Reviewerly/exHarmony huggingface dataset:
Visual context learning based on textual knowledge for image–text retrieval

Image-text bidirectional retrieval is a significant task within cross-modal learning field. The main issue lies on the jointly embedding learning and accurately measuring image-text matching score. Most prior works make use of either intra-modality methods performing within two separate modalities or inter-modality ones combining two modalities tightly. However, intra-modality methods remain ambiguous when learning visual context due to the existence of redundant messages. And inter-modality methods increase the complexity of retrieval because of unifying two modalities closely when learning modal features. In this research, we propose an eclectic Visual Context Learning based on Textual knowledge Network (VCLTN), which transfers textual knowledge to visual modality for context learning and decreases the discrepancy of information capacity between two modalities. Specifically, VCLTN merges label semantics into corresponding regional features and employs those labels as intermediaries between images and texts for better modal alignment. Contextual knowledge of those labels learned within textual modality is utilized to guide the visual context learning. Besides, considering the homogeneity within each modality, global features are merged into regional features for assisting in the context learning. In order to alleviate the imbalance of information capacity between images and texts, entities together with relations inside the given caption are extracted and an auxiliary caption is sampled for attaching supplementary messages to textual modality. Experiments performed on Flickr30K and MS-COCO reveal that our model VCLTN achieves best results compared with the state-of-the-art methods.
Medical image segmentation using scalable functional variational Bayesian neural networks with Gaussian processes

Bayesian neural networks (BNNs) are widely used in medical image segmentation tasks because they provide a probabilistic view of deep learning models by placing a prior distribution over the model parameters, and allow the representation of uncertainties through posterior distribution over the model parameters. However, it is difficult to choose meaningful prior distributions and fit accurate posterior distributions in a high-dimensional weight space, which limit the practical effectiveness of BNNs. In this study, we propose a scalable functional variational BNN with Gaussian processes (GPs) for the purpose of medical image segmentation. Specifically, we regard the prior and variational posterior distributions as GPs, and perform variational inference in function space. We employ a variant of the functional evidence lower bound (fELBO) with a β-weight on the Kullback–Leibler divergence term as the loss objective, and design a content-aware UNet segmentation network, which utilizes content-aware reassembly of features (CARAFE) as an upsampling operator to extract semantic information from input feature maps. The proposed BNN framework enables efficient training and can perform predictive inference with one forward pass. Extensive experiments are conducted on four segmentation datasets; the experimental results demonstrate that the proposed approach improves the segmentation performance metrics and uncertainty estimates compared with several state-of-the-art methods.
A comparison between CPU and GPU for image classification using Convolutional Neural Networks

Deep learning has emerged as an effective solution for analyzing complex datasets such as images, videos, text and speech. Today, one of the most popular algorithms to perform image classification is Convolutional Neural Networks(CNN) and its various implementations such as VGG, ResNet, Inception etc. The recent advances in hardware has led to emergence of Graphical Processing Units (GPU) as a solution for speeding up the process of executing complex deep learning algorithms. Although GPU offers massive parallelism through a large number of cores, it is not always the optimum choice for executing training of all deep learning models. This work performs a comparative analysis on CPU and GPU for two datasets using two different Convolutional Neural Network models. The experiments have been conducted on three different types of datasets and each trained with a separate CNN model. All three experiments indicate that the CPU trains upto 1.5x-3x times faster than the GPU.
From environmental sound representation to robustness of 2D CNN models against adversarial attacks

This paper investigates the impact of different standard environmental sound representations (spectrograms) on the recognition performance and adversarial attack robustness of a victim residual convolutional neural network, namely ResNet-18. Our main motivation for focusing on such a front-end classifier rather than other complex architectures is balancing recognition accuracy and the total number of training parameters. Herein, we measure the impact of different settings required for generating more informative Mel-frequency cepstral coefficient (MFCC), short-time Fourier transform (STFT), and discrete wavelet transform (DWT) representations on our front-end model. This measurement involves comparing the classification performance over the adversarial robustness. We demonstrate an inverse relationship between recognition accuracy and model robustness against six benchmarking attack algorithms on the balance of average budgets allocated by the adversary and the attack cost. Moreover, our experimental results have shown that while the ResNet-18 model trained on DWT spectrograms achieves a high recognition accuracy, attacking this model is relatively more costly for the adversary than other 2D representations. We also report some results on different convolutional neural network architectures such as ResNet-34, ResNet-56, AlexNet, and GoogLeNet, SB-CNN, and LSTM-based.
Accuracy Comparison of Different Batch Size for a Supervised Machine Learning Task with Image Classification

Machine learning is a type of artificial intelligence where computers solve issues by considering examples of real-world data. Within machine learning, there are various types of techniques or tasks such as supervised, unsupervised, reinforcement, and many hyperparameters have to be tuned to have high accuracy especially in image classification. The batch size refers to the total number of images required to train a single reverse and forward pass. It is one of the most essential hyperparameters. In our paper, we have studied the supervised task with image classification by changing batch size with epoch. The characterization effect of increasing the batch size on training time and how this relationship varies with the training model have been studied, which leads to extremely large variation between them. According to our results, a larger batch size does not always result in high accuracy.
INVESTIGATION OF THE FEASIBILITY OF INCLUDING DIFFERENT CRYPTOCURRENCIES IN THE INVESTMENT PORTFOLIO FOR ITS DIVERSIFICATION

Purpose – the main aim of this article is to identify cryptocurrencies suitable for investment and portfolio diversification. Research methodology – the methodology of empirical research includes methods of scientific literature analysis, statistical data analysis, multicriteria evaluation, correlation analysis. Findings – Bitcoin is the leading cryptocurrency, but this result could have been due to an exceptionally high market capitalization. Based on the results of the analysis, the inclusion of Bitcoin, Etherium and Dogecoin in the investment portfolio of S&amp;P500, Euro Stoxx 50, DAX and CAC 40 indexes could be considered. Terra could be an interesting investment when considering the benefits of diversification. Research limitations – based on the results of the study, the inclusion of all studied cryptocurrencies in the investment portfolio could be considered in order to diversify the portfolio, taking into account their investment attractiveness. Practical implications – Cryptocurrencies attract investors not only because of the returns they receive, but also because of the absence of intermediaries, which allows them to reduce transaction costs. High returns are associated with high risks, so it is necessary to conduct as much research as possible to identify the benefits of cryptocurrencies and to find risk management strategies. One such benefit of cryptocurrencies highlighted in research is diversification. Originality/Value – the novelty of the study lies in evaluation of 10 selected cryptocurrencies according to different criteria using a multi-criteria valuation method to identify cryptocurrencies that are non-correlated or weakly correlated with traditional assets and the most suitable for investment and for portfolio diversification.
A Uniform Latency Model for DNN Accelerators with Diverse Architectures and Dataflows

In the early design phase of a Deep Neural Network (DNN) acceleration system, fast energy and latency estimation are important to evaluate the optimality of different design candidates on algorithm, hardware, and algorithm-to-hardware mapping, given the gigantic design space. This work proposes a uniform intra-layer analytical latency model for DNN accelerators that can be used to evaluate diverse architectures and dataflows. It employs a 3-step approach to systematically estimate the latency breakdown of different system components, capture the operation state of each memory component, and identify stall-induced performance bottlenecks. To achieve high accuracy, different memory attributes, operands' memory sharing scenarios, as well as dataflow implications have been taken into account. Validation against an in-house taped-out accelerator across various DNN layers has shown an average latency model accuracy of 94.3%. To showcase the capability of the proposed model, we carry out 3 case studies to assess respectively the impact of mapping, workloads, and diverse hardware architectures on latency, driving design insights for algorithm-hardware-mapping co-optimization.
Recovering Realistic Details for Magnification-Arbitrary Image Super-Resolution

The emergence of implicit neural representations (INR) has shown the potential to represent images in a continuous form by mapping pixel coordinates to RGB values. Recent work is capable of recovering arbitrary-resolution images from the continuous representations of the input low-resolution (LR) images. However, it can only super-resolve blurry images and lacks the ability to generate perceptual-pleasant details. In this paper, we propose implicit pixel flow (IPF) to model the coordinate dependency between the blurry INR distribution and the sharp real-world distribution. For each pixel near the blurry edges, IPF assigns offsets for the coordinates of the pixel so that the original RGB values can be replaced by the RGB values of a neighboring pixel which are more appropriate to form sharper edges. By modifying the relationship between the INR-domain coordinates and the image-domain pixels via IPF, we convert the original blurry INR distribution to a sharp one. Specifically, we adopt convolutional neural networks to extract continuous flow representations and employ multi-layer perceptrons to build the implicit function for calculating pixel flow. In addition, we propose a new double constraint module to search for more stable and optimal pixel flows during training. To the best of our knowledge, this is the first method to recover perceptually-pleasant details for magnification-arbitrary single image super-resolution. Experimental results on public benchmark datasets demonstrate that we successfully restore shape edges and satisfactory textures from continuous image representations.
SCI-FI: Control Signal, Code, and Control Flow Integrity against Fault Injection Attacks

Fault injection attacks have become a serious threat against embedded systems. Recently, Laurent et al. have reported that some faults inside the microarchitecture escape all typical software fault models and so software counter-measures. Moreover, state-of-the-art counter-measures, hardware-only or with hardware support, do not consider the integrity of microarchitectural control signals that are the target of these faults. We present SCI-FI, a counter-measure for Control Signal, Code, and Control-Flow Integrity against Fault Injection attacks. SCI-FI combines the protection of pipeline control signals with a fine-grained code and control-flow integrity mechanism, and can additionally provide code authentication. We evaluate SCI-FI by extending a RISC-V core. The average hardware area overheads range from 6.5% to 23.8%, and the average code size and execution time increase by 25.4% and 17.5% respectively.
FCM Clustering Approach Optimization Using Parallel High-Speed Intel FPGA Technology

Fuzzy C-Means (FCM) is a widely used clustering algorithm that performs well in various scientific applications. Implementing FCM involves a massive number of computations, and many parallelization techniques based on GPUs and multicore systems have been suggested. In this study, we present a method for optimizing the FCM algorithm for high-speed field-programmable gate technology (FPGA) using a high-level C-like programming language called open computing language (OpenCL). The method was designed to enable the high-level compiler/synthesis tool to manipulate a task-parallelism model and create an efficient design. Our experimental results (based on several datasets) show that the proposed method makes the FCM execution time more than 186 times faster than the conventional design running on a single-core CPU platform. Also, its processing power reached 89 giga floating points operations per second (GFLOPs).
Improving novelty detection using the reconstructions of nearest neighbours

We show that using nearest neighbours in the latent space of autoencoders (AE) significantly improves performance of semi-supervised novelty detection in both single and multi-class contexts. Autoencoding methods detect novelty by learning to differentiate between the non-novel training class(es) and all other unseen classes. Our method harnesses a combination of the reconstructions of the nearest neighbours and the latent-neighbour distances of a given input's latent representation. We demonstrate that our nearest-latent-neighbours (NLN) algorithm is memory and time efficient, does not require significant data augmentation, nor is reliant on pre-trained networks. Furthermore, we show that the NLN-algorithm is easily applicable to multiple datasets without modification. Additionally, the proposed algorithm is agnostic to autoencoder architecture and reconstruction error method. We validate our method across several standard datasets for a variety of different autoencoding architectures such as vanilla, adversarial and variational autoencoders using either reconstruction, residual or feature consistent losses. The results show that the NLN algorithm grants up to a 17% increase in Area Under the Receiver Operating Characteristics (AUROC) curve performance for the multi-class case and 8% for single-class novelty detection.
Blockchain technology and universal health coverage: Health data space in global migration

The increase of forcibly displaced people worldwide is a challenge for health systems and their ability to provide access and equity in Health as a universal right. Health information systems should be strengthened to collect and disseminate migrant health data enabling analytics for strategic decisions.This Viewpoint focuses on blockchain technology as an emergent digital tool to improve communication and overcome gaps in medical data sharing, conceptualizing a global health space. Anchored in the security, privacy, and medico-legal regulation of medical data, Blockchain technology would empower inter-organizational services or workflows, in real-time, by the users, inside and outside the national health systems, anywhere in the world. As an innovative approach, this Viewpoint highlights the future directions in IT-supported health.
Entire Deformable ConvNets for semantic segmentation

Deformable ConvNets achieve excellent performance due to learnable spatial sampling locations. However, the spatial support may deviate from the region of interest, according to observations of its behavior. More Deformable ConvNets reduce abnormal behavior mainly by relying on a modulation mechanism, but they do not identify the reason for the behavior. In this work, we find that the unusual behavior is caused by the interaction between offsets, and we present a novel method named Entire Deformable ConvNets for reducing its influence. This updated version helps the spatial support better focus on pertinent image content. In the new method, deformable convolution is replaced with a new module, namely, entire deformable convolution. The new module avoids mutual interference by changing the manner of offset allocation. Using this simple pattern, entire deformable convolution inherits all the advantages of deformable convolution and achieves better performance. Furthermore, this strategy is also applicable to More Deformable ConvNets. Similarly, Entire More Deformable ConvNets contain a new module called entire modulated deformable convolution. Extensive experiments on PASCAL VOC and MS COCO benchmarks show that our methods realize excellent improvement over the original versions.
Perception for Autonomous Systems: A Measurement Perspective on Localization and Positioning

Autonomous systems are nowadays having an undisputed pervasiveness in modern society. Autonomous driving cars as well as applications of service robots (e.g., cleaning robots, companion robots, intelligent healthcare solutions, tour guided systems) are becoming more and more popular, and a general acceptance is now developing around such systems. Nonetheless, one of the major hurdles in building such applications relies on the capability of autonomous systems to understand their surroundings and then plan proper actions. The most popular solutions, which are gaining more and more attention, rely on artificial intelligence and deep learning as a means to perceive the structured and complex natural environment. Nonetheless, classical concepts of metrology, such as standard uncertainty, accuracy and precision, are still necessary for a clear and effective understanding of modern autonomous systems applications. In this paper, some fundamental measurement concepts will be reviewed in light of the autonomous systems domain, with an emphasis on localization and positioning problems for mobile robots. In particular, we will discuss and present the main issues and concepts that build around the statistical approach to measurements and the main role of uncertainties.
A two-stage container management in the cloud for optimizing the load balancing and migration cost

Container technology is driving the transformation of Container as a Service (CaaS), which calls for an optimal solution of container management. In this paper, we present a two-stage framework of container management in the cloud, which includes container placement and container migration. Specifically, we investigate the container placement strategy of cloud servers for load balancing in a long time scale. In a short time scale, we investigate the container migration strategy among the cloud servers for minimizing the migration cost while maintaining the degree of load balance. Both of the strategies are formulated as constrained optimization problems. We propose Balance Aware Container Placement (BACP) algorithm and Adaptive Threshold Container Migration (ATCM) algorithm to solve the optimization problems in different time scales. Furthermore, workload prediction of the containers is proposed to enhance the solution. We show by experiments that we can achieve the load balance while reducing the migration cost. Moreover, the proposed algorithm outperforms the meta-heuristic algorithms, i.e., PSO (Particle Swarm Optimization) and CRO (Coral Reef Optimization).
Using Textual Analysis to Detect Initial Coin Offering Frauds

ABSTRACT Initial coin offering (ICO) has attracted a lot of attention from the public in recent years due to its association with potentially fraudulent activities. In order to offer practical implications to investors and regulators when evaluating ICO projects, this study examines the use of textual analysis in detecting potential ICO fraud cases. By using Linguistic Inquiry and Word Count (LIWC), we extracted the textual characteristics of 1,402 English whitepapers that may have been indicators of potential fraud based on the prior literature, including first-person plural pronouns, adverbs, and certainty, and formed a risk index for potentially problematic ICOs. Our findings suggest that the use of these words reflects the warning signals raised by the Securities and Exchange Commission (SEC) about potentially problematic ICO projects, which can therefore be used by regulators and investors when evaluating ICOs. Implications are discussed.
DTQAtten: Leveraging Dynamic Token-based Quantization for Efficient Attention Architecture

Models based on the attention mechanism, i.e. transformers, have shown extraordinary performance in Natural Language Processing (NLP) tasks. However, their memory footprint, inference latency, and power consumption are still prohibitive for efficient inference at edge devices, even at data centers. To tackle this issue, we present an algorithm-architecture co-design with dynamic and mixed-precision quantization, DTQAtten. We present empirically that the tolerance to the noise varies from token to token in attention-based NLP models. This finding leads us to quantize different tokens with mixed levels of bits. Thus, we design a compression framework that (i) dynamically quantizes tokens while they are forwarded in the models and (ii) jointly determines the ratio of each precision. Moreover, due to the dynamic mixed-precision tokens caused by our framework, previous matrix-multiplication accelerators (e.g. systolic array) cannot effectively exploit the benefit of the compressed attention computation. We thus design our accelerator with the variable-speed systolic array (VSSA) and propose an effective optimization strategy to alleviate the pipeline-stall problem in VSSA without hardware overhead. We conduct experiments with existing attention-based NLP models, including BERT and GPT-2 on various language tasks. Our results show that DTQAtten outperforms the previous neural network accelerator Eyeriss by 13.12× in terms of speedup and 3.8× in terms of energy-saving. Compared with the state-of-the-art attention accelerator SpAtten, our DTQAtten achieves at least 2.65× speedup and 3.38× energy efficiency improvement.