Reviewerly/exHarmony huggingface dataset:
Accelerating Deep Learning using Multiple GPUs and FPGA-Based 10GbE Switch

A back-propagation algorithm following a gradient descent approach is used for training deep neural networks. Since it iteratively performs a large number of matrix operations to compute the gradients, GPUs (Graphics Processing Units) are efficient especially for the training phase. Thus, a cluster of computers each of which equips multiple GPUs can significantly accelerate the training phase. Although the gradient computation is still a major bottleneck of the training, gradient aggregation and parameter optimization impose both communication and computation overheads, which should also be reduced for further shortening the training time. To address this issue, in this paper, multiple GPUs are interconnected with a PCI Express (PCIe) over 10 Gbit Ethernet (10GbE) technology. Since these remote GPUs are interconnected via network switches, gradient aggregation and optimizers (e.g., SGD, Adagrad, Adam, and SMORMS3) are offloaded to an FPGA-based network switch between a host machine and remote GPUs; thus, the gradient aggregation and optimization are completed in the network. Evaluation results using four remote GPUs connected via the FPGA-based 10GbE switch that implements the four optimizers demonstrate that these optimization algorithms are accelerated by up to 3. 0x and 1. 25x compared to CPU and GPU implementations, respectively. Also, the gradient aggregation throughput by the FPGA-based switch achieves 98.3% of the 10GbE line rate.
Multi-agent Reinforcement Learning Using Simulated Quantum Annealing

With quantum computers still under heavy development, already numerous quantum machine learning algorithms have been proposed for both gate-based quantum computers and quantum annealers. Recently, a quantum annealing version of a reinforcement learning algorithm for grid-traversal using one agent was published. We extend this work based on quantum Boltzmann machines, by allowing for any number of agents. We show that the use of quantum annealing can improve the learning compared to classical methods. We do this both by means of actual quantum hardware and by simulated quantum annealing.
Improving Cross-Subject Activity Recognition via Adversarial Learning

Deep learning has been widely used for implementing human activity recognition from wearable sensors like inertial measurement units. The performance of deep activity recognition is heavily affected by the amount and variability of the labeled data available for training the deep learning models. On the other hand, it is costly and time-consuming to collect and label data. Given limited training data, it is hard to maintain high performance across a wide range of subjects, due to the differences in the underlying data distribution of the training and the testing sets. In this work, we develop a novel solution that applies adversarial learning to improve cross-subject performance by generating training data that mimic artificial subjects - i.e. through data augmentation - and enforcing the activity classifier to ignore subject-dependent information. Contrary to domain adaptation methods, our solution does not utilize any data from subjects of the test set (or target domain). Furthermore, our solution is versatile as it can be utilized together with any deep neural network as the classifier. Considering the open dataset PAMAP2, nearly 10% higher cross-subject performance in terms of F1-score can be achieved when training a CNN-LSTM-based classifier with our solution. A performance gain of 5% is also observed when our solution is applied to a state-of-the-art HAR classifier composed of a combination of inception neural network and recurrent neural network. We also investigate different influencing factors of classification performance (i.e. selection of sensor modalities, sampling rates and the number of subjects in the training data), and summarize a practical guideline for implementing deep learning solutions for sensor-based human activity recognition.
A Named Entity Based Approach to Model Recipes

Traditional cooking recipes follow a structure which can be modelled very well if the rules and semantics of the different sections of the recipe text are analyzed and represented accurately. We propose a structure that can accurately represent the recipe as well as a pipeline to infer the best representation of the recipe in this uniform structure. The Ingredients section in a recipe typically lists down the ingredients required and corresponding attributes such as quantity, temperature, and processing state. This can be modelled by defining these attributes and their values. The physical entities which make up a recipe can be broadly classified into utensils, ingredients and their combinations that are related by cooking techniques. The instruction section lists down a series of events in which a cooking technique or process is applied upon these utensils and ingredients. We model these relationships in the form of tuples. Thus, using a combination of these methods we model cooking recipe in the dataset RecipeDB [1] to show the efficacy of our method. This mined information model can have several applications which include translating recipes between languages, determining similarity between recipes, generation of novel recipes and estimation of the nutritional profile of recipes. For the purpose of recognition of ingredient attributes, we train the Named Entity Relationship (NER) models and analyze the inferences with the help of K-Means clustering. Our model presented with an F1 score of 0.95 across all datasets. We use a similar NER tagging model for labelling cooking techniques (F1 score =0.88) and utensils (F1 score =0.90) within the instructions section. Finally, we determine the temporal sequence of relationships between ingredients, utensils and cooking techniques for modeling the instruction steps.
Mutual Information Based Learning Rate Decay for Stochastic Gradient Descent Training of Deep Neural Networks

This paper demonstrates a novel approach to training deep neural networks using a Mutual Information (MI)-driven, decaying Learning Rate (LR), Stochastic Gradient Descent (SGD) algorithm. MI between the output of the neural network and true outcomes is used to adaptively set the LR for the network, in every epoch of the training cycle. This idea is extended to layer-wise setting of LR, as MI naturally provides a layer-wise performance metric. A LR range test determining the operating LR range is also proposed. Experiments compared this approach with popular alternatives such as gradient-based adaptive LR algorithms like Adam, RMSprop, and LARS. Competitive to better accuracy outcomes obtained in competitive to better time, demonstrate the feasibility of the metric and approach.
DDoS vulnerabilities analysis and mitigation model in cloud computing

Abstract Cloud systems deals with various computations and storage amenities with increased uptime to the services at low price. Cloud consumers are charged for arrangement of virtual machines by the cloud providers. Though, these machines can be offline or online may face several security problems. The significant aspect of infrastructures of the cloud systems deals with confidentiality, truthfulness, accessibility and ease of access. Security and accessibility is the serious concern in providing facilities to the cloud handlers without distractions in which various privacy problems are happened that disturbs the cloud practices. One of the serious concerns is the Distributed Denial of Service attack which is one of the key threats for cloud safety. The cloud possession are despoiled and deals with the critical factors which are having high impact in the cloud environment in case of the genuine consumers who are prominent to severe commercial losses, data robberies. This paper deals with the optimize model (Swarm Intelligence i.e. PSO and firefly) for the security analysis and vulnerabilities of the threats by considering the cloud computing scenario to achieve high protection for the users to have fault free processes.
Characterization of organic matter and its implications for pyrite hosted refractory gold mineralization along the South Purulia Shear Zone, eastern India

Refractory gold bearing sulfide mineralization is spatially associated with the carbonaceous matter rich meta-sedimentary rocks of the Proterozoic South Purulia Shear Zone (SPSZ). The carbon rich layers occurring as black, continuous laminae are the metamorphosed derivatives of carbonaceous matter, which were originally present in the sedimentary rocks and bear the characteristics of initial sedimentary environment of the refractory gold bearing formations of SPSZ. The carbonaceous laminae aligned along the bedding/foliation planes got remobilized into the sulfide bearing quartz ± carbonate veins during the metamorphic and deformational events. The typical disordered bands as well as graphitic band in the Raman spectra indicate that the carbonaceous matter is partially graphitized from the precursor carbon rich layers. The light and homogeneous carbon isotopic values (δ13C = −26.7 to −28.2‰) of the graphitized carbon suggests its origin due to the evolution from an organic precursor. The δ13C values fall within the range typical for organic carbon deposited during the Paleo to Meso-proterozoic period. The organic matter is considered to have played an essential role in buffering the oxygen fugacity of the initial sedimentary basin. The dissolved sulfate ions in seawater were reduced resulting in sulfidation and adsorption of Au in sedimentary pyrites to form refractory gold. Later during the deformational stages, the hydrothermal fluids interacted with the graphitized matter which provided suitable redox conditions for the precipitation of auriferous sulfides such as pyrite, pyrrhotite, chalcopyrite and arsenopyrite within the quartz ± carbonate veins.
The Role of Viewing Distance and Feedback on Affordance Judgments in Augmented Reality

The effectiveness of Augmented Reality (AR) for spatial applications is likely influenced by the extent to which users perceive that they can act on virtual objects as if they are real. While there are some promising initial results that action capabilities in AR are perceived similarly to the real world, AR differs from completely real-world viewing both in the visual information available within a restricted field of view (FOV) and in the ways that perceptual-motor feedback can be provided for the outcome of actions. We addressed both of these differences in a study of judgments of passing through an AR aperture defined by two moveable virtual walls presented via the Microsoft HoloLens. First, we investigated the possible effects of viewing distance on affordance judgments, predicting greater error in judgments at a close distance because of the inability to see the entirety of the aperture within the FOV. Second, we explored the effect of feedback on affordance judgments, predicting that verbal feedback about whether a judgment was correct would be sufficient to see improvements in judgments over time. In contrast to our predictions for distance, we found that judgments for passing through were closer to actual shoulder width when viewed at a distance near the aperture (0.85 m) compared to a farther viewing point (3.20 m). Verbal feedback reduced error over trials, but only at the farther distance, possibly because the judgments at the near distance were close to the ceiling of performance. These findings have implications for tasks that require accurate perception of action capabilities in AR, such as training procedures.
Multi-Atlas Brain Parcellation Using Squeeze-and-Excitation Fully Convolutional Networks

Multi-atlas parcellation (MAP) is carried out on a brain image by propagating and fusing labelled regions from brain atlases. Typical nonlinear registration-based label propagation is time-consuming and sensitive to inter-subject differences. Recently, deep learning parcellation (DLP) has been proposed to avoid nonlinear registration for better efficiency and robustness than MAP. However, most existing DLP methods neglect using brain atlases, which contain high-level information (e.g., manually labelled brain regions), to provide auxiliary features for improving the parcellation accuracy. In this paper, we propose a novel multi-atlas DLP method for brain parcellation. Our method is based on fully convolutional networks (FCN) and squeeze-and-excitation (SE) modules. It can automatically and adaptively select features from the most relevant brain atlases to guide parcellation. Moreover, our method is trained via a generative adversarial network (GAN), where a convolutional neural network (CNN) with multi-scale l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> loss is used as the discriminator. Benefiting from brain atlases, our method outperforms MAP and state-of-the-art DLP methods on two public image datasets (LPBA40 and NIREP-NA0).
Distributed video coding based on classification of frequency bands with block texture conditioned key frame encoder for wireless capsule endoscopy

Wireless capsule endoscopy (WCE) has provided remarkable improvement in diagnosing gastrointestinal disorders by scanning the entire digestive tract. The system still need refinement, to upgrade the quality of images, frame rate and battery life. The principal component of the system that can address these issues is low complexity video compressor. Motivated by low computational complexity requirements of WCE video encoding, this paper presents a distributed video coding framework based on frequency bands classification. The lower frequency bands are used to generate good quality side information (SI) as they exhibit high temporal correlation. This reduces the complexity of hash generation at the encoder, thus eliminating the latency in SI creation. Apart from this, SI creation involves only a simple block search and doesn’t depend on Wyner–Ziv (WZ) bands. Also different approach for distributed coding of sub-sampled chroma components of WZ frame is proposed. Low complexity JPEG based key frame encoding is proposed that take advantage of WCE image textural properties to reduce the complexity of encoding smooth blocks by 81% at the quantization and encoding stage. Other novel features include use of discrete Tchebichef transform (DTT), Golomb–Rice code for entropy coding. Performance evaluation shows that the proposed method achieves 60% improvement in compression over Motion JPEG with low computational complexity.
Is the Pencil Mightier than the Keyboard? A Meta-Analysis Comparing the Method of Notetaking Outcomes

This meta-analysis compared the educational impact of the method of notetaking in the college classroom – hand written or using electronic device. The findings involved 14 studies combining 3,075 participants demonstrated that using electronic notetaking methods reduced measured outcomes (average r = −.142). Using the Binomial Effect Size Display, results indicated a decline of 25% of students scoring below the mean when electronic devices when compared to using handwritten notetaking. The study considers explanations for the decline and makes recommendations about the use of technology for notetaking in the classroom as well as paths for practical and pedagogical implications.
Self-Supervision vs. Transfer Learning: Robust Biomedical Image Analysis Against Adversarial Attacks

Deep neural networks are being increasingly used for disease diagnosis and lesion localization on biomedical images. However, training deep neural networks not only requires large sets of expensive ground truth (image labels or pixel annotations); they are also susceptible to adversarial attacks. Transfer learning alleviates the former problem to some extent, wherein the lower layers of a neural network are pre-trained on a large labeled dataset from a different domain (e.g., ImageNet), while only the upper layers are fine-tuned on the target domain (e.g., chest X-rays). An alternative to transfer learning is self-supervised learning, in which a supervised task is created using the unlabeled images from the target domain itself to pre-train the lower layers. In this work, we show that self-supervised learning combined with adversarial training offers additional advantages over transfer learning as well as vanilla self-supervised learning. In particular, the process of adversarial training itself acts as data augmentation for self-supervision. This adversarial data augmentation leads to both a reduction in the amount of supervised data required for comparable accuracy, as well as natural robustness to adversarial attacks. We support our claims using experiments on the two modalities and tasks - classification of chest X-rays, and segmentation of MRI images.
NLP's Clever Hans Moment has Arrived

Large, pretrained language models have led to a flurry of new state-of-the-art results being reported in many areas of natural language processing. However, recent work has also shown that such models tend to solve language tasks by relying on superficial cues found in benchmark datasets, instead of acquiring the capabilities envisioned by the task designers. In this short opinion piece, I review a report by Niven & Kao (2019) of this so-called Clever Hans effect on an argument reasoning task and discuss possible solutions for its prevention.
There Is More Than One Type of Hoarder: Collecting, Managing and Hoarding Digital Data in the Workplace

Abstract There are organizational and individual problems associated with the excessive accumulation of digital material, but little is known about why people hoard digital information in the workplace. We interviewed 20 participants from two large knowledge-intensive organizations (one academic, one commercial). These participants scored highly on the Digital Hoarding Questionnaire (DHQ). We asked them to discuss their information management practices, their reasons for keeping large amounts of digital data and the difficulties they faced in relation to deleting data. Using thematic analysis, we identified four underlying dimensions of digital hoarding (anxiety, disengagement, compliance and collection). We discuss these in relation to the consequences of digital hoarding for individuals and organizations—considering the implications for organizational culture, digital ownership and cybersecurity.
Trajectory Planning of Quadrotor Systems for Various Objective Functions

SUMMARY Quadrotors are unmanned aerial vehicles with many potential applications ranging from mapping to supporting rescue operations. A key feature required for the use of these vehicles under complex conditions is a technique to analytically solve the problem of trajectory planning. Hence, this paper presents a heuristic approach for optimal path planning that the optimization strategy is based on the indirect solution of the open-loop optimal control problem. Firstly, an adequate dynamic system modeling is considered with respect to a configuration of a commercial quadrotor helicopter. The model predicts the effect of the thrust and torques induced by the four propellers on the quadrotor motion. Quadcopter dynamics is described by differential equations that have been derived by using the Newton–Euler method. Then, a path planning algorithm is developed to find the optimal trajectories that meet various objective functions, such as fuel efficiency, and guarantee the flight stability and high-speed operation. Typically, the necessary condition of optimality for a constrained optimal control problem is formulated as a standard form of a two-point boundary-value problem using Pontryagin’s minimum principle. One advantage of the proposed method can solve a wide range of optimal maneuvers for arbitrary initial and final states relevant to every considered cost function. In order to verify the effectiveness of the presented algorithm, several simulation and experiment studies are carried out for finding the optimal path between two points with different objective functions by using MATLAB software. The results clearly show the effect of the proposed approach on the quadrotor systems.
Simulation technologies of virtual reality usage in the training of future ship navigators

Research goal: the research is aimed at the theoretical substantiation of the application of virtual reality technology simulators and their features in higher maritime educational institutions. Research objectives: to determine the role and place of simulation technology in the educational process in the training of future ship navigators in order to form the professional competence of navigation. Object of research: professional training of future ship navigators in higher maritime educational institutions. Subject of research: simulation technologies of virtual reality as a component of the educational process at higher educational maritime establishments. Research methods used: theoretical methods containing the analysis of scientific sources; empirical methods involving study and observation of the educational process. Research results: the analysis of scientific publications allows to define the concept of virtual reality simulators, their application in the training of future navigators, their use for assessing the acquired professional competence of navigation. Main conclusions: introduction of simulation technologies of virtual reality in the educational process in higher maritime educational institutions increases the efficiency of education, promotes the development of professional thinking of students, enhances the quality of professional competence development.
MILP-Based Differential Cryptanalysis on Round-Reduced Midori64

Mixed integer linear programming (MILP) model was presented by Sun et al. at Asiacrypt 2014 to search for differential characteristics of block ciphers. Based on this model, it is easy to assess block ciphers against differential attack. In this paper, the MILP model is improved to search for differential trails of Midori64 which is a family of lightweight block ciphers provided by Banik et al. at Asiacrypt 2015. We find the best 5-round differential characteristics of Midori64 with MILP-based model, and the probabilities are 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-52</sup> and 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-58</sup> respectively. Based on these distinguishers, we give key recovery attacks on the 11-round reduced Midori64 with data complexities of 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">55.6</sup> and 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">61.2</sup> , and time complexities of 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">109.35</sup> and 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">100.26</sup> .