Reviewerly/exHarmony huggingface dataset:
Learning hyperbolic attention-based embeddings for link prediction in knowledge graphs

Knowledge graph (KG) embedding methods aim to learn low-dimensional representations of entities and relations to predict new valid triples for KG completion. Most of the existing KG embedding models learn embeddings in Euclidean space, which cannot accurately capture hierarchical structures and complex properties of relations found in KGs. To this effect, a recent model MuRP, as a first hyperbolic method, learns the KG embeddings in hyperbolic space and outperforms existing Euclidean embedding models. However, MuRP treats the KG triples individually, and hence fails to capture the complex structural information inherent in the local vicinity of a node, leading to low-quality node embeddings. On the other hand, the recent hyperbolic graph neural network (HGNN) provides a way of learning high-quality hyperbolic node embeddings by capturing information from each node’s neighborhood. However, HGNN ignores the relation features and treats all neighboring nodes with equal importance. To this end, we propose RHGNN, which extends HGNN by including the relation features and performing a hyperbolic attention-based neighborhood aggregation. We then combine RHGNN with MuRP into a novel encoder–decoder hyperbolic embedding learning framework (which we call HyperGEL) for KG completion. RHGNN gathers information from the neighboring nodes to generate rich hyperbolic node embeddings, and MuRP uses these embeddings to predict new triples. Experimental results show the proposed framework’s effectiveness that consistently marks performance gains over several previous models on recent standard KG completion datasets.
Towards Interpretable Deep Metric Learning with Structural Matching

How do the neural networks distinguish two images? It is of critical importance to understand the matching mechanism of deep models for developing reliable intelligent systems for many risky visual applications such as surveillance and access control. However, most existing deep metric learning methods match the images by comparing feature vectors, which ignores the spatial structure of images and thus lacks interpretability. In this paper, we present a deep interpretable metric learning (DIML) method for more transparent embedding learning. Unlike conventional metric learning methods based on feature vector comparison, we propose a structural matching strategy that explicitly aligns the spatial embeddings by computing an optimal matching flow between feature maps of the two images. Our method enables deep models to learn metrics in a more human-friendly way, where the similarity of two images can be decomposed to several part-wise similarities and their contributions to the overall similarity. Our method is model-agnostic, which can be applied to off-the-shelf backbone networks and metric learning methods. We evaluate our method on three major benchmarks of deep metric learning including CUB200-2011, Cars196, and Stanford Online Products, and achieve substantial improvements over popular metric learning methods with better interpretability. Code is available at https://github.com/wl-zhao/DIML.
vPipe: A Virtualized Acceleration System for Achieving Efficient and Scalable Pipeline Parallel DNN Training

The increasing computational complexity of DNNs achieved unprecedented successes in various areas such as machine vision and natural language processing (NLP), e.g., the recent advanced Transformer has billions of parameters. However, as large-scale DNNs significantly exceed GPU's physical memory limit, they cannot be trained by conventional methods such as data parallelism. Pipeline parallelism that partitions a large DNN into small subnets and trains them on different GPUs is a plausible solution. Unfortunately, the layer partitioning and memory management in existing pipeline parallel systems are fixed during training, making them easily impeded by out-of-memory errors and the GPU under-utilization. These drawbacks amplify when performing neural architecture search (NAS) such as the evolved Transformer, where different network architectures of Transformer needed to be trained repeatedly. vPipe is the first system that transparently provides dynamic layer partitioning and memory management for pipeline parallelism. vPipe has two unique contributions, including (1) an online algorithm for searching a near-optimal layer partitioning and memory management plan, and (2) a live layer migration protocol for re-balancing the layer distribution across a training pipeline. vPipe improved the training throughput of two notable baselines (Pipedream and GPipe) by 61.4-463.4 percent and 24.8-291.3 percent on various large DNNs and training settings.
SpZip: Architectural Support for Effective Data Compression In Irregular Applications

Irregular applications, such as graph analytics and sparse linear algebra, exhibit frequent indirect, data-dependent accesses to single or short sequences of elements that cause high main memory traffic and limit performance. Data compression is a promising way to accelerate irregular applications by reducing memory traffic. However, software compression adds substantial overheads, and prior hardware compression techniques work poorly on the complex access patterns of irregular applications.We present SpZip, an architectural approach that makes data compression practical for irregular algorithms. SpZip accelerates the traversal, decompression, and compression of the data structures used by irregular applications. In addition, these activities run in a decoupled fashion, hiding both memory access and decompression latencies. To support the wide range of access patterns in these applications, SpZip is programmable, and uses a novel Dataflow Configuration Language to specify programs that traverse and generate compressed data. Our SpZip implementation leverages dataflow execution and time-multiplexing to implement programmability cheaply. We evaluate SpZip on a simulated multicore system running a broad set of graph and linear algebra algorithms. SpZip outperforms prior state-of-the art software-only (hardware-accelerated) systems by gmean 3.0× (1.5×) and reduces memory traffic by 1.7× (1.4×). These benefits stem from both reducing data movement due to compression, and offloading expensive traversal and (de)compression operations.
A Secure Authentication Scheme for Wireless Sensor Networks Based on DAC and Intel SGX

Wireless sensor networks (WSNs) are widely implemented in military, intelligent medical, intelligent transportation, space exploration, and other fields. However, the authentication and communication of WSNs are carried out in the harsh external environment via a public channel, which is more vulnerable to various attacks than the traditional networks. Authentication is the key technology of security measures, but a common authentication scheme is not suitable for WSNs due to limitations of memory, computing, and energy consumption. Therefore, designing a secure and efficient authentication scheme is essential in WSNs. In recent years, several studies proved that the dynamic authentication credential (DAC) is efficient for enhancing the security of the authentication scheme. This article designs a secure authentication scheme for WSNs based on DAC and Intel software guard extensions (SGX). Compared with other DAC authentication schemes, our scheme provides the confirmation action of DAC rotation, which can effectively prevent the asynchronous update problem caused by packet loss. In order to resist the privileged user attack and the authentication table leakage attack, we choose the SGX, which can protect the data in use, as the trusted execution environment in the gateway node, and we adopt SGX to store the master key for protecting the authentication table. Finally, the security of our authentication scheme is verified by BAN logic, the simulation tool AVISPA, and informal security analysis. Through the consumption overhead analysis, the NS3 simulation result, and detailed comparison with other recent schemes, we conclude that our scheme is practical and achieves better security with less overhead.
Stability-Based Analysis and Defense against Backdoor Attacks on Edge Computing Services

With the explosive development of mobile Internet and deep learning (DL), intelligent edge computing services based on collaborative learning are widely deployed in various application scenarios. These intelligent services include intelligent applications based on edge computing and DL-based optimization for edge computing (e.g., caching and communicating). However, in a wide variety of domains, DL has been found to be vulnerable to adversarial attacks, especially architecture-independent backdoor attacks. It embeds the attack pattern into the learned model and only performs the attack when it encounters the corresponding trigger. In this article, for the first time we analyze the impact of backdoor attacks on intelligent edge computing services. The simulation results demonstrate that once one or more edge nodes implement backdoor attacks, the embedded attack pattern will rapidly expand to all relevant edge nodes, which poses huge challenges to security-sensitive intelligent edge computing services. Subsequently, we analyze the trade-off between expected performance and ability to defend against backdoor attacks, which sheds new light on designing defense mechanisms for intelligent edge computing services. To address the challenges posed by backdoor attacks, we propose a stability-based defense mechanism. The experimental results demonstrate that the newly proposed defense mechanism can effectively defend against different levels of backdoor attacks without knowing whether there are adversaries, which is conducive to the deployment of the stability-based defense mechanism in real-world scenarios.
Nonlinear Random Forest Classification, a Copula-Based Approach

In this work, we use a copula-based approach to select the most important features for a random forest classification. Based on associated copulas between these features, we carry out this feature selection. We then embed the selected features to a random forest algorithm to classify a label-valued outcome. Our algorithm enables us to select the most relevant features when the features are not necessarily connected by a linear function; also, we can stop the classification when we reach the desired level of accuracy. We apply this method on a simulation study as well as a real dataset of COVID-19 and for a diabetes dataset.
Blockchain Technology, Its Applications and Open Research Challenges

Abstract Blockchain is a technology that operates on distributed peer-to-peer network and is widely adopted by large number of organizations due to its attractive features such as highly secured, transparent, traceable, low transaction fee, no need of central authority and confidentiality. There are lot of confusions and myths regarding the blockchain technology as it is emerging technology and too complex in nature. This paper is aimed at revealing blockchain technology in depth along with its applications and open research challenges. The past published literature was reviewed to achieve the objectives. The study found that apart from cryptocurrency, blockchain is also suitable for other applications such as smart cities, supply chain, healthcare, smart transportation and authenticating IoT (Internet of Things) devices. This study is also disclosing various open research challenges such as scalability issues, huge power consumption and delay in completing transactions. This paper is also sharing the mitigation approaches and the future of blockchain with smart contracts.
A deep neural network model for fashion collocation recommendation using side information in e-commerce

A fashion collocation recommendation model is a useful tool to enhance customer experiences and increase cross-selling in e-commerce. Prior research efforts focus on using titles and images to build the model. However, accessible side information in e-commerce, such as textual descriptions, purchase data and category information of items, generally bear valuable information regarding this task. This study seeks to develop a fashion collocation recommendation model that leverages accessible side information in e-commerce. First, a knowledge graph is built to model the purchase data and category information of items and an approach is subsequently proposed to learn the knowledge embeddings in the knowledge graph. To cope with the challenge of Chinese titles, a model is developed to encode the Chinese titles into vectors. Finally, a fashion collocation recommendation model combines the knowledge embeddings of the knowledge graph and the encoded titles to compute the probability of fashion collocation between items. The experiments are conducted on two datasets. Compared with baseline models, our model obtains nearly 6% and 1% AUC improvement in TIANCHI and POG dataset, respectively. The experimental results demonstrate that our efforts to leverage side information to improve fashion collocation recommendations are feasible and effective.
OpenDC 2.0: Convenient Modeling and Simulation of Emerging Technologies in Cloud Datacenters

Cloud datacenters are important for the digital society, serving stakeholders across industry, government, and academia.Simulation is a critical part of exploring datacenter technologies, enabling scalable experimentation with millions of jobs and hundreds of thousands of machines, and what-if analysis in a matter of minutes to hours.Although the community has already developed powerful simulators, emerging technologies and applications in modern datacenters require new approaches.Addressing this requirement, in this work we propose OpenDC, a new platform for datacenter simulation.OpenDC includes novel models for emerging cloud-datacenter technologies and applications, such as serverless computing with FaaS deployment and TensorFlow-based machine learning.Our design also focuses on convenience, with a web-based interface for interactive experimentation, support for experiment automation, a library of prefabs for constructing and sharing datacenter designs, and support for diverse input formats and output metrics.We implement, validate, and open-source OpenDC 2.0, a significant redesign and release after a multi-year research and development process.We demonstrate the benefits of OpenDC for the field through a set of representative use-cases: serverless, machine learning, procurement of HPC-as-a-Service infrastructure, educational practices, and reproducibility studies.Overall, OpenDC helps understand how datacenters work, design datacenter infrastructure, and train the next generation of experts.
Identifying wetland areas in historical maps using deep convolutional neural networks

The local environment and land usages have changed a lot during the past one hundred years. Historical documents and materials are crucial in understanding and following these changes. Historical documents are, therefore, an important piece in the understanding of the impact and consequences of land usage change. This, in turn, is important in the search of restoration projects that can be conducted to turn and reduce harmful and unsustainable effects originating from changes in the land-usage. This work extracts information on the historical location and geographical distribution of wetlands, from hand-drawn maps. This is achieved by using deep learning (DL), and more specifically a convolutional neural network (CNN). The CNN model is trained on a manually pre-labelled dataset on historical wetlands in the area of Jönköping county in Sweden. These are all extracted from the historical map called "Generalstabskartan". The presented CNN performs well and achieves a F1-score of 0.886 when evaluated using a 10-fold cross validation over the data. The trained models are additionally used to generate a GIS layer of the presumable historical geographical distribution of wetlands for the area that is depicted in the southern collection in Generalstabskartan, which covers the southern half of Sweden. This GIS layer is released as an open resource and can be freely used. To summarise, the presented results show that CNNs can be a useful tool in the extraction and digitalisation of non-textual information in historical documents, such as historical maps. A modern GIS material that can be used to further understand the past land-usage change is produced within this research. Previously, no material of this detail and extent have been available, due to the large effort needed to manually create such. However, with the presented resource better quantifications and estimations of historical wetlands that have been lost can be made.
Model checking C++ programs

Summary In the last three decades, memory safety issues in system programming languages such as C or C++ have been one of the most significant sources of security vulnerabilities. However, there exist only a few attempts with limited success to cope with the complexity of C++ program verification. We describe and evaluate a novel verification approach based on bounded model checking (BMC) and satisfiability modulo theories (SMT) to verify C++ programs. Our verification approach analyses bounded C++ programs by encoding into SMT various sophisticated features that the C++ programming language offers, such as templates, inheritance, polymorphism, exception handling, and the Standard Template Libraries. We formalize these features within our formal verification framework using a decidable fragment of first‐order logic and then show how state‐of‐the‐art SMT solvers can efficiently handle that. We implemented our verification approach on top of ESBMC. We compare ESBMC to LLBMC and DIVINE, which are state‐of‐the‐art verifiers to check C++ programs directly from the LLVM bitcode. Experimental results show that ESBMC can handle a wide range of C++ programs, presenting a higher number of correct verification results. Additionally, ESBMC has been applied to a commercial C++ application in the telecommunication domain and successfully detected arithmetic‐overflow errors, which could potentially lead to security vulnerabilities.
Graph convolutional autoencoders with co-learning of graph structure and node attributes

Recently, graph representation learning based on autoencoders has received much attention. However, these methods suffer from two limitations. First, most graph autoencoders ignore the reconstruction of either the graph structure or the node attributes, which often leads to a poor latent representation of the graph-structured data. Second, for existing graph autoencoders models, the encoder and decoder are mainly composed of an initial graph convolutional network (GCN) or its variants. These traditional GCN-based graph autoencoders more or less encounter the problem of incomplete filtering, which causes these models to be unstable in practical applications. To address the above issues, this paper proposes the Graph convolutional Autoencoders with co-learning of graph Structure and Node attributes (GASN) based on variational autoencoders. Specifically, the proposed GASN encodes and decodes the node attributes and graph structure comprehensively in the graph-structured data. Furthermore, we design a completely low-pass graph encoder and a high-pass graph decoder. The experimental results on real-world datasets demonstrate that the proposed GASN achieves state-of-the-art performance on node clustering, link prediction, and visualization tasks.
Text Backdoor Detection Using an Interpretable RNN Abstract Model

Deep neural networks (DNNs) are known to be inherently vulnerable to malicious attacks such as the adversarial attack and the backdoor attack. The former is crafted by adding small perturbations to benign inputs so as to fool a DNN. The latter generally embeds a hidden pattern in a DNN by poisoning the dataset during the training process, which causes the infected model to misbehave on predefined inputs with a specific trigger and normally perform for others. Much work has been conducted on defending against the adversarial samples, while the backdoor attack received much less attention, especially in recurrent neural networks (RNNs), which play an important role in the text processing field. Two main limitations make it hard to directly apply existing image backdoor detection approaches to RNN-based text classification systems. First, a layer in an RNN does not preserve the same feature latent space function for different inputs, making it impossible to map the inserted specific pattern with the neural activations. Second, the text data is inherently discrete, making it hard to optimize the text like image pixels. In this work, we propose a novel backdoor detection approach named InterRNN for RNN-based text classification systems from the interpretation perspective. Specifically, we first propose a novel RNN interpretation technique by constructing a nondeterministic finite automaton (NFA) based abstract model, which can effectively reduce the analysis complexity of an RNN while preserving its original logic rules. Then, based on the abstract model, we can obtain interpretation results that explain the fundamental reason behind the decision for each input. We then detect trigger words by leveraging the differences between the behaviors in the backdoor sentences and those in the normal sentences. The extensive experiment results on four benchmark datasets demonstrate that our approach can generate better interpretation results compared to state-of-the-art approaches and effectively detect backdoors in RNNs.
Super-resolution technique for dense 3D reconstruction in fringe projection profilometry

Fringe projection profilometry (FPP) is one of the most widely used 3D reconstruction techniques. A higher-resolution fringe pattern produces a more detailed and accurate 3D point cloud, which is critical for 3D sensing. However, there is no effective way to achieve FPP super-resolution except by using greater hardware. Therefore, this Letter proposes a dual-dense block super-resolution network (DdBSRN) to extend the fringe resolution and reconstruct a high-definition 3D shape. Especially, a novel dual-dense block structure is designed and embedded into a multi-path structure to fully utilize the local layers and fuse multiple discrete sinusoidal signals. Furthermore, a fully functional DdBSRN can be obtained even when training with a smaller data sample. Experiments demonstrate that the proposed DdBSRN method is stable and robust, and that it outperforms standard interpolation methods in terms of accuracy and 3D details.
Blockchain Based Architecture and Solution for Secure Digital Payment System

With the evolution of Internet Technology, payment methods have undergone drastic changes from entity exchange to Internet banking. Almost every sector has gone through the transformation from conventional technologies to digital technologies. With the arrival of online payment and digital wallet system, making payments has become easier than ever and with the increasing demand for such services, the number of users using instant money transfer systems are growing rapidly. However, the existing online payment system has issues like a single point of failure, transparency, and insider problem. Also, security in such online payments is crucial to mitigate risks and financial inefficiencies. In this paper, a private and permissioned Blockchain-based Payment System for the financial sector in India is proposed. The proposed architecture is based on Istanbul Byzantine Fault Tolerance (IBFT) consensus and it also discusses the integration of banks with the system.