Reviewerly/exHarmony huggingface dataset:
Histo-fetch – on-the-fly processing of gigapixel whole slide images simplifies and speeds neural network training

Training convolutional neural networks using pathology whole slide images (WSIs) is traditionally prefaced by the extraction of a training dataset of image patches. While effective, for large datasets of WSIs, this dataset preparation is inefficient.We created a custom pipeline (histo-fetch) to efficiently extract random patches and labels from pathology WSIs for input to a neural network on-the-fly. We prefetch these patches as needed during network training, avoiding the need for WSI preparation such as chopping/tiling.We demonstrate the utility of this pipeline to perform artificial stain transfer and image generation using the popular networks CycleGAN and ProGAN, respectively. For a large WSI dataset, histo-fetch is 98.6% faster to start training and used 7535x less disk space.
Feature Importance Explanations for Temporal Black-Box Models

Models in the supervised learning framework may capture rich and complex representations over the features that are hard for humans to interpret. Existing methods to explain such models are often specific to architectures and data where the features do not have a time-varying component. In this work, we propose TIME, a method to explain models that are inherently temporal in nature. Our approach (i) uses a model-agnostic permutation-based approach to analyze global feature importance, (ii) identifies the importance of salient features with respect to their temporal ordering as well as localized windows of influence, and (iii) uses hypothesis testing to provide statistical rigor.
Complete Video-Level Representations for Action Recognition

In most of the existing work for activity recognition, 3D ConvNets show promising performance for learning spatiotemporal features of videos. However, most methods sample fixed-length frames from the original video, which are cropped to a fixed size and fed into the model for training. In this manner, two problems limit the model performance for recognition. First, the cropped video clips are incomplete or even distorted in appearance, resulting in a large gap between the feature representation and semantics of human activity. Second, the useful features of longer video frame sequences are weakened by the repeated stacking of 3D convolution over deep networks due to the limitations of GPU memory and computing ability. This article proposes a method based on a 3D backbone network for multi scale spatial feature representation, which uses a pyramid pooling layer to allow the input of video frames at different scales, and then aggregates short-term spatial–temporal features into a long-term video-level representation. Objection detection is used as a component of model testing to explore the improvement of activity recognition considering the large amount of space–time redundancy in real life videos. An experiment is performed on the principal video dataset, UCF101, and the proposed method presents a competitive performance.
Image-based Kinship Verification Using Dual VGG-Face Classifie

In this paper, we investigated dual VGG-Face convolutional neural network classifier for image-based kinship verification problems. The proposed classifier is formed by paralleling convolutional layers of VGG CNN architecture and combined it with several fully-connected layers. Although the VGG CNN architecture is consists of huge parameters, the number of parameters in the classifier will be reduced by more than 80% by removing the fully-connected layers of the original classifier. We use the multi-task loss function for the training process to ensure that the features learned by the classifier are also can be used for family classification. Experiments on the FIW kinship verification dataset show that dual VGG-Face CNN classifiers can achieve an average accuracy of 64.71% on a single classifier and 65.49% on ensemble configuration. Based on our experiments, the lowest accuracy is produced for second-generation kinship (grandchild - grandparent) which have a low number of examples on FIW kinship.
Research on the Application of the Function of Computer Management System in College Aerobics Teaching

Abstract The progress of society and technology cannot be separated from the support of computer science and technology. Computer science and technology have become an irreplaceable part of people’s life, work and study and they play an important role in the daily development of the country. Computer science and technology have changed people’s traditional way of life. Its appearance has improved people’s lives, improved people’s quality of life and greatly promoted human progress and historical development. Computer technology has brought great convenience to people’s lifestyles and has also triggered major innovations in technology and methods in the field of manufacturing. Computer technology is occupying every corner of modern society with its absolute superiority and it also has an important influence on people’s way of thinking. This article analyzes its application mode in college calisthenics teaching.
Breast cancer magnification-independent multi-class histopathology classification using dual-step model

Computer-aided classification of breast cancer using histopathological images can play a significant role in clinical practice by detecting the distinct type of malignant and/or benign tumor. However, currently proposed deep learning models developed using the BreakHis dataset only conduct a binary classification between benign and malignant tumors, and are also scale-dependent. This study utilizes a ResNet-50 implementation to transform images from the four magnification factors such that all images can be used for training the deep neural network. This process yields a larger training set that is also scale-independent. For this paper, we utilized a dual step approach with the first pass being binary classification and the second pass being a multi-class classifier of malignant tumors that offers higher clinical utility.
Thermal Infrared Face Recognition

The technology for deep learning in the field of thermal infrared face recognition has recently become more available for use in research, therefore allowing for the many groups working on this subject to achieve many novel findings.Thermal infrared face recognition helps recognize faces that are not able to be recognized in visible light and can additionally recognize facial blood vessel structure.Previous research regarding temperature variations, mathematical formulas, wave types, and methods in thermal infrared face recognition is reviewed.
Multi-server verifiable delegation of computations: Unconditional security and practical efficiency

Outsourcing computation has gained significant popularity in recent years due to the prevalence of cloud computing. There are two main security concerns in outsourcing computation: how to guarantee the cloud server performs the computation correctly and how to keep the client's data secret. The {\em single-server verifiable computation} (SSVC) of Gennaro, Gentry and Parno (Crypto'10) enables a client to delegate the computation of a function $f$ on any input $x$ with both concerns highly relieved, but only results in {\em computationally secure} schemes that {\em lack practical efficiency}. While the SSVC schemes use a single server, in this paper we develop a {\em multi-server verifiable computation} (MSVC) model where the client shares both $f$ and $x$ among multiple servers, each server performs a set of computations on its shares, and finally the client reconstructs $f(x)$ from all servers' results. In this MSVC model we propose a generic construction for outsourcing computations of the form $F{\bf x}$, where $F$ is a matrix and $\bf x$ is a vector. Our generic construction achieves {\em information-theoretic security, input privacy} and {\em function privacy}. By optimizing the parameters, we obtain both a 3-server scheme,which uses the least number of servers, and a 4-server scheme, which incurs the least workload. By decomposing many polynomial computations as a two-stage computation, where the first-stage has the form $F{\bf x}$ and the second-stage is fast, and delegating the first-stage computation, we obtain MSVC schemes for these polynomials. We implement our MSVC schemes and show that they are among the most {\em practical} ones to date.
Using Human-Centered Design to Adapt Supply Chains and Digital Solutions for Community Health Volunteers in Nomadic Communities of Northern Kenya

<h3>ABSTRACT</h3> <h3>Background:</h3> Unreliable and nonexistent supply chain procedures and processes are one of the primary barriers to achieving functional community health units in nomadic communities in the arid/semiarid counties of Kenya. <h3>Methods:</h3> We used a human-centered design (HCD) approach to engage communities and community health volunteers (CHVs) in redesigning a proven data-centric supply chain approach that included a digital solution, called cStock, for this challenging context. We conducted the HCD process in 4 phases: (1) understanding intent, (2) research and insights, (3) ideation and prototyping, and (4) supply chain design and requirements building. Data collection used qualitative methods and involved a range of stakeholders including CHVs, supervisors, and local beneficiaries. CHVs and their supervisors also participated in cStock usability testing. Drawing on insights and personas generated from the research, stakeholders ideated and codesigned supply chain tools. <h3>Results:</h3> The research identified critical insights for informing the redesign of cStock for nomadic communities. These insights were categorized into supply chain, information systems, human resources, behaviors, service delivery infrastructure, and connectivity. Four supply chain data solutions were designed, prototyped, tested, and iterated: a stock recording paper-based form, a user-friendly cStock application, a supervisor cStock application, and an unstructured supplementary service data reporting system using feature phones. <h3>Conclusions:</h3> Using the HCD process incorporated the perspective of CHVs and their communities and provided key insights to inform the design of the supply chain and adapt cStock. The process helped make cStock to be inclusive and have the potential to have a meaningful impact on strengthening the supply chain for seminomadic and nomadic communities in northern Kenya. A strong supply chain for these CHVs will increase access to essential and reproductive health commodities and contribute to improving the overall health and well-being of these communities, especially women and children.
Coherence of assistance and assisted maximally coherent states

Abstract Coherence and entanglement are fundamental concepts in resource theory. The coherence (entanglement) of assistance is the coherence (entanglement) that can be extracted assisted by another party with local measurement and classical communication. We introduce and study the general coherence of assistance. First, in terms of real symmetric concave functions on the probability simplex, the coherence of assistance and the entanglement of assistance are shown to be in one-to-one correspondence. We then introduce two classes of quantum states: the assisted maximally coherent states and the assisted maximally entangled states. They can be transformed into maximally coherent or entangled pure states with the help of another party using local measurement and classical communication. We give necessary conditions for states to be assisted maximally coherent or assisted maximally entangled. Based on these, a unified framework between coherence and entanglement including coherence (entanglement) measures, coherence (entanglement) of assistance, coherence (entanglement) resources is proposed. Then we show that the coherence of assistance as well as entanglement of assistance are strictly larger than the coherence of convex roof and entanglement of convex roof for all full rank density matrices. So all full rank quantum states are distillable in the assisted coherence distillation.
Accelerating Deep Neuroevolution on Distributed FPGAs for Reinforcement Learning Problems

Reinforcement learning, augmented by the representational power of deep neural networks, has shown promising results on high-dimensional problems, such as game playing and robotic control. However, the sequential nature of these problems poses a fundamental challenge for computational efficiency. Recently, alternative approaches such as evolutionary strategies and deep neuroevolution demonstrated competitive results with faster training time on distributed CPU cores. Here we report record training times (running at about 1 million frames per second) for Atari 2600 games using deep neuroevolution implemented on distributed FPGAs. Combined hardware implementation of the game console, image preprocessing and the neural network in an optimized pipeline, multiplied with the system level parallelism enabled the acceleration. These results are the first application demonstration on the IBM Neural Computer, which is a custom designed system that consists of 432 Xilinx FPGAs interconnected in a 3D mesh network topology. In addition to high performance, experiments also showed improvement in accuracy for all games compared to the CPU implementation of the same algorithm.
What If Bitcoin Futures Had Never Been Introduced?

Bitcoin futures were introduced in December 2017 and was seen by some as a sign that the most popular cryptocurrency was being accepted by the financial community. In this paper, we examine what the Bitcoin market would look like if Bitcoin futures were never introduced. We implement a Bayesian diffusion-regression (state-space) structural time-series model that enables us to predict the counterfactual market response in a synthetic control that would have occurred had no futures market been introduced. Our results indicate that if bitcoin futures had never been introduced, the USD bitcoin spot market return and skewness would be higher, while volatility, kurtosis and liquidity would be lower. Therefore, our paper offers important insights for investors and regulators, while also providing some guidance to the potential impact of futures markets on other cryptocurrencies.
Object Detection Combining CNN and Adaptive Color Prior Features

When compared with the traditional manual design method, the convolutional neural network has the advantages of strong expressive ability and it is insensitive to scale, light, and deformation, so it has become the mainstream method in the object detection field. In order to further improve the accuracy of existing object detection methods based on convolutional neural networks, this paper draws on the characteristics of the attention mechanism to model color priors. Firstly, it proposes a cognitive-driven color prior model to obtain the color prior features for the known types of target samples and the overall scene, respectively. Subsequently, the acquired color prior features and test image color features are adaptively weighted and competed to obtain prior-based saliency images. Finally, the obtained saliency images are treated as features maps and they are further fused with those extracted by the convolutional neural network to complete the subsequent object detection task. The proposed algorithm does not need training parameters, has strong generalization ability, and it is directly fused with convolutional neural network features at the feature extraction stage, thus has strong versatility. Experiments on the VOC2007 and VOC2012 benchmark data sets show that the utilization of cognitive-drive color priors can further improve the performance of existing object detection algorithms.
Comprehensive Multi-Modal Interactions for Referring Image Segmentation

We investigate Referring Image Segmentation (RIS), which outputs a segmentation map corresponding to the natural language description. Addressing RIS efficiently requires considering the interactions happening across visual and linguistic modalities and the interactions within each modality. Existing methods are limited because they either compute different forms of interactions sequentially (leading to error propagation) or ignore intramodal interactions. We address this limitation by performing all three interactions simultaneously through a Synchronous Multi-Modal Fusion Module (SFM). Moreover, to produce refined segmentation masks, we propose a novel Hierarchical Cross-Modal Aggregation Module (HCAM), where linguistic features facilitate the exchange of contextual information across the visual hierarchy. We present thorough ablation studies and validate our approach's performance on four benchmark datasets, showing considerable performance gains over the existing state-of-the-art (SOTA) methods.
Benchmarking quantum state transfer on quantum devices

Quantum state transfer (QST) provides a method to send arbitrary quantum states from one system to another. Such a concept is crucial for transmitting quantum information into the quantum memory, quantum processor, and quantum network. The standard benchmark of QST is the average fidelity between the prepared and received states. In this work, we provide a new benchmark which reveals the non-classicality of QST based on spatio-temporal steering (STS). More specifically, we show that the local-hidden-state (LHS) model in STS can be viewed as the classical strategy of state transfer. Therefore, we can quantify the non-classicality of QST process by measuring the spatio-temporal steerability. We then apply the spatio-temporal steerability measurement technique to benchmark quantum devices including the IBM quantum experience and QuTech quantum inspire under QST tasks. The experimental results show that the spatio-temporal steerability decreases as the circuit depth increases, and the reduction agrees with the noise model, which refers to the accumulation of errors during the QST process. Moreover, we provide a quantity to estimate the signaling effect which could result from gate errors or intrinsic non-Markovian effect of the devices.
Boosting Multilabel Semantic Segmentation for Somata and Vessels in Mouse Brain

Deep convolutional neural networks (DCNNs) are widely utilized for the semantic segmentation of dense nerve tissues from light and electron microscopy (EM) image data; the goal of this technique is to achieve efficient and accurate three-dimensional reconstruction of the vasculature and neural networks in the brain. The success of these tasks heavily depends on the amount, and especially the quality, of the human-annotated labels fed into DCNNs. However, it is often difficult to acquire the gold standard of human-annotated labels for dense nerve tissues; human annotations inevitably contain discrepancies or even errors, which substantially impact the performance of DCNNs. Thus, a novel boosting framework consisting of a DCNN for multilabel semantic segmentation with a customized Dice-logarithmic loss function, a fusion module combining the annotated labels and the corresponding predictions from the DCNN, and a boosting algorithm to sequentially update the sample weights during network training iterations was proposed to systematically improve the quality of the annotated labels; this framework eventually resulted in improved segmentation task performance. The microoptical sectioning tomography (MOST) dataset was then employed to assess the effectiveness of the proposed framework. The result indicated that the framework, even trained with a dataset including some poor-quality human-annotated labels, achieved state-of-the-art performance in the segmentation of somata and vessels in the mouse brain. Thus, the proposed technique of artificial intelligence could advance neuroscience research.
Comparative Study of Asean Countries Research Productivity in Library Science

Library science is one of the fastest-growing fields of knowledge, especially in Southeast Asian countries. This study aims to determine library science publications in Southeast Asia with approaches, namely bibliometrics, social network analysis, almetrics, and linear regression test. This study's data were taken from the Scopus web to obtain bibliographic data from library science publications in ten countries published in 1960-2019. Bibliometrics analysis uses Microsoft Excel calculations, mapping/scientometrics uses Vos viewer and Gephi, while altmetrics analysis uses altmetric.com. Based on the bibliometric analysis, the results showed that the most productive country is Malaysia, with 287 articles. The most sponsored publication’s country is Malaysia, with 57 articles. International journals that are popular as destinations for publication of library science articles in Southeast Asia are the Malaysian Journal of Library and Information Science (MJLIS). The country that has the most impact factor for citation is Singapore, with 4.922 citations. The country with the highest level of collaboration is Malaysia, with a total collaboration of 243 articles. The research results are based on social network analysis that the country that has the highest influence is Malaysia with a degree centrality (DC) of 301, closeness centrality (CC) of 0.61, and between centrality (BC) 0.66. The results of the study are based on altmetrics analysis. The country with the most shares/mentions on social media is Singapore, with 5.223 shares/mentions, the second is Malaysia 1467 shares/mentions. Meanwhile, based on the linear regression test that there is an influence between social media and citation, it is indicated by the Sig value of 0.001. Meanwhile, the large correlation between social media and citations was 97.8%.
Two-Server Delegation of Computation on Label-Encrypted Data

Catalano and Fiore propose a scheme to transform a linearly-homomorphic encryption into a homomorphic encryption scheme capable of evaluating quadratic computations on ciphertexts. Their scheme is based on the linearly-homomorphic encryption (such as Goldwasser-Micali, Paillier and ElGamal) and need to perform large integer operation on servers. Then, their scheme have numerous computations on the servers. At the same time, their scheme cannot verify the computations and cannot evaluate more than degree-4 computations. To solve these problems, we no longer use linearly-homomorphic encryption which based on number theory assumptions. We use label and pseudorandom function to encrypt message, which significantly reduce the computations on the servers and enable us to use homomorphic MACs technology to realize verifiable computations naturally. We also extend the method to construct <inline-formula><tex-math notation="LaTeX">$d$</tex-math></inline-formula> -server schemes, which allow the client to delegate degree- <inline-formula><tex-math notation="LaTeX">$d$</tex-math></inline-formula> computations on outsourced data.