Reviewerly/exHarmony huggingface dataset:
A note on Assmus–Mattson type theorems

In the present paper, we give Assmus–Mattson type theorems for codes and lattices. We show that a binary doubly even self-dual code of length 24m with minimum weight 4m provides a combinatorial 1-design and an even unimodular lattice of rank 24m with minimum norm 2m provides a spherical 3-design. We remark that some of such codes and lattices give t-designs for higher t. As a corollary, we give some restrictions on the weight enumerators of binary doubly even self-dual codes of length 24m with minimum weight 4m. Ternary and quaternary analogues are also given.
A New Architecture of Feature Pyramid Network for Object Detection

In recent years, object detectors generally use the feature pyramid network (FPN) to solve the problem of scale variation in object detection. In this paper, we propose a new architecture of feature pyramid network which combines a top-down feature pyramid network and a bottom-up feature pyramid network. The main contributions of the proposed method are two-fold: (1) We design a more complex feature pyramid network to get the feature maps for object detection. (2) By combining these two architectures, we can get the feature maps with richer semantic information to solve the problem of scale variation better. The proposed method experiments on PASCAL VOC2007 dataset. Experimental results show that the proposed method can improve the accuracy of detectors using the FPN by about 1.67%.
Personalized content recommendations on smart TV: Challenges, opportunities, and future research directions

Web recommender systems play a significant role in different domains, such as movies, books, music, etc., and contributes to not only user satisfaction but also to e-business and e-commerce. It utilizes the user’s profile information, preferences, and activities for recommendations of different objects. However, the distinct nature of watching smart TV greatly affects the accuracy and efficiency of recommender systems due to the reasons that it is a lean-back and multi-user device. Hence, the predictions and calculations of a user’s profile, preferences, and activities cannot be accurately utilized by the recommender systems for recommendations to the exact viewer(s) watching smart TV. This paper presents a critical review of existing recommender systems in the perspectives of smart TV watching scenarios. It highlights the issues and challenges and presents some research opportunities to deal with it. It further presents a subjective study for validating the highlighted factors that affect the recommendation results specifically on a smart TV. Results show that watching activities on a smart TV is significantly different from other devices, such as smartphones and computers. It further shows that smart TV is a non-personalized device and normally enjoyed in groups. Hence, personalized recommendations on smart TV need further investigation. The paper concludes that the existing recommender systems need further improvement to cope with issues of recommendations on a non-personalized device i.e., smart TV. Improving the recommender system for smart TVs may contribute not only to the viewer’s satisfaction but also to the conversion rate.
A Framework for Customizable FPGA-based Image Registration Accelerators

Image Registration is a highly compute-intensive optimization procedure that determines the geometric transformation to align a floating image to a reference one. Generally, the registration targets are images taken from different time instances, acquisition angles, and/or sensor types. Several methodologies are employed in the literature to address the limiting factors of this class of algorithms, among which hardware accelerators seem the most promising solution to boost performance. However, most hardware implementations are either closed-source or tailored to a specific context, limiting their application to different fields. For these reasons, we propose an open-source hardware-software framework to generate a configurable architecture for the most compute-intensive part of registration algorithms, namely the similarity metric computation. This metric is the Mutual Information, a well-known calculus from the Information Theory, used in several optimization procedures. Through different design parameters configurations, we explore several design choices of our highly-customizable architecture and validate it on multiple FPGAs. We evaluated various architectures against an optimized Matlab implementation on an Intel Xeon Gold, reaching a speedup up to 2.86x, and remarkable performance and power efficiency against other state-of-the-art approaches.
Evaluating user reputation of online rating systems by rating statistical patterns

Numerous complex systems such as rating systems are highly affected by a small number of spamming attackers. How to design a fast and effective ranking method under the threat of spamming attacks is significant in practice. In this paper, we extract the user’s rating characteristics from personal historical ratings to determine whether the user is normal. It is discovered that reliable users have little bias and their rating scores follow the pattern of peak distribution. On the opposite, malicious users usually have biased ratings and their rating scores scarcely follow a known pattern. A new reputation ranking method IOR (Iterative Optimization Ranking) is proposed based on user rating deviation and rating characteristics. The experimental results on three real datasets show that this method is more efficient than existing states of art methods. This new fundamental idea can be contributed to a new way to solve spammer attacking problem. It can also be applied in large and sparse bipartite rating networks in a short time.
Understanding Dynamic Scenes using Graph Convolution Networks

We present a novel Multi-Relational Graph Convolutional Network (MRGCN) based framework to model on-road vehicle behaviors from a sequence of temporally ordered frames as grabbed by a moving monocular camera. The input to MRGCN is a multi-relational graph where the graph's nodes represent the active and passive agents/objects in the scene, and the bidirectional edges that connect every pair of nodes are encodings of their Spatio-temporal relations.We show that this proposed explicit encoding and usage of an intermediate spatio-temporal interaction graph to be well suited for our tasks over learning end-end directly on a set of temporally ordered spatial relations. We also propose an attention mechanism for MRGCNs that conditioned on the scene dynamically scores the importance of information from different interaction types.The proposed framework achieves significant performance gain over prior methods on vehicle-behavior classification tasks on four datasets. We also show a seamless transfer of learning to multiple datasets without resorting to fine-tuning. Such behavior prediction methods find immediate relevance in a variety of navigation tasks such as behavior planning, state estimation, and applications relating to the detection of traffic violations over videos.
The GEOFON Program in 2020

Abstract The GEOFON program consists of a global seismic network, a seismological data center, and a global earthquake monitoring system. The seismic network has regional focus in Europe and North Africa as well as throughout the Indian Ocean, but it operates stations on all continents, including Greenland on the North American continental plate and Antarctica. The data center provides real-time seismic data through the SeedLink protocol and historical data from its large archive that currently comprises 120 TB of temporary and permanent seismic network data from GeoForschungsZentrums and third-party partners made available via standard services as part of the European Integrated Data Archive and within the International Federation of Digital Seismograph Networks. GEOFON also provides global and rapid earthquake information. The rapid earthquake information service prioritizes fast information dissemination globally after moderate and large earthquakes based on automatic processing. Most operations are carried out using the SeisComP system. GEOFON distributes findable, accessible, interoperable, reusable data, services, products, and software free of charge, and it is used worldwide by hundreds of users and other data centers.
Frame-based Neural Network for Machine Reading Comprehension

Machine Reading Comprehension (MRC) is one of the most challenging tasks in Natural Language Understanding (NLU). In particular, MRC systems typically answer a question by only utilizing the information contained in a given piece of text passage itself, while human beings can easily understand the meanings of the passage based on their background knowledge. To bridge the gap, we propose a novel Frame-based Neural Network for Machine Reading Comprehension(FNN-MRC) method, which employs Frame semantic knowledge to facilitate question answering. Specifically, different from existing Frame based methods that only model lexical units (LUs), our FNN-MRC has a Frame representation model, which utilizes both LUs in Frame and Frame-to-Frame (F-to-F) relations, designed to model Frames and sentences (in passage) together with attention schema. In addition, FNN-MRC has a Frame-based Sentence Representation (FSR) model, which is able to integrate multiple-Frame semantic information to obtain much better sentence representation. As such, FNN-MRC explicitly leverages the above Frame knowledge to assist its semantic understanding and representation. Extensive experiments demonstrate that our FNN-MRC method is able to achieve better results than existing state-of-the-art techniques across multiple datasets.
Skill-Sleeves: Designing Electrode Garments for Wearability

Many existing explorations of wearables for HCI consider functionality first and wearability second. Typically, as the technologies, designs, and experiential understandings develop, attention can shift towards questions of deployment and wearability. To support this shift of focus we present a case study of the iterative design of electrode sleeves. We consider the design motivations and background that led to the existing, prototype EMS sleeves, and the resultant challenges around their wearability. Through our own design research practice, we seek to reveal design criteria towards the wearability of such a sleeve, and provide designs that optimise for those criteria. We contribute (1) new electrode sleeve designs, which begin to make it practicable to take EMS beyond the lab, (2) new fabrication processes that support rapid production and personalisation, and (3) reflections on criteria for wearability across new eTextile garments.
Gaussian smoothing and modified histogram normalization methods to improve neural-biomarker interpretations for dyslexia classification mechanism

Achieving biologically interpretable neural-biomarkers and features from neuroimaging datasets is a challenging task in an MRI-based dyslexia study. This challenge becomes more pronounced when the needed MRI datasets are collected from multiple heterogeneous sources with inconsistent scanner settings. This study presents a method of improving the biological interpretation of dyslexia’s neural-biomarkers from MRI datasets sourced from publicly available open databases. The proposed system utilized a modified histogram normalization (MHN) method to improve dyslexia neural-biomarker interpretations by mapping the pixels’ intensities of low-quality input neuroimages to range between the low-intensity region of interest ( ROI low ) and high-intensity region of interest ( ROI high ) of the high-quality image. This was achieved after initial image smoothing using the Gaussian filter method with an isotropic kernel of size 4mm. The performance of the proposed smoothing and normalization methods was evaluated based on three image post-processing experiments: ROI segmentation, gray matter (GM) tissues volume estimations, and deep learning (DL) classifications using Computational Anatomy Toolbox (CAT12) and pre-trained models in a MATLAB working environment. The three experiments were preceded by some pre-processing tasks such as image resizing, labelling, patching, and non-rigid registration. Our results showed that the best smoothing was achieved at a scale value, σ = 1.25 with a 0.9% increment in the peak-signal-to-noise ratio (PSNR). Results from the three image post-processing experiments confirmed the efficacy of the proposed methods. Evidence emanating from our analysis showed that using the proposed MHN and Gaussian smoothing methods can improve comparability of image features and neural-biomarkers of dyslexia with a statistically significantly high disc similarity coefficient (DSC) index, low mean square error (MSE), and improved tissue volume estimations. After 10 repeated 10-fold cross-validation, the highest accuracy achieved by DL models is 94.7% at a 95% confidence interval (CI) level. Finally, our finding confirmed that the proposed MHN method significantly outperformed the normalization method of the state-of-the-art histogram matching.
Performance of roof-top PV systems in selected European countries from 2012 to 2019

The electrical energy generated by solar photovoltaic (PV) has a significant contribution to the transition to green energy production. A comprehensive analysis of the yield data and the performance of PV systems is required to be able to plan future energy generation through solar photovoltaics. In this publication, publicly available yield data, configuration, and location of 32,744 roof–top PV systems are evaluated. The analyzed period is from 2012 to 2019 and has a temporal resolution of one day. The considered PV systems are installed in the Netherlands, Belgium, Luxembourg, Germany, France, and Italy. The evaluation conducted here focuses on the widespread, long–term specific yield and performance ratio analysis. The specific yield over the entire area shows the expected south-north gradient. The long–term mean specific yields and performance ratios of systems, that ran continuously from 2015 to 2019, were 947 kWh/kWp, 0.73 in the Netherlands, 959 kWh/kWp, 0.73 in Belgium, 979 kWh/kWp, 0.73 in Luxembourg, 990 kWh/kWp, 0.74 in Germany, 1102 kWh/kWp, 0.73 in France, and 1195 kWh/kWp, 0.73 in Italy. The average performance ratio of PV systems that ran each of five consecutive years from 2015 to 2019 shows no correlation with system capacity. There was no increase in performance trend of PV systems that were installed after 2008.
Cloud Quantum Computing Concept and Development: A Systematic Literature Review

A cloud quantum computer is a quantum computer that can be accessed in a cloud environment through a network. Today, there are numbers of cloud quantum computing services that can be accessed by users. They are used to solve complex problems that require powerful computing. Different cloud quantum computing services deliver different architecture and performances. In our study, we conducted a research on some services to test and evaluate the performances of different cloud quantum computing services and make a comparison out of it. The test will be conducted using two different methods such as visual programming and qiskit. From the result, we can see that the amount of qubit per backend and shots per run pretty much affect the execution time of a cloud quantum computing. This test will give the users some insight and enables them to decide which cloud quantum computing services deliver better performance or faster execution time based on the specification each cloud quantum computer offers.
Differential Privacy on the Unit Simplex via the Dirichlet Mechanism

As members of network systems share more information among agents and with network providers, sensitive data leakage raises privacy concerns. Motivated by such concerns, we introduce a novel mechanism that privatizes vectors belonging to the unit simplex. Such vectors can be found in many applications, such as privatizing a decision-making policy in a Markov decision process. We use differential privacy as the underlying mathematical framework for this work. The introduced mechanism is a probabilistic mapping that maps a vector within the unit simplex to the same domain using a Dirichlet distribution. We find the mechanism well-suited for inputs within the unit simplex because it always returns a privatized output that is also in the unit simplex. Therefore, no further projection back onto the unit simplex is required. We verify and quantify the privacy guarantees of the mechanism for three cases: identity queries, average queries, and general linear queries. We establish a trade-off between the level of privacy and the accuracy of the mechanism output, and we introduce a parameter to balance the trade-off between them. Numerical results illustrate the proposed mechanism.
WebMedic

Users in developing regions still suffer from poor web performance, mainly due to their unique landscape of low-end devices. In this paper, we uncover a root cause of this suboptimal performance by cross-analyzing longitudinal resource (in particular, memory) profiles from a large social network, and the memory consumption of modern webpages in five regions. We discover that the primary culprit for hitting memory constraints is JavaScript execution which existing optimizations are ill-suited to alleviate. To handle this, we propose WebMedic, an approach that trades-off less critical functionality of a webpage to directly address memory and performance problems.
Detection of Hate Tweets using Machine Learning and Deep Learning

Cyberbullying has become a highly problematic occurrence due to its potential of anonymity and its ease for others to join in the harassment of victims. The distancing effect that technological devices have, has led to cyberbullies say and do harsher things compared to what is typical in a traditional face-to-face bullying situation. Given the great importance of the problem, detection is becoming a key area of cyberbullying research. Therefore, it is highly necessary for a framework to accurately detect new cyberbullying instances automatically. To review the machine learning and deep learning approaches, two datasets were used. The first dataset was provided by the University of Maryland consisting of over 30,000 tweets, whereas the second dataset was based on the article `Automated Hate Speech Detection and the Problem of Offensive Language' by Davidson et al., containing roughly 25,000 tweets. The paper explores machine learning approaches using word embeddings such as DBOW (Distributed Bag of Words) and DMM (Distributed Memory Mean) and the performance of Word2vec Convolutional Neural Networks (CNNs) to classify online hate.
ICPR 2020 - Competition on Harvesting Raw Tables from Infographics

This work summarizes the results of the second Competition on Harvesting Raw Tables from Infographics (ICPR 2020 CHART-Infographics). Chart Recognition is difficult and multifaceted, so for this competition we divide the process into the following tasks: Chart Image Classification (Task 1), Text Detection and Recognition (Task 2), Text Role Classification (Task 3), Axis Analysis (Task 4), Legend Analysis (Task 5), Plot Element Detection and Classification (Task 6.a), Data Extraction (Task 6.b), and End-to-End Data Extraction (Task 7). We provided two sets of datasets for training and evaluation of the participant submissions. The first set is based on synthetic charts (Adobe Synth) generated from real data sources using matplotlib. The second one is based on manually annotated charts extracted from the Open Access section of the PubMed Central (UB PMC). More than 25 teams registered out of which 7 submitted results for different tasks of the competition. While results on synthetic data are near perfect at times, the same models still have room to improve when it comes to data extraction from real charts. The data, annotation tools, and evaluation scripts have been publicly released for academic use.
Sampling Based Tumor Recognition in Whole-Slide Histology Image With Deep Learning Approaches

Histopathological identification of tumor tissue is one of the routine pathological diagnoses for pathologists. Recently, computational pathology has been successfully interpreted by a variety of deep learning-based applications. Nevertheless, the high-efficient and spatial-correlated processing of individual patches have always attracted attention in whole-slide image (WSI) analysis. In this paper, we propose a high-throughput system to detect tumor regions in colorectal cancer histology slides precisely. We train a deep convolutional neural network (CNN) model and design a Monte Carlo (MC) adaptive sampling method to estimate the most representative patches in a WSI. Two conditional random field (CRF) models are designed, namely the correction CRF and the prediction CRF are integrated for spatial dependencies of patches. We use three datasets of colorectal cancer from The Cancer Genome Atlas (TCGA) to evaluate the performance of the system. The overall diagnostic time can be reduced from 56.7 percent to 71.7 percent on the slides of a varying tumor distribution, with an increase in classification accuracy.
StructADMM: Achieving Ultrahigh Efficiency in Structured Pruning for DNNs

Weight pruning methods of deep neural networks (DNNs) have been demonstrated to achieve a good model pruning rate without loss of accuracy, thereby alleviating the significant computation/storage requirements of large-scale DNNs. Structured weight pruning methods have been proposed to overcome the limitation of irregular network structure and demonstrated actual GPU acceleration. However, in prior work, the pruning rate (degree of sparsity) and GPU acceleration are limited (to less than 50%) when accuracy needs to be maintained. In this work, we overcome these limitations by proposing a unified, systematic framework of structured weight pruning for DNNs. It is a framework that can be used to induce different types of structured sparsity, such as filterwise, channelwise, and shapewise sparsity, as well as nonstructured sparsity. The proposed framework incorporates stochastic gradient descent (SGD; or ADAM) with alternating direction method of multipliers (ADMM) and can be understood as a dynamic regularization method in which the regularization target is analytically updated in each iteration. Leveraging special characteristics of ADMM, we further propose a progressive, multistep weight pruning framework and a network purification and unused path removal procedure, in order to achieve higher pruning rate without accuracy loss. Without loss of accuracy on the AlexNet model, we achieve 2.58× and 3.65× average measured speedup on two GPUs, clearly outperforming the prior work. The average speedups reach 3.15× and 8.52× when allowing a moderate accuracy loss of 2%. In this case, the model compression for convolutional layers is 15.0× , corresponding to 11.93× measured CPU speedup. As another example, for the ResNet-18 model on the CIFAR-10 data set, we achieve an unprecedented 54.2× structured pruning rate on CONV layers. This is 32× higher pruning rate compared with recent work and can further translate into 7.6× inference time speedup on the Adreno 640 mobile GPU compared with the original, unpruned DNN model. We share our codes and models at the link http://bit.ly/2M0V7DO.