Reviewerly/exHarmony huggingface dataset:
Loads of Pollution to Lake Toba and Their Impacts

Lakes provide various ecosystem services that support biotic habitats and human life. In contrast, many lakes in the world are degraded due to pollutant supply from surrounding areas and human activities in the lake. Lake Toba, which is the largest lake in Indonesia, has indicated a polluted condition. However, the source and load of each pollutant are not yet known. A study has been conducted to determine nutrient and organic load levels entering the lake represented by Total Phosphorus (TP) and Chemical Oxygen Demand (COD), respectively. Observations were carried out in November 2017 at 22 locations, i.e., at 12 inlet rivers debouching to the lake and 10 sites at the lake. The pollutant impact was assessed from water class criteria based on COD, waters trophic status based on TP, and vertical oxygen profile representing cage aquaculture (CA) and non-cage aquaculture (NCA) areas. Based on COD and government regulation number 82/2001, water quality at the lake inlets was class III and IV. In the lake area, water class in NCA was III, while in CA the water class tends to be III and IV. Estimated TP loading from the catchment area was 138 tonnes/yr, while that from cage aquaculture activity was 570.33 tonnes/yr. Pollutants have caused the worsening of water class, increasing water column anoxia in the hypolimnion layer and eutrophication in Lake Toba.
Say their names: Resurgence in the collective attention toward Black victims of fatal police violence following the death of George Floyd

The murder of George Floyd by police in May 2020 sparked international protests and brought unparalleled levels of attention to the Black Lives Matter movement. As we show, his death set record levels of activity and amplification on Twitter, prompted the saddest day in the platform's history, and caused his name to appear among the ten most frequently used phrases in a day, where he is the only individual to have ever received that level of attention who was not known to the public earlier that same week. Importantly, we find that the Black Lives Matter movement's rhetorical strategy to connect and repeat the names of past Black victims of police violence-foregrounding racial injustice as an ongoing pattern rather than a singular event-was exceptionally effective following George Floyd's death: attention given to him extended to over 185 prior Black victims, more than other past moments in the movement's history. We contextualize this rising tide of attention among 12 years of racial justice activism on Twitter, demonstrating how activists and allies have used attention and amplification as a recurring tactic to lift and memorialize the names of Black victims of police violence. Our results show how the Black Lives Matter movement uses social media to center past instances of police violence at an unprecedented scale and speed, while still advancing the racial justice movement's longstanding goal to "say their names."
RoboMAX: Robotic Mission Adaptation eXemplars

Emerging and future applications of robotic systems pose unique self-adaptation challenges. To support the research needed to address these challenges, we provide an extensible repository of robotic mission adaptation exemplars. Co-designed with robotic application stakeholders including researchers, developers, operators, and end-users, our repository captures key sources of uncertainty, adaptation concerns, and other distinguishing characteristics of such applications. An online form enables external parties to supply new exemplars for curation and inclusion into the repository. We envisage that our RoboMAX repository will enable the development, evaluation, and comparison of self-adaptation approaches for the robotic systems domain.
Artificial bee colony algorithm with directed scout

As a relatively new model, the artificial bee colony algorithm (ABC) has shown impressive success in solving optimization problems. Nevertheless, its efficiency is still not satisfactory for some complex optimization problems. This paper has modified ABC and its other recent variants to improve its performance by modifying the scout phase. This modification enhances its exploitation ability by intensifying the regions in the search space, which probably includes reasonable solutions. The experiments were performed on CEC2014, and CEC2015 benchmark suites, real-life problems. And the proposed modification was applied to basic ABC, Gbest-Guided ABC, Depth First Search ABC, and Teaching–Learning Based ABC, and they were compared with their modified counterparts. The results have shown that our modification can successfully increase the performance of the original versions. Moreover, the proposed modified algorithm was compared with the state-of-the-art optimization algorithms, and it produced competitive results.
Analyzing Zoombombing as a new communication tool of cyberhate in the COVID-19 era

Purpose The aims of this paper are to explore the rise of cyberhate on the Zoom video conferencing platform at the outset of the coronavirus disease 2019 (COVID-19) pandemic and to examine victimized cases of Zoombombing where it was used as a cyberhate tool. The COVID-19 pandemic has not only impacted our lives and modes of working and studying, but also created new environments for cybercriminals to engage in cybercrime, cyberhate and hacking by exploiting tools such as Zoom. This new phenomenon called “Zoombombing” was first reported in mid-March 2020, when the social distancing and stay-at-home policies in the United States were nationally introduced. Design/methodology/approach This research conducted a news media content analysis on cases of Zoombombing. To conduct this analysis empirically, a dataset with all of the reported Zoombombing cases from March to April 2020 was created. Google Trends, news media and tweets were used to analyze Zoombombing as a form of cyberhate, particularly digital racism. Findings The results reveal prevalent Zoom-mediated racism toward Asian Americans, African Americans and Jewish Americans. This study understands Zoombombing from a sociopolitical/cultural perspective through news reporting of victimized cases and explores various ways that Zoombombing shapes, mediates, transforms and escalates racism. Originality/value This study is one of the very first studies to analyze Zoombombing in a way that builds upon an emerging body of literature on cyberhate. This paper considers Zoom as a space where curious young people, cybercriminals, extremists and hackers impose their ideologies and beliefs upon newly established online learning and working environments and engage in a struggle for identity recognition in the midst of increasingly accessible vulnerable software and cyberspace.
TAYLOR–MONARCH BUTTERFLY OPTIMIZATION-BASED SUPPORT VECTOR MACHINE FOR ACUTE LYMPHOBLASTIC LEUKEMIA CLASSIFICATION WITH BLOOD SMEAR MICROSCOPIC IMAGES

Acute lymphoblastic leukemia (ALL) is a serious hematological neoplasis that is characterized by the development of immature and abnormal growth of lymphoblasts. However, microscopic examination of bone marrow is the only way to achieve leukemia detection. Various methods are developed for automatic leukemia detection, but these methods are costly and time-consuming. Hence, an effective leukemia detection approach is designed using the proposed Taylor–monarch butterfly optimization-based support vector machine (Taylor–MBO-based SVM). However, the proposed Taylor–MBO is designed by integrating the Taylor series and MBO, respectively. The sparking process is designed to perform the automatic segmentation of blood smear images by estimating optimal threshold values. By extracting the features, such as texture features, statistical, and grid-based features from the segmented smear image, the performance of classification is increased with less training time. The kernel function of SVM is enabled to perform the leukemia classification such that the proposed Taylor–MBO algorithm accomplishes the training process of SVM. However, the proposed Taylor–MBO-based SVM obtained better performance using the metrics, such as accuracy, sensitivity, and specificity, with 94.5751, 95.526, and 94.570%, respectively.
Enhancing Contextualised Language Models with Static Character and Word Embeddings for Emotional Intensity and Sentiment Strength Detection in Arabic Tweets

Many studies have focused on Arabic sentiment or emotion classification tasks. However, research on alternative aspects of affect, such as emotional intensity and sentiment strength tasks, has been somewhat limited. In this paper, we propose a method for enriching a contextualised language model that incorporates static character and word embeddings for emotional intensity and sentiment strength in Arabic tweets. We examine the assumption that models using static embeddings that are trained specifically on a corpus containing extensive Arabic affect-related words can boost the performance of language models. Through the development of character-level embeddings, we have found that our method is able to overcome the limitations associated with out-of-vocabulary words, which is a common problem when dealing with Arabic informal text. Given this, the method that we have developed achieves state-of-the-art results for the detection of the intensity of emotion and sentiment strength in Arabic social media.
A cyber situational awareness model to predict the implementation of cyber security controls and precautions by SMEs

Purpose There is widespread concern about the fact that small- and medium-sized enterprises (SMEs) seem to be particularly vulnerable to cyberattacks. This is perhaps because smaller businesses lack sufficient situational awareness to make informed decisions in this space, or because they lack the resources to implement security controls and precautions. Design/methodology/approach In this paper, Endsley’s theory of situation awareness was extended to propose a model of SMEs’ cyber situational awareness, and the extent to which this awareness triggers the implementation of cyber security measures. Empirical data were collected through an online survey of 361 UK-based SMEs; subsequently, the authors used partial least squares modeling to validate the model. Findings The results show that heightened situational awareness, as well as resource availability, significantly affects SMEs’ implementation of cyber precautions and controls. Research limitations/implications While resource limitations are undoubtedly a problem for SMEs, their lack of cyber situational awareness seems to be the area requiring most attention. Practical implications The findings of this study are reported and recommendations were made that can help to improve situational awareness, which will have the effect of encouraging the implementation of cyber security measures. Originality/value This is the first study to apply the situational awareness theory to understand why SMEs do not implement cyber security best practice measures.
Measures to combat cybercrime: analysis of international and Ukrainian experience

The article is dedicated to the study of the measures used to combat cybercrime in different countries. It is observed that the world's leading countries are actively expanding and creating units in the armed forces and intelligence services that should ensure the development of offensive capabilities in cyberspace. In particular, the operational cooperation of law enforcement agencies, such as Interpol, Europol and Eurojust, in the fight against cybercrime is being stepped up. Anti-cybercrime activities are carried out not only by individual states, but also by their blocs, including NATO. In Ukraine, unlike the developed countries of the world, measures to combat cybercrime are less developed. Despite the existence of special laws and strategies, in particular the Cyber Security Strategy of Ukraine, the fight against cybercrime is not effective due to the declarative nature of most of the provisions of this strategy. It is concluded that one of the problematic aspects of the phenomenon of cybercrime is the low level of education in information technologies of the population of Ukraine.
Optimizing Coherent Integrated Photonic Neural Networks under Random Uncertainties

We propose an optimization method to improve power efficiency and robustness in silicon-photonic-based coherent integrated photonic neural networks. Our method reduces the network power consumption by 15.3% and the accuracy loss under uncertainties by 16.1%.
Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks

We algorithmically identify label errors in the test sets of 10 of the most commonly-used computer vision, natural language, and audio datasets, and subsequently study the potential for these label errors to affect benchmark results. Errors in test sets are numerous and widespread: we estimate an average of 3.4% errors across the 10 datasets, where for example 2916 label errors comprise 6% of the ImageNet validation set. Putative label errors are found using confident learning and then human-validated via crowdsourcing (54% of the algorithmically-flagged candidates are indeed erroneously labeled). Surprisingly, we find that lower capacity models may be practically more useful than higher capacity models in real-world datasets with high proportions of erroneously labeled data. For example, on ImageNet with corrected labels: ResNet-18 outperforms ResNet-50 if the prevalence of originally mislabeled test examples increases by just 6%. On CIFAR-10 with corrected labels: VGG-11 outperforms VGG-19 if the prevalence of originally mislabeled test examples increases by 5%. Traditionally, ML practitioners choose which model to deploy based on test accuracy -- our findings advise caution here, proposing that judging models over correctly labeled test sets may be more useful, especially for noisy real-world datasets.
SightBi: Exploring Cross-View Data Relationships with Biclusters

Multiple-view visualization (MV) has been heavily used in visual analysis tools for sensemaking of data in various domains (e.g., bioinformatics, cybersecurity and text analytics). One common task of visual analysis with multiple views is to relate data across different views. For example, to identify threats, an intelligence analyst needs to link people from a social network graph with locations on a crime-map, and then search for and read relevant documents. Currently, exploring cross-view data relationships heavily relies on view-coordination techniques (e.g., brushing and linking), which may require significant user effort on many trial-and-error attempts, such as repetitiously selecting elements in one view, and then observing and following elements highlighted in other views. To address this, we present SightBi, a visual analytics approach for supporting cross-view data relationship explorations. We discuss the design rationale of SightBi in detail, with identified user tasks regarding the use of cross-view data relationships. SightBi formalizes cross-view data relationships as biclusters, computes them from a dataset, and uses a bi-context design that highlights creating stand-alone relationship-views. This helps preserve existing views and offers an overview of cross-view data relationships to guide user exploration. Moreover, SightBi allows users to interactively manage the layout of multiple views by using newly created relationship-views. With a usage scenario, we demonstrate the usefulness of SightBi for sensemaking of cross-view data relationships.
Deep Convolutional Neural Network for Network-wide QoT Estimation

We propose a novel Deep Convolutional Neural Network formulation for network-wide QoT classification tasks and show its effectiveness for networks with significant topological differences. Our formulation achieves ~99% accuracy on large and diverse test datasets.
Legal Protection of Artificial Intelligence Data and Algorithms from the Perspective of Internet of Things Resource Sharing

There are few laws and regulations related to privacy protection in the existing artificial intelligence data sharing environment, lack of practical operability, and low feasibility. The weakening of administrative management and industry self-discipline also reflects my country’s current weak protection of big data privacy. In order to solve the problem of sharing artificial intelligence data and algorithms, it becomes very important to study the legal protection of artificial intelligence data and algorithms from the perspective of Internet of Things resource sharing. This article is aimed at studying the use of laws to protect artificial intelligence data and algorithms. Aiming at reducing the bullwhip effect, a most effective bullwhip effect model derivation algorithm is proposed. This method can not only share customer demand information with members at all levels in the supply chain but also achieve information sharing among members at all levels. Calculate the proportion of the overall time of the program through multiple statistical data ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>m</mi> <mo>=</mo> <mn>30</mn> </math> , <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>k</mi> <mo>=</mo> <mn>12</mn> </math> ; and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mi>m</mi> <mo>=</mo> <mn>60</mn> </math> , <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <mi>k</mi> <mo>=</mo> <mn>15</mn> </math> ), and extract two special values representing the overall situation ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M5"> <mi>m</mi> <mo>=</mo> <mn>30</mn> </math> , <math xmlns="http://www.w3.org/1998/Math/MathML" id="M6"> <mi>k</mi> <mo>=</mo> <mn>12</mn> </math> ; <math xmlns="http://www.w3.org/1998/Math/MathML" id="M7"> <mi>m</mi> <mo>=</mo> <mn>60</mn> </math> , <math xmlns="http://www.w3.org/1998/Math/MathML" id="M8"> <mi>k</mi> <mo>=</mo> <mn>15</mn> </math> ). Most of the time consumption of this program is concentrated in the secret distribution stage, accounting for about 80% on average.
The Future of Software Engineering Work

Presents the introductory editorial for this issue of the publication.
Systematic Review of Enhancement of Artificial Bee Colony Algorithm Using Ant Colony Pheromone

The artificial bee colony (ABC) is a well-studied algorithm developed to solve continuous function optimization problems by Karboga and Akay in 2009. ABC has been proven to be more effective than other biological-inspired algorithms with good exploration. However, ABC suffers from low exploitation and slow convergence in some cases. The ABC algorithm study has risen significantly over the past decade, with many researchers trying to improve ABC performance and apply it to solve problems. One method to enhance ABC is to borrow exploration technique from other algorithms. Researchers use pheromone, which is a technique used by Ant Colony optimization algorithm, to enhance ABC and addressed several aspects of using a pheromone to enhance the ABC. This systematic review aims to review and analysis articles about using pheromone to enhance ABC. Articles on related topics were systematically searched in four major databases namely Scopus, Web of Science, Association for Computing Machinery ACM and Google Scholar. To ensure that all research articles were considered the start date is not restrictions the search carry out till February 2021.Five articles were selected based on our inclusion and exclusion criteria for the systematic review. The results show that the use Pheromone to enhance ABC can increase the ABC exploitation ability and overcoming the late convergence. This paper also illustrates several potential pheromone using for future work.
A Novel Framework for Anomaly Detection for Satellite Momentum Wheel Based on Optimized SVM and Huffman-Multi-Scale Entropy

The health status of the momentum wheel is vital for a satellite. Recently, research on anomaly detection for satellites has become more and more extensive. Previous research mostly required simulation models for key components. However, the physical models are difficult to construct, and the simulation data does not match the telemetry data in engineering applications. To overcome the above problem, this paper proposes a new anomaly detection framework based on real telemetry data. First, the time-domain and frequency-domain features of the preprocessed telemetry signal are calculated, and the effective features are selected through evaluation. Second, a new Huffman-multi-scale entropy (HMSE) system is proposed, which can effectively improve the discrimination between different data types. Third, this paper adopts a multi-class SVM model based on the directed acyclic graph (DAG) principle and proposes an improved adaptive particle swarm optimization (APSO) method to train the SVM model. The proposed method is applied to anomaly detection for satellite momentum wheel voltage telemetry data. The recognition accuracy and detection rate of the method proposed in this paper can reach 99.60% and 99.87%. Compared with other methods, the proposed method can effectively improve the recognition accuracy and detection rate, and it can also effectively reduce the false alarm rate and the missed alarm rate.
TanhSoft—Dynamic Trainable Activation Functions for Faster Learning and Better Performance

Deep learning, at its core, contains functions that are the composition of a linear transformation with a non-linear function known as activation function.In the past few years, there is an increasing interest in the construction of novel activation functions resulting in better learning.In this work, we propose three novel activation functions with learnable parameters, namely TanhSoft-1, TanhSoft-2, and TanhSoft-3, which are shown to outperform several well-known activation functions.For instance, replacing ReLU with TanhSoft-1, TanhSoft-2, and Tanhsot-3 improves top-1 classification accuracy by 6.06%, 5.75%, and 5.38% respectively on VGG-16(with batch-normalization), by 3.02%, 3.25% and 2.93% respectively on PreActResNet-34 in CIFAR-100 dataset, by 1.76%, 1.93%, and 1.82% respectively on WideResNet 28-10 in Tiny ImageNet dataset.TanhSoft-1, TanhSoft-2, and Tanhsot-3 outperformed ReLU on mean average precision (mAP) by 0.7%, 0.8%, and 0.6% respectively in object detection problem on SSD 300 model in Pascal VOC dataset.
Co-Designing Personal Health? Multidisciplinary Benefits and Challenges in Informing Diabetes Self-Care Technologies

Co-design is a widely applied design process with well-documented values, including mutual learning and collective creativity. However, the real-world challenges of conducting multidisciplinary co-design research to inform the design of self-care technologies are not well established. We provide a qualitative account of a multidisciplinary project that aimed to co-design machine learning applications for Type 1 Diabetes (T1D) self-management. Through interviews, we identify not only perceived social, technological and strategic benefits of co-design but also organisational, translational and pragmatic design challenges: participants with T1D experienced difficulties in co-designing systems that met their individual self-care needs as part of group activities; HCI and AI researchers described challenges resulting from applying co-design outcomes to data-driven ML work; and industry collaborators highlighted academic data sharing regulations as cross-organisational challenges that can impede co-design efforts. Based on this understanding, we discuss opportunities for supporting multidisciplinary collaborations and aligning individual health needs with collaborative co-design activities.