Reviewerly/exHarmony huggingface dataset:
IMAGINARY PARLIAMENTS: EXCAVATING THE METAPHOR OF CONSENSUS WITHIN BLOCKCHAIN TECHNOLOGIES

Blockchain technologies function partly according to consensus algorithms and protocols. Beyond a practical means of allowing these technologies to function, they can also be considered to have imagined affordances that “emerge between users’ perceptions, attitudes, and expectations; between the materiality and functionality of technologies; and between the intentions and perceptions of designers” (Nagy and Neff, 2015, p. 5). In the case of blockchains, these affordances are attached to representing social trust and decentralizing electoral processes. As such, blockchain technologies have been intimately woven into discourses that imagine the future of governance, transparency, and democratic decision-making. But how has this happened? In this paper I identify that at the root of these discourses is the ambiguous concept of "consensus" and that socio-political meanings of consensus have be re-written as a set of technical operations. Through a combination of metaphor analysis and content analysis of canonical computer science papers and news articles, I demonstrate how the oscillation between technical and political meanings became the basis of the celebratory discourses around blockchain technologies. Through this analysis I demonstrate how computer scientists used a metaphor to guide technical development, and then this metaphor took on a foundational aspect of the socio-political value of the technology in the form of visions for decentralization, autonomy, and democracy.
THE ALGORITHMIC FLOW OF HARMFUL INDUSTRIES ADVERTISING ON SOCIAL MEDIA PLATFORMS

Advertising shapes our larger public culture but the typical experience of advertising is now confined to our private and algorithmically-customised social media feeds. In this project, with our partner VicHealth, we used a participatory digital method to work with 204 young Australians aged 18 to 25 to collect 5169 examples of alcohol, gambling and fast food advertising from their social media feeds. We analyse the collections of advertisements each participant sent us. The patterns across participants’ collections illustrate how social media platforms’ advertising models ‘learn’ to reflect and reproduce the identities and subject positions of participants. The collections of ads we see on social media are an important object of study because they reveal not just the symbolic content and targeting patterns of particular ads, but also because they illustrate how advertising on social media algorithmically-curates an immersive cultural experience. Our study demonstrates how social media continues the larger social role advertising plays in the construction and maintenance of consumer subjectivities. The algorithmic flow of advertising on social media is now the basis of our everyday engagement with advertising. We need to conceptualise advertising on social media not only using concepts of ‘targeting’ that imply the precise identification of our characteristics, but instead as a complex feedback loop between the refinement of ad content and themes, the data-driven optimization of audiences, and our reflexive and fluid identities, interests and aesthetic sensibilities.
IN ON THE GRIFT: HOW PLATFORMS GOVERN AUTHENTICITY FOR NEO-COLONIAL ENDS

Cryptocurrency has attracted widespread criticism for its exploitative logics that consume resources and trap unwitting investors into highly volatile, unregulated markets with worsening wealth inequality. A key factor in the rise of crypto trading is social media, where an ecosystem of spammers, grifters, ‘crypto influencers’ and celebrities such as Elon Musk amplify hype and generate public interest. While platforms moderate orchestrated platform manipulation through the opaque concept of ‘coordinated inauthentic behaviour’ (CIB), here we argue that Twitter is largely ignoring widespread crypto-related CIB on its platform– because it is profitable. Informed by a study of 1.8 million tweets about the top five crypto coins, we develop a neocolonial perspective of CIB that highlights how Twitter exploits it strategically to turn a blind eye to financial exploitation and techno-utopian subjugation on the platform. While the line between authentic and inauthentic coordinated activity is not always clear cut, we suggest that platforms construct the problem in such a way that strategically obfuscates its distinction. This raises critical questions for content moderation and platform regulation that will require us to think outside the compromised nomenclature that platforms have invented for their own ends.
RECLAIMING DIGITAL INTIMACY FOR YOUTH: PILOTING DIGITAL SEXUAL VIOLENCE WORKSHOPS FOR UNDER-18S DURING THE COVID-19 PANDEMIC IN ENGLAND AND IRELAND

Online sexual abuse and violence have become an urgent global problem for women and girls, and in particular for poor women, women of colour and LGBTQ women (Ging and Siapera, 2019). Online sexual harassment and abuse is an especially urgent issue for young people, for whom digital spaces are key sites of communication, identity formation, self-expression and sexual interaction. The toxic dynamics that frequently underpin these complex entanglements thus pose a significant threat to ethical digital intimacy. This situation became substantially more extreme during COVID-19, with rates of online abuse and harassment rising as young people have been forced to spend more and more time online. During this period, usage of particular platforms (e.g. TikTok) dramatically increased. A substantial rise in screen time also impacted young people’s experiences and digital intimacies in important ways. This paper reports on the findings of a cross-national study conducted in England and Ireland, which explored pedagogical interventions into the continuum of online and offline sexual violence amongst young people in schools. In particular, we are interested in how different social media platforms are used to perpetuate different types of online abuse based on certain technological affordances, such as Snapchat quick adds, shout outs and streaks, and Instagram direct message and group chat features. We make a number of concrete recommendations, pointing in particular to urgent paradigm shifts in digital ethics and digital safety initiatives, with a particular focus on platform algorithims, policies and governance.
EXPLOITATION IN ONLINE CONTENT MODERATION

This paper presents a normative framework for evaluating the moral and epistemic exploitation that online content moderation workers experience while working for social media companies (often as subcontractors or as Mechanical Turk workers). I argue that the current labor model of commercial content moderation (CCM) is exploitative in ways that inflict profound moral harm and epistemic injustices on the workers. This detailed account of exploitation enables us to see more clearly the contours and causes of the moral and epistemic injustice involved in CCM, and helps us understand precisely why these forms of exploitation are unjust. It also suggests some practical solutions for a more just labor model for the moderation work that shapes our online ecosystem.
DATA COLONIALISM AND DATA SOVEREIGNTY IN INDIGENOUS SPACES

This paper will discuss a topic of prioritized importance in contemporary Indigenous research: data sovereignty. In the light of the historical and cultural contexts in which we conduct our research, we need to drastically question and decenter research practices that could contribute to reiterating, maintaining, and amplifying colonial practices and perspectives. Data harvesting is such a practice, and an issue of immediate interest on the agenda of Indigenous researchers and allies. Efforts toward ethically valid and cultural-sensitive modes of data use are taking shape in Indigenous research, calling for an increased awareness about the topic among scholars within the interdisciplinary field of digital research. This presentation takes its point of departure in Sápmi, the traditional area of settlement of the Indigenous Sámi people, and in ongoing research and initiatives in the area. The international research context constitutes the framework for contextualizing topical debates and issues in Sápmi. First, I will approach and examine practices and principles of data collection, data management and data curation through the lens of Indigenous research. Second, I will discuss examples of digital initiatives in curatorship of Indigenous traditional knowledge, for instance the labelling of cultural heritage, use of metadata and access to library and archive collections. Current debates and ongoing works about ethical guidelines and about the application of FAIR and CARE principles will be central in this discussion.
TOWARD INTIMATE DATA: RE-THINKING DIGITAL, SOCIAL, POLITICAL RELATIONS

Digital technologies enable the mass datafication of human activity in new and intimate ways, allowing for both active and passive tracking bodily functions, physical movements, consumption habits, social encounters, and even moods and feelings. Despite the seeming newness of these developments, however, Internet scholars recognize that data production and use has always been bound up with broader relations between individuals, communities, and claims to the privateness or publicness of certain bodies, spaces, and behaviors. Most recently, critical data scholars have illuminated the complex, often surreptitious contexts in which datafication occurs, offering a range of conceptual frameworks to contend with the meanings and implications of these deeply personal digital-human entanglements. In this paper we take up and recast the notion of “intimate data.” Elsewhere denoting a particular category of tracked activities deemed private or sensitive, we instead consider intimacy as marking a set of (often unequal) socio-political relationships. That is, we mobilize “intimate data” to attend to the processes by which individuals and collectives are datafied in ways that have repercussions for knowledges about oneself and others. In so doing, we sidestep hermetic liberal conceptions of data that center ideals like consent and exchange to think about data collection as eliciting confessions , vulnerabilities, monetizable practices, and new possibilities for governing (inter)personal and other relations. We advance different, alternative political responses, focusing specifically on (1) the (racialized, gendered, classed, sexualized) normativity of intimate data, (2) (re)considerations of privacy and surveillance, (3) tensions around visibility, and (4) responsibilization of individuals to police spaces.
A PROTOTYPE MODEL ON DEVELOPMENT OF WEB-BASED DECISION SUPPORT SYSTEM FOR EMPLOYEE PERFORMANCE ASSESSMENTS WITH SIMPLE ADDITIVE WEIGHTING METHOD

The manager or related supervisor performs employee performance assessments to measure the performance and competency of the company's employees. The results of employee assessments can be used to make decisions. Three problems arise in the performance assessment process for PT Tibeta Logistik Indonesia's current employees. The first is that the assessment process is carried out conventionally. So it takes longer, the assessment results can be more objective, and managers or superiors cannot monitor employee performance evaluation results. Based on the literature review, it is known that the Decision Support System is a method that performs assessments more quickly and objectively. Simple Additive Weighting (SAW) is the Decision Support System method for performance assessment. The decision support system will be implemented on a website-based system designed with a prototype model. This research produces a website-based decision support system that can contribute to companies in evaluating employee performance. Before implementation, the decision support system was tested using the User Acceptance Test (UAT) method and obtained very good average results. Then in the comparison between before and after using the system, the results of employee assessments when using the system get superior results in 3 aspects: Integration, security, and ranking.&#x0D; &#x0D; Kata kunci: DSS, Prototipe, SAW, UAT, website.
Decomposition of a fuzzy function by one-dimensional fuzzy multiresolution analysis

Signal compression and data compression are techniques for storing and transmitting signals using fewer bits as possible for encoding a complete signal. A good signal compression scheme requires a good signal decomposition scheme. The decomposition of the signal can be done as follows: The signal is split into a low-resolution part, described by a smaller number of samples than the original signal, and a signal difference, which describes the difference between the low-resolution signal and the real coded signal. Our paper deals with the proofs of these properties in a fuzzy environment. The proof of one- dimensional multiresolution analysis is given. The concept of fuzzy wavelets is introduced and as a byproduct a special fuzzy space of details of a signal is given and an orthonormal basis of
Algorithms for the extension two-dimensional fuzzy signal via decomposition and reconstruction in fuzzy wavelets

he decomposition of an image can be done in the following way: The image is split into a low-resolution part, which can be described by a smaller number of samples than the original image, and a signal difference, which describes the difference between the low-resolution image and the real coded image. Therefore, this low-resolution image is also decomposed into a low-resolution image and a difference image, making more efficient coding possible. This decomposition is repeated several times, so that a hierarchical image decomposition is created. Thus, the low-resolution image is only half the size of the original image. This reduced image is enlarged to the size of the original image. The result is a detailed image that is the same size as the original image. Our problem is: "Can we build algorithms allowing the decomposition and reconstruction of a signal in a fuzzy environment? We will answer in the affirmative. This construction is first made possible by one-dimensional fuzzy multiresolution analysis, which will later be extended to two dimensions. In the first part, we recall some definitions and main results obtained on the existence of fuzzy multiresolution analysis in the one-dimensional case, for the decomposition of a fuzzy image. The second part, based on this multi-resolution analysis, presents a fast construction algorithm for the analysis and synthesis of a fuzzy signal. Finally, the third part is nothing else than an extension of this algorithm to 2 dimensions, by the fuzzy multiresolution two-dimensional analysis
Over-the-horizon camera calibration on smartphones with planar mirror reflections

To address the camera calibration problems on multi-cameras photography without any common field of views (FOVs), an over-the-horizon camera calibration on smartphones with planar mirror reflections is proposed. The liquid crystal display (LCD) target is placed suitably in the FOV of front cameras assembled on smartphones, and the chessboard images are captured from different views to obtain the intrinsic and extrinsic parameters for the front cameras. Then the target is reflected into the FOV of rear cameras with planar mirror reflections, which present parallel characteristics with respect to the front cameras, and then the absolute poses between the LCD target and the rear cameras are estimated. The over-the-horizon camera calibration experiments on a single and double smartphones show that the avenue of planar mirror reflections is simple and delicate. The Kappa’s poses between the front and the rear cameras are exhibited in the three-dimensional diagrams along with the mirrored images, in which the poses transfer between a series of cameras and a pubic shared target are implemented. The method behaves significantly potentials to resolve some intractable problems on a multi-camera holography without any common FOVs.
Underwater visual image denoising based on total variation method

Optical imaging technology is one of the main technology for underwater detection and recognition. However, the serious image noise occurred in the optical imaging and the signal to noise ratio of image decreased, due to water scattering and turbulence, background light interference and other factors. In this paper, an underwater visual image denoising algorithm based on total variation method is proposed to improve the signal-to-noise ratio of the underwater image at the same time keep the detail information of images. Based on the linear noise model of the image, the total variation model of the image is established and the partial differential equation of image problem is converted to optimization problems, which can be solved by gradient descent algorithm and finite difference algorithm. The method based on total variation was proposed to reduce the noise of underwater images compare with other traditional algorithms and verified by the simulation experiment and underwater experiment. Experimental verifications: after the image processing of the algorithm, the objective reference image quality evaluation index image peak signal-to-noise ratio (PSNR) was improved to more than 20 dB, and the score of no reference image quality evaluation index was improved by 4.8 times. The method can be applied to an underwater exploration and image enhancement for scatterer imaging applications.
Reading and Listening Ability Relative to Students’ Level of Performance

The study aimed to determine the second language reading and listening abilities of Grade 11 students and their relationship to grammatical morphemes and lexico-grammatical knowledge by proposing engagement exercises to enhance students’ language performance in public secondary schools. The study used the descriptive research method to determine students’ performance levels in reading, and listening abilities. The respondents were grade 11 students enrolled in the selected secondary schools in the Division of Capiz. Purposive sampling method was used as this study is based upon various criteria, including the capacity and willingness of respondents to participate. The data-gathering instruments utilized in the study were documentary analysis and teacher-made tests. For a meaningful interpretation and analysis of the study, the data were subjected to statistical treatment such as frequency, percentage, ranking, mean, Pearson r product-moment correlation, and T-test. The study’s findings revealed that Grade 11 students’ performance levels are average in grammatical morphemes and lexico-grammatical knowledge, while the levels of students’ reading and listening abilities are generally average. Further, there are relationships between the reading and listening abilities and their socio-demographic attributes. The study recommends language engagement exercises that includes activities that could enhance the listening and reading abilities of Grade 11 students based on the areas needing enhancement.
Influencing Factors on the Selection of Language Learning Centres: Empirical Evidence from Myanmar

This study aimed to analyse the factors considered by the learners in choosing a language learning centre in Myanmar as an overview of the competitive advantage in the rapidly transforming cutthroat and globalised market competition. Deductive approach was applied as the base of conducting the study with exploratory research design using survey questionnaire in which the related quantitative and qualitative questions distributed to 108 students. The qualitative and quantitative data interpreted revealed two prominent factors, TV advertising and financial factor, have no impact on the students’ selection decision. However, the other three factors, social media advertising, academic quality, and social influence, have significant impact on the students’ decision of selecting a language institute. The competitive advantages based on the user experience have been identified as well. Because the research area is limited to only five influential factors, the number of variables can merely cover the narrow scope of the research area to foreground the whole resemblance of the students’ selection behaviour and attitude. However, the results imply that competitive advantage in the highly competitive market requires strategic formulation of marketing strategies.
A Data Set of Generalizable Python Code Change Patterns

Mining repetitive code changes from version control history is a common way of discovering unknown change patterns. Such change patterns can be used in code recommender systems or automated program repair techniques. While there are such tools and datasets exist for Java, there is little work on finding and recommending such changes in Python. In this paper, we present a data set of manually vetted generalizable Python repetitive code change patterns. We create a coding guideline to identify generalizable change patterns that can be used in automated tooling. We leverage the mined change patterns from recent work that mines repetitive changes in Python projects and use our coding guideline to manually review the patterns. For each change, we also record a description of the change and why it is applied along with other characteristics such as the number of projects it occurs in. This review process allows us to identify and share 72 Python change patterns that can be used to build and advance Python developer support tools.
Detecting Anomalous Microflows in IoT Volumetric Attacks via Dynamic Monitoring of MUD Activity

IoT networks are increasingly becoming target of sophisticated new cyber-attacks. Anomaly-based detection methods are promising in finding new attacks, but there are certain practical challenges like false-positive alarms, hard to explain, and difficult to scale cost-effectively. The IETF recent standard called Manufacturer Usage Description (MUD) seems promising to limit the attack surface on IoT devices by formally specifying their intended network behavior. In this paper, we use SDN to enforce and monitor the expected behaviors of each IoT device, and train one-class classifier models to detect volumetric attacks. Our specific contributions are fourfold. (1) We develop a multi-level inferencing model to dynamically detect anomalous patterns in network activity of MUD-compliant traffic flows via SDN telemetry, followed by packet inspection of anomalous flows. This provides enhanced fine-grained visibility into distributed and direct attacks, allowing us to precisely isolate volumetric attacks with microflow (5-tuple) resolution. (2) We collect traffic traces (benign and a variety of volumetric attacks) from network behavior of IoT devices in our lab, generate labeled datasets, and make them available to the public. (3) We prototype a full working system (modules are released as open-source), demonstrates its efficacy in detecting volumetric attacks on several consumer IoT devices with high accuracy while maintaining low false positives, and provides insights into cost and performance of our system. (4) We demonstrate how our models scale in environments with a large number of connected IoTs (with datasets collected from a network of IP cameras in our university campus) by considering various training strategies (per device unit versus per device type), and balancing the accuracy of prediction against the cost of models in terms of size and training time.