Reviewerly/exHarmony huggingface dataset:
Predicting cyber offenders and victims and their offense and damage time from routine chat times and online social network activities

Predicting offenders and victims and the timing of their offenses and damages has become a perennial challenge in global cyberspace. This study aimed to solve this challenge through analyses of routine chat times and online social networks. We sampled more than 550,000 online users over 6 months. We also used unsupervised and supervised machine learning to predict cyber offenders and victims and their offense and damage times, respectively. Our predictors, based on routine chat times and online social network inputs, identified future cyber offenders and victims within 2 months. Furthermore, we predicted the hours and days of their offenses and damages within a week. Extraction of routine chat times and online social networks from chat data can help predict future cyber offenders and victims. Our cyber offense and damage time predictor could help prevent future cyber offenses and damages and promote a safer cyberspace for everyone.
A Fuzzy Logic and Multilevel Analysis-Based Evaluation Algorithm for Digital Teaching Quality in Colleges and Universities

To address the limitations of the university digital teaching quality assessment algorithms as well as the large evaluation mistakes in the existing algorithms, this paper presents a unique university digital teaching quality evaluation method based on multilevel analysis. First, the existing state of digital teaching quality evaluation in colleges and universities is studied to develop an evaluation index for digital teaching quality. Then, to identify and compute the weight of digital teaching quality indicators, an index weight evaluation matrix is built and the weight of digital teaching quality assessment indicators is plotted using a multilevel structure tree model. Then, from the top to the bottom of the tree, this paper computes the hierarchical ranking of assessment indicators. Additionally, this paper computes the membership degree of index evaluation, normalises the evaluation indicators, and completes the digital teaching quality assessment with the digital teaching confidence calculation. The experimental results demonstrate that the proposed method’s digital teaching quality assessment index has a high degree of accuracy and low evaluation error.
Towards Autonomous Industrial Warehouse Inspection

In order to achieve autonomous warehouse inspection, a reliable rack monitoring and instant detection of rack is necessary. Damage detection is an essential task for pallet rack maintenance and it requires large amount of manual efforts. To address this problem, we employ deep learning to automatically detect damages with their per-pixel segmentation mask in this paper. We also investigated the impact of using related and unrelated data on the detection performance. For this purpose, we compared the performance of a detector when it is trained on: (1) the COCO dataset, (2) the ImageNet dataset and (3) a related task such as car damage dataset. Moreover, we evaluated the performance of the proposed detector based on different backbones. Experiments show that the detector with ResNet101 as feature extractor can achieve 93.45% accuracy in our real dataset. The code and dataset can be viewed at: https://gitlab.utu.fi/drone-warehouse/beam_defect_detection.git.
What do (some of) our association measures measure (most)? Association?

Abstract This paper discusses the degree to which some of the most widely-used measures of association in corpus linguistics are not particularly valid in the sense of actually measuring association rather than some amalgam of a lot of frequency and a little association. The paper demonstrates these issues on the basis of hypothetical and actual corpus data and outlines implications of the findings. I then outline how to design an association measure that only measures association and show that its behavior supports the use of the log odds ratio as a true association-only measure but separately from frequency; in addition, this paper sets the stage for an analogous review of dispersion measures in corpus linguistics.
ROFT: Real-Time Optical Flow-Aided 6D Object Pose and Velocity Tracking

6D object pose tracking has been extensively studied in the robotics and computer vision communities. The most promising solutions, leveraging on deep neural networks and/or filtering and optimization, exhibit notable performance on standard benchmarks. However, to our best knowledge, these have not been tested thoroughly against fast object motions. Tracking performance in this scenario degrades significantly, especially for methods that do not achieve real-time performance and introduce non negligible delays. In this work, we introduce ROFT, a Kalman filtering approach for 6D object pose and velocity tracking from a stream of RGB-D images. By leveraging real-time optical flow, ROFT synchronizes delayed outputs of low frame rate Convolutional Neural Networks for instance segmentation and 6D object pose estimation with the RGB-D input stream to achieve fast and precise 6D object pose and velocity tracking. We test our method on a newly introduced photorealistic dataset, Fast-YCB, which comprises fast moving objects from the YCB model set, and on the dataset for object and hand pose estimation HO-3D. Results demonstrate that our approach outperforms state-of-the-art methods for 6D object pose tracking, while also providing 6D object velocity tracking. A video showing the experiments is provided as supplementary material.
Multi-Server Weakly-Private Information Retrieval

Private information retrieval (PIR) protocols ensure that a user can download a file from a database without revealing any information on the identity of the requested file to the servers storing the database. While existing protocols strictly impose that no information is leaked on the file&#x2019;s identity, this work initiates the study of the tradeoffs that can be achieved by relaxing the perfect privacy requirement. We refer to such protocols as weakly-private information retrieval (WPIR) protocols. In particular, for the case of multiple noncolluding replicated servers, we study how the download rate, the upload cost, and the access complexity can be improved when relaxing the perfect privacy constraint. To quantify the information leakage on the requested file&#x2019;s identity we consider mutual information (MI), worst-case information leakage, and maximal leakage (MaxL). We present two WPIR schemes, denoted by Scheme A and Scheme B, based on two recent PIR protocols and show that the download rate of the former can be optimized by solving a convex optimization problem. We also show that Scheme A achieves an improved download rate compared to the recently proposed scheme by Samy <i>et al.</i> under the so-called <inline-formula> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula>-privacy metric. Additionally, a family of schemes based on partitioning is presented. Moreover, we provide an information-theoretic converse bound for the maximum possible download rate for the MI and MaxL privacy metrics under a practical restriction on the alphabet size of queries and answers. For two servers and two files, the bound is tight under the MaxL metric, which settles the WPIR capacity in this particular case. Finally, we compare the performance of the proposed schemes and their gap to the converse bound.
A new three-band spectral and metal element index for estimating soil arsenic content around the mining area

Owing to the advantages of fast and non-destructive measurement, visible and near-infrared reflectance (VNIR) spectra have been widely used in the study of heavy metal pollution. However, few studies have focused on the estimation of soil heavy metal concentration by the enhanced joint architecture of spectral indices and metal elements enriched in clay minerals. In this work, a new composite index, namely Three-band Spectral and Metal Element Index (TSMEI), is proposed to retrieve arsenic (As) in soil by utilizing the multi-view spectral information. Based on obtained data, including spectra and the concentration of iron (Fe), potassium (K), aluminum (Al), magnesium (Mg) and arsenic (As) of the soil around the open-pit coal mine area, the three-band spectral index (TBSI) for As, K, Fe, Mg and Al were calculated from four types of spectral data, that is, raw reflectance (R), the first-order derivative of the spectrum (FD), spectral continuum removal (CR) and spectral reciprocal logarithmic (RL). Then, the metal element index (MEI) for As concentration were constructed via estimated content of K, Fe, Mg and Al based on their TBSIs. Finally, the optimized TBSIs and MEIs were used to construct TSMEI, and it was combined with random forest to invert the As concentration. The following conclusions are drawn: TBSIs is significantly better than that dual-band spectral index and single-band spectrum for estimating As content, and the correlations between the TBSIs based on the FD and the As concentration perform best (r ≥ 0.7684). In addition, two/three element MEIs show higher correlation coefficients with As concentration compared to individual metal element. Furthermore, the proposed TSMEI allow high-precision estimation of As content, which acquired highest correlation coefficient and lowest RMSE (r = 0.9732, RMSE = 0.0703). The results confirm that the TSMEI is significantly effective in estimating soil As content.
Concept drift modeling for robust autonomous vehicle control systems in time-varying traffic environments

Autonomous vehicle systems (AVSs) are widely used to transfer wafers in semiconductor manufacturing. However, in such systems, robust traffic control is a significant challenge because all vehicles must be monitored and controlled in real time to cope with traffic congestion. Several predictive approaches have been proposed to prevent traffic congestion in stationary traffic environments. However, in real-life traffic situations, concept drifts exist, which are characterized by time-varying traffic conditions that hinder the accurate prediction of congestion. In this study, we propose a concept drift modeling framework for a robust vehicle control system. The proposed method combines a drift-adaptation learning technique with a drift detector to achieve adaptive traffic prediction in time-varying AVSs. We compare the effectiveness of the prediction and efficiency of model updates with representative methods. High-fidelity simulations based on actual data confirm that the proposed method outperforms alternative methods by detecting change patterns and updating prediction models whenever significant concept drifts occur in traffic patterns.
The soft-margin Support Vector Machine with ordered weighted average

This paper deals with a cost sensitive extension of the standard Support Vector Machine (SVM) using an ordered weighted sum of the deviations of misclassified individuals with respect to their corresponding supporting hyperplanes. In contrast with previous heuristic approaches, an exact method that applies the ordered weighted average operator in the classical SVM model is proposed. Specifically, when weights are sorted in non-decreasing order, a quadratic continuous formulation is developed. For general weights, a mixed integer quadratic formulation is proposed. In addition, our results prove that nonlinear kernel functions can be also applied to these new models extending its applicability beyond the linear case. Extensive computational results reported in the paper show that the predictive performance provided by the proposed exact solution approaches are better than the ones provided by the classical models (linear and nonlinear kernel) and similar or better than the previous ones provided by the heuristic solution by Maldonado et al. (2018).
Mining frequent weighted utility itemsets in hierarchical quantitative databases

Mining frequent itemsets in traditional databases and quantitative databases (QDBs) has drawn many researchers’ interest. Although many studies have been conducted on this topic, a major limitation of these studies is that they ignore the relationships between items. However, in real-life datasets, items are often related to each other through a generalization/specialization relationship. To consider the relationships and discover a more generalized form of patterns, this study proposes a new concept of mining frequent weighted utility itemsets in hierarchical quantitative databases (HQDBs). In this kind of databases, items are organized in a hierarchy. Using the extended dynamic bit vector structure with large integer elements, two efficient algorithms named MINE_FWUIS and FAST_MINE_FWUIS are developed. The empirical evaluations in terms of processing time between MINE_FWUIS and FAST_MINE_FWUIS are conducted. The experimental results indicate that FAST_MINE_FWUIS is recommended for mining frequent weighted utility itemsets in hierarchical QDBs.
Crouching TIGER, hidden structure: Exploring the nature of linguistic data using TIGER values

Abstract In recent years, techniques such as Bayesian inference of phylogeny have become a standard part of the quantitative linguistic toolkit. While these tools successfully model the tree-like component of a linguistic dataset, real-world datasets generally include a combination of tree-like and nontree-like signals. Alongside developing techniques for modeling nontree-like data, an important requirement for future quantitative work is to build a principled understanding of this structural complexity of linguistic datasets. Some techniques exist for exploring the general structure of a linguistic dataset, such as NeighborNets, δ scores, and Q-residuals; however, these methods are not without limitations or drawbacks. In general, the question of what kinds of historical structure a linguistic dataset can contain and how these might be detected or measured remains critically underexplored from an objective, quantitative perspective. In this article, we propose TIGER values, a metric that estimates the internal consistency of a genetic dataset, as an additional metric for assessing how tree-like a linguistic dataset is. We use TIGER values to explore simulated language data ranging from very tree-like to completely unstructured, and also use them to analyze a cognate-coded basic vocabulary dataset of Uralic languages. As a point of comparison for the TIGER values, we also explore the same data using δ scores, Q-residuals, and NeighborNets. Our results suggest that TIGER values are capable of both ranking tree-like datasets according to their degree of treelikeness, as well as distinguishing datasets with tree-like structure from datasets with a nontree-like structure. Consequently, we argue that TIGER values serve as a useful metric for measuring the historical heterogeneity of datasets. Our results also highlight the complexities in measuring treelikeness from linguistic data, and how the metrics approach this question from different perspectives.
Deep reinforcement learning with credit assignment for combinatorial optimization

Recent advances in Deep Reinforcement Learning (DRL) demonstrates the potential for solving Combinatorial Optimization (CO) problems. DRL shows advantages over traditional methods both on scalability and computation efficiency. However, the DRL problems transformed from CO problems usually have a huge state space, and the main challenge of solving them has changed from high computation complexity to high sample complexity. Credit assignment determines the contribution of each internal decision to the final success or failure, and it has been shown to be effective in reducing the sample complexity of the training process. In this paper, we resort to a model-based reinforcement learning method to assign credits for model-free DRL methods. Since heuristic methods plays an important role on state-of-the-art solutions for CO problems, we propose using a model to represent those heuristic knowledge and derive the credit assignment from the model. This model-based credit assignment can facilitate the model-free DRL to perform a more effective exploration, and the data collected by the model-free DRL refines the model continuously as the training progresses. Extensive experiments on various CO problems with different settings show that our framework outperforms previous state-of-the-art methods on performance and training efficiency.
A YOLO-Based Target Detection Model for Offshore Unmanned Aerial Vehicle Data

Target detection in offshore unmanned aerial vehicle data is still a challenge due to the complex characteristics of targets, such as multi-sizes, alterable orientation, and complex backgrounds. Herein, a YOLO-based detection model (YOLO-D) was proposed for target detection in offshore unmanned aerial vehicle data. Based on the YOLOv3 network, the residual module was improved by establishing dense connections and adding a dual-attention mechanism (CBAM) to enhance the use of features and global information. Then, the loss function of the YOLO-D model was added to the weight coefficients to increase detection accuracy for small-size targets. Finally, the feature pyramid network (FPN) was replaced by the secondary recursive feature pyramid network to reduce the impacts of a complicated environment. Taking the car, boat, and deposit near the coastline as the targets, the proposed YOLO-D model was compared against other models, including the faster R-CNN, SSD, YOLOv3, and YOLOv5, to evaluate its detection performance. The results showed that the evaluation metrics of the YOLO-D model, including precision (Pr), recall (Re), average precision (AP), and the mean of average precision (mAP), had the highest values. The mAP of the YOLO-D model increased by 37.95%, 39.44%, 28.46%, and 5.08% compared to the faster R-CNN, SSD, YOLOv3, and YOLOv5, respectively. The AP of the car, boat, and deposit reached 96.24%, 93.70%, and 96.79% respectively. Moreover, the YOLO-D model had a higher detection accuracy than other models, especially in the detection of small-size targets. Collectively, the proposed YOLO-D model is a suitable model for target detection in offshore unmanned aerial vehicle data.
Blood Coagulation Algorithm: A Novel Bio-Inspired Meta-Heuristic Algorithm for Global Optimization

This paper introduces a novel population-based bio-inspired meta-heuristic optimization algorithm, called Blood Coagulation Algorithm (BCA). BCA derives inspiration from the process of blood coagulation in the human body. The underlying concepts and ideas behind the proposed algorithm are the cooperative behavior of thrombocytes and their intelligent strategy of clot formation. These behaviors are modeled and utilized to underscore intensification and diversification in a given search space. A comparison with various state-of-the-art meta-heuristic algorithms over a test suite of 23 renowned benchmark functions reflects the efficiency of BCA. An extensive investigation is conducted to analyze the performance, convergence behavior and computational complexity of BCA. The comparative study and statistical test analysis demonstrate that BCA offers very competitive and statistically significant results compared to other eminent meta-heuristic algorithms. Experimental results also show the consistent performance of BCA in high dimensional search spaces. Furthermore, we demonstrate the applicability of BCA on real-world applications by solving several real-life engineering problems.
FARNN: FPGA-GPU Hybrid Acceleration Platform for Recurrent Neural Networks

GPU-based platforms provide high computation throughput for large mini-batch deep neural network computations. However, a large batch size may not be ideal for some situations, such as aiming at low latency, training on edge/mobile devices, partial retraining for personalization, and having irregular input sequence lengths. GPU performance suffers from low utilization especially for small-batch recurrent neural network (RNN) applications where sequential computations are required. In this article, we propose a hybrid architecture, called FARNN, which combines a GPU and an FPGA to accelerate RNN computation for small batch sizes. After separating RNN computations into GPU-efficient and GPU-inefficient tasks, we design special FPGA computation units that accelerate the GPU-inefficient RNN tasks. FARNN off-loads the GPU-inefficient tasks to the FPGA. We evaluate FARNN with synthetic RNN layers of various configurations on the Xilinx UltraScale+ FPGA and the NVIDIA P100 GPU in addition to evaluating it with real RNN applications. The evaluation result indicates that FARNN outperforms the P100 GPU platform for RNN training by up to 4.2 <inline-formula><tex-math notation="LaTeX">$\times {}$</tex-math></inline-formula> with small batch sizes, long input sequences, and many RNN cells per layer.
Evaluation of computer-aided diagnosis in breast ultrasonography: Improvement in diagnostic performance of inexperienced radiologists

To evaluate if a computer-aided diagnosis (CAD) system on ultrasound (US) can improve the diagnostic performance of inexperienced radiologists.We collected ultrasound images of 256 breast lesions taken between March and May 2020. We asked two experienced and two inexperienced radiologists to retrospectively review the US features of each breast lesion according to the Breast Imaging Reporting and Data System (BI-RADS) categories. A CAD examination with S-Detect™ software (Samsung Healthcare, Seoul, South Korea) was conducted retrospectively by another uninvolved radiologist blinded to the BIRADS values previously attributed to the lesions. Diagnostic performances of experienced and inexperienced radiologists and CAD were compared and the inter-observer agreement among radiologists was calculated.The diagnostic performance of the experienced group in terms of sensitivity was significantly higher than CAD (p < 0.001). Conversely, the diagnostic performance of inexperienced group in terms of both sensitivity and specificity was significantly lower than CAD (p < 0.001). We obtained an excellent agreement in the evaluation of the lesions among the two expert radiologists (Kappa coefficient: 88.7%), and among the two non-expert radiologists (Kappa coefficient: 84.9%).The US CAD system is a useful additional tool to improve the diagnostic performance of the inexperienced radiologists, eventually reducing the number of unnecessary biopsies. Moreover, it is a valid second opinion in case of experienced radiologists.
Towards Using Blockchain Technology to Prevent Diploma Fraud

After its debut with Bitcoin in 2009, Blockchain has attracted enormous attention and been used in many different applications as a trusted black box.Many applications focus on exploiting the Blockchain-native features (e.g.trust from consensus, and smart contracts) while paying less attention to the application-specific requirements.In this paper, we initiate a systematic study on the applications in the education and training sector, where Blockchain is leveraged to combat diploma fraud.We first present a general system structure for digitized diploma management systems and identify both functional and non-functional requirements.We then show that all existing Blockchain-based systems fall short in meeting these requirements.Inspired by the analysis, we propose a Blockchain-facilitated solution by leveraging some basic cryptographic primitives and data structures.Our analysis shows that the proposed solution respects all the identified requirements by design and can be further extended to enhance its security and privacy guarantees.Finally, we investigate the proposed solution's computational complexity and demonstrate its practicality.