Reviewerly/exHarmony huggingface dataset:
A Novel Hyper-Converged Architecture for Power Data Centers

With the rapid development of emerging technologies such as the Internet, cloud computing, and big data, the traditional data center of the Internet of Things has been addressed with problems such as complex operation and maintenance, low configuration efficiency, high construction costs, and lack of effective unified business monitoring methods. This paper, by studying the related technologies of hyper-convergence, proposes a plan for applying the hyper-converged architecture to the power data center. This solution will achieve the goals of high-efficiency integration of data center hardware, flexible resource scheduling, rapid system deployment, safe and reliable data, and unified business operation and maintenance. Facts have proved that using hyper-convergence to provide underlying support for the power data center is an ideal way to transform the data center architecture.
Lower Bounds for Leakage-Resilient Secret-Sharing Schemes against Probing Attacks

Historically, side-channel attacks have revealed partial information about the intermediate values and secrets of computations to compromise the security of cryptographic primitives. The objective of leakage-resilient cryptography is to model such avenues of information leakage and study techniques to realize them securely. This work studies the local leakage-resilience of prominent secret-sharing schemes like Shamir's secret-sharing scheme and the additive secret-sharing scheme against probing attacks that leak physical-bits from the memory hardware storing the secret shares. Consider the additive secret-sharing scheme among <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> parties over a prime field such that the prime needs <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\lambda$</tex> -bits for its binary representation, where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\lambda$</tex> is the security parameter. We prove that <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> must be at least <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\omega(\log\lambda/\log\log\lambda)$</tex> for the scheme to be secure against even one physical-bit leakage from each secret share. This result improves the previous state-of-the-art result where an identical lower bound was known for one-bit general leakage from each secret share (Benhamouda, Degwekar, Ishai, and Rabin, CRYPTO–2018). This lower bound on the reconstruction threshold extends to Shamir's secret-sharing scheme if one does not carefully choose the evaluation places for generating the secret shares. For this scheme, our result additionally improves another lower bound on the reconstruction threshold <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> of Shamir's secret-sharing scheme (Nielsen and Simkin, EUROCRYPT–2020) when the total number of parties is <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{O}(\lambda\log\lambda/\log\log\lambda)$</tex> . Our work provides the analysis of the recently-proposed (explicit) physical-bit leakage attack of Maji, Nguyen, Paskin-Cherniavsky, Suad, and Wang (EUROCRYPT–2021), namely the “parity of parity” attack. This analysis relies on lower-bounding the “discrepancy” of the Irwin-Hall probability distribution.
Virtual reality and its transformation in forensic education and research practices

Documentation and evidence analysis are major components in forensic investigation; hence two-dimensional (2D) photographs along with three-dimensional (3D) models and data are used to accomplish this task. Data generated through 3D scanning and photogrammetry are generally visualised on a computer screen. However, spatial details are lost on the visualisation of 3D data on 2D computer screens. Virtual reality (VR) is an immersive technology that allows a user to visualise 3D information by immersing oneself into the scene. In forensics, VR was particularly introduced for the visualising and plotting distances of crime scenes; however, this technology has wider applications in the field of forensics and for court presentation. This short communication outlines the concept of VR and its potential in the field of forensics.
Advances in spam detection for email spam, web spam, social network spam, and review spam: ML-based and nature-inspired-based techniques

Despite the great advances in spam detection, spam remains a major problem that has affected the global economy enormously. Spam attacks are popularly perpetrated through different digital platforms with a large electronic audience, such as emails, microblogging websites (e.g. Twitter), social networks (e.g. Facebook), and review sites (e.g. Amazon). Different spam detection solutions have been proposed in the literature, however, Machine Learning (ML) based solutions are one of the most effective. Nevertheless, most ML algorithms have computational complexity problem, thus some studies introduced Nature Inspired (NI) algorithms to further improve the speed and generalization performance of ML algorithms. This study presents a survey of recent ML-based and NI-based spam detection techniques to empower the research community with information that is suitable for designing effective spam filtering systems for emails, social networks, microblogging, and review websites. The recent success and prevalence of deep learning show that it can be used to solve spam detection problems. Moreover, the availability of large-scale spam datasets makes deep learning and big data solutions (such as Mahout) very suitable for spam detection. Few studies explored deep learning algorithms and big data solutions for spam detection. Besides, most of the datasets used in the literature are either small or synthetically created. Therefore, future studies can consider exploring big data solutions, big datasets, and deep learning algorithms for building efficient spam detection techniques.
Deep graph alignment network

Graph alignment, also known as network alignment has many applications in data mining tasks. It aims to find the node correspondence across disjoint graphs. Recently, various methods like representation learning methods, spectral methods have been proposed to solve the graph alignment problem, but they either only consider the local structure information but ignore the neighborhood similarity, or their alignment process is easy to be disturbed by nodes with similar structure or attribute. In this paper, we consider both center and neighborhood similarities, aiming to reduce the inconsistency between them and enlarge the difference among node representations. We further propose model DGAN(Deep Graph Alignment Network) containing the DNN module and GCN module to learn more unique node representations under the guidance of the attribute-supervised module. Moreover, we theoretically prove that most spectral methods can be unified into a linear GCN model. By extensive experiments on public benchmarks, we show that our model achieves a good balance between alignment accuracy and speed over multiple datasets compared with existing methods.
A Systematic Mapping Study on Issues and Importance of Documentation in Agile

Agile practices in software development are common, but still issues related to users’ requirements and customers’ satisfaction show a negative impact on software. One of the main reasons is due tonot adopting proper documentation in agile as compared to othertraditional methods in software development. In Agile Software Development (ASD), most of the worry is with overly detailed documentation; nevertheless, proponents of agile methodologies sometimes recommend utilizing “just enough” documentation. Thus, this article provides an analysis and review of published articles that focus on investigation related to documentation in agile so asto reveal their concerns or importance. Systematic Mapping Study (SMS) has been used to review the related articles systematically. From this mapping study, it is seen that 14 aspects related to documentation in agile were investigated, showing that how important is to have ‘just enough” documentation in agile. Based on these aspects it can be analyzed that why do projects lack in competing with time and cost. Also, how hard it becomes for developers while performing rapid changes iteratively to satisfy the customers’ needs.
IndoorCare: Low-Cost Elderly Activity Monitoring System through Image Processing

The Portuguese population is aging at an increasing rate, which introduces new problems, particularly in rural areas, where the population is small and widely spread throughout the territory. These people, mostly elderly, have low income and are often isolated and socially excluded. This work researches and proposes an affordable Ambient Assisted Living (AAL)-based solution to monitor the activities of elderly individuals, inside their homes, in a pervasive and non-intrusive way, while preserving their privacy. The solution uses a set of low-cost IoT sensor devices, computer vision algorithms and reasoning rules, to acquire data and recognize the activities performed by a subject inside a home. A conceptual architecture and a functional prototype were developed, the prototype being successfully tested in an environment similar to a real case scenario. The system and the underlying concept can be used as a building block for remote and distributed elderly care services, in which the elderly live autonomously in their homes, but have the attention of a caregiver when needed.
Toward Realistic Single-View 3D Object Reconstruction with Unsupervised Learning from Multiple Images

Recovering the 3D structure of an object from a single image is a challenging task due to its ill-posed nature. One approach is to utilize the plentiful photos of the same object category to learn a strong 3D shape prior for the object. This approach has successfully been demonstrated by a recent work of Wu et al. (2020), which obtained impressive 3D reconstruction networks with unsupervised learning. However, their algorithm is only applicable to symmetric objects. In this paper, we eliminate the symmetry requirement with a novel unsupervised algorithm that can learn a 3D reconstruction network from a multi-image dataset. Our algorithm is more general and covers the symmetry-required scenario as a special case. Besides, we employ a novel albedo loss that improves the reconstructed details and realisticity. Our method surpasses the previous work in both quality and robustness, as shown in experiments on datasets of various structures, including single-view, multiview, image-collection, and video sets. Code is available at: https://github.com/VinAIResearch/LeMul.
Secure and Efficient Software Masking on Superscalar Pipelined Processors

Physical side-channel attacks like power analysis pose a serious threat to cryptographic devices in real-world applications. Consequently, devices implement algorithmic countermeasures like masking. In the past, works on the design and verification of masked software implementations have mostly focused on simple microprocessors that find usage on smart cards. However, many other applications such as in the automotive industry require side-channel protected cryptographic computations on much more powerful CPUs. In such situations, the security loss due to complex architectural side-effects, the corresponding performance degradation, as well as discussions of suitable probing models and verification techniques are still vastly unexplored research questions.
MI3: Machine-initiated Intelligent Interaction for Interactive Classification and Data Reconstruction

In many applications, while machine learning (ML) can be used to derive algorithmic models to aid decision processes, it is often difficult to learn a precise model when the number of similar data points is limited. One example of such applications is data reconstruction from historical visualizations, many of which encode precious data, but their numerical records are lost. On the one hand, there is not enough similar data for training an ML model. On the other hand, manual reconstruction of the data is both tedious and arduous. Hence, a desirable approach is to train an ML model dynamically using interactive classification, and hopefully, after some training, the model can complete the data reconstruction tasks with less human interference. For this approach to be effective, the number of annotated data objects used for training the ML model should be as small as possible, while the number of data objects to be reconstructed automatically should be as large as possible. In this article, we present a novel technique for the machine to initiate intelligent interactions to reduce the user’s interaction cost in interactive classification tasks. The technique of machine-initiated intelligent interaction (MI3) builds on a generic framework featuring active sampling and default labeling. To demonstrate the MI3 approach, we use the well-known cholera map visualization by John Snow as an example, as it features three instances of MI3 pipelines. The experiment has confirmed the merits of the MI3 approach.
Who is closer: A computational method for domain gap evaluation

Domain gaps between different datasets limit the generalization ability of CNN models. Precise evaluation on the domain gap has potential to assist the promotion of CNN generalization ability. This paper proposes a computational framework to evaluate gaps between different domains, e.g., judging which one of source domains is closer to the target domain. Our model is based on the observation that, given a well-trained classifier on the source domain, the entropy of its classification scores of the output layer can be used as an indicator of the domain gap. For instance, smaller domain gap generally corresponds to smaller entropy of classification scores. To further boost the discriminative power in distinguishing domain gaps, a novel training strategy is proposed to supervise the model to produce smaller entropy on one source domain and larger entropy on other source domains. This supervision leads to an efficient and discriminative domain gap evaluation model. Extensive experiments on multiple datasets including faces, vehicles, fashions, and persons, etc. show that our method can reasonably measure domain gaps. We further conduct experiments on domain adaptive person ReID task and our method is adopted to pre-trained model selection, pre-trained model fusion, source dataset fusion, and source dataset selection. As shown in the experiments, our method substantially boosts the ReID accuracy. To the best of our knowledge, this is an original work focusing on computational domain gap evaluation. Our code is available at https://github.com/liu-xb/DomainGapEvaluation.
Privacy-Preserving Traceable Attribute-Based Keyword Search in Multi-Authority Medical Cloud

In cloud-based electronic medical record (EMR) systems, attribute-based encryption (ABE) has been utilized to protect the confidentiality of EMRs and provide keyword search over the encrypted EMRs. However, existing schemes are designed for a single attribute authority, and lack sufficient user privacy protection. In this article, we introduce TABKS, a privacy-preserving traceable attribute-based keyword search scheme in multi-authority medical cloud. First, we propose an anonymous EMR access control framework with multiple authorities, which provides user anonymity against the untrusted authorities. Second, we achieve traceable attribute-based Boolean keyword search, which enables the authorized user who satisfies the policy to conduct Boolean keyword search over the encrypted EMRs. In this process, TABKS improves the efficiency of legitimate users by partially decrypting the matched results, and also achieves efficient traitor trace by revealing the user identity from the trapdoor. Finally, we prove the security of TABKS against chosen plaintext attack and chosen keyword attack, and conduct extensive experiments with two real-world datasets to show the feasibility of TABKS.
Temporal Classification Constraint for Improving Handwritten Mathematical Expression Recognition

We present a temporal classification constraint as an auxiliary learning method for improving the recognition of Handwritten Mathematical Expression (HME). Connectionist temporal classification (CTC) is used to learn the temporal alignment of the input feature sequence and corresponding symbol label sequence. The CTC alignment is trained with the encoder-decoder alignment through a combination of CTC loss and encoder-decoder loss to improve the feature learning of the encoder in the encoder-decoder model. We show the effectiveness of the approach in improving symbol classification and expression recognition on the CROHME datasets.
Searching for Two-Stream Models in Multivariate Space for Video Recognition

Conventional video models rely on a single stream to capture the complex spatial-temporal features. Recent work on two-stream video models, such as SlowFast network and AssembleNet, prescribe separate streams to learn complementary features, and achieve stronger performance. However, manually designing both streams as well as the in-between fusion blocks is a daunting task, requiring to explore a tremendously large design space. Such manual exploration is time-consuming and often ends up with suboptimal architectures when computational resources are limited and the exploration is insufficient. In this work, we present a pragmatic neural architecture search approach, which is able to search for two-stream video models in giant spaces efficiently. We design a multivariate search space, including 6 search variables to capture a wide variety of choices in designing two-stream models. Furthermore, we propose a progressive search procedure, by searching for the architecture of individual streams, fusion blocks and attention blocks one after the other. We demonstrate two-stream models with significantly better performance can be automatically discovered in our design space. Our searched two-stream models, namely Auto-TSNet, consistently outperform other models on standard benchmarks. On Kinetics, compared with the SlowFast model, our Auto-TSNet-L model reduces FLOPS by nearly 11× while achieving the same accuracy 78.9%. On Something-Something-V2, Auto- TSNet-M improves the accuracy by at least 2% over other methods which use less than 50 GFLOPS per video.
Multi-Target Regression Using Convolutional Neural Network-Random Forests (CNN-RF) For Early Earthquake Warning System

Indonesia occupies a very active tectonic zone because the world's three large plates and nine other smaller plates meet each other in Indonesian territory and form a complex plate meeting path. East Java Province is part of the Sundanese arc, which has a relatively high level of seismicity and has a complex geological system resulting from the Indo-Australian plate. Therefore, a system that can provide earthquake early warning (EEW) is needed to reduce casualties. In this paper, we determine the epicenter and magnitude of the earthquake using the Multi-Target Regression Convolutional Neural Network-Random Forest (CNN-RF). This model uses Multi-Target Regression Convolutional Neural Network (CNN) as feature extraction and Multi-Target Regression Random Forest (RF) for multi-target regression. Earthquakes in East Java in 2009-2017 are used to train and validate the proposed model. Based on the experiment, the lowest error obtained from the Multi-Target Regression CNN-RF model is 16.3 km for longitude, 36.4 km for latitude, and 0.3095 for magnitude.
A Formally Verified Cut-Elimination Procedure for Linear Nested Sequents for Tense Logic

We port Dawson and Goré’s general framework of deep embeddings of derivability from Isabelle to Coq. By using lists instead of multisets to encode sequents, we enable the encoding of genuinely substructural logics in which some combination of exchange, weakening and contraction are not admissible. We then show how to extend the framework to encode the linear nested sequent calculus \(\mathsf {LNS}_\mathsf {Kt}\) of Goré and Lellmann for the tense logic \(\mathsf {Kt}\) and prove cut-elimination and all required proof-theoretic theorems in Coq, based on their pen-and-paper proofs. Finally, we extract the proof of the cut-elimination theorem to obtain a formally verified Haskell program that produces cut-free derivations from those with cut. We believe it is the first published formally verified computer program for eliminating cuts in any proof calculus.
SparkCruise

Today cloud companies offer fully managed Spark services. This has made it easy to onboard new customers but has also increased the volume of users and their workload sizes. However, both cloud providers and users lack the tools and time to optimize these massive workloads. To solve this problem, we designed SparkCruise that can help understand and optimize workload instances by adding a workload-driven feedback loop to the Spark query optimizer. In this paper, we present our approach to collecting and representing Spark query workloads and use it to improve the overall performance on the workload, all without requiring any access to user data. These methods scale with the number of workloads and apply learned feedback in an online fashion. We explain one specific workload optimization developed for computation reuse. We also share the detailed analysis of production Spark workloads and contrast them with the corresponding analysis of TPC-DS benchmark. To the best of our knowledge, this is the first study to share the analysis of large-scale production Spark SQL workloads.
Explaining inference queries with bayesian optimization

Obtaining an explanation for an SQL query result can enrich the analysis experience, reveal data errors, and provide deeper insight into the data. Inference query explanation seeks to explain unexpected aggregate query results on inference data; such queries are challenging to explain because an explanation may need to be derived from the source, training, or inference data in an ML pipeline. In this paper, we model an objective function as a black-box function and propose BOExplain, a novel framework for explaining inference queries using Bayesian optimization (BO). An explanation is a predicate defining the input tuples that should be removed so that the query result of interest is significantly affected. BO --- a technique for finding the global optimum of a black-box function --- is used to find the best predicate. We develop two new techniques (individual contribution encoding and warm start) to handle categorical variables. We perform experiments showing that the predicates found by BOExplain have a higher degree of explanation compared to those found by the state-of-the-art query explanation engines. We also show that BOExplain is effective at deriving explanations for inference queries from source and training data on a variety of real-world datasets. BOExplain is open-sourced as a Python package at https://github.com/sfu-db/BOExplain.