Reviewerly/exHarmony huggingface dataset:
Why Do National Cybersecurity Awareness Programmes Often Fail?

Nowadays, many cyber users do not understand how to protect themselves and their information within cyber space. One reason is that cyber users are unaware of possible cyber risks and threats that may occur within cyber space. The second reason is that citizens, businesses and users within the public sector may be aware of relevant cyber risks but do not really understand the seriousness of such risks and the consequences if they do realise. Therefore, cybersecurity awareness campaigns are an integral part of improving cybersecurity awareness. Based on in-country reviews conducted as part of the Global Cybersecurity Capacity Centre (GCSCC) programme, we observed that the campaigns to raise cybersecurity awareness throughout the country are often led by different ‘owners’ without co-ordination and adequate resources therefore creating fragmentation in the national cybersecurity awareness raising programme. This paper suggests that the development of a coordinated and coherent national cybersecurity awareness program is critical for building a basic level of aware-ness at the national level. We will examine the requirements needed to develop a coordinated national awareness raising programme by reviewing the existing literature, best practice approaches and the role of different stakeholders such as the government, private sector and civil society. We will draw conclusions on the main obstacles to ensure overall coherence between the actions of stakeholders and the efforts countries should prioritise in order to increase awareness of cyber risks at the national level.
A method for specific emitter identification based on surrounding-line bispectrum and convolutional neural network

Specific emitter identification (SEI) is an association of radar signal to specific emitter primarily. SEI has been widely used in military and civilian spectrum management applications. We propose a SEI method based on deep learning (convolutional neural network), which uses the characteristics of the received steady-state signal. Particularly, we calculate the bispectrum of the signal as the unique feature. Then, we use surrounding-line bispectrum to reduce the influence of noise. Finally, CNN is used to identify specific emitters by using the surrounding-line bispectrum. This method basically extracts the overall feature information hidden in the original signal. This can be used to improve recognition performance. The simulation results verify our conclusion that the proposed method is better than other existing solutions in the literature.
Visualizing differential principal turnover

Purpose The authors demonstrate the usage of data visualization for conveying educational administration research, with a specific focus on differential principal turnover. They model when and how principals move, over time, between six categories of turnover. Design/methodology/approach The authors construct several easy-to-interpret alluvial diagrams that reveal the patterns of differential turnover among 1,113 first-time Texas principals. Furthermore, the authors investigate how these patterns differ across educator characteristics (i.e. race and sex) and school contexts (i.e. school level and campus urbanicity). Findings Half of all first-time principals turn over within two years. Most principals who stay in leadership roles leave the district where they were first entered the principalship. Men are promoted more and women turn over less. In a connected finding, the authors conclude that elementary principals turn over less, and middle and high school principals are promoted more often. Principals of color are demoted more often than White principals. Urban school principals exit the system at a greater rate than rural principals. Originality/value The significance of this study lies in its direct response to two problems facing the administrator turnover knowledge base – a lack of methodological accessibility and the underutilization of data visualization. The authors’ is the first study to contain visualization of differential turnover outcomes over time. Second, the authors’ study provides a blueprint for data visualization that not only creates new knowledge but also speaks to a wider variety of education stakeholders by presenting complex data in a visual format.
Encodable: Configurable Grammar for Visualization Components

There are so many Libraries of visualization components nowadays with their APIs often different from one another. Could these components be more similar, both in terms of the APIs and common functionalities? For someone who is developing a new visualization component, how should the API look like? This work drew inspiration from visualization grammar, decoupled the grammar from its rendering engine and adapted it into a configurable grammar for individual components called Encodable. Encodable helps component authors define grammar for their components, and parse encoding specifications from users into utility functions for the implementation. This paper explains the grammar design and demonstrates how to build components with it.
Sentiment analysis of agricultural product ecommerce review data based on deep learning

The relevant comment texts of agricultural products sold on the e-commerce platform contain various and complex emotions of consumers, and reflect customers' consumer experience and views on those products. Compared with the features and advantages of English text segmentation structure, the analysis of Chinese sentiment polarity in a complex context has always been a problem in deep learning. This article explores the issue of sentiment polarity analysis of short texts of Chinese reviews of agricultural products by constructing Text-CNN, Bi-LSTM and BERT to fine-tune three deep learning models. The research object adopted is the review data of agricultural products on the e-commerce platform obtained based on crawler technology. The experimental results show that, compared with the Bi-LSTM and BERT fine-tuning models, the Text-CNN model's accuracy, precision, recall, and F1 value are all about 3 to 8 percentage points higher. The classification effect gradient gap between the three models is obvious. The accuracy of the Text-CNN convolutional neural network reached 99.92%. The results confirmed that Text-CNN has high sentiment analysis accuracy and good sentiment polarity classification effect in processing agricultural short text comment data, as an agricultural product e-commerce The emotional classification model of sales review data can provide an important reference for manufacturers to accurately distinguish user feedback and enhance market competitiveness.
Research on Global Path Planning of Artificial Intelligence Robot Based on Improved Ant Colony Algorithm

Abstract With the rapid development of science and technology and the improvement of people’s living standards and enjoyment consciousness, robots have come into people’s field of vision more and more. People’s use of robots in production and life has greatly increased. As an important function of the normal use of the robot and evaluating the quality level of the robot, the careful design and optimization of the path planning is the most important. This study discusses the basic situation of robot global path planning, the application, advantages and improvement measures of ant colony algorithm in robot path planning, which provides help for the further development and popularization of robot.
Training Users to Identify Phishing Emails

Phishing emails pose a serious threat to individuals and organizations. Users’ ability to identify phishing emails is critical to avoid becoming victims of these attacks. The current study examined the effectiveness of a short online phishing training program designed to help users identify phishing emails. Half of the participants were in the training group and the other half worked on a control filler task. The training group’s sensitivity ( d′) at correctly classifying emails as legitimate or phishing increased by 1.14 whereas the control group’s sensitivity increased by only 0.48. This difference in d' changes was significant, t(38) = 2.05, p = .048. This improvement in performance was likely due to users learning how to check reliable cues and interpret them. Despite a sizeable improvement in detecting phishing emails, the training group correctly classified only about two-thirds of phishing emails. Accordingly, a short training program appears beneficial, but a more comprehensive training program would be needed to reduce vulnerability to an acceptable level.
Accuracy Improvement in Binary and Multi-Class Classification of Breast Histopathology Images

Breast cancer is widely common in women. In order to recognize this type of cancer, experienced pathologists must evaluate cell shapes in breast histopathology images in different magnification levels. However, there are few pathologists per population in many countries, and there is always a possibility of human error. This study represents a deep transfer learning-based model that can improve some of the current state-of-the-art results in both binary and multi-class classification. The presented model has been examined in magnification-dependent and magnification-independent modes. As for multi-class classification, the proposed system grants 94-97% accuracy. In binary classification, the proposed system provides up to 99% accuracy. The results show higher accuracies than past studies in the classification of breast histological images in some cases with the same algorithm and setting for all the measurements.
Polygonal Perception for Mobile Robots

Geometric primitives are a compact and versatile representation of the environment and the objects within. From a motion planning perspective, the geometric structure can be leveraged in order to implement potentially faster and smoother motion control algorithms than it has been possible with grid-based occupancy maps so far. In this paper, we introduce a novel perception pipeline that efficiently processes the point cloud obtained from an RGB-D sensor in order to produce a floor-projected 2D map in the field-of-view of the robot where obstacles are represented as polygons rather than cells. These polygons can then be processed by path planning algorithms and obstacle avoidance controllers. Our pipeline includes a ground floor plane detector that performs significantly faster than other contemporary solutions and a grid segmentation algorithm that uses image processing techniques to identify the contours of obstacles in order to convert them to polygons. We demonstrate the performance of our approach in experiments with a wheeled and a humanoid robot and show that our polygonal perception pipeline works robustly even in the presence of the disturbances caused by the shaking of a walking robot.
Monocular Instance Motion Segmentation for Autonomous Driving: KITTI InstanceMotSeg Dataset and Multi-task Baseline

Moving object segmentation is a crucial task for autonomous vehicles as it can be used to segment objects in a class agnostic manner based on their motion cues. It enables the detection of unseen objects during training (e.g., moose or a construction truck) based on their motion and independent of their appearance. Although pixel-wise motion segmentation has been studied in autonomous driving literature, it has been rarely addressed at the instance level, which would help separate connected segments of moving objects leading to better trajectory planning. As the main issue is the lack of large public datasets, we create a new InstanceMotSeg dataset comprising of 12.9K samples improving upon our KITTIMoSeg dataset. In addition to providing instance level annotations, we have added 4 additional classes which is crucial for studying class agnostic motion segmentation. We adapt YOLACT and implement a motion-based class agnostic instance segmentation model which would act as a baseline for the dataset. We also extend it to an efficient multi-task model which additionally provides semantic instance segmentation sharing the encoder. The model then learns separate prototype coefficients within the class agnostic and semantic heads providing two independent paths of object detection for redundant safety. To obtain real-time performance, we study different efficient encoders and obtain 39 fps on a Titan Xp GPU using MobileNetV2 with an improvement of 10% mAP relative to the baseline. Our model improves the previous state of the art motion segmentation method by 3.3%. The dataset and qualitative results video are shared in our website at this https URL.
Multi-camera, multi-person, and real-time fall detection using long short term memory

Approximately 37 million falls occur each year worldwide requiring medical attention. Victims are often helpless and not able to call for help, which is a risk for elderly persons living alone. To detect falls at home, several approaches have been proposed. Video cameras are used increasingly. Recently, high accuracy in real-time human pose estimation in videos has been achieved by novel machine learning techniques. In this work, we propose a multi-camera system for video-based fall detection. We augment human pose estimation (OpenPifPaf algorithm) by support for multi-camera and multi-person tracking and a long short-term memory (LSTM) neural network to predict two classes: “Fall” or “No Fall”. From the poses, we extract five temporal and spatial features which are processed by the LSTM. For evaluation of identification and tracking with multiple cameras, we used videos recorded in a smart home (living lab) with two persons walking and interacting. For evaluation of fall detection, we used the UP-Fall Detection dataset and achieve an F1 score of 92.5%. We observed a tendency towards false positive classifications due to lack of activities in publicly available datasets that look similar to falls but stem from normal activity. Moreover, the lack of variation in the activities also results in a higher amount of false positives. This requires the acquisition of more balanced datasets in future work. In conclusion, real-time fall detection from multiple camera inputs and for multiple persons is feasible using a LSTM neural network combined with features obtained via human pose estimation. Source code is available at https://github.com/taufeeque9/HumanFallDetection.
A computational construction grammar approach to semantic frame extraction

Abstract This paper introduces a novel methodology for extracting semantic frames from text corpora. Building on recent advances in computational construction grammar, the method captures expert knowledge of how semantic frames can be expressed in the form of conventionalised form-meaning pairings, called constructions. By combining these constructions in a semantic parsing process, the frame-semantic structure of a sentence is retrieved through the intermediary of its morpho-syntactic structure. The main advantage of this approach is that state-of-the-art results are achieved, without the need for annotated training data. We demonstrate the method in a case study where causation frames are extracted from English newspaper articles, and compare it to a commonly used approach based on Conditional Random Fields (CRFs). The computational construction grammar approach yields a word-level F 1 score of 78.5%, outperforming the CRF approach by 4.5 percentage points.
An Overview of Real-Time Traffic Sign Detection and Classification

Traffic Sign Recognition systems (TSR) are important components of advanced driver assistance systems (ADAS), they are specifically designed to work in a real-time environment to increase driver safety by notifying the driver of the various traffic signs such as speed limits, priorities, restrictions, etc.. Nonetheless, in real world applications, these systems face a range of external challenges such as bad weather, broken signs, insufficient conditions of lighting (night-time), etc. However, the real-time challenge is still the biggest and most critical challenge facing ADAS, as it occurs in all challenges and increases their complexity, so all researchers in this field should give it the highest priority. In this paper, we will draw an overview of some recent and efficient real-time methods for the detection and classification of traffic signs.
Development of a Model and Algorithms for Servicing Traffic in a Cloud Computing System

There is a serious variance in the requirements for the provided resource in cloud systems. Also it's needed to quickly process incoming requests and maintain the proper level of quality of service. All these factors cause difficulties for cloud providers. The proposed analytical model is designed to process requests for a cloud computing system in the Processor Sharing (PS) service mode. It allows us to solve mentioned problems. In this work, the flow of service requests is described by the Poisson model which is a special case of the Engset model. The proposed model and the results of its analysis can be used to evaluate the main characteristics of cloud systems performance.
Combining Syntax Features and Word Embeddings in Bidirectional LSTM for Vietnamese Named Entity Recognition

Named Entity Recognition (NER) is the task to classify words into predefined target entity classes. This task is now considered a core of many natural language processing tasks such as document classification, machine translation, search engine, information extraction,… Distributed representation of words (word embeddings) is an important technique in current natural language processing. Word embeddings is a good feature but if we combine both word embeddings and syntax features can bring better effect. This paper proposes a new approach by combining automatic syntactic features with pre-trained word embedding in Bidirectional Long Short Term Memory (BiLSTM) deep learning method for Vietnamese Named Entity Recognition. The proposed system has achieved good results for the Vietnamese NER problem on the VLSP 2016 dataset.
Runtime Complexity Analysis of Logically Constrained Rewriting

Logically constrained rewrite systems (LCTRSs) are a versatile and efficient rewriting formalism that can be used to model programs from various programming paradigms, as well as simplification systems in compilers and SMT solvers. In this paper, we investigate techniques to analyse the worst-case runtime complexity of LCTRSs. For that, we exploit synergies between previously developed decomposition techniques for standard term rewriting by Avanzini et al. in conjunction with alternating time and size bound approximations for integer programs by Brockschmidt et al. and adapt these techniques suitably to LCTRSs. Furthermore, we provide novel modularization techniques to exploit loop bounds from recurrence equations which yield sublinear bounds. We have implemented the method in to test the viability of our method.
Discriminative Dictionary Design for Action Classification in Still Images and Videos

In this paper, we address the problem of action recognition from still images and videos. Traditional local features such as SIFT and STIP invariably pose two potential problems: 1) they are not evenly distributed in different entities of a given category and 2) many of such features are not exclusive of the visual concept the entities represent. In order to generate a dictionary taking the aforementioned issues into account, we propose a novel discriminative method for identifying robust and category specific local features which maximize the class separability to a greater extent. Specifically, we pose the selection of potent local descriptors as filtering-based feature selection problem, which ranks the local features per category based on a novel measure of distinctiveness. The underlying visual entities are subsequently represented based on the learned dictionary, and this stage is followed by action classification using the random forest model followed by label propagation refinement. The framework is validated on the action recognition datasets based on still images (Stanford-40) as well as videos (UCF-50). We get 51.2% and 66.7% recognition accuracy for Standford-40 and UCF-50, respectively. Compared to other representative methods from the literature, our approach exhibits superior performances. This proves the effectiveness of adaptive ranking methodology presented in this work.
Adversarial Training for Multi-domain Speaker Recognition

In real-life applications, the performance of speaker recognition systems always degrades when there is a mismatch between training and evaluation data. Many domain adaptation methods have been successfully used for eliminating the domain mismatches in speaker recognition. However, usually both training and evaluation data themselves can be composed of several subsets. These inner variances of each dataset can also be considered as different domains. Different distributed subsets in source or target domain dataset can also cause multi-domain mismatches, which are influential to speaker recognition performance. In this study, we propose to use adversarial training for multi-domain speaker recognition to solve the domain mismatch and the dataset variance problems. By adopting the proposed method, we are able to obtain both multi-domain-invariant and speaker-discriminative speech representations for speaker recognition. Experimental results on DAC13 dataset indicate that the proposed method is not only effective to solve the multi-domain mismatch problem, but also outperforms the compared unsupervised domain adaptation methods.
Calibration and uncertainty analysis of a combined tracking-based vision measurement system using Monte Carlo simulation

Abstract A global stereovision system combined with a local vision sensor is an effective approach to large-scale object measurement. However, obtaining the error distribution of such an approach remains a key research challenge in vision metrological applications. This paper investigates the calibration and the reconstruction uncertainty estimation method of the combined vision system. The measurement principle and the calibration method of the transformation matrix between the tracking-based measurement coordinate systems are presented. Furthermore, Monte Carlo simulation is utilized to determine the reconstruction uncertainty based on the theoretical measurement model and the experiment-based input uncertainty. The overall measurement uncertainty of the combined system is found to be 34.5% higher than that of the global vision system, which is more sensitive to the input pixel uncertainty than the local vision system. However, the combined vision system can achieve comparable measurement results within its larger working volume. This work contributes to a better understanding of the measurement uncertainty in combined tracking-based vision systems, as well as providing a few useful practice guidelines for using such a visual system.
Quantum firmware and the quantum computing stack

Integrated quantum-control protocols could bridge the gap between abstract algorithms and the physical manipulation of imperfect hardware.