Reviewerly/exHarmony huggingface dataset:
Machine Learning for Data Management: A System View

Machine learning techniques have been proposed to optimize data management in recent years. Compared with traditional empirical data management, learning-based methods extract knowledge from historical tasks, generalize the extracted knowledge to similar new tasks, and can achieve better performance in many scenarios (e.g., knob tuning, cardinality estimation). However, data management systems require to handle various and dynamic workloads in different scenarios, and there are some challenges in applying machine learning techniques for data management systems. First, with various workloads and hundreds of system metrics, how to select and characterize effective features for data management problems? Second, with diversified machine learning models, how to design the proper models? Third, with various data management requirements, how to validate whether the machine learning models can meet the requirements? In this tutorial, we discuss existing learning-based data management studies and how they solve the above challenges, and provide some future research directions.
A Knowledge Base Question Answering System for Cyber Threat Knowledge Acquisition

Open-source cyber threat intelligence (OSCTI) provides a form of evidence-based knowledge about cyber threats, enabling businesses to gain visibility into the fast-evolving threat landscape. Despite the pressing need for high-fidelity threat knowledge, existing cyber threat knowledge acquisition systems have primarily focused on providing low-level, isolated indicators. These systems have ignored the rich higher-level threat knowledge entities and their relationships presented in OSCTI reports, and do not provide a flexible and intuitive way for threat analysts to acquire the desired knowledge. To bridge the gap, we propose ThreatQA, a system that facilitates cyber threat knowledge acquisition via knowledge base question answering. Particularly, ThreatQA uses a combination of AI-based techniques to (1) automatically harvest comprehensive knowledge about trending threats from massive OSCTI reports from various sources and construct a large threat knowledge base, and (2) intelligently respond to an input natural language threat knowledge acquisition question by fetching the answer from the threat knowledge base via question answering.
XTree: Traversal-Based Partitioning for Extreme-Scale Graph Processing on Supercomputers

Graph algorithms, such as Breadth First Search (BFS), Single Source Shortest Path (SSSP), PageRank (PR), and Connected Components (CC), are increasingly important in big data processing and analytics. As graph scales (numbers of vertices and edges) have increased from billions to trillions, Supercomputers have huge numbers (up to hundreds of thousands) of computing nodes (CNs) that can provide ultra-high aggregate computing power and memory capacity, thus being particularly suitable for processing extreme-scale graphs with trillions of vertices and edges. However, existing cluster-based graph-parallel systems perform poorly when deployed on supercomputers, since their partitioning methods overlook the hierarchical nature of supercomputer networks and incur prohibitive communication storm. This paper presents XTree, an efficient traversal-based partitioning method for minimizing communication overhead of graph processing on supercomputers. We observe that supercomputers' huge numbers of CNs are usually organized into hierarchical communication domains, which can be modeled as a domain tree where communication in lower-level domains is significantly faster than that in higher-level ones. Therefore, the key idea of XTree's partitioning is to exploit hierarchical locality by viewing the graph as a BFS tree and leveraging the topology knowledge to map the graph's BFS tree onto the domain tree, We evaluate the effectiveness of XTree by running various graph algorithms, on both real-world big graphs and synthetic trillion-scale graphs. XTree substantially reduces communication overhead and achieves orders of magnitude speedup against the Graph500 reference implementations with the state-of-the-art 2D-decomposition partitioning.
Learning Graph Convolutional Networks based on Quantum Vertex Information Propagation (Extended Abstract)

This paper proposes a novel Quantum Spatial Graph Convolutional Neural Network (QSGCNN) model that can directly learn a classification function for graphs of arbitrary sizes. The main idea is to define a new quantum-inspired spatial graph convolution associated with pre-transformed fixed-sized aligned grid structures of graphs, in terms of quantum information propagation between grid vertices of each graph. We show that the proposed QSGCNN model can significantly reduce either the information loss or the notorious tottering problem arising in existing spatially-based Graph Convolutional Network (GCN) models. Experiments on benchmark graph datasets demonstrate the effectiveness of the proposed QSGCNN model.
Subgraph Query Generation with Fairness and Diversity Constraints

This paper studies the problem of subgraph query generation with guarantees on both diversity and group fairness. Given a query template (with parameterized search predicates) and a set of node groups in a graph, it is to compute a set of sub-graph queries that instantiate the query template, and each query ensures diversified answers that meanwhile covers each group with a desired number of nodes. Such need is evident in web and social search with fairness constraints, query optimization, and query benchmarking. We formalize a bi-criteria optimization problem that aims to find a Pareto optimal set of query instances in terms of diversity and fairness measures. We show the problem is in Δ <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$P$</tex> 2 and verify its hardness (NP-hard and fixed-parameter tractable). We provide (1) two efficient algorithms that can approximate Pareto optimal sets with E-dominance relations that yield representative query instances with a bounded size, and (2) an online algorithm that progressively generates and maintains fixed-size ∊-Pareto set with small delay time. We experimentally verify that our algorithms can efficiently generate queries with desired diversity and coverage properties for targeted groups.
CARE: Cloud Archival Repository Express via Algorithmic Machine Learning

CARE-Cloud Archive Repository Express has emerged from algorithmic machine learning, and acts like a "fastlane" to bridge between DATA and wi-seCIO where DATA stands for digital archiving & trans-analytics, and wise-CIO for web-based intelligent service.CARE incorporates DATA and wise-CIO into a triad for content management and delivery (CMD) to orchestrate Anything as a Service (XaaS) by using mathematical and computational solutions to cloud-based problems.This article presents algorithmic machine learning in CARE for "DNA-like" ingredients with trivial information eliminated through deep learning to support integral content management over DATA and informative delivery on wiseCIO.In particular with algorithmic machine learning, CARE creatively incorporates express tokens for information interchange (eTokin) to promote seamless intercommunications among the CMD triad that enables Anything as a Service and empowers ordinary users to be UNIQ professionals: such as ubiquitous manager on content management and delivery, novel designer on universal interface and user-centric experience, intelligent expert for business intelligence, and quinary liaison with XaaS without explicitly coding required.Furthermore, CMD triad harnesses rapid prototyping for user interface design and propels cohesive assembly from Anything orchestrated as a Service.More importantly, CARE collaboratively as a whole promotes instant publishing over DATA, efficient presentation to end-users via wiseCIO, and diligent intelligence for business, education, and entertainment (iBEE) through highly robotic process automation.
Private Stochastic Dual Averaging for Decentralized Empirical Risk Minimization

In this work, we study the decentralized empirical risk minimization problem under the constraint of differential privacy (DP). Based on the algorithmic framework of dual averaging, we develop a novel decentralized stochastic optimization algorithm to solve the problem. The proposed algorithm features the following: i) it perturbs the stochastic subgradient evaluated over individual data samples, with which the information about the dataset can be released in a differentially private manner; ii) it employs hyperparameters that are more aggressive than conventional decentralized dual averaging algorithms to speed up convergence. The upper bound for the utility loss of the proposed algorithm is proven to be smaller than that of existing methods to achieve the same level of DP. As a by-product, when removing the perturbation, the non-private version of the proposed algorithm attains the optimal O(1/t) convergence rate for non-smooth stochastic optimization. Finally, experimental results are presented to demonstrate the effectiveness of the algorithm.
A Photonics-Inspired Compact Network: Toward Real-Time AI Processing in Communication Systems

Machine learning methods are ubiquitous in communication systems and have proven powerful for applications including radio-frequency (RF) fingerprinting, automatic modulation classification, and signal recovery in communication systems. However, the high throughput requirement of a communication link makes AI models difficult to implement in real-time on edge devices. In this work, we address this issue by improving both the algorithm and hardware to target real-time AI processing in communication systems. For algorithm development, we propose the first compact deep network consisting of a silicon photonic recurrent neural network model in combination with a simplified convolutional neural network classifier to identify RF emitters by their random transmissions. Our model achieves 96.32% classification accuracy over a set of 30 identical ZigBee devices when using 50 times fewer training parameters than an existing state-of-the-art CNN classifier (Merchant et al., 2018). Thanks to the large reduction in network size, we emulate the system using a small-scale FPGA board, the PYNQ-Z1, and demonstrate real-time RF fingerprinting with 0.219 ms latency. In addition, for hardware implementation, we further demonstrate a fully-integrated silicon photonic neural network for fiber nonlinearity compensation (Huang et al., 2021), which improves the received signal by 0.60 dB.
Adopting Attention and Cross-Layer Features for Fine-Grained Representation

Fine-grained visual classification (FGVC) is challenging task due to discriminative feature representations.The attention-based methods show great potential for FGVC, which neglect that the deeply digging inter-layer feature relations have an impact on refining feature learning.Similarly, the associating cross-layer features methods achieve significant feature enhancement, which lost the long-distance dependencies between elements.However, most of the previous researches neglect that these two methods are mutually correlated to reinforce feature learning, which are independent of each other in related models.Thus, we adopt the respective advantages of the two methods to promote fine-gained feature representations.In this paper, we propose a novel CLNET network, which effectively applies attention mechanism and cross-layer features to obtain feature representations.Specifically, CL-NET consists of 1) adopting selfattention to capture long-rang dependencies for each element, 2) associating cross-layer features to reinforce feature learning,and 3) to cover more feature regions,we integrate attention-based operations between output and input.Experiments verify that CLNET yields new state-of-the-art performance in three widely used fine-grained benchmark datasets, including CUB-200-2011, Stanford Cars and FGVC-Aircraft.The url of our code is https://github.com/dlearing/CLNET.git.
The Growth Assessment of Renewable Energy in Russia: the Retrospective Analysis

Renewable energy is a driver for developing the global energy market, including Russia. Key approaches to assessing the growth prospects of the renewable energy segment in Russia are discussed. The data of the financial statements of Russian generating companies for the period 2015-2020 were analyzed. The purpose of the article is to assess the prospects for renewable energy business compared with traditional energy companies in the Russian Federation based on a retrospective analysis of the financial statements of independent companies in this sector. Conclusions are made about the overestimated forecast estimates of the investment attractiveness of the renewable energy segment in Russia for 2020. Nevertheless, according to independent organizations in the renewable energy sector in Russia, revenue growth is 44% (at current prices) for the period 2015-2020, while for the traditional generation, it is only 6.3%.
Anomaly Detection in Audio With Concept Drift Using Dynamic Huffman Coding

When detecting anomalies in audio, it can often be necessary to consider concept drift: the distribution of the data may drift over time because of dynamically changing environments, and anomalies may become normal as time elapses. We propose to use dynamic Huffman coding for anomaly detection in audio with concept drift. Compared with the existing method of adaptive Gaussian mixture modeling (AGMM), dynamic Huffman coding does not require a priori information about the clusters and can adjust the number of clusters dynamically depending on the amount of variation in the audio. To control the size of the Huffman tree, we propose to merge clusters that are close to each other instead of replacing rare clusters with new data. This reduces redundancy in the Huffman tree while ensuring that it never forgets past information. On audio datasets with concept drift which we have curated ourselves, our proposed method achieves a higher area under the curve (AUC) compared with AGMM and fixed-length Huffman trees.
A Role-Based Encryption (RBE) Scheme for Securing Outsourced Cloud Data in a Multi-Organization Context

Role-Based Encryption (RBE) is an emerging new technique that integrates role based access control (RBAC) model with encryption. RBE embeds RBAC access policies in encrypted data itself so that only users belonging to appropriate roles are able to decrypt and access the data. However, the existing RBE schemes have been focusing on the single-organization cloud storage system, where the stored data can be accessed by users of the same organization. This paper presents a novel RBE scheme with efficient user revocation for the multi-organization cloud storage system, where the data from multiple independent organizations are stored and can be accessed by the authorized users from any other organization. Additionally, an outsourced decryption mechanism is introduced which enables the users to delegate expensive cryptographic operations to the cloud, thereby reducing the overhead on the end-users. Security and performance analyses of the proposed scheme demonstrate that it is provably secure against Chosen Plaintext Attack and can be useful for practical applications due to its low computation overhead.
S-Box Attack Using FPGA Reverse Engineering for Lightweight Cryptography

Since the use of lightweight cryptography for Internet of Things (IoT) security increases, it is necessary to inform significant threats to IoT devices through research on the attacks of lightweight cryptography. This article demonstrates successful attacks on six lightweight cryptographies: DESL, LBlock, TWINE, PRESENT, KLEIN, and LED, after investigating over 50 modern lightweight cryptographies built on SRAM field-programmable gate arrays (FPGAs). We first describe the fundamental procedure of an S-box attack to detect and manipulate S-box within the FPGA bitstream and then carefully customize the S-box attacks for each lightweight cryptography in order to weaken plaintext or key information. For practical analysis, a typical IoT platform based on Cortex-M0 operating at 50 MHz is implemented along with a variety of cryptography algorithms and design options on three Xilinx FPGA chips: Spartan-6, Artix-7, and Kintex Ultrascale. According to experimental results, the proposed attack successfully extracts the full 64-bit plaintext for DESL, LBlock, and TWINE. For KLEIN and LED, the full 64-bit keys are recovered, and for PRESENT, 80% of the 64-bit keys out of the total 80-bit keys are partially retrieved. We emphasize that the purpose of this article is not to provide attackers with a feasible attack strategy, but rather to raise awareness about the possibility of an attacker manipulating the lightweight cryptography on FPGA devices.
Killing me softly: Creative and cognitive aspects of implicitness in abusive language online

Abstract Abusive language is becoming a problematic issue for our society. The spread of messages that reinforce social and cultural intolerance could have dangerous effects in victims’ life. State-of-the-art technologies are often effective on detecting explicit forms of abuse, leaving unidentified the utterances with very weak offensive language but a strong hurtful effect. Scholars have advanced theoretical and qualitative observations on specific indirect forms of abusive language that make it hard to be recognized automatically. In this work, we propose a battery of statistical and computational analyses able to support these considerations, with a focus on creative and cognitive aspects of the implicitness, in texts coming from different sources such as social media and news. We experiment with transformers, multi-task learning technique, and a set of linguistic features to reveal the elements involved in the implicit and explicit manifestations of abuses, providing a solid basis for computational applications.
Interactive optimization of embedding-based text similarity calculations

Comparing text documents is an essential task for a variety of applications within diverse research fields, and several different methods have been developed for this. However, calculating text similarity is an ambiguous and context-dependent task, so many open challenges still exist. In this paper, we present a novel method for text similarity calculations based on the combination of embedding technology and ensemble methods. By using several embeddings, instead of only one, we show that it is possible to achieve higher quality, which in turn is a key factor for developing high-performing applications for text similarity exploitation. We also provide a prototype visual analytics tool which helps the analyst to find optimal performing ensembles and gain insights to the inner workings of the similarity calculations. Furthermore, we discuss the generalizability of our key ideas to fields beyond the scope of text analysis.
An Adaptive Firefly Optimization (AFO) with Multi-Kernel SVM (MKSVM) Classification for Big Data Dimensionality Reduction

The data's dimensionality had already risen sharply in the last several decades. The "Dimensionality Curse" (DC) is a problem for conventional learning techniques when dealing with "Big Data (BD)" with a higher level of dimensionality. A learning model's performance degrades when there is a numerous range of features present. "Dimensionality Reduction (DR)" approaches are used to solve the DC issue, and the field of "Machine Learning (ML)" research is significant in this regard. It is a prominent procedure to use "Feature Selection (FS)" to reduce dimensions. Improved learning effectiveness such as greater classification precision, cheaper processing costs, and improved model comprehensibility are all typical outcomes of this approach that selects an optimal portion of the original features based on some relevant assessment criteria. An "Adaptive Firefly Optimization (AFO)" technique based on the "Map Reduce (MR)" platform is developed in this research. During the initial phase (mapping stage) the whole large "DataSet (DS)" is first subdivided into blocks of contexts. The AFO technique is then used to choose features from its large DS. In the final phase (reduction stage), every one of the fragmentary findings is combined into a single feature vector. Then the "Multi Kernel Support Vector Machine (MKSVM)" classifier is used as classification in this research to classify the data for appropriate class from the optimal features obtained from AFO for DR purposes. We found that the suggested algorithm AFO combined with MKSVM (AFO-MKSVM) scales very well to high-dimensional DSs which outperforms the existing approach "Linear Discriminant Analysis-Support Vector Machine (LDA-SVM)" in terms of performance. The evaluation metrics such as Information-Ratio for Dimension-Reduction, Accuracy, and Recall, indicate that the AFO-MKSVM method established a better outcome than the LDA-SVM method.
Research on global calibration method of large-scene multi-vision sensors in wheel alignment

Abstract Wheel measurement and positioning technology is an important technology in vehicle production, and is of great importance for the safety of vehicles. At present, visual measurement and other methods are widely used in automotive production and inspection, but these methods are limited to the measurement of regular-sized vehicles. There is no effective solution to the problem of wheel alignment for large special-purpose vehicles with multiple tires. In order to solve the wheel positioning problem of large-size special-purpose vehicles, this paper designs a vision measurement system for wheel parameters in large scenes by combining vision sensors with linear motion guides to extend the vision measurement range and complete the global calibration of multiple-vision sensors with the help of laser trackers and self-researched stereo targets. This paper proposes an Elementary–Advance global calibration algorithm to improve the accuracy of coordinate system conversion during global calibration. This paper establishes a correction model for the measurement errors that exist in the initial global calibration process, and iterates the objective function to optimize the coordinate system conversion parameters between multiple vision sensors to improve the measurement accuracy of the system. The experimental results show that the goodness of fit and the accuracy of fit for the feature cylinder are 98.31% and 99.03% based on the global calibration method of this paper. The standard deviation of measurements for the standard ruler of the Inva alloy is less than 0.391 mm in the large dimensional range of 6050 mm × 3500 mm × 800 mm. Finally, by comparing the measurement results with the TrukCam four-wheel alignment instrument, the feasibility of the visual measurement system designed in this paper for large scenarios of wheel parameters is verified, and the measurement accuracy requirements for four-wheel alignment of special-purpose vehicles are met.
Adiabatic control of decoherence-free subspaces in an open collective system

We propose a method to adiabatically control an atomic ensemble using a decoherence-free subspace (DFS) within a dissipative cavity. We can engineer a specific eigenstate of the system's Lindblad jump operators by injecting a field into the cavity which deconstructively interferes with the emission amplitude of the ensemble. In contrast to previous adiabatic DFS proposals, our scheme creates a DFS in the presence of collective decoherence. We therefore have the ability to engineer states that have high multi-particle entanglements which may be exploited for quantum information science or metrology. We further demonstrate a more optimized driving scheme that utilizes the knowledge of possible diabatic evolution gained from the so-called adiabatic criteria. This allows us to evolve to a desired state with exceptionally high fidelity on a time scale that does not depend on the number of atoms in the ensemble. By engineering the DFS eigenstate adiabatically, our method allows for faster state preparation than previous schemes that rely on damping into a desired state solely using dissipation.