Reviewerly/exHarmony huggingface dataset:
Online Training of Stereo Self-Calibration Using Monocular Depth Estimation

Stereo imaging is the most common passive method for producing reliable depth maps. Calibration is a crucial step for every stereo-based system, and despite all the advancements in the field, most calibrations are still done by the same tedious method using a checkerboard target. Monocular-based depth estimation methods do not require extrinsic calibration but generally achieve inferior depth accuracy. In this paper, we present a novel online self-calibration method, which makes use of both stereo and monocular depth maps to find the transformation required for extrinsic calibration by enforcing consistency between both maps. The proposed method works in a closed-loop and exploits the pre-trained networks' global context, and thus avoids feature matching and outliers issues. In addition to presenting our method using an image-based monocular depth estimation method, which can be implemented in most systems without additional changes, we also show that adding a phase-coded aperture mask leads to even better and faster convergence. We demonstrate our method on road scenes from the KITTI vision benchmark and real-world scenes using our prototype camera. Our code is publicly available at https://github.com/YotYot/CalibrationNet.
Efficient and Accurate Flow Record Collection With HashFlow

Traditional tools like NetFlow face great challenges as both the speed and the complexity of the network traffic increase. To keep the pace up, we propose HashFlow for more efficient and accurate collection of flow records. HashFlow keeps large flows in its main flow table and uses an ancillary table to summarize the other flows when the main table is full. With our <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">flow collision resolution</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">flow record promotion</i> schemes, a flow in the ancillary table is promoted back to the main flow table with a guaranteed probability when it becomes large enough. These operations can be performed highly efficiently, so HashFlow can keep up with ultra-high traffic speed. We implement HashFlow in a Tofino switch, and using traces from different operational networks, we compare its performance against some state-of-the-art flow measurement algorithms. Our experiments show that, for various types of traffic analysis applications, HashFlow consistently demonstrates clearly better performance than its competitors. For example, the performance of HashFlow in flow size estimation, flow size distribution estimation and heavy hitter detection is up to 21, 60 and 35 percent better than those of the best competitors respectively, and these merits of HashFlow come with almost no degradation of throughput.
Optimal Transportation Methods in Nonlinear Filtering

How data became one of the most powerful tools to fight an epidemic is a question that a recent (10 June 2020) The New York Times article poses in its title. Indeed, the spread of COVID-19 involves dynamically evolving hidden data (for example, the number of infected people, the number of asymptomatic people) that must be deduced from noisy and partially observed data (for example, the number of daily deaths, the number of daily hospitalizations, and the number of daily positive tests). The underlying mathematics for posing and solving this and several other partially observed dynamic problems is familiar to control theorists.
PointFace: Point Set Based Feature Learning for 3D Face Recognition

Though 2D face recognition (FR) has achieved great success due to powerful 2D CNNs and large-scale training data, it is still challenged by extreme poses and illumination conditions. On the other hand, 3D FR has the potential to deal with aforementioned challenges in the 2D domain. However, most of available 3D FR works transform 3D surfaces to 2D maps and utilize 2D CNNs to extract features. The works directly processing point clouds for 3D FR is very limited in literature. To bridge this gap, in this paper, we propose a light-weight framework, named PointFace, to directly process point set data for 3D FR. Inspired by contrastive learning, our PointFace use two weight-shared encoders to directly extract features from a pair of 3D faces. A feature similarity loss is designed to guide the encoders to obtain discriminative face representations. We also present a pair selection strategy to generate positive and negative pairs to boost training. Extensive experiments on Lock3DFace and Bosphorus show that the proposed PointFace outperforms state-of-the-art 2D CNN based methods.
Updating the Content of Higher Education in the Context of Modern Sociocultural Trends

In the article, on the basis of the culturological concept of the content of education as an adapted social experience, as well as the practice of its implementation in vocational training as purposeful and declared, the essence of its explicit, hidden and implicit components is differentiated for the first time. The leading role of the hidden content of education in the transmission of sociocultural values is revealed, which ensures the formation of the value-worldview characteristics of the personality of a competent graduate, capable of effective professional activity in the conditions of sociocultural transformations of the postindustrial era. The dynamic structure of the hidden education content is revealed, which includes communicative and subject components and which made it possible to determine the directions of its update by the sociocultural aspect. The update of the education content in the context of the growing role of the cultural factor in social development is realized through the sociocultural reflection of the transmitted social experience, for the development of which the diagnostic-forming methodology “My sociocultural values” has been developed. As a result of the update of the hidden education content by the sociocultural aspect, it is transformed into an implicit (updated hidden), which, while not being declared, but being adapted and purposeful in reflecting and translating sociocultural experience, improves the training of specialists in various directions and profiles, implements the leading principle of higher education.
Livelyzer

Over-the-top (OTT) live video traffic has grown significantly, fueled by fundamental shifts in how users consume video content (e.g., increased cord-cutting) and by improvements in camera technologies, computing power, and wireless resources. A key determining factor for the end-to-end live streaming QoE is the design of the first-mile upstream ingest path that captures and transmits the live content in real-time, from the broadcaster to the remote video server. This path often involves either a Wi-Fi or cellular component, and is likely to be bandwidth-constrained with time-varying capacity, making the task of high-quality video delivery challenging. Today, there is little understanding of the state of the art in the design of this critical path, with existing research focused mainly on the downstream distribution path, from the video server to end viewers.
Lightweight and Reliable Decentralized Reward System using Blockchain

Crowdsensing has become increasingly popular in recent years by leveraging the sensing capability of the ubiquitous Internet of Things (IoT) and mobile devices. A critical component to enable effective crowdsensing is a reliable reward system to motivate the participation of crowdsensing. In traditional crowdsensing applications, the assessment of data quality and the evaluation of each participant's rewards are performed by the central server. Hence, their fairness and reliability are based on the assumption that the server will behave correctly. Once the server suffers from software or hardware failures, or even cheats on purpose, the profit of participants cannot be guaranteed. In this paper, we propose a reliable decentralized reward system for crowdsensing by harnessing the blockchain technique. Different from existing blockchain-based crowdsensing solutions that utilize expensive consensus mechanisms in terms of computation or financial cost, we explore the power of reputation system that exists in most crowdsensing applications and securely integrate it into blockchain to design a proof of reputation consensus mechanism. On top of it, an efficient and reliable reward system using blockchain is further designed. We evaluated the performance of our proposed reward system using numerical analysis as well as simulation.
Multiple-source Data Collection and Processing into a Graph Database Supporting Cultural Heritage Applications

The continuous growth of available resources on the web, both in the form of Linked Open Data and on Social Networks, provides an important opportunity to gather information concerning specific kinds of touristic activities like, for example, cultural tourism, eco-tourism, bike-tourism, and so on. Both decision makers and tourists can take advantage from these data, as demonstrated by previous works, with institutional actors foreseeing an increase in the use of this data to substitute other time-consuming and expensive approaches. However, managing multiple sources built with different goals and structures is not straightforward, so specific design choices must be made when assembling this kind of information. Graph databases represent an ideal way to combine multiple-source data but, to be successful, strategies accounting for inconsistencies and format differences have to be defined to support coherent analysis. Also, the continuously changing nature of crowd-sourced data makes it difficult, for the research community, to compare technological approaches to the different tasks that are linked to cultural heritage, from recommendation to management. To support the research effort in this direction, we describe the data ingestion and enrichment procedure we followed to organise knowledge coming from three different sources, namely Wikidata, Wikipedia, and Flickr, into a single, application-oriented, resource organised as a graph database. We present the potential use of this resource to perform multiple source analyses targeting the specific case of cultural tourism on a nationwide scale, and we propose its use as a shared benchmark for technological applications designed to support optimal management of cultural resources.
Semi-Automated Classification of Arabic User Requirements into Functional and Non-Functional Requirements using NLP Tools

Functional and non-functional requirements are equally important in software engineering. Both of them are mixed together within the same software requirement document. Usually, they are expressed in natural languages. So, a lot of human effort is required to classify them. Software requirements classification is a challenging task. Requirements classification can help developers to deliver quality software that meets users' expectations completely. In this paper, we present a Semi-Automated classification approach of Arabic functional and non-functional requirements using a natural language processing (NLP) tool. We propose a set of heuristics based on basic constructs of Arabic sentences in order to extract information from Arabic software requirements to classify the requirements into functional and non-functional requirements. This research aims to help software engineers by reducing the cost and time required in performing manual classification of software requirements.
Aggregate Signature with Traceability of Devices Dynamically Generating Invalid Signatures

Aggregate signature schemes enable us to aggregate multiple signatures into a single short signature. One of its typical applications is sensor networks, where a large number of users and devices measure their environments, create signatures to ensure the integrity of the measurements, and transmit their signed data. However, if an invalid signature is mixed into aggregation, the aggregate signature becomes invalid, thus if an aggregate signature is invalid, it is necessary to identify the invalid signature. Furthermore, we need to deal with a situation where an invalid sensor generates invalid signatures probabilistically. In this paper, we introduce a model of aggregate signature schemes with interactive tracing functionality that captures such a situation, and define its functional and security requirements and propose aggregate signature schemes that can identify all rogue sensors. More concretely, based on the idea of Dynamic Traitor Tracing, we can trace rogue sensors dynamically and incrementally, and eventually identify all rogue sensors of generating invalid signatures even if the rogue sensors adaptively collude. In addition, the efficiency of our proposed method is also sufficiently practical.
Unsupervised Sentiment Analysis by Transferring Multi-source Knowledge

Sentiment analysis (SA) is an important research area in cognitive computation—thus, in-depth studies of patterns of sentiment analysis are necessary. At present, rich-resource data-based SA has been well-developed, while the more challenging and practical multi-source unsupervised SA (i.e., a target-domain SA by transferring from multiple source domains) is seldom studied. The challenges behind this problem mainly locate in the lack of supervision information, the semantic gaps among domains (i.e., domain shifts), and the loss of knowledge. However, existing methods either lack the distinguishable capacity of the semantic gaps among domains or lose private knowledge. To alleviate these problems, we propose a two-stage domain adaptation framework. In the first stage, a multi-task methodology-based shared-private architecture is employed to explicitly model the domain-common features and the domain-specific features for the labeled source domains. In the second stage, two elaborate mechanisms are embedded in the shared-private architecture to transfer knowledge from multiple source domains. The first mechanism is a selective domain adaptation (SDA) method, which transfers knowledge from the closest source domain. And the second mechanism is a target-oriented ensemble (TOE) method, in which knowledge is transferred through a well-designed ensemble method. Extensive experiment evaluations verify that the performance of the proposed framework outperforms unsupervised state-of-the-art competitors. What can be concluded from the experiments is that transferring from very different distributed source domains may degrade the target-domain performance, and it is crucial to choose proper source domains to transfer from.
Supervised multi-specialist topic model with applications on large-scale electronic health record data

Motivation: Electronic health record (EHR) data provides a new venue to elucidate disease comorbidities and latent phenotypes for precision medicine. To fully exploit its potential, a realistic data generative process of the EHR data needs to be modelled.
Parameter Tuning of MLP, RBF, and ANFIS Models Using Genetic Algorithm in Modeling and Classification Applications

Nowadays, soft computing algorithms are used to simulate and solve complex problems in different fields. Each of the existing algorithms requires the proper adjustment of parameters to produce the outputs tailored to the user's needs. Using trial-and-error methods to find the correct parameter values is time-consuming and does not guarantee the optimum value. In this paper, a genetic algorithm is employed to obtain optimal parameter values for multi-layer perceptrons, radial basis function, and adaptive neuro-fuzzy inference system algorithms. For each of these algorithms, different coding for chromosomes is provided according to the user's needs. In this approach, the network architecture also changes, unlike many existing schemes that initially assume the same topology for all algorithms. Simulations on standard datasets, including real and artificial datasets in the field of modeling and classification, are presented to verify the resulting performance.
Bias and comparison framework for abusive language datasets

Recently, numerous datasets have been produced as research activities in the field of automatic detection of abusive language or hate speech have increased. A problem with this diversity is that they often differ, among other things, in context, platform, sampling process, collection strategy, and labeling schema. There have been surveys on these datasets, but they compare the datasets only superficially. Therefore, we developed a bias and comparison framework for abusive language datasets for their in-depth analysis and to provide a comparison of five English and six Arabic datasets. We make this framework available to researchers and data scientists who work with such datasets to be aware of the properties of the datasets and consider them in their work.
Learning Then, Learning Now, and Every Second in Between: Lifelong Learning With a Simulated Humanoid Robot

Long-term human-robot interaction requires the continuous acquisition of knowledge. This ability is referred to as lifelong learning (LL). LL is a long-standing challenge in machine learning due to catastrophic forgetting, which states that continuously learning from novel experiences leads to a decrease in the performance of previously acquired knowledge. Two recently published LL approaches are the Growing Dual-Memory (GDM) and the Self-organizing Incremental Neural Network+ (SOINN+). Both are growing neural networks that create new neurons in response to novel sensory experiences. The latter approach shows state-of-the-art clustering performance on sequentially available data with low memory requirements regarding the number of nodes. However, classification capabilities are not investigated. Two novel contributions are made in our research paper: (I) An extended SOINN+ approach, called associative SOINN+ (A-SOINN+), is proposed. It adopts two main properties of the GDM model to facilitate classification. (II) A new LL object recognition dataset (v-NICO-World-LL) is presented. It is recorded in a nearly photorealistic virtual environment, where a virtual humanoid robot manipulates 100 different objects belonging to 10 classes. Real-world and artificially created background images, grouped into four different complexity levels, are utilized. The A-SOINN+ reaches similar state-of-the-art classification accuracy results as the best GDM architecture of this work and consists of 30 to 350 times fewer neurons, evaluated on two LL object recognition datasets, the novel v-NICO-World-LL and the well-known CORe50. Furthermore, we observe an approximately 268 times lower training time. These reduced numbers result in lower memory and computational requirements, indicating higher suitability for autonomous social robots with low computational resources to facilitate a more efficient LL during long-term human-robot interactions.
A Framework for Efficient and Binary Clustering in High-Dimensional Space

Today's applications generate a large amount of data where the majority of the data are not associated with any labels. Clustering methods are the most commonly used algorithms for data analysis, especially in healthcare. However, running clustering algorithms on embedded devices is significantly slow as the computation involves a large amount of complex pairwise similarity measurements. In this paper, we proposed FebHD, an adaptive framework for efficient and fully binary clustering in high-dimensional space. Instead of using complex similarity metrics, e.g., Euclidean distance, FebHD introduces a nonlinear encoder to map data points into sparse high-dimensional space. FebHD encoder simplifies the similarity search, the most costly and frequent clustering operation, to Hamming distance, which can be accelerated in today's hardware. FebHD performs clustering by assigning each data point to a set of initialized centers. It then updates the centers adaptively based on: (i) data points assigned to each cluster, and (ii) the confidence of the model on the clustering prediction. This adaptive update enables FebHD to provide a high quality of clustering with very few learning iterations. We also propose an end-to-end hardware accelerator that parallelizes the entire FebHD computation by exploiting FPGA bit-level granularity. Our evaluation shows that FebHD provides comparable accuracy to state-of-the-art clustering algorithms, while providing 6.2× and 9.1× (4.7× and 5.8×) faster and higher energy efficiency when running on the same FPGA (GPU) platform.
A Case for Emerging Memories in DNN Accelerators

The popularity of Deep Neural Networks (DNNs) has led to many DNN accelerator architectures, which typically focus on the on-chip storage and computation costs. However, much of the energy is spent on accesses to off-chip DRAM memory. While emerging resistive memory technologies such as MRAM, PCM, and RRAM can potentially reduce this energy component, they suffer from drawbacks such as low endurance that prevent them from being a DRAM replacement in DNN applications. In this paper, we examine how DNN accelerators can be designed to overcome these limitations and how emerging memories can be used for off-chip storage. We demonstrate that through (a) careful mapping of DNN computation to the accelerator and (b) a hybrid setup (both DRAM and an emerging memory), we can reduce inference energy over a DRAM-only design by a factor ranging from 1.12× on EfficientNetB7 to 6.3× on ResNet-50, while also increasing the endurance from 2 weeks to over a decade. As the energy benefits vary dramatically across DNN models, we also develop a simple analytical heuristic solely based on DNN model parameters that predicts the suitability of a given DNN for emerging-memory-based accelerators.
RingCNN: Exploiting Algebraically-Sparse Ring Tensors for Energy-Efficient CNN-Based Computational Imaging

In the era of artificial intelligence, convolutional neural networks (CNNs) are emerging as a powerful technique for computational imaging. They have shown superior quality for reconstructing fine textures from badly-distorted images and have potential to bring next-generation cameras and displays to our daily life. However, CNNs demand intensive computing power for generating high-resolution videos and defy conventional sparsity techniques when rendering dense details. Therefore, finding new possibilities in regular sparsity is crucial to enable large-scale deployment of CNN-based computational imaging.In this paper, we consider a fundamental but yet well-explored approach—algebraic sparsity—for energy-efficient CNN acceleration. We propose to build CNN models based on ring algebra that defines multiplication, addition, and non-linearity for n-tuples properly. Then the essential sparsity will immediately follow, e.g. n-times reduction for the number of real-valued weights. We define and unify several variants of ring algebras into a modeling framework, RingCNN, and make comparisons in terms of image quality and hardware complexity. On top of that, we further devise a novel ring algebra which minimizes complexity with component-wise product and achieves the best quality using directional ReLU. Finally, we design an accelerator, eRingCNN, to accommodate to the proposed ring algebra, in particular with regular ring-convolution arrays for efficient inference and on-the-fly directional ReLU blocks for fixed-point computation. We implement two configurations, n = 2 and 4 (50% and 75% sparsity), with 40 nm technology to support advanced denoising and super-resolution at up to 4K UHD 30 fps. Layout results show that they can deliver equivalent 41 TOPS using 3.76 W and 2.22 W, respectively. Compared to the real-valued counterpart, our ring convolution engines for n = 2 achieve 2.00× energy efficiency and 2.08× area efficiency with similar or even better image quality. With n = 4, the efficiency gains of energy and area are further increased to 3.84× and 3.77× with only 0.11 dB drop of peak signal-to-noise ratio (PSNR). The results show that RingCNN exhibits great architectural advantages for providing near-maximum hardware efficiencies and graceful quality degradation simultaneously.