Reviewerly/exHarmony huggingface dataset:
Efficient and scalable FPGA design of GF(2m) inversion for post-quantum cryptosystems

Post-quantum cryptosystems based on QC-MDPC codes are designed to mitigate the security threat posed by quantum computers to traditional public-key cryptography.The polynomial inversion is the core operation of key generation in such cryptosystems and the adoption of ephemeral keys imposes the execution of key generation for each session.To this end, there is a need for efficient and scalable hardware implementations of the binary polynomial inversion operation to support the key generation primitive across a wide range of computational platforms.This manuscript proposes an efficient and scalable architecture implementing the binary polynomial inversion at the hardware level.Our solution can deliver a performance-optimized implementation for the large polynomials used in post-quantum code-based cryptosystems and for each FPGA of the mid-range Xilinx Artix-7 family.The effectiveness of the proposed solution was validated by means of the BIKE and LEDAcrypt post-quantum QC-MDPC cryptosystems as representative use cases.Compared to the C11-and the optimized AVX2-based software implementations of LEDAcrypt, instances of the proposed architecture targeting the Artix-7 200 FPGA show an average performance improvement of 31.7 and 2.2 times, respectively.Moreover, the proposed architecture delivers a performance improvement up to 18.1 and 21.5 times for AES-128 and AES-192 security levels, respectively, compared to the BIKE hardware implementation.
From OXALID to GlobaLID: Introducing a modern and FAIR lead isotope database with an interactive application

Abstract Lead isotope provenance studies of archaeological artefacts rely crucially on the availability of reference data from raw material sources. The OXALID database, a collection of Excel sheets with Pb isotope data of ores and artefacts, is still the most cited data source in archaeometry. However, OXALID has not been updated for several years and contains almost exclusively data from Europe, particularly from the Mediterranean area. Digitalisation and open science philosophy currently revolutionise the use of machine‐generated data by developing digital data infrastructures. A modernised Pb isotope reference database, which is tailored for, but not limited to archaeometric research, is hence overdue. Here, we introduce GlobaLID, a FAIR (findable, accessible, interoperable, reusable) and extendable database and a web application with interactive and statistical tools for user‐friendly data handling up to publication quality. GlobaLID aims to be a collection point for worldwide Pb isotope data. It is designed as research infrastructure for Pb isotope studies in (for example) archaeometry, ore deposit and environmental geochemistry. As a community engagement project, GlobaLID invites scientists around the world to become active contributors to enable GlobaLID’s rapid growth. We released GlobaLID as a pilot study to showcase the infrastructure we build and provide a starting point for community discussion.
Smart Data Transfer For Data Monetization

Data transfer is a way to attain the goal of data monetization. While the data is being transferred, securing the records and information of users is the prime concern which needs to be taken care of. There is a strong necessity to find out a new, safe and reliable process in which information of customers should be transferred. This research paper provides a smart and secured method to transfer data from one organization to different organizations for data monetization. It focuses on achieving efficient transfer of data with the permission of the person whose credentials are getting shared, leading to economic growth of both the dealers. It also focuses on how different organizations can use data of a single organization at same time for data monetization without actually accessing the data with the help of the proposed methodology. Proposed methodology is time saving for the different organizations as insights helps to target the relevant people from the same domain.
Reproducible Model Selection Using Bagged Posteriors

Bayesian model selection is premised on the assumption that the data are generated from one of the postulated models. However, in many applications, all of these models are incorrect (that is, there is misspecification). When the models are misspecified, two or more models can provide a nearly equally good fit to the data, in which case Bayesian model selection can be highly unstable, potentially leading to self-contradictory findings. To remedy this instability, we propose to use bagging on the posterior distribution ("BayesBag") - that is, to average the posterior model probabilities over many bootstrapped datasets. We provide theoretical results characterizing the asymptotic behavior of the posterior and the bagged posterior in the (misspecified) model selection setting. We empirically assess the BayesBag approach on synthetic and real-world data in (i) feature selection for linear regression and (ii) phylogenetic tree reconstruction. Our theory and experiments show that, when all models are misspecified, BayesBag (a) provides greater reproducibility and (b) places posterior mass on optimal models more reliably, compared to the usual Bayesian posterior; on the other hand, under correct specification, BayesBag is slightly more conservative than the usual posterior, in the sense that BayesBag posterior probabilities tend to be slightly farther from the extremes of zero and one. Overall, our results demonstrate that BayesBag provides an easy-to-use and widely applicable approach that improves upon Bayesian model selection by making it more stable and reproducible.
Towards High Performance Low Complexity Calibration in Appearance Based Gaze Estimation

Appearance-based gaze estimation from RGB images provides relatively unconstrained gaze tracking. We have previously proposed a gaze decomposition method that decomposes the gaze angle into the sum of a subject-independent gaze estimate from the image and a subject-dependent bias. This paper extends that work with a more complete characterization of the interplay between the complexity of the calibration dataset and estimation accuracy. We analyze the effect of the number of gaze targets, the number of images used per gaze target and the number of head positions in calibration data using a new NISLGaze dataset, which is well suited for analyzing these effects as it includes more diversity in head positions and orientations for each subject than other datasets. A better understanding of these factors enables low complexity high performance calibration. Our results indicate that using only a single gaze target and single head position is sufficient to achieve high quality calibration, outperforming state-of-the-art methods by more than 6.3%. One of the surprising findings is that the same estimator yields the best performance both with and without calibration. To better understand the reasons, we provide a new theoretical analysis that specifies the conditions under which this can be expected.
A sampling-based motion planning method for active visual measurement with an industrial robot

This paper proposes a universal and complete framework for active visual measurement with an industrial robot based on sampling-based motion planning techniques with high efficiency and excellent performance. The proposed path planner is based on a tree-based randomized planning scheme to generate a point-to-point path satisfying the camera's field of view constraints, the occlusion-free and collision-free constraints in the joint space. The speed planner first smoothes the path based on the B-spline curve and then carries out the time-optimal speed planning satisfying the joint velocity, acceleration, jerk constraints, and the camera's velocity constraints. The proposed method in this paper focuses on improving the motion performance of the visual measurement. The satisfaction of the joint jerk constraints and the camera's velocity constraints enables the robot to run smoothly along the generated trajectory, reducing the vibration of the camera fixed on the end effector significantly. Therefore, the pictures taken by the camera at a certain frequency can all be used for the measured workpiece's point cloud reconstruction, and the experiments verified the feasibility and effectiveness of our proposed framework.
Improving the Consistency of the Failure Mode Effect Analysis (FMEA) Documents in Semiconductor Manufacturing

Digitalization of causal domain knowledge is crucial. Especially since the inclusion of causal domain knowledge in the data analysis processes helps to avoid biased results. To extract such knowledge, the Failure Mode Effect Analysis (FMEA) documents represent a valuable data source. Originally, FMEA documents were designed to be exclusively produced and interpreted by human domain experts. As a consequence, these documents often suffer from data consistency issues. This paper argues that due to the transitive perception of the causal relations, discordant and merged information cases are likely to occur. Thus, we propose to improve the consistency of FMEA documents as a step towards more efficient use of causal domain knowledge. In contrast to other work, this paper focuses on the consistency of causal relations expressed in the FMEA documents. To this end, based on an explicit scheme of types of inconsistencies derived from the causal perspective, novel methods to enhance the data quality in FMEA documents are presented. Data quality improvement will significantly improve downstream tasks, such as root cause analysis and automatic process control.
Efficient Deep Learning Methods for Sarcasm Detection of News Headlines

Sarcasm is a combination of irony and mocking used to communicate contempt for someone or something. It can be found in textual form in various places, including reviews from customers about products or services, news stories, and most notably, social networking sites. Sarcastic utterances can conceal the negative connotation with a positive outlook. This deceptive nature of sarcasm has always been an exciting and crucial aspect of sentiment analysis. With the advent of false and manipulative news and the popularity of humorous articles, detecting sarcasm in news articles has turned out to be crucial. In this paper, we evaluated various vectorization and machine learning models to detect sarcastic headlines. Our experiments show that pre-trained transformer-based embeddings combined with Long Short-Term Memory (LSTM) networks provide better recall, accuracy, and F1 score.
Blockchain opportunities for construction industry in Hong Kong: a case study of RISC and site diary

Purpose Blockchain as a disruptive technology has revolutionized many industries, such as finance, logistics and manufacturing sectors. This paper aims to explore Blockchain applications in construction to effectively manage project workflows and uplift construction efficiency. Design/methodology/approach Semi-structured in-depth interviews were conducted with various stakeholders from a public infrastructure project to analyze Blockchain applications in the construction industry. Eleven key construction workflows are selected to identify and discover the necessity and significance of Blockchain applications in the construction industry. They are related to construction program, daily operations and payment. Based on workflow analysis, several issues are discussed to design a development roadmap of Blockchain in the construction industry. Findings The findings show Blockchain can improve construction efficiency, reduce paper-based manual operation and address the fragmentation and communication issue. However, the development of Blockchain in the construction industry is still impeded by some challenges. Traditional work modes might be existing even though Blockchain-enabled systems are introduced, which may increase the workload of construction practitioners. Thus, a feasible and practical development roadmap is proposed to instruct implementation of Blockchain in construction. Originality/value This research systematically analyzes the development of Blockchain in the construction industry from several existing workflows, based on a real-life case study. Several considerations are given to provide referential value for applying Blockchain in the construction industry, including opportunities and challenges, cost–benefit analysis, obstacles and possible solutions, as well as development strategies with a prototype system.
Vu1SPG: Vulnerability detection based on slice property graph representation learning

Vulnerability detection is an important issue in software security. Although various data-driven vulnerability detection methods have been proposed, the task remains challenging since the diversity and complexity of real-world vulnerable code in syntax and semantics make it difficult to extract vulnerable features with regular deep learning models, especially in analyzing a large program. Moreover, the fact that real-world vulnerable codes contain a lot of redundant information unrelated to vulnerabilities will further aggravate the above problem. To mitigate such challenges, we define a novel code representation named Slice Property Graph (SPG), and then propose VulSPG, a new vulnerability detection approach using the improved R-GCN model with triple attention mechanism to identify potential vulnerabilities in SPG. Our approach has at least two advantages over other methods. First, our proposed SPG can reflect the rich semantics and explicit structural information that may be relevance to vulnerabilities, while eliminating as much irrelevant information as possible to reduce the complexity of graph. Second, VulSPG incorporates triple attention mechanism in R-GCNs to achieve more effective learning of vulnerability patterns from SPG. We have extensively evaluated VulSPG on two large-scale datasets with programs from SARD and real-world projects. Experimental results prove the effectiveness and efficiency of VulSPG.
Motion State Recognition and Trajectory Prediction of Hypersonic Glide Vehicle Based on Deep Learning

Hypersonic glide vehicle (HGV) has brought severe challenges to the existing defense system due to its characteristics of high maneuverability, high speed and high precision. Simultaneously, these characteristics also bring great difficulties to trajectory prediction. In this paper, a method for HGV motion state recognition and trajectory prediction based on deep learning is proposed. The proposed method consists of two modules, namely the motion state recognition module and the trajectory prediction module. The motion state recognition module can identify the HGV’s motion state according to state information, and divide it into eight categories. The softmax function is added to the state recognition module to calculate the probability of each motion state. The trajectory prediction module comprises a nonlinear prediction part and a linear prediction part. According to the result of motion state recognition, the appropriate prediction scheme is adopted to better extract the linear and nonlinear characteristics of HGV trajectory, which improves the robustness and prediction accuracy of the proposed method. The experimental results of HGV trajectory prediction show that the proposed method can maintain good stability when the HGV maneuver state changes, and has higher accuracy than the four benchmark methods.
An Adaptive Agent-Specific Sub-Optimal Bounding Approach for Multi-Agent Path Finding

A Multi-Agent Path Finding (MAPF) problem involves multiple agents who want to reach their destinations without obstructing other agents. Although a MAPF problem needs to be solved for many real-world deployments, solving such a problem optimally is NP-hard. Many approaches have been proposed in the literature that offers sub-optimal solutions to this problem. For example, the Enhanced Conflict Based Search (ECBS) algorithm compromises the solution quality up to a constant factor to gain a notable runtime improvement. However, these algorithms use a fixed global sub-optimal bound for all agents, regardless of their preferences. In effect, with the increase in the number of agents, the runtime performance degrades. Against this backdrop, with the intent to further speed up the runtime, we propose an adaptive agent-specific sub-optimal bounding approach, called ASB-ECBS, that can be executed statically or dynamically. Specifically, ASB-ECBS can assign sub-optimal bound considering an individual agent’s requirement. Additionally, we theoretically prove that the solution cost of ASB-ECBS remains within the sub-optimal bound. Finally, our extensive empirical results depict a notable improvement in the runtime by using ASB-ECBS while reducing the search space compared to the state-of-the-art MAPF algorithms.
Forest Fire Surveillance using Live Video Streaming from UAV via Multiple LTE Networks

Forest fires are one of most serious societal problems all over the world. In recent years, many forest fire surveillance systems using unmanned aerial vehicles (UAVs) have been proposed. This paper proposes a system based on live video streaming from a UAV. The system has a low introduction cost, is easy-to-use, and has wide coverage. For a low introduction cost and ease of use, it uses light-weight devices for video streaming so that a small and cheap consumer-oriented UAV can be selected. Also, for wide coverage, the system utilizes two mobile operators at the same time and distributes video data to minimize the expected delay with a proposed multipath video streaming method. The authors conducted a field test in Palangka Raya, Indonesia, and confirmed the effectiveness of the system.
CPFL: An Effective Secure Cognitive Personalized Federated Learning Mechanism for Industry 4.0

While promoting the intelligence in industrial production, Industry 4.0 has also caused privacy leaks concurrently. As a possible solution, the existing personalized federated learning relies too much on a good global model to fine-tune or limit local drift, which lacks intelligent cognitive ability. When faced with heterogeneous data or poisoning attacks, even a few low-quality local models will affect the whole federation effect. In this article, we design a cognitive personalized federated learning (CPFL) mechanism for Industry 4.0, which can selectively improve the collaboration capabilities between more relevant devices. We use the parameters in the local training process as the cognitive basis and calculate Earth mover’s distance to quantify the differences between different models. When the gradient distribution is closer, the local data are more similar. By adaptively adjusting the weight distribution during the aggregation process, self-learning and cooperative learning are balanced, and the interference of heterogeneous data on the federated training process is reduced. Therefore, the global model can better fit most heterogeneous industrial data and achieve personalization. Comparative experimental results show that our proposed CPFL mechanism can increase the average accuracy of personalized models by 5%–10% in non independent and identically distributed situations, and it has certain effects against poisoning attacks and noise interference.
Convolutional neural networks

Convolutional neural networks (CNNs) have been a core element in the recent great advances of deep learning. This chapter introduces the basic concepts related to CNNs. The first part of the chapter focuses on the CNN structure and describes common layer types with the main focus on the convolutional layers. Some of the most famous CNN architectures are also briefly presented. The second part of the chapter focuses on training of CNNs. This part first describes how backpropagation is performed on CNNs and introduces common loss functions and different optimizers. Finally, it discusses typical challenges in CNN training and presents some of the most widely spread solutions to these challenges.
DualGCN: An Aspect-Aware Dual Graph Convolutional Network for review-based recommender

Recently, a variety of review-based recommendation systems that incorporate the valuable information extracted from user-generated textual reviews into user and item modeling have been proposed. However, the existing recommendations normally model reviews at the sentence level. They ignore the modeling of aspect words in reviews, which fails to capture user preferences and item attributes in a fine-grained way. In addition, few studies consider constructing user–item interaction based on review information extracted from the aspect level. In this paper, we are motivated to propose an Aspect-Aware Dual Graph Convolutional Network (DualGCN). Specifically, we first design an Aspect-GCN layer to model the message diffusion of an aspect graph constructed from reviews, capturing the overall description of an aspect in all reviews. We then propose a UI-GCN layer to model a user's fine-grained preferences toward interacted items at the aspect level. Finally, we adopt a Factorization Machine model to accomplish the recommendation task. The experimental results demonstrate that our model significantly outperforms the related approaches w.r.t. the accuracy of both rating prediction and top-K ranking on Amazon and Yelp datasets.
ISP can identify YouTube videos that you just watched

Video streaming service providers are increasingly using end-to-end traffic encryption protocols, such as HTTPS. However, these protocols alone are not sufficient to hide the identification of the videos being watched by the clients on the network from anyone who can sniff the network traffic, such as the network administrator or ISP. In this work, we have explored the YouTube video streams for video identification. We have presented a side-channel attack and a sequential convolutional neural network (SCNN) for the task. The results have shown that even with a series of bytes per second from a two-minute sniffing of network traffic taken as a feature in the SCNN can identify the video with high accuracy without breaking the encryption.
CenGCN: Centralized Convolutional Networks with Vertex Imbalance for Scale-Free Graphs

Graph Convolutional Networks (GCNs) have achieved impressive performance in a wide variety of areas, attracting considerable attention. The core step of GCNs is the information-passing framework that considers all information from neighbors to the central vertex to be equally important. Such equal importance, however, is inadequate for scale-free networks, where hub vertices propagate more dominant information due to vertex imbalance. In this paper, we propose a novel centrality-based framework named CenGCN to address the inequality of information. This framework first quantifies the similarity between hub vertices and their neighbors by label propagation with hub vertices. Based on this similarity and centrality indices, the framework transforms the graph by increasing or decreasing the weights of edges connecting hub vertices and adding self-connections to vertices. In each non-output layer of the GCN, this framework uses a hub attention mechanism to assign new weights to connected non-hub vertices based on their common information with hub vertices. We present two variants CenGCN\_D and CenGCN\_E, based on degree centrality and eigenvector centrality, respectively. We also conduct comprehensive experiments, including vertex classification, link prediction, vertex clustering, and network visualization. The results demonstrate that the two variants significantly outperform state-of-the-art baselines.