Reviewerly/exHarmony huggingface dataset:
Building cyber security awareness in a developing country: Lessons from Myanmar

The paper discusses the arguments for inclusion of cybersecurity awareness building in development assistance programs. We note that cybersecurity awareness building can strengthen the resilience of productivity-enhancing services, such as mobile banking and e-payment systems, and benefit economic growth. We develop a typology of cybersecurity strategies involving government, the private sector and the international community. The paper describes an Australian-funded program delivered by Monash University to overcome cyber errorism by strengthening cybersecurity capacity and awareness in Myanmar and explains how and why it was delivered. Drawing on the program's experience, the paper identifies various challenges to managing an effective program as well as eight lessons learned which might be applicable to other programs. Key amongst the lessons learned is the need for a solid understanding of how the internet is accessed and used in the host country, and the need for local content to attract target audience attention.
Blockchain architecture: A design that helps CPA firms leverage the technology

Blockchain technology has garnered the interest of the accounting profession in recent years. However, when considering whether to adopt this technology, many business professionals have voiced a lack of a compelling use case as a major challenge. To utilize the technology effectively, first, we need to establish how organizations will connect to the blockchain that will also provide a compelling use-case for CPA firms. In this paper, we design a blockchain architecture for organizations that will facilitate effective connectivity to a blockchain while enabling auditors to leverage this technology to provide audit and assurance services. To design the architecture, we consider two broad questions: first, how do CPA firms gain access to reliable audit evidence and, second, how can client firms maintain confidentiality and security of their data given a decentralized and distributed immutable ledger (i.e., a blockchain). Consequently, the proposed architecture will help auditors gain access to reliable digital audit evidence while incentivizing client firms to adopt blockchain technology by substantially reducing the costs of replacing existing information systems. Given this architecture, auditors could also design continuous audit procedures for their respective clients without having to incur substantial investments in software integration. Further, the architecture can be expanded to include various use cases and supply chain participants, other CPA firms, customers, and regulators.
The Arwen Trading Protocols

The Arwen Trading Protocols are layer-two blockchain protocols for traders to securely trade cryptocurrencies at a centralized exchange, without ceding custody of their coins to the exchange. Before trading begins, traders deposit their coins in an on-blockchain escrow where the agent of escrow is the blockchain itself. Each trade is backed by the coins locked in escrow. Each trade is fast, because it happens off-blockchain, and secure, because atomic swaps prevent even a hacked exchange from taking custody of a trader's coins. Arwen is designed to work even with the "lowest common denominator" of blockchains—namely Bitcoin-derived coins without SegWit support. As a result, Arwen supports essentially all "Bitcoin-derived" coins e.g., BTC, LTC, BCH, ZEC, as well as Ethereum. Our protocols support Limit and RFQ order types, we implemented our RFQ protocol and it is available for use at arwen.io .
Nested Enclave: Supporting Fine-grained Hierarchical Isolation with SGX

Although hardware-based trusted execution environments (TEEs) have evolved to provide strong isolation with efficient hardware supports, their current monolithic model poses challenges in representing common software structures with modules produced from potentially untrusted 3rd parties. For better mapping of such modular software designs to trusted execution environments, it is necessary to extend the current monolithic model to a hierarchical one, which provides multiple inner TEEs within a TEE. For such hierarchical compartmentalization within a TEE, this paper proposes a novel hierarchical TEE called nested enclave, which extends the enclave support from Intel SGX. Inspired by the multi-level security model, nested enclave provides multiple inner enclaves sharing the same outer enclave. Inner enclaves can access the context of the outer enclave, but they are protected from the outer enclave and non-enclave execution. Peer inner enclaves are isolated from each other while accessing the execution environment of the shared outer enclave. Both of the inner and outer enclaves are protected from vulnerable privileged software and physical attacks. Such fine-grained nested enclaves allow secure multitiered environments using software modules from untrusted 3rd parties. The security-sensitive modules run on the inner enclave with the higher security level, while the 3rd party modules on the outer enclave. It can be further extended to provide a separate inner module for each user to process privacy-sensitive data while sharing the same library with efficient hardwareprotected communication channels. This study investigates three case scenarios implemented with an emulated nested enclave support, proving the feasibility and security improvement of the nested enclave model.
HyP-DESPOT: A hybrid parallel algorithm for online planning under uncertainty

Robust planning under uncertainty is critical for robots in uncertain, dynamic environments, but incurs high computational cost. State-of-the-art online search algorithms, such as DESPOT, have vastly improved the computational efficiency of planning under uncertainty and made it a valuable tool for robotics in practice. This work takes one step further by leveraging both CPU and GPU parallelization in order to achieve real-time online planning performance for complex tasks with large state, action, and observation spaces. Specifically, Hybrid Parallel DESPOT (HyP-DESPOT) is a massively parallel online planning algorithm that integrates CPU and GPU parallelism in a multi-level scheme. It performs parallel DESPOT tree search by simultaneously traversing multiple independent paths using multi-core CPUs; it performs parallel Monte Carlo simulations at the leaf nodes of the search tree using GPUs. HyP-DESPOT provably converges in finite time under moderate conditions and guarantees near-optimality of the solution. Experimental results show that HyP-DESPOT speeds up online planning by up to a factor of several hundred in several challenging robotic tasks in simulation, compared with the original DESPOT algorithm. It also exhibits real-time performance on a robot vehicle navigating among many pedestrians.
Spara: An Energy-Efficient ReRAM-Based Accelerator for Sparse Graph Analytics Applications

Resistive random access memory (ReRAM) addresses the high memory bandwidth requirement challenge of graph analytics by integrating the computing logic in the memory. Due to the matrix-structured crossbar architecture, existing ReRAM-based accelerators, when handling real-world graphs that often have the skewed degree distribution, suffer from the severe sparsity problem arising from zero fillings and activation nondeterminism, incurring substantial ineffectual computations.In this paper, we observe that the sparsity sources lie in the consecutive mapping of source and destination vertex index onto the wordline and bitline of a crossbar. Although exhaustive graph reordering improves the sparsity-induced inefficiency, its totally-random (source and destination) vertex mapping leads to expensive overheads. This work exploits the insight in a mid-point vertex mapping with the random wordlines and consecutive bitlines. A cost-effective preprocessing is proposed to exploit the insight by rapidly exploring the crossbar-fit vertex reorderings but ignores the sparsity arising from activation dynamics. We present a novel ReRAM-based graph analytics accelerator, named Spara, which can maximize the workload density of crossbars dynamically by using a tightly-coupled bank parallel architecture further proposed. Results on real-world and synthesized graphs show that Spara outperforms GraphR and GraphSAR by 8.21 × and 5.01 × in terms of performance, and by 8.97 × and 5.68× in terms of energy savings (on average), while incurring a reasonable (<; 9.98%) pre-processing overhead.
Adaptive AI-based auto-scaling for Kubernetes

Kubernetes, the prevalent container orchestrator for cloud-deployed web applications, offers an automatic scaling feature for the application provider in order to meet the ever-changing amount of demand from its clients. This auto-scaling service, however, requires a seemingly difficult parameter set to be customized by the application provider, and those management parameters are static while incoming web request dynamics often change, not to mention the fact that scaling decisions are inherently reactive, instead of being proactive. Therefore we set the ultimate goal of making cloud-based web applications' management easier and more effective. We propose a Kubernetes scaling engine that makes the auto-scaling decisions apt for handling the actual variability of incoming requests. In this engine various AI-based forecast methods compete with each other via a short-term evaluation loop in order to always give the lead to the method that suits best the actual request dynamics, as soon as possible. We also introduce a compact management parameter for the cloud-tenant application provider in order to easily set their sweet spot in the resource over-provisioning vs. SLA violation trade-off. The multi-forecast scaling engine and the proposed management parameter are evaluated both in simulations and with measurements on our collected web traces to show the improved quality of fitting provisioned resources to service demand. We find that with just a few competing forecast methods, our auto-scaling engine, implemented in Kubernetes, results in significantly less lost requests with slightly more provisioned resources compared to the default baseline.
Describing Physics <i>For</i> Physical Reasoning: Force-Based Sequential Manipulation Planning

Physical reasoning is a core aspect of intelligence in animals and humans. A central question is what model should be used as a basis for reasoning. Existing work considered models ranging from intuitive physics and physical simulators to contact dynamics models used in robotic manipulation and locomotion. In this work we propose descriptions of physics which directly allow us to leverage optimization methods for physical reasoning and sequential manipulation planning. The proposed multi-physics formulation enables the solver to mix various levels of abstraction and simplifications for different objects and phases of the solution. As an essential ingredient, we propose a specific parameterization of wrench exchange between object surfaces in a path optimization framework, introducing the point-of-attack as decision variable. We demonstrate the approach on various robot manipulation planning problems, such as grasping a stick in order to push or lift another object to a target, shifting and grasping a book from a shelve, and throwing an object to bounce towards a target.
A Fog-Augmented Machine Learning based SMS Spam Detection and Classification System

Smart cities and societies are driving unprecedented technological and socioeconomic growth in everyday life albeit making us increasingly vulnerable to infinitely and incomprehensibly diverse threats. Short Message Service (SMS) spam is one such threat that can affect mobile security by propagating malware on mobile devices. A security breach could also cause a mobile device to send spam messages. Many works have focused on classifying incoming SMS messages. This paper proposes a tool to detect spam from outgoing SMS messages, although the work can be applied to both incoming and outgoing SMS messages. Specifically, we develop a system that comprises multiple machine learning (ML) based classifiers built by us using three classification methods -- Naïve Bayes (NB), Support Vector Machine (SVM), and Naïve Bayes Multinomial (NBM)- and five preprocessing and feature extraction methods. The system is built to allow its execution in cloud, fog or edge layers, and is evaluated using 15 datasets built by 4 widely-used public SMS datasets. The system detects spam SMSs and gives recommendations on the spam filters and classifiers to be used based on user preferences including classification accuracy, True Negatives (TN), and computational resource requirements.
Optimized homomorphic encryption solution for secure genome-wide association studies

Abstract Background Genome-Wide Association Studies (GWAS) refer to observational studies of a genome-wide set of genetic variants across many individuals to see if any genetic variants are associated with a certain trait. A typical GWAS analysis of a disease phenotype involves iterative logistic regression of a case/control phenotype on a single-neuclotide polymorphism (SNP) with quantitative covariates. GWAS have been a highly successful approach for identifying genetic-variant associations with many poorly-understood diseases. However, a major limitation of GWAS is the dependence on individual-level genotype/phenotype data and the corresponding privacy concerns. Methods We present a solution for secure GWAS using homomorphic encryption (HE) that keeps all individual data encrypted throughout the association study. Our solution is based on an optimized semi-parallel GWAS compute model, a new Residue-Number-System (RNS) variant of the Cheon-Kim-Kim-Song (CKKS) HE scheme, novel techniques to switch between data encodings, and more than a dozen crypto-engineering optimizations. Results Our prototype can perform the full GWAS computation for 1,000 individuals, 131,071 SNPs, and 3 covariates in about 10 minutes on a modern server computing node (with 28 cores). Our solution for a smaller dataset was awarded co-first place in iDASH’18 Track 2: “Secure Parallel Genome Wide Association Studies using HE”. Conclusions Many of the HE optimizations presented in our paper are general-purpose, and can be used in solving challenging problems with large datasets in other application domains.
NERNet: Noise estimation and removal network for image denoising

While some denoising methods based on deep learning achieve superior results on synthetic noise, they are far from dealing with photographs corrupted by realistic noise. Denoising on real-world noisy images faces more significant challenges due to the source of it is more complicated than synthetic noise. To address this issue, we propose a novel network including noise estimation module and removal module (NERNet). The noise estimation module automatically estimates the noise level map corresponding to the information extracted by symmetric dilated block and pyramid feature fusion block. The removal module focuses on removing the noise from the noisy input with the help of the estimated noise level map. Dilation selective block with attention mechanism in the removal module adaptively not only fuses features from convolution layers with different dilation rates, but also aggregates the global and local information, which is benefit to preserving more details and textures. Experiments on two datasets of synthetic noise and three datasets of realistic noise show that NERNet achieves competitive results in comparison with other state-of-the-art methods.
Overcoming the security risks of remote working

The Covid-19 pandemic has transformed the way businesses operate. Around the world, governments have implemented regulation to protect people and slow the spread of the virus. This includes strict lockdown and social distancing measures that have prompted entire workforces to start working remotely. The Covid-19 pandemic has transformed the way businesses operate. As millions of people work from the safety of their homes, we have witnessed the creation of the largestever global remote workforce. Moving millions of employees, their computers and their data away from a secure office environment presents tremendous data security risks, ranging from simple technical glitches to full-scale ransomware attacks. The role of IT teams in business continuity has never been more important, says Florian Malecki of StorageCraft.
Multiple Kernel Driven Clustering With Locally Consistent and Selfish Graph in Industrial IoT

In the cognitive computing of intelligent industrial Internet of Things, clustering is a fundamental machine learning problem to exploit the latent data relationships. To overcome the challenge of kernel choice for nonlinear clustering tasks, multiple kernel clustering (MKC) has attracted intensive attention. However, existing graph-based MKC methods mainly aim to learn a consensus kernel as well as an affinity graph from multiple candidate kernels, which cannot fully exploit the latent graph information. In this article, we propose a novel pure graph-based MKC method. Specifically, a new graph model is proposed to preserve the local manifold structure of the data in kernel space so as to learn multiple candidate graphs. Afterward, the latent consistency and selfishness of these candidate graphs are fully considered. Furthermore, a graph connectivity constraint is introduced to avoid requiring any postprocessing clustering step. Comprehensive experimental results demonstrate the superiority of our method.
Universal Chosen-Ciphertext Attack for a Family of Image Encryption Schemes

In recent decades, there has been considerable popularity in employing nonlinear dynamics and permutation-substitution structures for image encryption. Three procedures generally exist in such image encryption schemes: the key schedule module for producing encryption elements, permutation for image scrambling and substitution for pixel modification. This paper cryptanalyzes a family of image encryption schemes that adopt pixel-level permutation and modular addition-based substitution. The security analysis first reveals a common defect in the studied image encryption schemes. Specifically, the mapping from the differentials of the ciphertexts to those of the plaintexts is found to be linear and independent of the key schedules, permutation techniques and encryption rounds. On this theory basis, a universal chosen-ciphertext attack is further proposed. Experimental results demonstrate that the proposed attack can recover the plaintexts of the studied image encryption schemes without a security key or any encryption elements. Related cryptographic discussions are also given.
Application of ensemble RNN deep neural network to the fall detection through IoT environment

The emerging of new models in machine learning enhances the performance of algorithms proposed to address several challenging tasks such as object recognition, classification and identification purpose. Nowadays. the deep learning algorithms are playing a massive role in accurately addressing complex problems due to their capability of learning various complex features from data. One limitation of deep learning is a lack of sufficient data for training. In this study, we proposed variants of Long Short Term Memory (LSTM) model and ensemble learning methods such as XGBoost, AdaBoost, Bagging, Stacking and Random forest. The experimentation is carried out on Time series data generated from the Internet of Things (IoT) devices. To validate the proposed method, we have used a freely available dataset on the web namely Smart-Fall datasets. To measure the performance of the proposed method, we have used standard performance measures namely, accuracy, precision, recall, f-score, specificity, geometric mean and confusion matrix. A set of experimental details are carried out on the SmartFall dataset and the experimental results exhibit that the Random forest algorithm performs better when compared with a single deep LSTM model and different ensemble techniques.
The interpolation phase transition in neural networks: Memorization and generalization under lazy training

Modern neural networks are often operated in a strongly overparametrized regime: they comprise so many parameters that they can interpolate the training set, even if actual labels are replaced by purely random ones. Despite this, they achieve good prediction error on unseen data: interpolating the training set does not lead to a large generalization error. Further, overparametrization appears to be beneficial in that it simplifies the optimization landscape. Here, we study these phenomena in the context of two-layers neural networks in the neural tangent (NT) regime. We consider a simple data model, with isotropic covariates vectors in d dimensions, and N hidden neurons. We assume that both the sample size n and the dimension d are large, and they are polynomially related. Our first main result is a characterization of the eigenstructure of the empirical NT kernel in the overparametrized regime Nd≫n. This characterization implies as a corollary that the minimum eigenvalue of the empirical NT kernel is bounded away from zero as soon as Nd≫n and, therefore, the network can exactly interpolate arbitrary labels in the same regime. Our second main result is a characterization of the generalization error of NT ridge regression including, as a special case, min-ℓ2 norm interpolation. We prove that, as soon as Nd≫n, the test error is well approximated by the one of kernel ridge regression with respect to the infinite-width kernel. The latter is in turn well approximated by the error of polynomial ridge regression, whereby the regularization parameter is increased by a "self-induced" term related to the high-degree components of the activation function. The polynomial degree depends on the sample size and the dimension (in particular on logn/logd).
Dynamic Sample Selection for Federated Learning with Heterogeneous Data in Fog Computing

Federated learning is a state-of-the-art technology used in the fog computing, which allows distributed learning to train cross-device data while achieving efficient performance. Many current works have optimized the federated learning algorithm in homogeneous networks. However, in the actual application scenario of distributed learning, data is independently generated by each device, and this non-homologous data has different distribution characteristics. Therefore, the data used by each device for local learning is unbalanced and non-IID, and the heterogeneity of data affects the performance of federated learning and slows down the convergence. In this paper, we present a dynamic sample selection optimization algorithm, FedSS, to tackle heterogeneous data in federated learning. FedSS dynamically selects the training sample size during the gradient iteration based on the locally available data size, to settle the expensive evaluations of the local objective function with a massive amount of dataset. We theoretically analyze the convergence and present the complexity estimates of our framework when learning large data from unbalanced distribution. Our experimental results show that the use of dynamic sampling methods can effectively improve the convergence speed with heterogeneous data, and keep computational costs low while achieving the desired accuracy.