Reviewerly/exHarmony huggingface dataset:
An evaluation of a virtual atlas of portion sizes (VAPS) mobile augmented reality for portion size estimation

Abstract Food portion size estimation is a critical yet challenging task in dietary assessment. Augmented reality technology enables the presentation of food dimensions and volume in a virtual three-dimensional object. It has the potential to improve perception and estimation of portion sizes. This study aims to develop and evaluate a novel mobile augmented reality application, namely Virtual Atlas of Portion Sizes (VAPS), as a portion size estimation aid. The development methodology of VAPS involves food photography, reconstruction of 3D models using photogrammetry method and presenting them in an AR environment. The 3D food models displayed in either semi-transparent or vivid mode for users to perform food portion estimation. Users can then resize and rotate the 3D models to fit the virtual model with the actual food. A total of thirty-six participants were involved in the evaluation and were divided into a health science and a non-health science background group. VAPS received good usability level with 76 SUS score. In terms of task completion time, unsurprisingly, the health science group performed faster. However, both groups have equivalent accuracy on the food portion estimation task using VAPS: 22.5% for non-health science group and 26.6% for health science group. The health science group liked and have better accuracy in vivid 3D food models (37.5%). Meanwhile, the non-health science group preferred semi-transparent 3D food models, but the accuracy is not significantly different between semi-transparent (25%) and vivid 3D food model (20%). Results demonstrate the potential of VAPS to aid in portion size estimation for dietary assessment, and participants’ feedback will be incorporated in the future for improvement of the app.
T-Lease

A lease is an important primitive for building distributed protocols, and it is ubiquitously employed in distributed systems. However, the scope of the classic lease abstraction is restricted to the trusted computing infrastructure. Unfortunately, this important primitive cannot be employed in the untrusted computing infrastructure because the trusted execution environments (TEEs) do not provide a trusted time source. In the untrusted environment, an adversary can easily manipulate the system clock to violate the correctness properties of lease-based systems.
Regularization with Latent Space Virtual Adversarial Training

Virtual Adversarial Training (VAT) has shown impressive results among recently developed regularization methods called consistency regularization. VAT utilizes adversarial samples, generated by injecting perturbation in the input space, for training and thereby enhances the generalization ability of a classifier. However, such adversarial samples can be generated only within a very small area around the input data point, which limits the adversarial effectiveness of such samples. To address this problem we propose LVAT (Latent space VAT), which injects perturbation in the latent space instead of the input space. LVAT can generate adversarial samples flexibly, resulting in more adverse effect and thus more effective regularization. The latent space is built by a generative model, and in this paper we examine two different type of models: variational auto-encoder and normalizing flow, specifically Glow. We evaluated the performance of our method in both supervised and semi-supervised learning scenarios for an image classification task using SVHN and CIFAR-10 datasets. In our evaluation, we found that our method outperforms VAT and other state-of-the-art methods.
Transfer subspace learning based on structure preservation for JPEG image mismatched steganalysis

In real-world steganalysis applications, the traditional steganalysis methods built by a set of training data coming from a source may be applied to detect data from another new different source. In this case, the steganalyzers will face a serious problem that training data and test data are no longer subjected to the same distribution, and thus the detection performance would degrade rapidly. To address this problem, a novel transfer subspace learning method with structure preservation for image steganalysis is proposed in this paper. It aims to alleviate the mismatch between the training and test data so as to improve the detection performance. Specifically, a discriminant projection matrix is learned for the training and test data such that the projected data of both sets lie in a common subspace where each sample can be linearly reconstructed by a combination of the training data. In this way, the difference between the training and test sets is decreased. Further, in order to preserve the structure information of features in the projection subspace, a Frobenius-norm based regularization term is introduced into the objective function. Moreover, to mitigate the negative impacts of noises and outliers, a structurally sparse error matrix is introduced to model the noise and outlier information. The formulation of the proposed method can be efficiently solved by an alternating optimization algorithm. The extensive experiments compared with prior arts show the validity of the proposed method for JPEG image mismatched steganalysis.
Deep Transfer Learning Model for Automated Screening of Cervical Cancer Cells Using Multi-cell Images

In the automated screening of cervical cancer using raw micro scoping images, the segmentation of individual cells is time taking and error prone. Therefore, extracting individual cells automatically during inference time is not possible. The automated classification models based on the extraction of hand-crafted features such as texture, morphology, etc. are not very accurate always. In this article, a transfer learning based deep EfficientNet model is used for screening of cervical cancer without any segmentation for reducing manual errors and improving time constraints. Initially, the EfficientNet is trained on the dataset containing original images from the ImageNet dataset and then on the dataset containing the cervical cell microscopic images that consists of different categories of cervix cells. This method has been evaluated on the Herlev Pap smear dataset. Instead of working on individual cells like previous methods, we used to work on images having multiple cells. The number of cells inferred per unit time drastically increases. Results show that EfficientNet model performs well in classification, when applied to the Herlev benchmark Pap smear dataset and evaluated using ten-fold cross-validation. The used model is promising in terms of the time required for the inference as compared to other methods. The performance comparison with other models shows that the accuracy and other scores obtained by EfficientNet is improved with reduction processing time.
Current research information systems and institutional repositories: From data ingestion to convergence and merger

Current research information systems (CRIS) and institutional repositories (IR) were developed as clearly distinguished systems, with different objectives and functionalities, standards and data models, and for different needs and user groups. While academic librarians are often deeply committed to the management of open access and IR, they are less involved and familiar with research evaluation and CRIS. After a period of separate implementation of CRIS and IR, both systems started to converge and even to merge. Today IR often fulfil the requirements of monitoring and assessment of institutional research performance while CRIS, beyond the processing of metadata, begin to store, preserve, and disseminate research papers. This chapter describes and explains the underlying dynamics, with examples to illustrate the benefits, risks, and the potential barriers of this convergence. Special attention is paid to the role of data quality and user acceptance, and to the implications for the academic librarian.
Fantastic Knowledge Graph Embeddings and How to Find the Right Space for Them

During the last few years, several knowledge graph embedding models have been devised in order to handle machine learning problems for knowledge graphs. Some of the models which were proven to be capable of inferring relational patterns, such as symmetry or transitivity, show lower performance in practice than those not allowing to infer those patterns. It is often unknown what factors contribute to such performance differences among KGE models in the inference of particular patterns. We develop the concept of a solution space as a factor that has a direct influence on the practical performance of knowledge graph embedding models as well as their capability to infer relational patterns. We showcase the effect of solution space on a newly proposed model dubbed SpacE $$^{ss}$$ . We describe the theoretical considerations behind the solution space and evaluate our model against state-of-the-art models on a set of standard benchmarks namely WordNet and FreeBase.
Peafowl

The traffic load sent to key-value (KV) stores varies over long timescales of hours to short timescales of a few microseconds. Long-term variations present the opportunity to save power during low or medium periods of utilization. Several techniques exist to save power in servers, including feedback-based controllers that right-size the number of allocated CPU cores, dynamic voltage and frequency scaling (DVFS), and c-state (idle-state) mechanisms. In this paper, we demonstrate that existing power saving techniques are not effective for KV stores. This is because the high rate of traffic even under low load prevents the system from entering low power states for extended periods of time. To achieve power savings, we must unbalance the load among the CPU cores so that some of them can enter low power states during periods of low load. We accomplish this by introducing the notion of in-application CPU scheduling. Instead of relying on the kernel to schedule threads, we pin threads to bypass the kernel CPU scheduler and then perform the scheduling within the KV store application. Our design, Peafowl, is a KV store that features an in-application CPU scheduler that monitors the load to learn the workload characteristics and then scales the number of active CPU cores when the load drops, leading to notable power savings during low or medium periods of utilization. Our experiments demonstrate that Peafowl uses up to 40--54% lower power than state of the art approaches such as Rubik and μDPM.
Automated Item Generation: Model and Development Technique

The development of Computer Assisted Assessment (CAA) has helped the assessment process. Some of the advantages offered by these tools are effectivity and speed. But there are still obstacles in the process of compiling the items. Therefore, we need tools to overcome this problem. Automated Item Generation (AIG) is a tool to generate questions using computer devices. Several models and techniques in AIG had been developed. AIG research is grouped into 2 models i.e. the framework model and special model. In the previous study, the AIG was divided into two approaches, automatic and semi-automatic. Several techniques were developed to produce high-quality and variety of items. Each technique has advantages and disadvantages in generating items. Thus, there is still a need for developing AIG to answer the challenges that have not been resolved in the previous study. This paper presents a literature review to identify a specific problem in each model and technique especially for mathematic and non-verbal items, validity and reliability of the items, and the schema or infrastructure presented to generate the items. This paper also gives possible suggestions on how to make an improvement the items generated to be better in the future.
The Impact of the COVID-19 Pandemic on the Use of Digital Technologies in Ensuring the Efficient e-Learning Process at the Technical University of Moldova

The Technical University of Moldova (TUM) is the only state-accredited technical higher education institution in the Republic of Moldova.In the face of the magnitude of the health crisis, TUM had to quickly adapt its mode of operation to ensure the continuity of its activities and to accompany its students in the study process in an efficient way.In this paper, we analyzed the impact of the COVID-19 pandemic on eLearning and on the use of Moodle and Microsoft 365 educational platforms, implemented at the Technical University of Moldova.The data were exported from M365 Usage Report in excel format for a period of 180 days (from 15.02.2020 to 15.08.2020) and from the Moodle database for a period of about 210 days (period 31.12.2019-31.07.2020).Those intervals include the pre-pandemic period, the lockdown period and the student holiday period.The aim of the research was to analyze the potential of the university to continue and ensure the online education process internally, to share the lived experience, the measures taken to anticipate the widening of the digital divide, but also for the identification of vulnerabilities and reasoned planning of exit strategies, in an appropriate frame of reference, depending on subsequent developments related to the COVID-19 pandemic.The Technical University of Moldova anticipated and undertook multiple measures that demonstrated the potential to ensure the continuity and good development of the teaching process during the pandemic by: reducing the digital divide; reducing the risks of inequalities in education for students from disadvantaged backgrounds; ensuring the social binder (im-How to cite this paper:
Concept Extraction Using Pointer–Generator Networks and Distant Supervision for Data Augmentation

Concept extraction is crucial for a number of downstream applications. However, surprisingly enough, straightforward single token/nominal chunk–concept alignment or dictionary lookup techniques such as DBpedia Spotlight still prevail. We propose a generic open domain-oriented extractive model that is based on distant supervision of a pointer–generator network leveraging bidirectional LSTMs and a copy mechanism and that is able to cope with the out-of-vocabulary phenomenon. The model has been trained on a large annotated corpus compiled specifically for this task from 250K Wikipedia pages, and tested on regular pages, where the pointers to other pages are considered as ground truth concepts. The outcome of the experiments shows that our model significantly outperforms standard techniques and, when used on top of DBpedia Spotlight, further improves its performance. The experiments furthermore show that the model can be readily ported to other datasets on which it equally achieves a state-of-the-art performance.
Punch Typing

A common way to perform data entry in virtual reality remains using virtual laser pointers to select characters from a flat 2D keyboard in 3-dimensional space. In this demo, we present a data input method that takes advantage of 3D space by interacting with a keyboard with keys arranged in three dimensions. Each hand is covered by a hemisphere of keys based on the QWERTY layout, allowing users to type by moving their hands in a motion similar to punching. Although the goal is to achieve a gesture more akin to tapping, current controllers or hand tracking technology doesn't allow such high fidelity. Thus, the presented interaction using VR controllers is more comparable to punching.
Detecting Uncertain BNN Outputs on FPGA Using Monte Carlo Dropout Sampling

Monte Carlo dropout sampling (MC Dropout), which approximates a Bayesian Neural Network, is useful for measuring the uncertainty in the output of a Deep Neural Network (DNN). However, because it takes a long time to sample DNN's output for calculating its distribution, it is difficult to apply it to edge computing where resources are limited. Thus, this research proposes a method of reducing a sampling time required for MC Dropout in edge computing by parallelizing the calculation circuit using FPGA. To apply MC dropout in an FPGA, this paper shows an efficient implementation by binarizing the neural network and simplifying dropout computation by pre-dropout and localizing parallel circuits. The proposed method was evaluated using the MNIST dataset and a dataset of satellite images of ships at sea captured. As a result, it was possible to reject approximately 60% of data which the model had not learned as "uncertain" on a classification identification problem of the image on an FPGA. Furthermore, for 20 units in parallel, the amount of increase in the circuit scale was only 2–3 times that of non-parallelized circuits. In terms of inference speed, parallelization of dropout circuits has achieved up to 3.62 times faster.
Blockchain vs GDPR in Collaborative Data Governance

Data Governance is the trending topic in today's security-privacy-concerned digital ecosystem. Blockchain technology is probably one of the most acclaimed evolutions in recent times. Blockchain technologies can be a game-changer for data governance in the areas of transparency and data provenance. As a distributed ledger technology (DLT), blockchain is being touted as a potentially transformational force in collaborative data governance. The General Data Protection Regulation (GDPR) entered into force on May 25, 2018. It is the latest in a series of European Union (EU) legislative measures designed to give EU citizens more control over their data. GDPR, which directs a centralized 'data controller' (GDPR Article 4) to manage user data, clashes with the blockchain's decentralized data storage and management process. The GDPR and the blockchain both have a common ideological ground, emphasizing the need for a change in managing personal data. While GDPR takes care of the policy side by setting up a standard, the blockchain helps enable the implementation side by providing a unique framework. In this paper, the authors analyze the clashes between the two and the potential solutions to those clashes for blockchain to comply with GDPR.
Disambiguating Arabic Words According to Their Historical Appearance in the Document Based on Recurrent Neural Networks

How can we determine the semantic meaning of a word in relation to its context of appearance? We eventually have to grabble with this difficult question, as one of the paramount problems of Natural Language Processing (NLP). In other words, this issue is commonly defined as Word Sense Disambiguation (WSD). The latter is one of the crucial difficulties within the NLP field. In this respect, word vectors extracted from a neural network model have been successfully applied for resolving the WSD problem. Accordingly, this article presents an unprecedented method to disambiguate Arabic words according to both their contextual appearance in a source text and the era in which they emerged. In fact, in the few previous decades, many researchers have been grabbling with Arabic Word Sense Disambiguation. It should be noted that the Arabic language can be divided into three major historical periods: old Arabic, middle-age Arabic, and contemporary Arabic. Actually, contemporary Arabic has proved to be the greatest concern of many researchers. The main gist of our work is to disambiguate Arabic words according to the historical period in which they appeared. To perform such a task, we suggest a method that deploys contextualized word embeddings to better gather valid syntactic and semantic information of the same word by taking into account its contextual uses. The preponderant thing is to convert both the senses and the contextual uses of an ambiguous item to vectors, then determine which of the possible conceptual meanings of the target word is closer to the given context.
Person Detection in Drone Imagery

The use of drones in search and rescue operations has become standard almost everywhere in the world. A special challenge in the search and rescue operation is the automatic detection of persons in different terrains, in different situations and body positions, in different weather conditions, and from different shooting heights during a drone flight. This paper investigates the accuracy of people detection in drone images on existing VisDrone, Okutama - Action datasets, and on a custom SARD image dataset built to simulate search and rescue scenes. A Faster R-CNN with FPN as the backbone, pre-trained on the COCO data set, was used as a detector. The person detector is additionally trained on the SARD data set containing 1,981 images and on the subset of the VisDrone set. After transfer learning, a significant improvement in the detection results of persons in the images taken by the drone was achieved concerning mAP and precision and recall.
TinyGarble2

We present TinyGarble2 -- a C++ framework for privacy-preserving computation through the Yao's Garbled Circuit (GC) protocol in both the honest-but-curious and the malicious security models. TinyGarble2 provides a rich library with arithmetic and logic building blocks for developing GC-based secure applications. The framework offers abstractions among three layers: the C++ program, the GC back-end and the Boolean logic representation of the function being computed. TinyGarble2 thus allowing the most optimized versions of all pertinent components. These abstractions, coupled with secure share transfer among the functions make TinyGarble2 the fastest and most memory-efficient GC framework. In addition, the framework provides a library for Convolutional Neural Networks (CNN). Our evaluations show that TinyGarble2 is the fastest among the current end-to-end GC frameworks while also being scalable in terms of memory footprint. Moreover, it performs 18x faster on the CNN LeNet-5 compared to the existing scalable frameworks.
Memory Network for Linguistic Structure Parsing

Memory-based learning can be characterized as a lazy learning method in machine learning terminology because it delays the processing of input by storing the input until needed. Linguistic structure parsing, which has been in a performance improvement bottleneck since the latest series of works was presented, determines the syntactic or semantic structure of a sentence. In this article, we construct a memory component and use it to augment a linguistic structure parser which allows the parser to directly extract patterns from the known training treebank to form memory. The experimental results show that existing state-of-the-art parsers reach new heights of performance on the main benchmarks for dependency parsing and semantic role labeling with this memory network.
My Little Robot: User Preferences in Game Agent Customization

Agents are part of our daily lives. They are in the games we play and in our phones as personal assistants. As users develop relationships with these agents, one question is about how to cultivate those relationships. This study chose one design feature, customization, to understand whether it would affect user experiences and whether there are optimal number of choices users prefer. This paper describes the results of two studies. The first, a small scale (N=15), preliminary study explored user preferences when customizing an on-screen, robot-like, virtual agent partner. The data includes eye-tracking, screen capture, and a post-survey. Findings from this study suggests that a robot's clothes (both top and bottom) are the most important for users to customize. Informed by the first study, a custom application was developed to test user preferences for the number of choices and attributes in a large scale online study (N = 343). Findings from the second study suggest that participants preferred at least 8 different attributes to customize and they showed no decrease in satisfaction with the most number of choices (i.e., 24) per attribute. These findings are different from previous studies on choice overload and contribute to further understanding of choices in games and digital spaces.