Reviewerly/exHarmony huggingface dataset:
Multi-View Clustering for Open Knowledge Base Canonicalization

Open information extraction (OIE) methods extract plenty of OIE triples <noun phrase, relation phrase, noun phrase> from unstructured text, which compose large open knowledge bases (OKBs). Noun phrases and relation phrases in such OKBs are not canonicalized, which leads to scattered and redundant facts. It is found that two views of knowledge (i.e., a fact view based on the fact triple and a context view based on the fact triple's source context) provide complementary information that is vital to the task of OKB canonicalization, which clusters synonymous noun phrases and relation phrases into the same group and assigns them unique identifiers. However, these two views of knowledge have so far been leveraged in isolation by existing works. In this paper, we propose CMVC, a novel unsupervised framework that leverages these two views of knowledge jointly for canonicalizing OKBs without the need of manually annotated labels. To achieve this goal, we pro- pose a multi-view CH K-Means clustering algorithm to mutually reinforce the clustering of view-specific embeddings learned from each view by considering their different clustering qualities. In order to further enhance the canonicalization performance, we propose a training data optimization strategy in terms of data quantity and data quality respectively in each particular view to refine the learned view-specific embeddings in an iterative manner. Additionally, we propose a Log-Jump algorithm to predict the optimal number of clusters in a data-driven way without requiring any labels. We demonstrate the superiority of our framework through extensive experiments on multiple real-world OKB data sets against state-of-the-art methods.
An Innovative Sensing Machine Learning Technique to Detect Credit Card Frauds in Wireless Communications

There has been an increase in credit card fraud as e-commerce has become more widespread. Financial transactions are essential to our economy, so detecting bank fraud is essential. Experiments on automated and real-time fraud detection are needed here. There are numerous machine learning techniques for identifying credit card fraud, and the most prevalent are support vector machine (SVM), logic regression, and random forest. When models penalise all errors equally during training, the quality of these detection approaches becomes crucial. This paper uses an innovative sensing method to judge the classification algorithm by considering the misclassification cost and at the same time by employing SVM hyperparameter optimization using grid search cross-validation and separating the hyperplane using the theory of reproducing kernels like linear, Gaussian, and polynomial, and the robustness is maintained. Because of this, credit card fraud has been identified significantly more successful than in the past.
Efficient Design Space Exploration for Sparse Mixed Precision Neural Architectures

Pruning and Quantization are two effective Deep Neural Network (DNN) compression methods for efficient inference on various hardware platforms. Pruning refers to removing unimportant weights or nodes, whereas Quantization converts the floating-point parameters to low-bit fixed integer representation. The pruned and low precision models result in smaller and faster inference models on hardware platforms with almost the same accuracy as the unoptimized network. Tensor Cores in Nvidia Ampere 100 (A100) GPU supports (1) 2:4 fine-grained sparse pruning where 2 out of every 4 elements are pruned, and (2) traditional dense multiplication to achieve a good accuracy and performance trade-off. The A100 Tensor Core also takes advantage of 1-bit, 4-bit, and 8-bit multiplication to speed up the inference of a model. Hence, finding the right matrix type (dense or 2:4 sparse) along with the precision for each layer becomes a combinatorial problem. Neural Architecture Search (NAS) can alleviate such problems by automating the architecture design process instead of a brute-force search. In this paper, we propose (i) Mixed Sparse and Precision Search (MSPS), a NAS framework to search for efficient sparse and mixed-precision quantized model within the predefined search space and fixed backbone neural network (Eg. ResNet50), and (ii) Architecture, Sparse and Precision Search (ASPS) to jointly search for kernel size and number of filters, and sparse-precision combination of each layer. We illustrate the effectiveness of our methods targeting A100 Tensor Core on Nvidia GPUs by searching efficient sparse-mixed precision networks on ResNet50 and achieving better accuracy-latency trade-off models compared to the manually designed Uniform Sparse Int8 networks.
On the problem of documentology and its methodology

The author's appeal to this issue is caused by ambiguous assessments of documentology in the scientific publications as the universal document developed by Yu. N. Stolyarov. The scientific and methodological prerequisites for the development of documentology, its hypothesis, the theory of relativity of the document as its methodological basis, as well as the laws of documentology, are analyzed in detail. In his analysis, the author comes to the conclusion that the expressed hypothesis about the division of the concept of a document into the substantive and the functional is based on common ideas on using artificially created objects for the intended or unintended purposes. Therefore, this hypothesis is seen as ascientific. The theoretical ascent is based on the discussion of a number of incorrectly formulated examples taken, as they say, "out of the hat". The scientific argumentation is replaced by manipulative reasoning, starting with the justification of the relevance and practical significance of the study and ending with citing both individual authors and regulatory documents. Reasoning contradicts the rules of for-mal logic. There is a constant confusion of theoretical and empirical reasoning. The provisions on the relative, conditional and conventional status of the document are scientifically incorrect. The proposed laws are not scientific. At the same time, the author believes that his comments reflect the results of following methodological guidelines introduced by Paul Otlet, far from indisputable, and thus the theoretical work done by Yu. N. Stolyarov is of great scientific importance.
A novel algorithm for tracking a maneuvering target in clutter

An algorithm for tracking a maneuvering target under measurement origin uncertainties is derived based on the approximation and propagation of the target state posterior distribution by combining Bayesian decision theory and suitable hypothesis merging procedures. Simulation results show that the main feature of this algorithm is its ability to significantly reduce the estimation error during non-maneuvering periods, making it quite suitable for tracking low maneuvering aircrafts. At the same time, the proposal is able to keep very low levels of track loss rate even for scenarios with high false alarm probability and trajectories with high degree of maneuverability. This proposal presents overall superiority when compared to the Interacting Multiple-Model with Probabilistic Data Filtering (IMMPDAF) solution, with an affordable increase in computational cost.
Modeling craftspeople for cultural heritage: A case study

Abstract Intangible heritage is often linked to human actions and performances. The use of digital humans (DHs) for its digital representation and therefore its preservation, allows reframing the way to transmit and deal with content that is difficult to visualize. To that end, the digital human becomes an important element establishing the connection between the action, the objects, the knowledge, and the environment. In this article, we describe the development of DHs acting as practitioners and storytellers for traditional craft processes within virtual environments. We present the process and the tasks involved in modeling, designing, and animating DHs, detailing the underlying technological background. Animations were completely based on real humans' motion extraction while working on the corresponding craft. As a result, we present the different DHs models created for three specific heritage crafts: mastic cultivation, glass blowing, and silk weaving as well as an AR application, built to augment exhibits of the Chios Mastic Museum. This article is a practical description of the steps to model and animate virtual humans. The work aims to bring a methodology for achieving DHs creation for CH applications.
An approach for semantic interoperability in autonomic distributed intelligent systems

Abstract The complexity and dynamism of distributed intelligent systems have motivated the utilization of autonomic computing in such systems. However, the interoperability of agents in order to attain self‐managing properties is itself a great challenge that requires more attention. Although a number of semantic approaches exist for central autonomic systems, there are not many attempts to develop mechanisms for facilitating semantic interoperability to attain a distributed feedback loop. In this paper, a semantic approach is presented to overcome this challenge. The main component of this approach is AutonoML language that provides the shared semantics for autonomic computing in distributed environments. Besides this language, the method of its application for enhancing the interoperability of the distributed MAPE‐K loop is presented. In order to evaluate this mechanism, three well‐known metrics from the ontological assessment area are utilized, and the results are compared with an existing autonomic ontology that shows greater relational and attributes richness of the proposed semantic structures. Moreover, in order to evaluate the applicability of the mechanism, a case from NASA‐ANTS project is studied, and the results show that the proposed mechanism is capable of facilitating the achievement of more stable self‐adaptive solutions in less time and by exchanging fewer messages.
A Multilevel Collective Framework for Internet of Things Digital Forensic Investigation

Little attention has been paid to how to investigate crimes related to the Internet of Things (IoT). We propose a forensic investigation framework that considers various aspects of IoT devices and evaluate it with 32 users, including investigators, law enforcement officers, and incident responders.
Cross Chain Atomic Swaps in the Absence of Time via Attribute Verifiable Timed Commitments

A Hash Time Lock Contract (HTLC) is a protocol that is commonly used to exchange payments across different blockchains. Using HTLC as a building block for cross blockchain atomic swaps has its drawbacks: The notion of time is handled differently in each blockchain, be it private or public. Additionally, if the swap ends up aborted, the funds are locked in escrow until the safety timeout expires. In this work we formulate a new cryptographic primitive: Attribute Verifiable Timed Commitment which enables to prove that a timed commitment commits to a value which possesses certain attributes. Using our cryptographic primitive, we describe a new cross chain atomic swap protocol that operates without blockchain derived time and unlike the state of the art, all parties can instantly abort the swap without waiting for the safety timeouts to expire. In order to prove in zero knowledge that a secret committed to using a timed commitment has a claimed hash value, we employ the “MPC in the head” technique by Ishai et al. and implement our zero-knowledge proof protocol and evaluate its performance. As part of our techniques, we develop a novel and efficient procedure for integer Lower-Than validation in arithmetic circuits which may be of independent interest.
Exploration on Task Scheduling using Optimization Algorithm in Cloud computing

Cloud computing has become a prevalent technology. With rising demands, the demand for computing resources is also increasing. Therefore there is a necessity to support multiple clients in the same physical infrastructure. With increasing levels of tasks and the dynamic nature of cloud resources, there is a consequential issue of load balancing, task allocation, resource utilization, system performance, etc. The proposed research study has compared different algorithms, which are used to schedule different tasks in the cloud. Task scheduling is an important and challenging issue in Cloud computing. The main objective is to utilize resource efficiency, increase performance, and approximate an optimal solution in a heterogeneous computing environment. The algorithms for scheduling should arrange tasks so that there is a balance between efficiency, resource utilization, and QoS, performance, etc. To resolve this issue, numerous researchers have applied diverse scheduling techniques this paper reviews some current task scheduling and resource utilizing operation in cloud computing framework.
Tensor-based plenoptic image denoising by integrating super-resolution

In this paper, we propose a novel tensor-based denoising method targeting at plenoptic images which contain 4D light field (2D angular + 2D spatial) and 5D hyperspectral light field (2D angular + 2D spatial + 1D spectral). In order to make use of the high-dimension structural property of plenoptic images, we first generalize the intrinsic tensor sparsity measure to plenoptic images by extending the nonlocal similarity from the spatial dimension to the angular dimension. Second, to eliminate the sub-pixel misalignment of different views, we integrate the spatial super-resolution into denoising and exploit the spatial-angular correlation by utilizing the nonlocal similarity of the refined high-resolution central view. In the procedure of super-resolution, we utilize an intensity consistency criterion and a coordinate rationality criterion to facilitate the process of projection. The denoising performance can be boosted after back-projection performed on the refined high-resolution central view. Experimental results validate the superior performance of the proposed method on several plenoptic image datasets in terms of both subjective and objective quality.
A Novel Broad Echo State Network for Time Series Prediction: Cascade of Mapping Nodes and Optimization of Enhancement Layer

Time series prediction is crucial for advanced control and management of complex systems, while the actual data are usually highly nonlinear and nonstationary. A novel broad echo state network is proposed herein for the prediction problem of complex time series data. Firstly, the framework of the broad echo state network with cascade of mapping nodes (CMBESN) is designed by embedding the echo state network units into the broad learning system. Secondly, the number of enhancement layer nodes of the CMBESN is determined by proposing an incremental algorithm. It can obtain the optimal network structure parameters. Meanwhile, an optimization method is proposed based on the nonstationary statistic metrics to determine the enhancement layer. Finally, experiments are conducted both on the simulated and actual datasets. The results show that the proposed CMBESN and its optimization have good prediction capability for nonstationary time series data.
Ephemeral Encoding Message in the Elliptic Curve Cryptography for IoT

Wireless Sensor Network (WSN) is a network composed of constrained devices which are limited by their energetic autonomy, computing power and storage capacity. Nevertheless WSNs have demonstrated novel research and developmental interests for military applications. In order to protect the WSN, lightweight and robust cryptographic protocols are needed. Various cryptographic systems were proposed, and most of them are based on public-key algorithms. Elliptic Curve El Gamal (ECEG) is one of the most promising, since it offers a strong protection with smaller sizes of keys. To implement an ECEG cryptographic system, we need to encode the message before encryption and decode it after decryption. In this paper, we propose a new encoding method that improves the ones proposed in the literature. Experimental tests were made and the performances of the new methods are provided.
Multi-branch feature aggregation based on multiple weighting for speaker verification

Multi-branch feature aggregation has recently been introduced and shows superior performance for speaker verification. It is often implemented via simple operations, such as element-wise addition or concatenation, but this might lead to suboptimal results. In this paper, we propose a novel multi-branch feature aggregation method based on multiple weighting (MBFA-MW), which adaptively learns attention weights for each branch to extract discriminative information that is beneficial to speaker verification. This method contains two weighting strategies, point attention and channel attention. Point attention learns a point-wise weight to emphasize salient local information from the time–frequency domain, and channel attention learns a channel-wise weight to enhance the correlation between the key information and the channel from the frequency domain. Combining the time–frequency domain and the frequency domain, the two strategies complement each other and extract informational features from multiple branch. In addition, we compared different multi-branch feature aggregation methods in the same environment. Experimental results on the datasets of Voxceleb and Cnceleb show the proposed method achieves performance improvements compared to other multi-branch feature aggregation methods and other mainstream methods.
Covert Channels in Network Time Security

Network Time Security (NTS) specified in RFC8915 is a mechanism to provide cryptographic security for clock synchronization using the Network Time Protocol (NTP) as foundation. By using Transport Layer Security (TLS) and Authenticated Encryption with Associated Data (AEAD) NTS is able to ensure integrity and authenticity between server and clients synchronizing time. However, in the past it was shown that time synchronisation protocols such as the Network Time Protocol (NTP) and the Precision Time Protocol (PTP) might be leveraged as carrier for covert channels, potentially infiltrating or exfiltrating information or to be used as Command-and-Control channels in case of malware infections. By systematically analyzing the NTS specification, we identified 12 potential covert channels, which we describe and discuss in this paper. From the 12 channels, we exemplary selected an client-side approach for a proof-of-concept implementation using NTS random UIDs. Further, we analyze and investigate potential countermeasures and propose a design for an active warden capable of mitigating the covert channels described in this paper.
DaisyRec 2.0: Benchmarking Recommendation for Rigorous Evaluation

Recently, one critical issue looms large in the field of recommender systems - there are no effective benchmarks for rigorous evaluation - which consequently leads to unreproducible evaluation and unfair comparison. We, therefore, conduct studies from the perspectives of practical theory and experiments, aiming at benchmarking recommendation for rigorous evaluation. Regarding the theoretical study, a series of hyper-factors affecting recommendation performance throughout the whole evaluation chain are systematically summarized and analyzed via an exhaustive review on 141 papers published at eight top-tier conferences within 2017-2020. We then classify them into model-independent and model-dependent hyper-factors, and different modes of rigorous evaluation are defined and discussed in-depth accordingly. For the experimental study, we release DaisyRec 2.0 library by integrating these hyper-factors to perform rigorous evaluation, whereby a holistic empirical study is conducted to unveil the impacts of different hyper-factors on recommendation performance. Supported by the theoretical and experimental studies, we finally create benchmarks for rigorous evaluation by proposing standardized procedures and providing performance of ten state-of-the-arts across six evaluation metrics on six datasets as a reference for later study. Overall, our work sheds light on the issues in recommendation evaluation, provides potential solutions for rigorous evaluation, and lays foundation for further investigation.
Hare

Distributed machine learning (DML) has shown great promise in accelerating model training on multiple GPUs. To increase GPU utilization, a common practice is to let multiple learning jobs share GPU clusters, where the most fundamental and critical challenge is how to efficiently schedule these jobs on GPUs. However, existing works about DML job scheduling are constrained to settings with homogeneous GPUs. GPU heterogeneity is common in practice, but its influence on multiple DML job scheduling has been seldom studied. Moreover, DML jobs have internal structures that contain great parallelism potentials, which have not yet been fully exploited in the heterogeneous computing environment. In this paper, we propose Hare, a DML job scheduler that exploits both inter-job and intra-job parallelism in a heterogeneous GPU cluster. Hare has three novel designs. First, Hare optimizes GPU execution environment to reduce task switching overhead by exploiting unique features of DML scheduling. Second, Hare adopts a relaxed fixed-scale synchronization scheme that allows independent tasks to be flexibly scheduled within a training round. Finally, we propose a fast heuristic algorithm to minimize the total weighted job completion time by jointly considering job features and hardware heterogeneity. Its theoretical bound is derived. We evaluate Hare using a small-scale testbed and a trace-driven simulator. The results show that it can outperform the state-of-the-art by about 2x.
BlindSpot: Watermarking Through Fairness

With the increasing development of machine learning models in daily businesses, a strong need for intellectual property protection arised. For this purpose, current works suggest to leverage backdoor techniques to embed a watermark into the model, by overfitting to a set of particularly crafted and secret input-output pairs called triggers. By sending verification queries containing triggers, the model owner can analyse the behavior of any suspect model on the queries to claim its ownership. However, when it comes to scenarios where frequent monitoring is needed, the computational overhead of these verification queries in terms of volume demonstrates that backdoor-based watermarking appears to be too sensitive to outlier detection attacks and cannot guarantee the secrecy of the triggers.
Underwater image restoration using level adjustment and colour equalization

Due to the light absorption and scattering by water, the underwater image quality is often severely degraded. To solve this problem, we propose an underwater image restoration method that uses level adjustment and colour equalization algorithms to obtain high-quality underwater images. A human visual perception-based underwater imaging model is first built to generate images with high contrast and rich colours. Then, a level adjustment algorithm is developed to improve the brightness of the image. Finally, a colour equalization algorithm is designed to filter the local characteristics and achieve image brightness, colour, and contrast adjustment with nonlinear and filtered local characteristics. Extensive numerical and experimental results show that the proposed method can improve the underwater images and is superior to the recently reported methods.