Reviewerly/exHarmony huggingface dataset:
Venturing Beyond the Imposition of a Postdigital, Anti-human Higher Education

In an important footnote in Capital, Marx highlights how an analysis of technology enables us to reveal the dialectical construction of our dominant mode of reproducing everyday life. This links humans, infrastructures, modes of production, cultural conceptions, social relations, governance arrangements, and nature, in order to describe the dominant political economic context for living. In terms of higher education, this enables an understanding of the relationship between humans and technologies, in order to maintain the reproduction of capitalism. This chapter argues that such a relationship is morbid and lengthens the shadow of value over what it means to be human. Here, the drive, shaped competitively and through commodification, is for productive human capital, the management of risk, and value-for-money. Thus, the idea of what it means to be educated is restructured by economic value, rather than humane values. This is the terrain upon which conceptions of postdigital higher education might be analysed, in order to offer alternatives away from the dehumanising universe of value. The chapter utilises the metaphor of composting as a way that individuals in their environments process anger, grief, and trauma and find new ways of living. It pivots around the potential for a reimagining of the integration of the digital and the human through mass intellectuality, as a new form of sociability.
COVIDSeer

We develop an enhanced version of CORD-19 dataset released by the Allen Institute for AI. Tools in the SeerSuite project are used to exploit information in original articles not directly provided in the CORD-19 datasets. We add 728 new abstracts, 70,102 figures and 31,446 tables with captions that are not provided in the current data release. We also built a vertical search engine COVIDSeer based on the new dataset we created. COVIDSeer has a relatively simple architecture with features like keyword filtering, and similar paper recommendation. The goal was to provide a system and dataset that can help scientists better navigate through the literature concerning COVID-19. The enriched dataset can serve as a supplement to the existing dataset. The search engine, which offers keyphrase-enhanced search, will hopefully help biomedical and life science researchers, medical students, and the general public to more effectively explore coronavirus-related literature. The entire data set and the system will be made open source.
Digital Piano Keyboard Design using FPGA Implementation for Beginner

Abstract With the future technology and architecture, the digital musical instruments have been designed into a pack of electronic hardware and software with no strings or animal skins for producing an instruments sound. One of the musical instruments with high interest to learn by a beginner is piano. To solve this problem, this research aimed to design and development of basic digital piano keyboard using FPGA for rapid prototyping. The core of this project is keynotes which is a common input peripheral device that is connected with FPGA which is DE1-SoC-MTL2 and speaker to complete a basic learning system of piano. The architecture is designed using Verilog HDL through the Altera Quartus Prime software. Verilog has been designed to export every 13 keys to 13 certain note then implemented on DE1-SoC-MTL2. The basic system process is a keynote press is needed to activate or produce the certain piano sound. MTL2 used to display a pressed keynote. There are 13 keys in total of piano keynotes for this design where each key represents a different note or frequency to match the existing piano at the present day. The 13 different notes are low octave C, C#, D, D#, E, F, F#, G#, A#, B and high octave C basically for beginner user knowledge. All the components of the system are verified using oscilloscope. This digital piano keyboard designed on FPGA has been implemented for user beginner needs, who want to learn to play piano at very basic stage.
Weight Erosion: An Update Aggregation Scheme for Personalized Collaborative Machine Learning

Background. In medicine and other applications, the copying and sharing of data is impractical for a range of well-considered reasons. With federated learning (FL) techniques, machine learning models can be trained on data spread across several locations without such copying and sharing. While good privacy guarantees can often be made, FL does not automatically incentivize participation and the resulting model can suffer if data is non-identically distributed (non-IID) across locations. Model personalization is a way of addressing these concerns. Methods. In this study, we introduce Weight Erosion: an SGD-based gradient aggregation scheme for personalized collaborative ML. We evaluate this scheme on a binary classification task in the Titanic data set. Findings. We demonstrate that the novel Weight Erosion scheme can outperform two baseline FL aggregation schemes on a classification task, and is more resistant to over-fitting and non-IID data sets.
Password Guessing-Based Legacy-UI Honeywords Generation Strategies for Achieving Flatness

The legacy-UI honeywords generation approach is more favored due to its high usability compared to the modified-UI approach that sometimes becomes unusable in practice. However, several prior arts on legacy-UI based honeywords generation methods often fail to obtain the security standard, especially the flatness criterion. In this work, we propose two legacy-UI honeywords generation strategies based on two password guessing methods: PassGAN and Probabilistic Context-Free Grammar (PCFG). Besides, we also introduce two hybrid strategies by combining PassGAN, PCFG, and random-based methods. We empirically examine the flatness of the proposed honeywords generation strategy against Top Password (Top-PW) attack using real-world datasets, instead of only providing heuristic security arguments. The experiment results show that three of the proposed methods (the PassGAN-based and the two hybrid methods) have lower flatness value than all previous legacy-UI methods and able to meet the "perfectly flat" criterion.
Learning Representations of Hierarchical Slates in Collaborative Filtering

We are interested in building collaborative filtering models for recommendation systems where users interact with slates instead of individual items. These slates can be hierarchical in nature. The central idea of our approach is to learn low dimensional embeddings of these slates. We present a novel way to learn these embeddings by making use of the (unknown) statistics of the underlying distribution generating the hierarchical data. Our representation learning algorithm can be viewed as a simple composition rule that can be applied recursively in a bottom-up fashion to represent arbitrarily complex hierarchical structures in terms of the representations of its constituent components. We demonstrate our ideas on two real world recommendation systems datasets including the one used for the RecSys 2019 challenge. For that dataset, we improve upon the performance achieved by the winning team’s model by incorporating embeddings as features generated by our approach in their solution.
Realization of Vehicle Classification System Based on Deep Learning

Vehicle positioning and classification is an important technology in intelligent transportation and autonomous driving. The system uses SSD algorithm to achieve vehicle classification and positioning, from the picture collection, picture calibration, model training, model detection several aspects of the detailed introduction of vehicle classification process. Pre-labeled can be adopted in image annotation to improve annotation efficiency. The SSD algorithm is a mainstream deep learning-based target detection algorithm in recent years. The algorithm has high detection efficiency and correct rate, and is widely used in classification recognition and target positioning.
SDRP: Safe, Efficient, and SLO-Aware Workload Consolidation Through Secure and Dynamic Resource Partitioning

Workload consolidation is a widely-used technique to improve the resource utilization of services computing systems by consolidating latency-critical (LC) and batch workloads on the same physical server. The resource manager for workload consolidation dynamically allocates hardware resources (e.g., cores, caches) to the workloads to maximize the resource utilization while satisfying the service-level objective (SLO) of the LC workloads. Since security-critical hardware resources are dynamically allocated across consolidated workloads, information leakages can be created among workloads through microarchitectural side-channel (SC) attacks. Despite extensive prior works, it is yet to investigate efficient system software support for achieving high resource utilization without compromising the SLO and security of consolidated workloads. To bridge this gap, we propose SDRP, secure and dynamic resource partitioning for safe, efficient, and SLO-aware workload consolidation. As with the state-of-the-art techniques, SDRP dynamically allocates hardware resources to enhance the resource utilization and provide the SLO guarantees. In contrast to the state-of-the-art techniques, SDRP dynamically sanitizes security-critical hardware resources to robustly defeat microarchitectural SC attacks. Our quantitative evaluation demonstrates that SDRP achieves high resource sanitization quality, introduces low performance overheads, delivers high resource utilization with the SLO and security guarantees, and defeats the last-level cache (LLC)-based SC attack.
Mass formulae for Euclidean self-orthogonal and self-dual codes over finite commutative chain rings

In this paper, we obtain explicit mass formulae for all Euclidean self-orthogonal and self-dual codes of an arbitrary length over finite commutative chain rings of odd characteristic. With the help of these mass formulae, we classify all Euclidean self-orthogonal and self-dual codes of lengths 2,3,4,5 over the chain ring F5[u]∕<u2> and of lengths 2,3,4 over the chain ring F7[u]∕<u2>.
TORIQ: Android-based mobile learning for vector lessons

Abstract This research aims to produce an Android-based learning media using Adobe Flash CS6 Professional named TORIQ—Vector Intelligence Quiz. The product was developed by attention to aspects of validity, practicality, and effectiveness in X-grade students’ mathematics learning of Airlangga Health Vocational School of Balikpapan. The implementation used the design of MDLC model development which consists of concept, design, material collecting, assembly, testing, and distribution. The results were obtained from media testing consisting of Alpha 1 test, Alpha 2 test, Beta test in small group, and Beta test in large group. The results concluded that the Android-based TORIQ learning media that have been developed are included in the category of highly valid with a percentage of 86.928%, very practical with an average percentage of 85.208%; and effective with a percentage of completeness of 93.333%. Thus, as a whole, the TORIQ learning media developed can be an alternative media in learning activities, especially on vector material.
A Game about our Neighbourhood: A Case Study of Participatory Game Design with Pre-school Children

Game design can have a positive impact on children's game literacy skills, on domain specific knowledge, and on cognitive skills such as problem solving. This paper reports a participatory game design activity involving early childhood education students. Seven preschoolers, aged 4-5, were asked to design a digital game in the framework of a year-long school programme of activities on architecture. An exploratory approach using a qualitative research methodology was adopted and data from participatory observation and video recordings were analysed. Our findings indicate the potential of preschool children for the design of digital games, as well as their perceptions on digital games. Implications on the design of participatory game design activities in preschool education are further discussed. Further research in the field of participatory game design in preschool education can provide valuable insights regarding children's needs and requirements in relation to digital games, as well as their perceptions and approaches to game design.
Two Decades of Empirical Research on Developers' Information Needs

Over the last two decades, developers' daily intake of information has been constantly on the rise and so has the interest of research in investigating the information needs of developers. Knowledge about what information they seek and which sources they rely on is scarce and has to be updated regularly to match the rapid changes in development practices. In this paper, we reflect on the scientific studies published in this field over the last two decades. We present preliminary results of our analysis of a study sample where we particularly focus on the research methods used, the number of recruited participants, and the organisational context in which they emerged. We have investigated a total of 54 studies from 41 publications and found that convenience sampling is the predominant sampling strategy with a prevalence of the industrial organisational context. Moreover, the majority of studies had a reduced sample size and draw participants from a single organisation resulting in high sample homogeneity. Among the studies carried out in industry 51.9% recruited participants from Microsoft.
GNE: Generic Heterogeneous Information Network Embedding

As an effective approach to solve graph mining problems, network embedding aims to learn low-dimensional latent representation of nodes in a network. We develop a representation learning method called GNE for generic heterogeneous information networks to learn the vertex representations for generic HINs. Greatly different from previous works, our model consists two components. First, GNE assigns the probability of each random walk step according to vertex centrality, weight of relations and structural similarity for neighbors on premise of performing a biased self-adaptive random walk generator. Second, to learn more desirable representations for generic HINs, we then design an advanced joint optimization framework by accounting for both the explicit (1st-order) relations and implicit (higher-order) relations.
New Order-Revealing Encryption with Shorter Ciphertexts

As data outsourcing services have been becoming common recently, developing skills to search over encrypted data has received a lot of attention. Order-revealing encryption (OREnc) enables performing a range of queries on encrypted data through a publicly computable function that outputs the ordering information of the underlying plaintexts. In 2016, Lewi et al. proposed an OREnc scheme that is more secure than the existing practical (stateless and non-interactive) schemes by constructing an ideally-secure OREnc scheme for small domains and a “domain-extension” scheme for obtaining the final OREnc scheme for large domains. They encoded a large message into small message blocks of equal size to apply them to their small-domain scheme, thus their resulting OREnc scheme reveals the index of the first differing message block. In this work, we introduce a new ideally-secure OREnc scheme for small domains with shorter ciphertexts. We also present an alternative message-block encoding technique. Combining the proposed constructions with the domain-extension scheme of Lewi et al., we can obtain a new large-domain OREnc scheme with shorter ciphertexts or with different leakage information, but longer ciphertexts.
Mid Day Meals Scheme Monitoring System in School using Image Processing Techniques

The Government of India introduced the Mid - Day meal scheme which is a school meal programme and that also helps to enhance the nutritional standing of school students. This program is designed to support for both primary and secondary school education. This mid day meal scheme provides cost free lunches on all working days for children in primary and upper primary classes in Government schools. In our work Ensuring number of students that took meal is same number as reported. This System monitoring comprise of human face detection through a webcam where identification of image done using an effective algorithm called Viola jones Algorithm. Feature Extraction is a technique that process the captured visual content into image of indexing and retrieval. After face detection, then it checks whether the food is served in plate or not. If food is served then count is increased, else no change in count. Food is recognized through Deep learning approach. With the improvement of deep CNN learning algorithm, Food identification and recognition estimation have been developed. we use Deep Convolutional neural network for visual recognition. So admin can verify lunch served is same as published/reported menu. Alerting in case of discrepancy and capability to centrally see past records / proof (numbers and visual) for any school. In order to observe this activity, we need image processing.
Digital Disease Surveillance

The fight against future pandemics will likely involve digital disease surveillance: the use of digital technology to enhance traditional public-health techniques like contact tracing, isolation, and quarantine. But legal scholarship on digital disease surveillance is still in its infancy. This Article fills that gap. Part I explains the role that digital disease surveillance could have played in responding to coronavirus, and the role it likely will play in future infectious-disease outbreaks. Part II explains how the “special needs” exception to the Fourth Amendment’s warrant requirement permits almost any rationally designed disease surveillance program. Part III suggests safeguards beyond what Fourth Amendment doctrine currently requires that could protect rights without diminishing surveillance effectiveness, including: review for effectiveness and equality, procedural requirements, and periodic legislative authorization. Part IV proposes a mixed standard for judicial review: courts should require these safeguards under an evolving understanding of Fourth Amendment reasonableness while tempering their review with deference to the political branches. Part IV concludes by outlining how the doctrinal evolution spurred by digital disease surveillance programs—the development of a “special needs with bite” standard—might advance a key research agenda in criminal procedure: how to apply the Fourth Amendment to modern, data-driven surveillance regimes.
Image domain adaption of simulated data for human pose estimation

Leveraging the power of deep neural networks, single-person pose estimation has made substantial progress throughout the last years. More recently, multi-person pose estimation has also become of growing importance, mainly driven by the high demand for reliable video surveillance systems in public security. To keep up with these demands, certain efforts have been made to improve the performance of such systems, which is yet limited by the insufficient amount of available training data. This work addresses this lack of labeled data: by diminishing the often faced problem of domain shift between synthetic images from computer game graphics engines and real world data, annotated training data shall be provided at zero labeling-cost. To this end, generative adversarial networks are applied as domain adaption framework, adapting the data of a novel synthetic pose estimation dataset to several real world target domains. State-of-the-art domain adaption methods are extended to meet the important requirement of exact content preservation between synthetic and adapted images. Experiments, that are subsequently conducted, indicate the improved suitability of the adapted data as human pose estimators trained on this data outperform those which are trained on purely synthetic images.
Toward Classification of Arabic Manuscripts Words Based on the Deep Convolutional Neural Networks

Deep learning is an area that has seen many developments in recent years. One of these algorithms that have provided good results is Deep Convolutional Neural Networks (DCNN). It is proven to be effective in various fields such as natural language processing, pattern recognition, computer vision, object detection in images, etc. Despite the development of these technologies, Arabic manuscripts in digital libraries still use traditional indexing methods based on metadata, annotation or transcription. In this article, we propose two methods of word classification based on deep learning, the first one uses a simple Neural Network (DNN) and the last one uses a Convolutional Neural Network (DCNN). The idea is to segment words of Arabic manuscripts images and predict the class of each word. The experimental results show the efficient of this classification system based on the DCNN. By comparing the results obtained, we can observe that the DCNN method provides excellent results than those obtained with the DNN method.
Multi-Scale Adaptive Graph Convolutional Network for Skeleton-Based Action Recognition

Skeleton-based action recognition is a branch of action recognition which uses dynamic skeletons as input. Recent research based on graph convolutional networks (GCN) has achieved remarkable performance in this area. However, feature extraction and fusion at different physical scales have not been well studied. To solve these issues, we propose a novel MultiScale Adaptive Graph Convolutional Network (MSGCN) which contains a Multi-Scale Graph Convolutional Module and a MultiScale Selective Fusion Module. Extensive experiments on NTU-RGBD dataset demonstrate the effectiveness of our method, our method achieved competitive performance on NTU-RGBD dataset.
EMOTIONET: A Multi-Convolutional Neural Network Hierarchical Approach to Facial and Emotional Classification using TPUs

Machine Learning and the tasks of recognition and detection have become a salient topic in recent years. [1] This study is focused on improving the task of using deep convolutional neural networks for facial emotion recognition in the wild with a two-part hierarchical architecture made up of two deep neural networks that are focused on two different tasks. The first task uses a convolutional neural network to classify face or not face. The second task ascertains using another neural network if a face is displaying the emotion within a selected suite of emotions, depicting joyful and sad faces. Factoring the recognition task in this way has produced an observed improvement on previous published work with an overall accuracy of 90% compared to platforms on a CPU cluster and also using TPUs to increase computational power.
A Partial Materialization-Based Approach to Scalable Query Answering in OWL 2 DL

This paper focuses on the efficient ontology-mediated querying (OMQ) problem. Compared with query answering in plain databases, which deals with fixed finite database instances, a key challenge in OMQ is to deal with the possibly infinite large set of consequences entailed by the ontology, i.e., the so-called chase. Existing techniques mostly avoid materializing the chase by query rewriting to address this issue, which, however, comes at the cost of query rewriting and query evaluation at runtime, and the possibility of missing optimization opportunity at the data level. Instead, pure materialization technology is adopted in this paper. The query-rewriting is unnecessary at materialization. A query analysis algorithm (QAA) is proposed for ensuring the completeness and soundness of OMQ over partial materialization for rooted queries in \(\textit{DL-Lite}^{\mathcal {N}}_{ horn } \). We also soundly and incompletely expand our method to deal with OWL 2 DL. Finally, we implement our approach as a prototype system SUMA by integrating off-the-shelf efficient SPARQL query engines. The experiments show that SUMA is complete on each test ontology and each test query, which is the same as Pellet and outperforms PAGOdA. In addition, SUMA is highly scalable on large data sets.