Reviewerly/exHarmony huggingface dataset:
Visual histological assessment of morphological features reflects the underlying molecular profile in invasive breast cancer: a morphomolecular study

Tumour genotype and phenotype are related and can predict outcome. In this study, we hypothesised that the visual assessment of breast cancer (BC) morphological features can provide valuable insight into underlying molecular profiles.The Cancer Genome Atlas (TCGA) BC cohort was used (n = 743) and morphological features, including Nottingham grade and its components and nucleolar prominence, were assessed utilising whole-slide images (WSIs). Two independent scores were assigned, and discordant cases were utilised to represent cases with intermediate morphological features. Differentially expressed genes (DEGs) were identified for each feature, compared among concordant/discordant cases and tested for specific pathways. Concordant grading was observed in 467 of 743 (63%) of cases. Among concordant case groups, eight common DEGs (UGT8, DDC, RGR, RLBP1, SPRR1B, CXorf49B, PSAPL1 and SPRR2G) were associated with overall tumour grade and its components. These genes are related mainly to cellular proliferation, differentiation and metabolism. The number of DEGs in cases with discordant grading was larger than those identified in concordant cases. The largest number of DEGs was observed in discordant grade 1:3 cases (n = 1185). DEGs were identified for each discordant component. Some DEGs were uniquely associated with well-defined specific morphological features, whereas expression/co-expression of other genes was identified across multiple features and underlined intermediate morphological features.Morphological features are probably related to distinct underlying molecular profiles that drive both morphology and behaviour. This study provides further evidence to support the use of image-based analysis of WSIs, including artificial intelligence algorithms, to predict tumour molecular profiles and outcome.
From Adiabatic to Dispersive Readout of Quantum Circuits

Spectral properties of a quantum circuit are efficiently read out by monitoring the resonance frequency shift it induces in a microwave resonator coupled to it. When the two systems are strongly detuned, theory attributes the shift to an effective resonator capacitance or inductance that depends on the quantum circuit state. At small detuning, the shift arises from the exchange of virtual photons, as described by the Jaynes-Cummings model. Here we present a theory bridging these two limits and illustrate, with several examples, its necessity for a general description of quantum circuits readout.
Improving random forest algorithm by Lasso method

The random forest (RF) algorithm is a very practical and excellent ensemble learning algorithm. In this paper, we improve the random forest algorithm and propose an algorithm called ‘post-selection boosting random forest’ (PBRF). This algorithm combines the original random forest and the Lasso method, without giving the number of decision trees for final prediction in advance, it can dynamically obtain the decision trees according to different input samples to output the prediction results. Meanwhile, we verify that the proposed algorithm can improve the performance of the model through simulation studies and real data analysis.
HierHAR: Sensor-Based Data-Driven Hierarchical Human Activity Recognition

Pervasive computing greatly advances the automatic recognition and understanding of human activities and effectively bridges the gap between the low-level sensor signals and high-level human-centric applications. The inherent complexity of human behavior, however, inevitably poses a huge challenge to the design of a robust activity recognizer, especially in classifying similar activities. In this study, we present a hierarchical framework, named HierHAR, that infers on-going activities in a multi-stage process for better distinguishing similar activities and improving the overall performance. Specifically, we propose a data-driven approach, rather than heavily rely on prior domain knowledge, to automatically determining the relationships among activities. Afterwards, we use the relationships to organize the activities into a tree structure and accordingly construct and train a tree-based activity recognition model. Furthermore, we train a graph-based model that aims to reduce the compounding errors induced by the prediction process of the tree-based model. Finally, extensive comparative experiments are conducted on public datasets and results demonstrate the power of HierHAR in facilitating the automatic organization of activities and the design of hierarchical recognizers without prior knowledge about activities. Besides, the graph-based activity recognizer generally generalizes better across different scenarios and outperforms the tree-based model.
Semisupervised charting for spectral multimodal manifold learning and alignment

For one given scene, multimodal data are acquired from multiple sensors. They share some similarities across the sensor types (redundant part of the information, also called coupling part) and they also provide modality-specific information (dissimilarities across the sensors, also called decoupling part). Additional critical knowledge about the scene can hence be extracted, which is not extractable from each modality alone. For the processing of multimodal data, we propose in this paper a model to simultaneously learn the underlying low-dimensional manifold in each modality, and locally align these manifolds across different modalities. For each pair of modalities we first build a common manifold that represents the corresponding (redundant) part of information, ignoring non-corresponding (modality specific) parts. We propose a semi-supervised learning model, using a limited amount of prior knowledge about the coupling and decoupling components of the different modalities. We propose a localized version of Laplacian eigenmaps technique specifically designed to handle multimodal manifold learning, in which the ideas of local patching of the manifolds, also known as manifold charting, is combined with the joint spectral analysis of the graph Laplacians of the different modalities. The limited given supervised information is then extending on the manifold of each modality. The idea of functional mapping is finally used to align the different manifolds across modalities. The evaluation of the proposed model using synthetic and real-world multimodal problems shows promising results, compared to several related techniques.
Deep Learning-Based Efficient Model Development for Phishing Detection Using Random Forest and BLSTM Classifiers

With the increase in the number of electronic devices and developments in the communication system, security becomes one of the challenging issues. Users are interacting with each other through different heterogeneous devices such as smart sensors, actuators, and many other devices to process, monitor, and communicate different scenarios of real life. Such communication needs a secure medium through which users can communicate in a secure and reliable way so that their information may not be lost. The proposed study is an endeavor toward the detection of phishing by using random forest and BLSTM classifiers. The experimental results of the proposed study are promising in phishing detection, and the study reflects the applicability of the proposed algorithms in the information security. The experimental results show that the BLSTM-based phishing detection model is prominent in ensuring the network security by generating a recognition rate of 95.47% compared to the conventional RF-based model that generates a recognition rate of 87.53%. This high recognition rate for the BLSTM-based model reflects the applicability of the proposed model for phishing detection.
VB-Net: Voxel-Based Broad Learning Network for 3D Object Classification

Point clouds have been widely used in three-dimensional (3D) object classification tasks, i.e., people recognition in unmanned ground vehicles. However, the irregular data format of point clouds and the large number of parameters in deep learning networks affect the performance of object classification. This paper develops a 3D object classification system using a broad learning system (BLS) with a feature extractor called VB-Net. First, raw point clouds are voxelized into voxels. Through this step, irregular point clouds are converted into regular voxels which are easily processed by the feature extractor. Then, a pre-trained VoxNet is employed as a feature extractor to extract features from voxels. Finally, those features are used for object classification by the applied BLS. The proposed system is tested on the ModelNet40 dataset and ModelNet10 dataset. The average recognition accuracy was 83.99% and 90.08%, respectively. Compared to deep learning networks, the time consumption of the proposed system is significantly decreased.
Uncertain Autoregressive Model via LASSO Procedure

Uncertain time series analysis is a method to predict future values based on imprecisely observed values. As a basic model of uncertain time series, an uncertain autoregressive model has been presented. However, the existing paper ignores the temporal dependence information embedded in time-series data. In dealing with this issue, this paper adds a least absolute shrinkage and selection operator penalty to the traditional uncertain autoregressive model and selects the optimum order of the model according to Akaike’s final prediction error criterion. Finally, two numerical examples are given to illustrate the effectiveness of the model and compare the results predicted by the uncertain autoregressive model with the principle of least squares.
Using ID-Based Authentication and Key Agreement Mechanism for Securing Communication in Advanced Metering Infrastructure

Smart metering technology plays a key role in Advanced Metering Infrastructure (AMI) in Smart Grid (SG). Smart Meters (SM) measure Power Consumption Data (PCD) of household devices and send it to DSO (Distributed System Operator) for further processing. DSO utilizes PCD for different applications such as monthly billing, demand response and other applications related to power system operation and energy markets. Secure communication between SM and DSO is of paramount importance. Certificate-based authentication is a de facto mechanism in ensuring legitimacy of communicating parties. But it incurs more computational delay as it involves intensive processes such as certificate management, revocation, and verification. ID-based authentication eliminates the risks associated with certificate management. A key agreement protocol based on ID-based authentication mechanism is proposed and analyzed for computational performance. This paper analyzes and evaluates both authentication mechanisms: certificate and ID-based mechanism based on computational times for suitability in AMI network. The experimental results show that ID-based authentication and key agreement mechanism are suitable for securing communication in AMI network.
Traffic Accident Benchmark for Causality Recognition

We propose a brand new benchmark for analyzing causality in traffic accident videos by decomposing an accident into a pair of events, cause and effect. We collect videos containing traffic accident scenes and annotate cause and effect events for each accident with their temporal intervals and semantic labels; such annotations are not available in existing datasets for accident anticipation task. Our dataset has the following two advantages over the existing ones, which would facilitate practical research for causality analysis. First, the decomposition of an accident into cause and effect events provides atomic cues for reasoning on a complex environment and planning future actions. Second, the prediction of cause and effect in an accident makes a system more interpretable to humans, which mitigates the ambiguity of legal liabilities among agents engaged in the accident. Using the proposed dataset, we analyze accidents by localizing the temporal intervals of their causes and effects and classifying the semantic labels of the accidents. The dataset as well as the implementations of baseline models are available in the code repository ( https://github.com/tackgeun/CausalityInTrafficAccident ).
A Multilingual View of Unsupervised Machine Translation

We present a probabilistic framework for multilingual neural machine translation that encompasses supervised and unsupervised setups, focusing on unsupervised translation. In addition to studying the vanilla case where there is only monolingual data available, we propose a novel setup where one language in the (source, target) pair is not associated with any parallel data, but there may exist auxiliary parallel data that contains the other. This auxiliary data can naturally be utilized in our probabilistic framework via a novel cross-translation loss term. Empirically, we show that our approach results in higher BLEU scores over state-of-the-art unsupervised models on the WMT’14 English-French, WMT’16 English-German, and WMT’16 English-Romanian datasets in most directions.
CDEvalSumm: An Empirical Study of Cross-Dataset Evaluation for Neural Summarization Systems

Neural network-based models augmented with unsupervised pre-trained knowledge have achieved impressive performance on text summarization.However, most existing evaluation methods are limited to an in-domain setting, where summarizers are trained and evaluated on the same dataset.We argue that this approach can narrow our understanding of the generalization ability for different summarization systems.In this paper, we perform an indepth analysis of characteristics of different datasets and investigate the performance of different summarization models under a crossdataset setting, in which a summarizer trained on one corpus will be evaluated on a range of out-of-domain corpora.A comprehensive study of 11 representative summarization systems on 5 datasets from different domains reveals the effect of model architectures and generation ways (i.e.abstractive and extractive) on model generalization ability.Further, experimental results shed light on the limitations of existing summarizers.Brief introduction and supplementary code can be found in https://github.com/zide05/CDEvalSumm.
What is More Likely to Happen Next? Video-and-Language Future Event Prediction

Given a video with aligned dialogue, people can often infer what is more likely to happen next.Making such predictions requires not only a deep understanding of the rich dynamics underlying the video and dialogue, but also a significant amount of commonsense knowledge.In this work, we explore whether AI models are able to learn to make such multimodal commonsense nextevent predictions.To support research in this direction, we collect a new dataset, named Video-and-Language Event Prediction (VLEP), with 28,726 future event prediction examples (along with their rationales) from 10,234 diverse TV Show and YouTube Lifestyle Vlog video clips.In order to promote the collection of non-trivial challenging examples, we employ an adversarial humanand-model-in-the-loop data collection procedure.We also present a strong baseline incorporating information from video, dialogue, and commonsense knowledge.Experiments show that each type of information is useful for this challenging task, and that compared to the high human performance on VLEP, our model provides a good starting point but leaves large room for future work. 1
University students' attitude to distance learning in situation of uncertainty

The article presents an analysis of using distance learning technology in university education in the context of the COVID-19 pandemic and the attitude of students to this format of education. The authors justify the urgency of the problem under study for modern education and update the basic requirements for education in the situation of growing uncertainty. The paper describe the methodological foundations and genesis of the development of distance learning, distinguishes the concepts of "distance education" and "distance learning". The authors determine the features of distance learning in modern conditions and highlight the main problems and contradictions that were provoked by the COVID-19 pandemic and the social isolation. The aim of this study is to evaluate emotional attitude of university students of the humanities and natural sciences to distance learning, implemented in the pandemic situation. The presented results of an empirical study reveal the pros and cons of distance learning, the active use of Internet resources by students in learning, and the willingness to rebuild in a changing environment.
TDOA and track optimization of UAV swarm based on D-optimality

To solve the problem of time difference of arrival (TDOA) positioning and tracking of targets by the unmanned aerial vehicles (UAV) swarm in future air combat, this paper adopts the TDOA positioning method and uses time difference sensors of the UAV swarm to locate target radiation sources. Firstly, a TDOA model for the target is set up for the UAV swarm under the condition that the error variance varies with the received signal-to-noise ratio. The accuracy of the positioning error is analyzed by geometric dilution of precision (GDOP). The D-optimality criterion of the positioning model is theoretically derived. The target is positioned and settled, and the maximum value of the Fisher information matrix determinant is used as the optimization objective function to optimize the track of the UAV in real time. Simulation results show that the track optimization improves the positioning accuracy and stability of the UAV swarm to the target.
Leveraging Edge Intelligence for Video Analytics in Smart City Applications

In smart city scenarios, the huge proliferation of monitoring cameras scattered in public spaces has posed many challenges to network and processing infrastructure. A few dozen cameras are enough to saturate the city’s backbone. In addition, most smart city applications require a real-time response from the system in charge of processing such large-scale video streams. Finding a missing person using facial recognition technology is one of these applications that require immediate action on the place where that person is. In this paper, we tackle these challenges presenting a distributed system for video analytics designed to leverage edge computing capabilities. Our approach encompasses architecture, methods, and algorithms for: (i) dividing the burdensome processing of large-scale video streams into various machine learning tasks; and (ii) deploying these tasks as a workflow of data processing in edge devices equipped with hardware accelerators for neural networks. We also propose the reuse of nodes running tasks shared by multiple applications, e.g., facial recognition, thus improving the system’s processing throughput. Simulations showed that, with our algorithm to distribute the workload, the time to process a workflow is about 33% faster than a naive approach.
Skenario Pengembangan Fasilitas Sistem Pengolahan Sampah Dengan Pendekatan Cost Benefit Analysis Di Kelurahan Kedamaian Kota Bandar Lampung

Bandar Lampung City is part of the major city of Lampung province. The problem that arises in Bandar Lampung City is solid waste management. The amount of solid waste in Bandar Lampung City reaches 1000 tons/day. One of the district of Bandar Lampung named Kedamaian district still uses the old method (collecting-transport-throw) in solid waste management, which causes the load of waste to accumulate in the landfill. The constraints encountered this time are the absence of sorting locations and limited land. Kedamaian district needs to develop waste treatment facilities, an approach based on Cost Benefit Analysis (CBA). The results show from the analysis that BCR in the scenario A &lt;1 is 0.85 with NPV - Rp. 72.968.828. the activity in the existing conditions can still be carried out, but the waste reduction has not been maximized to the TPS (Container). Scenario B, NPV is Rp. 95.319.338 and BCR is 1.13, which value of BCR is &gt;1. This indicates that a waste processing facility in the Kedamaian District is suitable to operate with TPS-3R. The results of scenario B are obtained by increasing the effectiveness of processing facilities to 100% by 2025. In 2025, with the implementation of the TPS-3R obtained, BCR is 1.20 with NPV is Rp. 218.447.621.
Android code smells: From introduction to refactoring

Object-oriented code smells are well-known concepts in software engineering that refer to bad design and development practices commonly observed in software systems. With the emergence of mobile apps, new classes of code smells have been identified by the research community as mobile-specific code smells. These code smells are presented as symptoms of important performance issues or bottlenecks. Despite the multiple empirical studies about these new code smells, their diffuseness and evolution along change histories remains unclear. We present in this article a large-scale empirical study that inspects the introduction, evolution, and removal of Android code smells. This study relies on data extracted from 324 apps, a manual analysis of 561 smell-removing commits, and discussions with 25 Android developers. Our findings reveal that the high diffuseness of mobile-specific code smells is not a result of releasing pressure. We also found that the removal of these code smells is generally a side effect of maintenance activities as developers do not refactor smell instances even when they are aware of them.
Privacy protection for blockchains with account and multi-asset model

The blockchain technology has been applied to wide areas. However, the open and transparent properties of the blockchains pose serious challenges to users' privacy. Among all the schemes for the privacy protection, the zero-knowledge proof algorithm conceals most of the private information in a transaction, while participants of the blockchain can validate this transaction without the private information. However, current schemes are only aimed at blockchains with the UTXO model, and only one type of assets circulates on these blockchains. Based on the zero-knowledge proof algorithm, this paper proposes a privacy protection scheme for blockchains that use the account and multi-asset model. We design the transaction structure, anonymous addresses and anonymous asset metadata, and also propose the methods of the asset transfer and double-spending detection. The zk-SNARKs algorithm is used to generate and to verify the zero-knowledge proof. And finally, we conduct the experiments to evaluate our scheme.