Reviewerly/exHarmony huggingface dataset:
Can non‐developers learn a simplified modeling notation quickly?

Abstract The User Interface Transition Diagram (UITD) is a simplified modeling notation to describe the transitions between the UIs of a software system. It is designed to be used for customer‐developer s improving the models. This study investigates (i) which concepts of the UITD language are easy to understand and which ones require more effort to be understood and (ii) to what extent UITD comprehensibility depends on the user's programming skill level. A questionnaire was applied to a group of 334 non‐developers after they received a very brief explanation of the UITD concepts. The results were analyzed using statistical methods. The overall average score of the group was 70.42%, which is quite acceptable considering the briefness of the explanation. We observed a very low correlation between the subjects' programming skill level and their scores. This suggests that, even for people who have little or no programming skills, the entirety of the UITD symbols and concepts should quickly be understood with little further training.
Digital displacement of youth offending: addressing the issue

Purpose Global evidence suggests a potential displacement of youth offending from the physical to the digital landscape, requiring revision of existing detection and intervention methods. This study aims to explore pathways from harmful to illegal online activity perpetrated by young people, legislation and police perspectives, current detection methods and interventions. Design/methodology/approach This perspective paper examines issues observed within a larger systematic literature review on digital youth offending. Findings A trajectory from acceptable to harmful and subsequently illegal behaviour was identified, with a particular pathway from unethical video game activity to digitally dependent offending. Legislation and police perspectives vary by jurisdiction, with a common theme that increased officer education is key to the level of preparedness to investigate cases. Machine learning and automatic prevention show promise as detection and disruption processes, with education recommended for young people as a deterrent and redirection of skills to positive outcomes. Research limitations/implications Recommendations for further research include a broad survey of school students to include all identified areas of digital offending, which could drive the development of targeted education by law enforcement and partner agencies for young people. Practical implications The shift in youth offending requires the justice and educational systems to adjust how they respond to youth crime. Policy and practise shifts can include further exploration of investigative hacking, education for law enforcement and educational prevention and redirection programmes aimed at youth. Originality/value The digital displacement of youth offending is a progressively emerging concept. This paper examines the current state of response from educational and law enforcement agencies and discusses the next steps based on what is currently known.
Multimodal Remote Sensing Image Registration Algorithm Based on a New Edge Descriptor

Image registration of multimodal remote sensing images plays a vital role in remote sensing image analysis. However, there are significant nonlinear intensity differences between multimodal remote sensing image pairs, making it difficult for most traditional image registration algorithms to meet the registration requirements. In this paper, we propose a novel edge descriptor utilizing edge information, which has not only affine invariance but is also insensitive to nonlinear intensity differences. Moreover, we utilize the proposed descriptor to design a multimodal image registration algorithm. We use several different multimodal image pairs to evaluate the proposed algorithm. The experimental results show that the proposed algorithm holds a stable performance and can still achieve accurate spatial alignment even with the huge nonlinear intensity differences.
Edge artificial intelligence camera network: an efficient object detection and tracking framework

Computer-intensive tasks can be distributed to edge devices in edge computing, which has many benefits, including distributed processing, low latency, better security, privacy, etc. In traditional video surveillance, video is cached in the data center for post analysis and processing, but it is impossible to analyze the specific behaviors of specific objects intelligently in real-time scene. Taking this into consideration, we propose an efficient object detection and tracking framework via edge computing, which distributes the object detection and tracking tasks to edge cameras within the network instead of sending all the data to a centralized server. Its advantages can be summarized as follows: (1) instead of analyzing and processing video in the cloud, we analyze them locally, which greatly reduces the bandwidth and delay caused by the transmission of large amounts of data and ensures the security of the data. (2) we propose a neural network model compression framework for object detection with less storage and computing cost, which reduces the number of parameters and floating point operations by 39.3% and 17.28%. We use the detection results for tracking, combined with a simple tracking method, we can achieve a real-time object detection and tracking framework and more quickly determine the type and trajectory of moving objects. (3) We propose a communication module, which enables data interaction among cameras, and makes it easier for us to find the moving range of the object in the cross-camera area. In the experiment, network cameras and edge devices were used to build a framework, and an offline inference method was used to complete the detection and tracking of objects and judge the motion trajectory to verify the feasibility of the framework.
BatchHL: Answering Distance Queries on Batch-Dynamic Networks at Scale

Many real-world applications operate on dynamic graphs that undergo rapid changes in their topological structure over time. However, it is challenging to design dynamic algorithms that are capable of supporting such graph changes efficiently. To circumvent the challenge, we propose a batch-dynamic framework for answering distance queries, which combines offline labelling and online searching to leverage the advantages from both sides - accelerating query processing through a partial distance labelling that is of limited size but provides a good approximation to bound online searches. We devise batch-dynamic algorithms to dynamize a distance labelling efficiently in order to reflect batch updates on the underlying graph. In addition to providing theoretical analysis for the correctness, labelling minimality, and computational complexity, we have conducted experiments on 14 real-world networks to empirically verify the efficiency and scalability of the proposed algorithms.
Cost-efficiency and Performance Robustness in Serverless Data Exchange

Enterprises increasingly store their sporadically used data on cheap, elastic cloud storage to save costs. Analytical workloads on that data often appear in infrequent bursts presenting a high disparity in input data sizes. Using conservatively over-provisioned compute resources for this class of workloads is not cost-efficient as a fixed amount of resources only matches steady demand. Elastic query processors with a serverless architecture resolve this issue by running workers in cloud functions [3][9][10]. These systems can start thousands of functions within seconds, enabling elasticity down to query pipeline granularity. Moreover, serverless query processors come at no cost for idle times.
Local ternary pattern based multi-directional guided mixed mask (MDGMM-LTP) for texture and material classification

Local feature descriptors (LFDs) have been researched and developed for decades, and their applications are still being examined nowadays. In addition, the fact that LFDs are significant in a variety of computer vision applications and have excellent performances that should be improved, especially in challenging conditions, is the key reason for the ongoing work in this area. Therefore, we design in this paper a novel model of local feature descriptor for texture representation. The essence of the proposed method, referred to as multi directional guided mixed mask based local ternary pattern (MDGMM-LTP), is to progressively extract comprehensive micro structure features by analyzing the differential excitation and directional information according to relationships between pixels sampled in a variety of spatial arrangements within each 3 × 3 neighborhood. The process of MDGMM-LTP consists first in constructing a new mixed hybrid directional kernel based on a combination of several edge directional kernels in eight directions, including Kirsch, Robinson, Prewitt and Frei–Chen masks and then both local and non-local pixel interactions are encoded according to a new compact pattern encoding scheme integrating both LTP’s and LDP’s concepts. MDGMM-LTP operator has good ability to capture stable and more discriminant micro structure information through complementary information resulting from both LTP’s and LDP’s encoders’ combination. With the help of the K-Nearest Neighbors (KNN) classifier, the experiments conducted on sixteen challenging texture datasets revealed that the proposed MDGMM-LTP descriptor outperforms the other commonly used LFDs-based and deep learning-based techniques in terms of overall performance. To further show the effectiveness of the proposed method, a statistical experiment applied over all the tested datasets through the Wilcoxon signed rank test is performed, which statistically validates the efficacy of the proposed method.
Patterns and Opportunities for the Design of Human-Plant Interaction

The emergence of living organisms as entities in HCI presents an opportunity to collaborate with other beings through technology, align interspecies motives, and nurture greater empathy for the non-human. Plants are particularly interesting because of their natural ability to sense and respond to environmental stimuli and potential to enable more sustainable interaction design. However, due to the cross-disciplinary and emerging nature of this space, there is a need to identify overarching patterns and discern opportunities for unifying future research. This paper aims to systematically analyze existing Human-Plant Interaction (HPI) works by presenting a survey of projects across HCI, art/design, architecture, and bioengineering. We identify core design paradigms along the dimensions of HPI System Architecture, Plant I/O Coupling, Plant Interfacing and Manipulation Techniques, Application Context, and Scale. From these themes, we assemble a framework for HCI practitioners to approach HPI, and discuss opportunities and open questions for future exploration.
Multiagent Culture Algorithm-Based Interactive Design of College English Online Teaching Process

Traditional educational techniques and conceptions no longer match the need of the society for talent training. It is due to the fact that educational reforms are deepening and more open interactive teaching models have begun to be gradually implemented in college English instruction. The method's innovation and reform can not only fully respect the students' dominating position but also entice students to devote themselves to the English classroom with greater zeal which is helpful in allowing for deeper knowledge discovery and thinking. This study has the motive to examine the interactive teaching mode's application tactics in college English classrooms, particularly with the goal of improving the effectiveness of English classroom teaching and promoting the high-quality growth of today's college students. Furthermore, in order to improve the interactivity of online English teaching in colleges and universities, this research proposes an intelligent conversation system based on multiagents to aid in the improvement of online teaching. Finally, using a public discourse corpus dataset for implementation, the proposed model proves to have better performance than existing state of the art approaches.
Action Bias and the Two Most Dangerous Words in Cybersecurity Incident Response: An Argument for More Measured Incident Response

An inherent aspect of cybersecurity is managing incidents where people expect to see an immediate response. Action bias is our tendency to favor action over inaction because it makes us feel better—even if we have made things worse.
Image Captioning Using Detectors and Swarm Based Learning Approach for Word Embedding Vectors

IC (Image Captioning) is a crucial part of Visual Data Processing and aims at understanding for providing captions that verbalize an image's important elements.However, in existing works, because of the complexity in images, neglecting major relation between the object in an image, poor quality image, labelling it remains a big problem for researchers.Hence, the main objective of this work attempts to overcome these challenges by proposing a novel framework for IC.So in this research work the main contribution deals with the framework consists of three phases that is image understanding, textual understanding and decoding.Initially, the image understanding phase is initiated with image pre-processing to enhance image quality.Thereafter, object has been detected using IYV3MMDs (Improved YoloV3 Multishot Multibox Detectors) in order to relate the interrelation between the image and the object, and then it is followed by MBFOCNNs (Modified Bacterial Foraging Optimization in Convolution Neural Networks), which encodes and provides final feature vectors.Secondly, the textual understanding phase is performed based on an image which is initiated with preprocessing of text where unwanted words, phrases, punctuations are removed in order to provide a healthy text.It is then followed by MGloVEs (Modified Global Vectors for Word Representation), which provides a word embedding of features with the highest priority towards the object present in an image.Finally, the decoding phase has been performed, which decodes the image whether it may be a normal or complex scene image and provides an accurate text by its learning ability using MDAA (Modified Deliberate Adaptive Attention).The experimental outcome of this work shows better accuracy of shows 96.24% when compared to existing and similar methods while generating captions for images.
The marginalization of southern researchers in Development

We analyze the share and quality of publications of southern-based authors in top development economic journals between 1990 and 2019. We consider researchers as southern if they are based in low, lower-middle and upper-middle income countries (LIMCs), according to the World Bank country classification. Our results indicate that 74% of publications in top development journals are written by researchers not based in southern countries. Northern-based researchers’ prevalence is amplified by the fact that they are more often cited than their counterparts from developing countries. Southern-based researchers’ marginalization is also shown by the composition of editorial teams in development journals: only 17% of the editors of our selected journals are based in LIMCs. Interestingly, we find a strong correlation between the composition of the editorial board and the origin of publications. Despite the lower share of southern-based researchers, collaborations between northern- and southern-based researchers have grown significantly in the past thirty years, suggesting a potential decline in the relative academic isolation of southern scholars that may work to boost quality research and access to academic development.
Personalized College English Learning Based on Deep Learning under the Background of Big Data

Generally, in-depth learning has been extensively employed in numerous industries to enhance the growth of economic globalization since the dawn of the big data age. At the same time, the demand for foreign language talent has risen dramatically, and more and more businesses are steadily raising their English proficiency standards. International interactions, as well as scientific and technical exchanges, are influenced by English communicating competence. As a result, colleges and universities place a high value on students studying English. However, English learning is also a compulsory course in Colleges and universities, which is different from other courses. Therefore, in setting teaching objectives, arranging teaching activities, and compiling teaching contents, we should highlight students' personalized needs and formulate learning plans in combination with the individual requirements of each student. Aiming at this problem, this paper adopts the deep learning method to study the personalized learning of College English. Deep learning algorithm utilization in the development of college students' English learning reduces teachers' workload and focuses more on students' personalized needs, which is beneficial to teachers' teaching and students' learning. By analyzing the deep learning neural network model, the process of English speech recognition is described, and the characteristic parameters of English speech are extracted. Then compare and explain the characteristics of deep learning and shallow learning, analyze the relationship between them, and clarify the route of deep learning. Finally, the effect of College Students' personalized learning is analyzed by random sampling, and the effect of College English personalized learning based on deep learning is explained from two aspects: listening effect and reading practice.
Assessing and Enhancing Adversarial Robustness of Predictive Analytics: An Empirically Tested Design Framework

As predictive analytics increasingly applies supervised machine learning (SML) models to inform mission-critical decision-making, adversaries become incentivized to exploit the vulnerabilities of these SML models and mislead predictive analytics into erroneous decisions. Due to the limited understanding and awareness of such adversarial attacks, the predictive analytics knowledge and deployment need a principled technique for adversarial robustness assessment and enhancement. In this research, we leverage the technology threat avoidance theory as the kernel theory and propose a research framework for assessing and enhancing the adversarial robustness of predictive analytics applications. We instantiate the proposed framework by developing a robust text classification system, the ARText system. The proposed system is rigorously evaluated in comparison with benchmark methods on two tasks extensively enabled by SML: spam review detection and spam email detection, which then confirmed the utility and effectiveness of our ARText system. Results from numerous experiments revealed that our proposed framework could significantly enhance the adversarial robustness of predictive analytics applications.
Keypoint-based copy-move forgery detection in digital images: a survey

Copy-move (called copy-paste) is one of the most common image forgery, where one or more areas of an image are copied and pasted into another location of the same image. The objective of such a forgery is to hide useful elements and perform area duplication in some sections of an image. Thereby, copy-move forgery (CMF) poses a serious threat to society and forensic experts. Many methods have been proposed for copy-move forgery detection (CMF), which can be categorized into keypoint-based and block-based methods. Generally, the performance of keypoint-based methods is relatively higher in terms of computational efficiency, complexity, and robustness against many transformations. In this paper, a comprehensive survey of the recent keypoint-based methods based on Scale Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF), Oriented FAST and Rotated Brief (ORB), KAZE, and Binary Robust Invariant Scalable Keypoints (BRISK) is presented.
Revisiting ensemble adversarial attack

Deep neural networks have shown vulnerability to adversarial attacks. Adversarial examples generated with an ensemble of source models can effectively attack unseen target models, posing a security threat to practical applications. In this paper, we investigate the manner of ensemble adversarial attacks from the viewpoint of network gradients with respect to inputs. We observe that most ensemble adversarial attacks simply average gradients of the source models, ignoring their different contributions in the ensemble. To remedy this problem, we propose two novel ensemble strategies, the Magnitude-Agnostic Bagging Ensemble (MABE) strategy and Gradient-Grouped Bagging And Stacking Ensemble (G2BASE) strategy. The former builds on a bagging ensemble and leverages a gradient normalization module to rebalance the ensemble weights. The latter divides diverse models into different groups according to the gradient magnitudes and combines an intragroup bagging ensemble with an intergroup stacking ensemble. Experimental results show that the proposed methods enhance the success rate in white-box attacks and further boost the transferability in black-box attacks.
Bot Detection Using Multi-Input Deep Neural Network Model in Social Media

Recently, social media has been viewed as a critical tool for businesses to engage with other users, customers, and future consumers to build popularity, solicit ideas and opinions from users, or influence another user. This massive amount of data quickly becomes a valuable asset for businesses and organizations as a powerful tool for gaining insights and making critical decisions. Unfortunately, certain disinformation operations have been orchestrated by using bots, which are social media accounts operated by computer scripts that attempt to pass as actual human users to sway public opinion and disseminate misleading information. Bot identification is difficult since many bots deliberately strive to avoid detection. There are several approaches to distinguishing a bot from a legitimate account. This paper uses a multi-input deep neural network model with word embedding vectors to represent textual data for content-based bot detection, using fewer data and getting greater accuracy. Our proposed model, which includes two parallel convolutional neural network channels and fully connected neural networks to combine them, outperforms other newly proposed models in bot detection. We reached 93.25% accuracy in distinguishing between bot and human Twitter accounts using this novel technique.
Bolt

Random forests use ensembles of decision trees to boost accuracy for machine learning tasks. However, large ensembles slow down inference on platforms that process each tree in an ensemble individually. We present Bolt, a platform that restructures whole random forests, not just individual trees, to speed up inference. Conceptually, Bolt maps every path in each tree to a lookup table which, if cache were large enough, would allow inference with just one memory access. When the size of the lookup table exceeds cache capacity, Bolt employs a novel combination of lossless compression, parameter selection, and bloom filters to shrink the table while preserving fast inference. We compared inference speed in Bolt to three state-of-the-art platforms: Python Scikit-Learn, Ranger, and Forest Packing. We evaluated these platforms using datasets with vision, natural language processing and categorical applications. We observed that on ensembles of shallow decision trees Bolt can run 2--14X faster than competing platforms and that Bolt's speedups persist as the number of decision trees in an ensemble increases.