Reviewerly/exHarmony huggingface dataset:
A systematic method for measuring the performance of a cyber security operations centre analyst

Analysts who work in a Security Operations Centre (SOC) play an essential role in supporting businesses to protect their computer networks against cyber attacks. To manage analysts efficiently and effectively, SOC managers and stakeholders use Key Performance Indicators (KPIs) to evaluate their performance. However, existing literature suggests a lack of a systematic approach for assessing analysts' performance. Even though cyber security researchers advocate for research into this area, little effort has been made by researchers to address this gap. Drawing on the results of a Delphi panel with industry experts and the principles of the Analytic Hierarchy Process (AHP), this paper interrogates the problem and proposes a systematic weighted approach for measuring the performance of an analyst in a SOC. The proposed method, referred to as a SOC Analyst Assessment Method (SOC-AAM), was evaluated in two SOCs as a part of an experimental case study. The results of the empirical evaluation show that the SOC-AAM enables SOC managers and stakeholders to quantify and assess analysts' performance in a systematic manner. The SOC-AAM also provides a novel guideline for assessing the quality of incident analysis and the quality of incident reports. This study will be of interest to practitioners and cyber security researchers seeking to understand the operations of a SOC analyst.
Self-Adapting Particle Swarm Optimization for continuous black box optimization

This paper introduces a new version of a hyper-heuristic framework: Generalized Self-Adapting Particle Swarm Optimization with samples archive (M-GAPSO). This framework is based on the authors previous works on hybridization of optimization algorithms and enhancing population based optimization with model based optimization. The paper presents the structure of the proposed framework and analyzes the impact of its modules on the final system performance. M-GAPSO hybridizes Particle Swarm Optimization, Differential Evolution and model based optimizers. A ratio of particular algorithms within a population is regulated by an adaptation scheme. The applicability of the proposed hybrid method to black-box optimization is verified on 24 continuous benchmark functions from the COCO test set and 29 functions from the CEC-2017 test set. On the BBOB test set a hybrid of PSO and DE with adaptation obtained 11 significantly better and 2 significantly worse results on 5 and 20 dimensional functions than the basic DE. Further inclusion of the model based optimizers led to 15 significantly better and 2 significantly worse results compared to the PSO-DE hybrid. On the CEC-2017 test set, M-GAPSO was significantly better than both Red Fox Optimization and Dual Opposition-Based Learning for Differential Evolution (DOBL) on 7 functions in 30 dimensions and 12 functions in 50 dimensions. • A hyper-heuristic approach to continuous global optimization problems. • An archive of function samples stored in an R-Tree based indexed. • Seamless hybridization of population-based and model-based optimizers. • A roulette-based algorithm selector. • Restart methods considering locations of discovered optima.
Framework for experimental evaluation of software solutions in a virtual environment

Objectives . Ready-made information technology solutions used when developing software have various characteristics depending on the objectives to be experimentally obtained. While the selection of appropriate technologies and software tools used in experimental software engineering can be time-consuming, experimental complexity can be reduced by providing the researcher with domain-specific tools. The aim of the study is to design and develop a domain-specific software framework for experimental evaluation of the characteristics of information technology solutions in a virtual environment. Methods . To determine the required characteristics of the software framework, an analysis of software tools for conducting experimental studies to evaluate the characteristics of information technology solutions in a virtual environment was conducted. Methods of decomposition, structural design, and software development were applied to design and develop the framework. Results . A software framework for conducting experimental research has been developed. The design results, key features of the framework and a description of the functionality are presented. The implementation of the framework comprises commands for managing virtual machines and commands for scaffolding. A technique for conducting experimental studies using the framework is proposed. Conclusions . The developed domain-specific software framework addresses shortcomings of existing tools to reduce labor costs when conducting experiments to evaluate information technology solutions. The developed framework and proposed methodology allows the number of programming and markup languages required for setting up a software experiment to be reduced from 3 to 1.
Augmented reality during parotid surgery: real-life evaluation of voice control of a head mounted display

Augmented Reality can improve surgical planning and performance in parotid surgery. For easier application we implemented a voice control manual for our augmented reality system. The aim of the study was to evaluate the feasibility of the voice control in real-life situations.We used the HoloLens 1® (Microsoft Corporation) with a special speech recognition software for parotid surgery. The evaluation took place in a audiometry cubicle and during real surgical procedures. Voice commands were used to display various 3D structures of the patient with the HoloLens 1®. Commands had different variations (male/female, 65 dB SPL)/louder, various structures).In silence, 100% of commands were recognized. If the volume of the operation room (OR) background noise exceeds 42 dB, the recognition rate decreases significantly, and it drops below 40% at > 60 dB SPL. With constant speech volume at 65 dB SPL male speakers had a significant better recognition rate than female speakers (p = 0.046). Higher speech volumes can compensate this effect. The recognition rate depends on the type of background noise. Mixed OR noise (52 dB(A)) reduced the detection rate significantly compared to single suction noise at 52 dB(A) (p ≤ 0.00001). The recognition rate was significantly better in the OR than in the audio cubicle (p = 0.00013 both genders, 0.0086 female, and 0.0036 male).The recognition rate of voice commands can be enhanced by increasing the speech volume and by singularizing ambient noises. The detection rate depends on the loudness of the OR noise. Male voices are understood significantly better than female voices.
On the Origin of Venn Diagrams

Abstract In this paper we argue that there were several currents, ideas and problems in 19th-century logic that motivated John Venn to develop his famous logic diagrams. To this end, we first examine the problem of uncertainty or over-specification in syllogistic that became obvious in Euler diagrams. In the 19th century, numerous logicians tried to solve this problem. The most famous was the attempt to introduce dashed circles into Euler diagrams. The solution that John Venn developed for this problem, however, came from a completely different area of logic: instead of orienting to syllogistic like Euler diagrams, Venn applied Boolean algebra to improve visual reasoning. Venn’s contribution to solving the problem of elimination also played an important role. The result of this development is still known today as the ‘Venn Diagram’.
Research on the Uyghur morphological segmentation model with an attention mechanism

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Effects of spatial constraints and ages on children’s upper limb performance in mid-air gesture interaction

Through two controlled experiments, including a pie menu study and a target acquisition study, this paper investigates children’s performance on mid-air gesture interactions in different spatial constraints (i.e., in different orientations/distances), as well as the age effect on such interaction scenarios. The first experiment recorded children’s speeds and accuracies following certain directions under menus with different numbers of items, while the second evaluated the speed–accuracy trade-off (SAT) of children’s arm movements. We also compared performance differences between two age-related groups (i.e., 6–8 years old and 9–12 years old). Based on these experiments, we propose an improved design for UI menus based on mid-air gesture interaction for children. The improved design provides suggestions for setting appropriate directions and difficulty indexes, which makes it much easier and quicker for children to use the menus with mid-air interaction.
A microscopic view of bursts, buffer contention, and loss in data centers

Managing data center networks with low loss requires understanding traffic dynamics at short (millisecond) time-scales, especially the burstiness of traffic, and to what extent bursts contend for switch buffer resources. Yet, monitoring traffic over such intervals is a challenge at scale.
Real-time semantic segmentation on FPGAs for autonomous vehicles with hls4ml

Abstract In this paper, we investigate how field programmable gate arrays can serve as hardware accelerators for real-time semantic segmentation tasks relevant for autonomous driving. Considering compressed versions of the ENet convolutional neural network architecture, we demonstrate a fully-on-chip deployment with a latency of 4.9 ms per image, using less than 30% of the available resources on a Xilinx ZCU102 evaluation board. The latency is reduced to 3 ms per image when increasing the batch size to ten, corresponding to the use case where the autonomous vehicle receives inputs from multiple cameras simultaneously. We show, through aggressive filter reduction and heterogeneous quantization-aware training, and an optimized implementation of convolutional layers, that the power consumption and resource utilization can be significantly reduced while maintaining accuracy on the Cityscapes dataset.
Classification of peripheral blood neutrophils using deep learning

Deep learning has been used to classify the while blood cells in peripheral blood smears. However, the classification of developing neutrophils is rarely studied. Moreover, it is still unknown whether deep learning can work well on the data coming from different sources. In this study, we therefore investigate the classification performance of deep learning for immature and mature neutrophils. In particular, we used three open-access datasets obtained from different imaging systems: CellaVision DM 96, CellaVision DM 100, and iCELL ME-150. A total of 26,050 images identified by one laboratory technologist were randomly split into training, validation, and testing datasets. A total of 10 convolutional neural networks were trained to classify six blood cell types: myeloblast, promyelocyte, myelocyte, metamyelocyte, banded neutrophil, and segmented neutrophil. The experimental results showed that compared to any single model, the average ensemble model could achieve a better classification performance and provide a testing accuracy of 90.1%. The sensitivity and specificity of the average ensemble model for the six blood cell types were above 83.5% and 96.9%, respectively. Our results suggest that deep learning is a promising tool for the classification of developing neutrophils, but further improvement is required.
COVID-19 Cryptocurrency Investment: Wealth Disparities and Portfolio Diversification

The introduction of cryptocurrency and blockchain technology has provided many investors the option to engage in the market, diversify their portfolios, and accumulate wealth. The high return on cryptocurrency during the pandemic has served as an incentive for all ethnic groups to participate in the market. Cryptocurrency is perceived as a hedging instrument for wealth prospects across races during COVID-19. Considering the return on investment, to what extent is blockchain a good hedging instrument for minority investors? Using weekly trade price data from Yahoo Finance, market valuations from coinranking.com, and asset/equity variables from the Federal Reserve Bank, this paper examines investment strategies of different racial/ethnic groups in cryptocurrency during the pandemic in a panel data model from 2019 to 2021. Should investors use public coins such as Bitcoin and Ethereum as part of their investment portfolio mix during the pandemic? We find that an increase in the price of Bitcoin and other cryptocurrencies during the pandemic may repress the investment strategy for marginalized groups.
Using the e-questionnaire in qualitative applied linguistics research

The e-questionnaire (electronic questionnaire) offers remarkable opportunities for qualitative researchers aiming to capture the diversity of ways language learners and teachers experience and interpret language phenomena. Nevertheless, the literature on the use of the e-questionnaire in qualitative research, especially in applied linguistics, is sparse and fragmented. The purpose of this tutorial is therefore to advance the use of the e-questionnaire and to consider design issues and administration strategies. It aims to show that the e-questionnaire constitutes a valuable tool for qualitative applied linguistics research, one that offers many benefits, including broadened access to study participants and their views and experiences, greater efficiency in data collection, and reduced research costs.
Driver gaze tracking and eyes off the road detection

Driver distraction is the main cause of vehicle collisions rather than mechanical faults. This paper proposes a vision-based continuous driver gaze tracking and eyes off the road detection system. The main blocks of the system are Facial points and head pose tracking, Gaze prediction, and Eyes off the road detection. The IR cut filter camera is fitted on the dashboard in line with the steering wheel which continuously records the driver's facial region. This feed is processed to track facial features and estimate head pose. Subsequent deep learning algorithm predicts the gaze region using features like yaw, pitch, roll, number of pixels in the facial area, and eye distance from the top edge. The IR cut filter camera can produce good quality images for both day and night conditions. The system detects the eyes off the road in real-time with 20 fps and 96% accuracy. The system can also perform in diver-independent calibration mode with 85% accuracy.
Fast Differentiable Matrix Square Root and Inverse Square Root

Computing the matrix square root and its inverse in a differentiable manner is important in a variety of computer vision tasks. Previous methods either adopt the Singular Value Decomposition (SVD) to explicitly factorize the matrix or use the Newton-Schulz iteration (NS iteration) to derive the approximate solution. However, both methods are not computationally efficient enough in either the forward pass or the backward pass. In this paper, we propose two more efficient variants to compute the differentiable matrix square root and the inverse square root. For the forward propagation, one method is to use Matrix Taylor Polynomial (MTP), and the other method is to use Matrix Padé Approximants (MPA). The backward gradient is computed by iteratively solving the continuous-time Lyapunov equation using the matrix sign function. A series of numerical tests show that both methods yield considerable speed-up compared with the SVD or the NS iteration. Moreover, we validate the effectiveness of our methods in several real-world applications, including de-correlated batch normalization, second-order vision transformer, global covariance pooling for large-scale and fine-grained recognition, attentive covariance pooling for video recognition, and neural style transfer. The experiments demonstrate that our methods can also achieve competitive and even slightly better performances. Code is available at https://github.com/KingJamesSong/FastDifferentiableMatSqrt.
Support in the Moment: Benefits and use of video-span selection and search for sign-language video comprehension among ASL learners

As they develop comprehension skills, American Sign Language (ASL) learners often view challenging ASL videos, which may contain unfamiliar signs. Current dictionary tools require students to isolate a single sign they do not understand and input a search query, by selecting linguistic properties or by performing the sign into a webcam. Students may struggle with extracting and re-creating an unfamiliar sign, and they must leave the video-watching task to use an external dictionary tool. We investigate a technology that enables users, in the moment, i.e., while they are viewing a video, to select a span of one or more signs that they do not understand, to view dictionary results. We interviewed 14 American Sign Language (ASL) learners about their challenges in understanding ASL video and workarounds for unfamiliar vocabulary. We then conducted a comparative study and an in-depth analysis with 15 ASL learners to investigate the benefits of using video sub-spans for searching, and their interactions with a Wizard-of-Oz prototype during a video-comprehension task. Our findings revealed benefits of our tool in terms of quality of video translation produced and perceived workload to produce translations. Our in-depth analysis also revealed benefits of an integrated search tool and use of span-selection to constrain video play. These findings inform future designers of such systems, computer vision researchers working on the underlying sign matching technologies, and sign language educators.
A novel bio-inspired approach with multi-resolution mapping for the path planning of multi-robot system in complex environments

Abstract For multi-robot systems (MRSs), conventional path planning with single resolution mapping is challenging to balance information and computation. Regarding path planning of MRS, the previous research lacked systematic definition, quantitative evaluation, and the consideration of complex environmental factors. In this paper, a new systematic formulation is proposed to redefine the multi-robot path planning problem in complex environments, and evaluate the related solutions of this problem. To solve this problem, a novel bio-inspired approach based on reaction-diffusion system is given to deal with the path planning of MRS in complex environments, such as electromagnetic interference, ocean currents, and so on. Furthermore, a multi-layer neural dynamic network is proposed to describe environments with multiple resolutions, which can improve time performance while ensuring the integrity of environmental information. Comparative experimental results indicate that the proposed approach shows the excellent path planning performance of MRS in complex environments. The stability of the proposed method is determined by the mathematical basis.
Forensic authentication method for audio recordings generated by Voice Recorder application on Samsung Galaxy Watch4 series

The number of smartwatch users has been rapidly increasing in recent years. A smartwatch is a wearable device that collects various types of data using sensors and provides basic functions, such as healthcare-related measurements and audio recording. In this study, we proposed the forensic authentication method for audio recordings from the Voice Recording application in the Samsung Galaxy Watch4 series. First, a total of 240 audio recordings from each of the four different models, paired with four different smartphones for synchronization via Bluetooth, were collected and verified. To analyze the characteristics of smartwatch audio recordings, we examined the transition of the audio latency, writable audio bandwidth, timestamps, and file structure between those generated in the smartwatches and those edited using the Voice Recording application of the paired smartphones. In addition, the devices with the audio recordings were examined via the Android Debug Bridge (ADB) tool and compared with the timestamps stored in the file system. The experimental results showed that the audio latency, writable audio bandwidth, and file structure of audio recordings generated by smartwatches differed from those generated by smartphones. Additionally, by analyzing the file structure, audio recordings can be classified as unmanipulated, manipulation has been attempted, or manipulated. Finally, we can forensically authenticate the audio recordings generated by the Voice Recorder application in the Samsung Galaxy Watch4 series by accessing the smartwatches and analyzing the timestamps related to the audio recordings in the file system.
Convolutional Neural Networks: A Roundup and Benchmark of Their Pooling Layer Variants

One of the essential layers in most Convolutional Neural Networks (CNNs) is the pooling layer, which is placed right after the convolution layer, effectively downsampling the input and reducing the computational power required. Different pooling methods have been proposed over the years, each with its own advantages and disadvantages, rendering them a better fit for different applications. We introduce a benchmark between many of these methods that highlights an optimal choice for different scenarios depending on each project’s individual needs, whether it is detail retention, performance, or overall computational speed requirements.
Question routing via activity-weighted modularity-enhanced factorization

Abstract Question Routing (QR) in Community-based Question Answering (CQA) websites aims at recommending newly posted questions to potential users who are most likely to provide “accepted answers”. Most of the existing approaches predict users’ expertise based on their past question answering behavior and the content of new questions. However, these approaches suffer from challenges in three aspects: (1) sparsity of users’ past records results in lack of personalized recommendation that at times does not match users’ interest or domain expertise, (2) modeling based on all questions and answers content makes periodic updates computationally expensive, and (3) while CQA sites are highly dynamic, they are mostly considered as static. This paper proposes a novel approach to QR that addresses the above challenges. It is based on dynamic modeling of users’ activity on topic communities. Experimental results on three real-world datasets demonstrate that the proposed model significantly outperforms competitive baseline models.
The MSR-Video to Text dataset with clean annotations

Video captioning automatically generates short descriptions of the video content, usually in form of a single sentence. Many methods have been proposed for solving this task. A large dataset called MSR Video to Text (MSR-VTT) is often used as the benchmark dataset for testing the performance of the methods. However, we found that the human annotations, i.e., the descriptions of video contents in the dataset are quite noisy, e.g., there are many duplicate captions and many captions contain grammatical problems. These problems may pose difficulties to video captioning models for learning underlying patterns. We cleaned the MSR-VTT annotations by removing these problems, then tested several typical video captioning models on the cleaned dataset. Experimental results showed that data cleaning boosted the performances of the models measured by popular quantitative metrics. We recruited subjects to evaluate the results of a model trained on the original and cleaned datasets. The human behavior experiment demonstrated that trained on the cleaned dataset, the model generated captions that were more coherent and more relevant to the contents of the video clips.