input, we first define a set of primitive operations, and then construct a deep encoder-decoder based unified backbone, where each encoder or decoder block corresponds to an operation searched from a predefined operation pool. On top of the unified backbone, we attach task-specific heads to tackle different multimodal learning tasks. By using a gradient-based NAS algorithm, the optimal architectures for different tasks are learned efficiently. Extensive ablation studies, comprehensive analysis, and comparative experimental results show that the obtained MMnasNet significantly outperforms existing state-of-the-art approaches across three multimodal learning tasks (over five datasets), including visual question answering, image-text matching, and visual grounding.
An efficient approach for privacy preserving decentralized deep learning models based on secure multi-party computation

This paper aims to develop a new efficient framework named Secure Decentralized Training Framework (SDTF) for Privacy Preserving Deep Learning models. The main feature of the proposed framework is its capable of working on a decentralized network setting that does not need a trusted third-party server while simultaneously ensuring the privacy of local data with a low cost of communication bandwidth. Particularly, we first propose a so-called Efficient Secure Sum Protocol (ESSP) that enables a large group of parties to jointly calculate a sum of private inputs. ESSP can work not only with integer number but also with floating point number without any data conversion. We then propose a Secure Model Sharing Protocol that enables a group of parties securely train and share the local models to be aggregated into a global model. Secure Model Sharing Protocol exploits randomization techniques and ESSP to protect local models from any honest-but-curious party even n-2 of n parties colluding. Eventually, these protocols are employed for collaborative training decentralized deep learning models. We conduct theoretical evaluation of privacy and communication cost as well as empirical experiments on balance class image datasets (MNIST) and an unbalance class text dataset (UCI SMS Spam). These experiments demonstrate the proposed approach can obtain high accuracy (i.e. 97% baseline accuracy in only 10 training rounds with MNIST, 100 training rounds with SMS Spam) and robust to the heterogeneity decentralized network, with non-IID and unbalance data distributions. We also show a reduction in required rounds of training to achieve the accuracy baseline by 5× as compared to Downpour SGD. It is shown that the proposed approach can achieve both the privacy at the level of cryptographic approaches and efficiency at the level of randomization techniques, while it also retains higher model’s utility than differential privacy approaches.
STATICA: A 512-Spin 0.25M-Weight Annealing Processor With an All-Spin-Updates-at-Once Architecture for Combinatorial Optimization With Complete Spin–Spin Interactions

This article presents a high-performance annealing processor named STochAsTIc Cellular automata Annealer (STATICA) for solving combinatorial optimization problems represented by fully connected graphs. Supporting fully connected graphs is strongly required for dealing with realistic optimization problems. Unlike previous annealing processors that follow Glauber dynamics, our proposed annealer can update multiple states of fully connected spins simultaneously by introducing different dynamics called stochastic cellular automata annealing. It allows us to utilize the pipeline-level and memory-bank-level parallelization in addition to the PE-level parallelization originally adopted in the previous annealers. The STATICA prototype chip, which supports 512-spin fully connected graph, has been fabricated with the 65-nm CMOS technology and realized as a 3 mm × 4 mm chip. Using the fabricated 512-spin chip and numerical projections for a 2048-spin chip, we have conducted experiments to reveal the annealing performance of STATICA and examined how to control its annealing process efficiently.
Revisiting Internal Covariate Shift for Batch Normalization

Despite the success of batch normalization (BatchNorm) and a plethora of its variants, the exact reasons for its success are still shady. The original BatchNorm article explained it as a mechanism that reduces the internal covariate shift (ICS), i.e., the distribution shifts in the input of the layers during training. Recently, some articles manifested skepticism on this hypothesis and provided alternative explanations for the success of BatchNorm, such as the applicability of very high learning rates and the ability to smooth the landscape in optimization. In this work, we counter these alternative arguments by demonstrating the importance of reduction in ICS following an empirical approach. We demonstrated various ways to achieve the abovementioned alternative properties without any performance boost. In this light, we explored the importance of different BatchNorm parameters (i.e., batch statistics and affine transformation parameters) by visualizing their effectiveness in the performance and analyzed their connections with ICS. Afterward, we showed a different normalization scheme that fulfills all the alternative explanations except reduction in ICS. Despite having all the alternative properties, we observed its poor performance, which nullifies the alternative claims, rather signifies the importance of the ICS reduction. We performed comprehensive experiments on many variants of BatchNorm, finding that all of them similarly reduce ICS.