Reviewerly/exHarmony huggingface dataset:
Breast Cancer Pathological Image Classification Based on the Multiscale CNN Squeeze Model

The use of an automatic histopathological image identification system is essential for expediting diagnoses and lowering mistake rates. Although it is of enormous clinical importance, computerized breast cancer multiclassification using histological pictures has rarely been investigated. A deep learning-based classification strategy is suggested to solve the challenge of automated categorization of breast cancer pathology pictures. The attention model that acts on the feature channel is the channel refinement model. The learned channel weight may be used to reduce superfluous features when implementing the feature channel. To increase classification accuracy, calibration is necessary. To increase the accuracy of channel recalibration findings, a multiscale channel recalibration model is provided, and the msSE-ResNet convolutional neural network is built. The multiscale properties flow through the network’s highest pooling layer. The channel weights obtained at different scales are delivered into line fusion and used as input to the next channel recalibration model, which may improve the results of channel recalibration. The experimental findings reveal that the spatial recalibration model fares poorly on the job of classifying breast cancer pathology pictures when applied to the semantic segmentation of brain MRI images. The public BreakHis dataset is used to conduct the experiment. The network performs benign/malignant breast pathology picture classiﬁcation collected at various magnifications with a classification accuracy of 88.87 percent, according to experimental data. The diseased images are also more resilient. Experiments on pathological pictures at various magnifications show that msSE-ResNet34 is capable of performing well when used to classify pathological images at various magnifications.
Adaptation of IoT with Blockchain in Food Supply Chain Management: An Analysis-Based Review in Development, Benefits and Potential Applications

In today's scenario, blockchain technology is an emerging area and promising technology in the field of the food supply chain industry (FSCI). A literature survey comprising an analytical review of blockchain technology with the Internet of things (IoT) for food supply chain management (FSCM) is presented to better understand the associated research benefits, issues, and challenges. At present, with the concept of farm-to-fork gaining increasing popularity, food safety and quality certification are of critical concern. Blockchain technology provides the traceability of food supply from the source, i.e., the seeding factories, to the customer's table. The main idea of this paper is to identify blockchain technology with the Internet of things (IoT) devices to investigate the food conditions and various issues faced by transporters while supplying fresh food. Blockchain provides applications such as smart contracts to monitor, observe, and manage all transactions and communications among stakeholders. IoT technology provides approaches for verifying all transactions; these transactions are recorded and then stored in a centralized database system. Thus, IoT enables a safe and cost-effective FSCM system for stakeholders. In this paper, we contribute to the awareness of blockchain applications that are relevant to the food supply chain (FSC), and we present an analysis of the literature on relevant blockchain applications which has been conducted concerning various parameters. The observations in the present survey are also relevant to the application of blockchain technology with IoT in other areas.
POMCoP: Belief Space Planning for Sidekicks in Cooperative Games

We present POMCoP, a system for online planning in collaborative domains that reasons about how its actions will affect its understanding of human intentions, and demonstrate its use in building sidekicks for cooperative games. POMCoP plans in belief space. It explicitly represents its uncertainty about the intentions of its human ally, and plans actions which reveal those intentions or hedge against its uncertainty. This allows POMCoP to reason about the usefulness of incorporating information gathering actions into its plans, such as asking questions, or simply waiting to let humans reveal their intentions. We demonstrate POMCoP by constructing a sidekick for a cooperative pursuit game, and evaluate its effectiveness relative to MDP-based techniques that plan in state space, rather than belief space.
Automatic topography of high-dimensional data sets by non-parametric density peak clustering

Data analysis in high-dimensional spaces aims at obtaining a synthetic description of a data set, revealing its main structure and its salient features. We here introduce an approach providing this description in the form of a topography of the data, namely a human-readable chart of the probability density from which the data are harvested. The approach is based on an unsupervised extension of Density Peak clustering and a non-parametric density estimator that measures the probability density in the manifold containing the data. This allows finding automatically the number and the height of the peaks of the probability density, and the depth of the "valleys" separating them. Importantly, the density estimator provides a measure of the error, which allows distinguishing genuine density peaks from density fluctuations due to finite sampling. The approach thus provides robust and visual information about the density peaks' height, their statistical reliability, and their hierarchical organization, offering a conceptually powerful extension of the standard clustering partitions. We show that this framework is particularly useful in the analysis of complex data sets.
Neural Predictive Coding Using Convolutional Neural Networks Toward Unsupervised Learning of Speaker Characteristics

Learning speaker-specific features is vital in many applications like speaker recognition, diarization and speech recognition. This paper provides a novel approach, we term Neural Predictive Coding (NPC), to learn speaker-specific characteristics in a completely unsupervised manner from large amounts of unlabeled training data that even contain many non-speech events and multi-speaker audio streams. The NPC framework exploits the proposed short-term active-speaker stationarity hypothesis which assumes two temporally-close short speech segments belong to the same speaker, and thus a common representation that can encode the commonalities of both the segments, should capture the vocal characteristics of that speaker. We train a convolutional deep siamese network to produce "speaker embeddings" by learning to separate `same' vs `different' speaker pairs which are generated from an unlabeled data of audio streams. Two sets of experiments are done in different scenarios to evaluate the strength of NPC embeddings and compare with state-of-the-art in-domain supervised methods. First, two speaker identification experiments with different context lengths are performed in a scenario with comparatively limited within-speaker channel variability. NPC embeddings are found to perform the best at short duration experiment, and they provide complementary information to i-vectors for full utterance experiments. Second, a large scale speaker verification task having a wide range of within-speaker channel variability is adopted as an upper-bound experiment where comparisons are drawn with in-domain supervised methods.
Deformable Object Tracking With Gated Fusion

The tracking-by-detection framework receives growing attention through the integration with the convolutional neural networks (CNNs). Existing tracking-by-detection-based methods, however, fail to track objects with severe appearance variations. This is because the traditional convolutional operation is performed on fixed grids, and thus may not be able to find the correct response while the object is changing pose or under varying environmental conditions. In this paper, we propose a deformable convolution layer to enrich the target appearance representations in the tracking-by-detection framework. We aim to capture the target appearance variations via deformable convolution, which adaptively enhances its original features. In addition, we also propose a gated fusion scheme to control how the variations captured by the deformable convolution affect the original appearance. The enriched feature representation through deformable convolution facilitates the discrimination of the CNN classifier on the target object and background. The extensive experiments on the standard benchmarks show that the proposed tracker performs favorably against the state-of-the-art methods.
The KnowRef Coreference Corpus: Removing Gender and Number Cues for Difficult Pronominal Anaphora Resolution

We introduce a new benchmark for coreference resolution and NLI, KNOWREF, that targets common-sense understanding and world knowledge.Previous coreference resolution tasks can largely be solved by exploiting the number and gender of the antecedents, or have been handcrafted and do not reflect the diversity of naturally occurring text.We present a corpus of over 8,000 annotated text passages with ambiguous pronominal anaphora.These instances are both challenging and realistic.We show that various coreference systems, whether rule-based, feature-rich, or neural, perform significantly worse on the task than humans, who display high interannotator agreement.To explain this performance gap, we show empirically that state-ofthe art models often fail to capture context, instead relying on the gender or number of candidate antecedents to make a decision.We then use problem-specific insights to propose a data-augmentation trick called antecedent switching to alleviate this tendency in models.Finally, we show that antecedent switching yields promising results on other tasks as well: we use it to achieve state-of-the-art results on the GAP coreference task.
Optimal procedure for the identification of constitutive parameters from experimentally measured displacement fields

Measured displacement fields constitute a rich database for the identification of mechanical behavior. A variety of methods are available today to address this problem. A formalism is proposed showing that they all revert to the minimization of a quadratic norm (or semi-norm) between measured and computed displacement fields. However, they differ in the chosen metric. Transporting image noise through Digital Image Correlation and identification procedures, the uncertainty in the measured displacement fields (i.e., their full covariance) and the resulting uncertainty in the estimated constitutive parameters is assessed. Consequently, an optimal choice of metric (with respect to image noise) is proposed so that the resulting identification uncertainty is minimized.
Topical Co-Attention Networks for hashtag recommendation on microblogs

Hashtags provide a simple and natural way of organizing content in microblog services. Along with the fast growing of microblog services, the task of recommending hashtags for microblogs has been given increasing attention in recent years. However, much of the research depends on hand-crafted features. Motivated by the successful use of neural models for many natural language processing tasks, in this paper, we adopt an attention based neural network to learn the representation of a microblog post. Unlike previous works, which only focus on content attention of microblogs, we propose a novel Topical Co-Attention Network (TCAN) that jointly models content attention and topic attention simultaneously, in the sense that the content representation(s) are used to guide the topic attention and the topic representation is used to guide content attention. We conduct experiments and test with different settings of TCAN on a large real-world dataset. Experimental results show that our model significantly outperforms various competitive baseline methods. Furthermore, the incorporation of topical co-attention mechanism gives more than 13.6% improvement in F1 score compared with the standard LSTM based methods.
A Multi-Agent Communication Framework for Question-Worthy Phrase Extraction and Question Generation

Question generation aims to produce questions automatically given a piece of text as input. Existing research follows a sequence-to-sequence fashion that constructs a single question based on the input. Considering each question usually focuses on a specific fragment of the input, especially in the scenario of reading comprehension, it is reasonable to identify the corresponding focus before constructing the question. In this paper, we propose to identify question-worthy phrases first and generate questions with the assistance of these phrases. We introduce a multi-agent communication framework, taking phrase extraction and question generation as two agents, and learn these two tasks simultaneously via message passing mechanism. The results of experiments show the effectiveness of our framework: we can extract question-worthy phrases, which are able to improve the performance of question generation. Besides, our system is able to extract more than one question worthy phrases and generate multiple questions accordingly.
Can Wearable Haptic Devices Foster the Embodiment of Virtual Limbs?

Increasing presence is one of the primary goals of virtual reality research. A crucial aspect is that users are capable of distinguishing their self from the external virtual world. The hypothesis we investigate is that wearable haptics play an important role in the body experience and could thereby contribute to the immersion of the user in the virtual environment. A within-subject study (n=32) comparing the embodiment of a virtual hand with different implementations of haptic feedback (force feedback, vibrotactile feedback, and no haptic feedback) is presented. Participants wore a glove with haptic feedback devices at thumb and index finger. They were asked to put virtual cubes on a moving virtual target. Touching a virtual object caused vibrotactile-feedback, force-feedback or no feedback depending on the condition. These conditions were provided both synchronously and asynchronously. Embodiment was assessed quantitatively with the proprioceptive drift and subjectively via a questionnaire. Results show that haptic feedback significantly improves the subjective embodiment of a virtual hand and that force feedback leads to stronger responses to certain subscales of subjective embodiment. These outcomes are useful guidelines for wearable haptic designer and represent a basis for further research concerning human body experience, in reality, and in virtual environments.
Quantum Network Communication With a Novel Discrete-Time Quantum Walk

In this paper, we propose a scheme using a new kind of quantum walk to realize quantum communication well.In our scheme, obviously, there are three advantages.First, the initial states can be transferred from the source to the target by finite steps, which will make execution easier in practice.Second, due to fewer restrictions to the network models, our scheme can be adapted to more general network models, such as the butterfly network, the inverted crown network, the grail network, and the generalized butterfly network G k .Finally, arbitrary single-qubit and two-qubit initial states can be transferred to different kinds of network models.At the same time, the fidelity and probability equal 1.Our work promotes more applications of combining quantum computation with quantum communication in the future.
Improving Accuracy of the Kalman Filter Algorithm in Dynamic Conditions Using ANN-Based Learning Module

Prediction algorithms enable computers to learn from historical data in order to make accurate decisions about an uncertain future to maximize expected benefit or avoid potential loss. Conventional prediction algorithms are usually based on a trained model, which is learned from historical data. However, the problem with such prediction algorithms is their inability to adapt to dynamic scenarios and changing conditions. This paper presents a novel learning to prediction model to improve the performance of prediction algorithms under dynamic conditions. In the proposed model, a learning module is attached to the prediction algorithm, which acts as a supervisor to monitor and improve the performance of the prediction algorithm continuously by analyzing its output and considering external factors that may have an influence on its performance. To evaluate the effectiveness of the proposed learning to prediction model, we have developed the artificial neural network (ANN)-based learning module to improve the prediction accuracy of the Kalman filter algorithm as a case study. For experimental analysis, we consider a scenario where the Kalman filter algorithm is used to predict actual temperature from noisy sensor readings. the Kalman filter algorithm uses fixed process error covariance R, which is not suitable for dynamic situations where the error in sensor readings varies due to some external factors. In this study, we assume variable error in temperature sensor readings due to the changing humidity level. We have developed a learning module based on ANN to estimate the amount of error in current readings and to update R in the Kalman filter accordingly. Through experiments, we observed that the Kalman filter with the learning module performed better (4.41%–11.19%) than the conventional Kalman filter algorithm in terms of the root mean squared error metric.
SEP-graph

In general, the performance of parallel graph processing is determined by three pairs of critical parameters, namely synchronous or asynchronous execution mode (Sync or Async), Push or Pull communication mechanism (Push or Pull), and Data-driven or Topology-driven traversing scheme (DD or TD), which increases the complexity and sophistication of programming and system implementation of GPU. Existing graph-processing frameworks mainly use a single combination in the entire execution for a given application, but we have observed their variable and suboptimal performance. In this paper, we present SEP-Graph, a highly efficient software framework for graph-processing on GPU. The hybrid execution mode is automatically switched among three pairs of parameters, with an objective to achieve the shortest execution time in each iteration. We also apply a set of optimizations to SEP-Graph, considering the characteristics of graph algorithms and underlying GPU architectures. We show the effectiveness of SEP-Graph based on our intensive and comparative performance evaluation on NVIDIA 1080, P100, and V100 GPUs. Compared with existing and representative GPU graph-processing framework Groute and Gunrock, SEP-Graph can reduce execution time up to 45.8 times and 39.4 times.
Performance Analyses of Differential Evolution Algorithm Based on Dynamic Fitness Landscape

Dynamic fitness landscape analyses contain different metrics to attempt to analyze optimization problems. In this article, some of dynamic fitness landscape metrics are selected to discuss differential evolution (DE) algorithm properties and performance. Based on traditional differential evolution algorithm, benchmark functions and dynamic fitness landscape measures such as fitness distance correlation for calculating the distance to the nearest global optimum, ruggedness based on entropy, dynamic severity for estimating dynamic properties, a fitness cloud for getting a visual rendering of evolvability and a gradient for analyzing micro changes of benchmark functions in differential evolution algorithm, the authors obtain useful results and try to apply effective data, figures and graphs to analyze the performance differential evolution algorithm and make conclusions. Those metrics have great value and more details as DE performance.
An approach for approximate subgraph matching in fuzzy RDF graph

Subgraph pattern matching is important for a wide variety of applications such as social network analysis and knowledge discovery. Despite extensive research over the past few decades, subgraph pattern matching is still challenging, especially when it comes with new conditions and constraints. In this paper, we tackle the problem of answering queries on fuzzy RDF data graphs. Specifically, we model RDF data by fuzzy graphs, and an RDF query is equivalent to a search over subgraphs of fuzzy graphs that have high possibility to match with a given query graph. After establishing a path-based RDF graph indexing, we give an efficient approximate subgraph matching algorithm which consists of three main steps: decomposing the query graph into a set of paths, obtaining a set of candidates for every path in the decomposition, and joining the candidate paths together to build the answers of the query graph by the k-partition intersection graph. Finally, a comprehensive experimental evaluation demonstrates the effectiveness of the proposed measure.
Analysis of Backpropagation Algorithm in Predicting the Most Number of Internet Users in the World

The Internet today has become a primary need for its users. According to market research company e-Marketer, there are 25 countries with the largest internet users in the world. Indonesia is in the sixth position with a total of 112.6 million internet users. With the increasing number of internet users are expected to help improve the economy and also education in a country. To be able to increase the number of internet users, especially in Indonesia, it is necessary to predict for the coming years so that the government can provide adequate facilities and pre-facilities in order to balance the growth of internet users and as a precautionary step when there is a decrease in the number of internet users. The data used in this study focus on data on the number of internet users in 25 countries in 2013-2017. The algorithm used is Artificial Neural Network Backpropagation. Data analysis was processed by Artificial Neural Network using Matlab R2011b (7.13). This study uses 5 architectural models. The best network architecture generated is 3-50-1 with an accuracy of 92% and the Mean Squared Error (MSE) is 0.00151674.
Learning document representation via topic-enhanced LSTM model

Document representation plays an important role in the fields of text mining, natural language processing, and information retrieval. Traditional approaches to document representation may suffer from the disregard of the correlations or order of words in a document, due to unrealistic assumption of word independence or exchangeability. Recently, long–short-term memory (LSTM) based recurrent neural networks have been shown effective in preserving local contextual sequential patterns of words in a document, but using the LSTM model alone may not be adequate to capture global topical semantics for learning document representation. In this work, we propose a new topic-enhanced LSTM model to deal with the document representation problem. We first employ an attention-based LSTM model to generate hidden representation of word sequence in a given document. Then, we introduce a latent topic modeling layer with similarity constraint on the local hidden representation, and build a tree-structured LSTM on top of the topic layer for generating semantic representation of the document. We evaluate our model in typical text mining applications, i.e., document classification, topic detection, information retrieval, and document clustering. Experimental results on real-world datasets show the benefit of our innovations over state-of-the-art baseline methods.