Reviewerly/exHarmony huggingface dataset:
A blockchain-based smart contract trading mechanism for energy power supply and demand network

To overcome the high cost, high risk and poor efficiency of traditional centralized electric energy trading method, this paper proposes an efficient trading mechanism for energy power supply and demand network (EPSDN) based on blockchain smart contract, considering the opening of the sales side market in China.Specifically, the encourage-real-quotation (ERQ) rule was adopted to determine the clearing queue and price, thus smoothing the supply and demand interaction between the EPSDN node.Meanwhile, the blockchain smart contract was introduced into the transaction to form a sealed quotation function, which eliminates the centralization and high cost and solves the poor transparency and trust in traditional transaction.In addition, the transaction efficiency was improved through the construction of an efficient power trading system and a secure trading environment.A case study is given in the end of the paper.Case study shows that the blockchain-based smart contract trading system for the EPSDN can achieve desirable security and effectiveness, and effectively solve the problems of the traditional centralized trading method.The research findings lay solid theoretical and decision-making bases for small-scale transactions in the electric energy market.
Truncated lognormal distributions and scaling in the size of naturally defined population clusters

Using population data of high spatial resolution for a region in the south of Europe, we define cities by aggregating individuals to form connected clusters. The resulting cluster-population distributions show a smooth decreasing behavior covering six orders of magnitude. We perform a detailed study of the distributions, using state-of-the-art statistical tools. By means of scaling analysis we rule out the existence of a power-law regime in the low-population range. The logarithmic-coefficient-of-variation test allows us to establish that the power-law tail for high population, characteristic of Zipf's law, has a rather limited range of applicability. Instead, lognormal fits describe the population distributions in a range covering from a few dozen individuals to more than 1×10^{6} (which corresponds to the population of the largest cluster).
A Secure Container Deployment Strategy by Genetic Algorithm to Defend against Co-Resident Attacks in Cloud Computing

Linux container mechanism is constantly gaining ground in the virtualization landscape as a lightweight and efficient alternative to hypervisor-based Virtual Machine (VM). Lightweight virtualization brings convenience and high speed, but the kernel-sharing property also means that isolation is incomplete, so it is more vulnerable to co-resident attacks which could incur information leakage. Previous works mainly eliminate co-resident attacks by fixing hardware and guest operating systems, but these methods cannot be generalized to future co-resident attacks. The threats of co-resident attacks can also be migrated by reducing the probability of co-residency with appropriate deployment of containers. In this paper, we present SecCDS, a Secure Container Deployment Strategy by genetic algorithm (GA) to defend against co-resident attacks in container clouds. SecCDS detects the co-residency of the cloud in real time, and uses a deployment strategy to migrate containers and separate tenants with high risk. In designing SecCDS, we (1) present a formal model and establish metrics to describe deployment and co-residency of container clouds; (2) develop a dynamic container deployment strategy based on GA as a robust defense for arbitrary co-residency; (3) implement, test, and prove the effectiveness of the strategy. The results show that SecCDS can reduce co-residency by 50% compared with existing strategies, and is scalable and flexible to large cloud deployments and different security level requirements.
On-Device Power Analysis Across Hardware Security Domains.

Side-channel power analysis is a powerful method of breaking secure cryptographic algorithms, but typically power analysis is considered to require specialized measurement equipment on or near the device. Assuming an attacker first gained the ability to run code on the unsecure side of a device, they could trigger encryptions and use the on-board ADC to capture power traces of that hardware encryption engine.This is demonstrated on a SAML11 which contains a M23 core with a TrustZone-M implementation as the hardware security barrier. This attack requires 160 × 106 traces, or approximately 5 GByte of data. This attack does not use any external measurement equipment, entirely performing the power analysis using the ADC on-board the microcontroller under attack. The attack is demonstrated to work both from the non-secure and secure environment on the chip, being a demonstration of a cross-domain power analysis attack.To understand the effect of noise and sample rate reduction, an attack is mounted on the SAML11 hardware AES peripheral using classic external equipment, and results are compared for various sample rates and hardware setups. A discussion on how users of this device can help prevent such remote attacks is also presented, along with metrics that can be used in evaluating other devices. Complete copies of all recorded power traces and scripts used by the authors are publicly presented.
Integrating Dense LiDAR-Camera Road Detection Maps by a Multi-Modal CRF Model

Road detection is an important task in autonomous navigation systems. In this paper, we propose a road detection method via a LiDAR-camera fusion strategy to exploit both the range and color information. The whole system consists of three parts. In the LiDAR based part, we transform the discrete 3D LiDAR point clouds to continuous 2D LiDAR range images and propose a distance-aware height-difference based scanning approach to get the road estimations quickly. In the camera based part, we apply a light-weight transfer learning based road segmentation network. In the LiDAR-camera fusion part, we transform the detection results from LiDAR and camera to dense and binary ones to solve the data imbalance problem and fuse them in a multi-modal conditional random field (MM-CRF) framework. Experiments show that the proposed MM-CRF fusion method can operate in real-time and achieve competitive performance compared with the state-of-the-art road detection algorithms on the KITTI-Road benchmark.
Team Tactics Estimation in Soccer Videos Based on a Deep Extreme Learning Machine and Characteristics of the Tactics

A novel method for estimating team tactics in soccer videos based on a Deep Extreme Learning Machine (DELM) and unique characteristics of tactics is presented in this paper. The proposed method estimates the tactics of each team from players' formations and enables successful training from a limited amount of training data. Specifically, the estimation of tactics consists of two stages. First, by utilizing two DELMs corresponding to the two teams, the proposed method estimates the provisional tactics of each team. Second, the proposed method updates the team tactics based on unique characteristics of soccer tactics, the relationship between tactics of the two teams and information on ball possession. Consequently, since the proposed method estimates the team tactics that satisfy these characteristics, accurate estimation results can be obtained. In an experiment, the proposed method is applied to actual soccer videos to verify its effectiveness.
Detecting Lesion Bounding Ellipses with Gaussian Proposal Networks

Lesions characterized by computed tomography (CT) scans, are arguably often elliptical objects. However, current lesion detection systems are predominantly adopted from the popular Region Proposal Networks (RPNs) [9] that only propose bounding boxes without fully leveraging the elliptical geometry of lesions. In this paper, we present Gaussian Proposal Networks (GPNs), a novel extension to RPNs, to detect lesion bounding ellipses. Instead of directly regressing the rotation angle of the ellipse as the common practice, GPN represents bounding ellipses as 2D Gaussian distributions on the image plane and minimizes the Kullback-Leibler (KL) divergence between the proposed Gaussian and the ground truth Gaussian for object localization. Experiments on the DeepLesion [13] dataset show that GPN significantly outperforms RPN for lesion bounding ellipse detection thanks to lower localization error.
The learning management system: Variables that determine its use

Teachers consider the Virtual Learning Environment a technological tool with strong didactic potential. This perception is validated by the frequency of its use both in technical and didactic spheres, which frequently interweave with each other. The main assumption of our research is that those technical and pedagogical competences of the digital tools have promoted the use of the Virtual Learning Environment (AVA) in class causing a change of the methodology used as a result. The study is a non-experimental one but an ex post facto with non-probabilistic sampling. The survey was carried out on a population of 640 teachers of a professional technical higher education institution in Chile. The results showed that, in order to incorporate digital tools into their educational practice, teachers should emphasize the didactic domain of those device over the technical one. In conclusion, it has been highlighted that the teaching staff uses AVA mainly to accomplish administrative tasks to the detriment of the didactic use.
Efficient Classical Simulation of Clifford Circuits with Nonstabilizer Input States

We investigate the problem of evaluating the output probabilities of Clifford circuits with nonstabilizer product input states. First, we consider the case when the input state is mixed, and give an efficient classical algorithm to approximate the output probabilities, with respect to the $l_1$ norm, of a large fraction of Clifford circuits. The running time of our algorithm decreases as the inputs become more mixed. Second, we consider the case when the input state is a pure nonstabilizer product state, and show that a similar efficient algorithm exists to approximate the output probabilities, when a suitable restriction is placed on the number of qubits measured. This restriction depends on a magic monotone that we call the Pauli rank. We apply our results to give an efficient output probability approximation algorithm for some restricted quantum computation models, such as Clifford circuits with solely magic state inputs (CM), Pauli-based computation (PBC) and instantaneous quantum polynomial time (IQP) circuits.
A Hierarchical Attention Model for CTR Prediction Based on User Interest

The prediction of click-through rate is a challenging problem in the aspect of online advertising. Recently, researchers have proposed deep learning-based models that follow a similar embedding and multilayer perceptron paradigm. Although encouraging successes have been obtained, the importance of capturing the latent user interest behind user behavior data was ignored by most of the methods, which has the potential to effectively learn the feature interactions. In this article, we propose an attentive-deep-interest-based model to fill these gaps. Specifically, we capture the interest sequence in the interest extractor layer, and the auxiliary losses are employed to produce the interest state with deep supervision. First, we use the bidirectional long short-term memory network to model the dependence between behaviors. Next, an interest evolving layer is proposed to extract the interest evolving process that is related to the target. Then, the model learns highly nonlinear interactions of features based on stack autoencoders. An experiment is conducted using four real-world datasets. The experimental results show that the proposed model achieves 1.8% improvement in the Amazon datasets than the existing state-of-the-art models.
Continuous Bitrate &amp; Latency Control with Deep Reinforcement Learning for Live Video Streaming

In this paper, we introduce a continuous bitrate control and latency control model for the Live Video Streaming Challenge. Our model is based on Deep Deterministic Policy Gradient, popular on continuous control tasks. Simultaneously, it can take a fine-grained control through continuous control and does not need to discrete the continuous "latency limit", which is a buffer threshold to minimize end-to-end delay by frame skipping. In all considered live video scenarios, our model can provide a better quality of experience with improvements in average QoE of 3.6% than DQN which discrete the "latency limit". Additionally, challenge results show the effectiveness and applicability of the proposed model, which achieved top performance in 3 different networks that include high, low and oscillating throughput, and ranked the second place in the network with medium throughput.
Model-Agnostic Adversarial Detection by Random Perturbations

Adversarial examples induce model classification errors on purpose, which has raised concerns on the security aspect of machine learning techniques. Many existing countermeasures are compromised by adaptive adversaries and transferred examples. We propose a model-agnostic approach to resolve the problem by analysing the model responses to an input under random perturbations, and study the robustness of detecting norm-bounded adversarial distortions in a theoretical framework. Extensive evaluations are performed on the MNIST, CIFAR-10 and ImageNet datasets. The results demonstrate that our detection method is effective and resilient against various attacks including black-box attacks and the powerful CW attack with four adversarial adaptations.
A dataset build using wearable inertial measurement and ECG sensors for activity recognition, fall detection and basic heart anomaly detection system

This paper defines two major data sets 1) from wearable inertial measurement sensors and 2) wearable ECG SHIMMER™ sensors. The first dataset is devised to benchmark techniques dealing with human behavior analysis based on multimodal inertial measurement wearable SHIMMER™ sensors unit during research studies "Fall Detection System for the Elderly Based on the Classification of Shimmer Sensor Prototype Data" [2] and "A novel fall detection algorithm for elderly using SHIMMER wearable sensors" [3]. The SHIMMER inertial sensor is a lightweight sensing device, incorporated with tri-axial accelerometer, a tri-axial gyroscope and tri-axial magnetometer, mounted on the waist of the subjects. The second dataset is developed to assess the feasibility of using SHIMMER™ wearable third generation ECG sensors for identification of basic heart anomalies by remote ECG analysis. The experimental protocol was carried out according to the Timed Up and Go (TUG) test [1], which is mainly used in fall detection and fall risk assessment systems specially designed for elderly. Three daily life activities such as standing still, walking and sitting on chair and getup were performed along with fall activity in controlled environment. This dataset is available on Data in Brief Dataverse [4] and a data repository [5].
Hierarchical Comprehensive Context Modeling for Chinese Text Classification

The Chinese text classification task is challenging compared to tasks based on other languages such as English due to the characteristics of the Chinese text itself.In recent years, some popular methods based on deep learning have been used for text classification, such as the convolutional neural network (CNN) and the long short-term memory (LSTM) network.However, some problems are still encountered when classifying Chinese text.For example, important but obscure context information in Chinese text is not easily extracted.To improve the effect of Chinese text classification, we propose a novel classification model in this paper named the hierarchical comprehensive context modeling network (HCCMN) that can extract more comprehensive context.Our approach aims to extract contextual information and integrate it with the original input and then extract hierarchically more context, spatial information and high-weight local features from the integrated results.In addition, our method can remember long-term historical obscure information.Since Chinese radiology texts are complicated and difficult to obtain, we collected a Chinese radiology medical text dataset (CIRTEXT) containing more than 56,000 real-world data samples to verify the effect of this work.We conducted experiments on four datasets and showed that our HCCMN performs at state-of-the-art levels on three selected evaluation metrics compared to baselines.We present promising results showing that our hierarchical context modeling network extracts useful context from Chinese text more effectively and comprehensively.
IoT based heart monitoring and alerting system with cloud computing and managing the traffic for an ambulance in India

&lt;p&gt;Global Burden of Disease Report, released in Sept 2017, shows that Cardio- vascular Diseases caused 1.7 million deaths (17.8%) in 2016 and it is the leading cause of deaths in India [1]. According to the Indian Heart Association, 25% of all heart attacks happen under the age of 40. In most cases, the initial heart attacks are often ignored. Even post-diagnosis, as per government data [2], 50% of heart attack cases reach the hospital in more than 400 minutes against the ideal window time of 180 minutes; post which damage is irreversible. The delay is often attributed to delay in reaching a hospital or receiving primary aid. In India, traffic conditions also add to the grimace of the situation. Although the government is taking various measures; a holistic solution is required to minimize the delay at each of the steps like accessing the patient situation, contacting the Medical aid or making available the nearest aid possible. In this paper, we aim at providing the holistic solution using the Internet of Things technology (IOT) along with data analytics. IoT enables real-time capturing and computation of medical data from smart sensors built-in wearable devices. The amalgamation of Internet-based services with Medical Things (Smart sensors) enhance the chances of survival of patients. The proposed system analyses the inputs collected from the sensors fit with the patients prone to cardiovascular diseases to ascertain the emergency situation. In addition, to these data, the system also considers age, maximum and minimum heart rate. Based on computational results received from the input parameters, the system triggers the alert to emergency contacts such as the close relatives of the patient, doctors, the hospitals and nearby ambulance. The proposed system combines with the optimized navigation platform to guide the medical assistance to find the fastest route.&lt;/p&gt;
Hybrid approach: naive bayes and sentiment VADER for analyzing sentiment of mobile unboxing video comments

&lt;p align="justify"&gt;Revolution in social media has attracted the users towards video sharing sites like YouTube. It is the most popular social media site where people view, share and interact by commenting on the videos. There are various types of videos that are shared by the users like songs, movie trailers, news, entertainment etc. Nowadays the most trending videos is the unboxing videos and in particular unboxing of mobile phones which gets more views, likes/dislikes and comments. Analyzing the comments of the mobile unboxing videos provides the opinion of the viewers towards the mobile phone. Studying the sentiment expressed in these comments show if the mobile phone is getting positive or negative feedback. A Hybrid approach combining the lexicon approach Sentiment VADER and machine learning algorithm Naive Bayes is applied on the comments to predict the sentiment. Sentiment VADER has a good impact on the Naive Bayes classifier in predicting the sentiment of the comment. The classifier achieves an accuracy of 79.78% and F1 score of 83.72%.&lt;/p&gt;
An Exponential-Type Anti-Noise Varying-Gain Network for Solving Disturbed Time-Varying Inversion Systems

To solve the disturbed time-varying inversion problem, an exponential-type anti-noise varying-gain network (EAVGN) is proposed and analyzed. To do so, a vector-based error function is first defined. By using the varying-gain neural dynamic design method, an EAVGN model is then formulated. Furthermore, the differentiation error and the model-implementation error are considered into the model, and the perturbed EAVGN model is obtained. For better illustrations, comparisons between the EAVGN and the conventional fixed-parameter recurrent neural network (FP-RNN) are conducted to illustrate the advantages of the proposed EAVGN. Mathematical proof demonstrates that the proposed EAVGN has much better anti-noise properties than FP-RNN. On one hand, the residual error of EAVGN can be reduced to zero in any case, but that of FP-RNN is large and cannot be convergent, in particular when the bound of Frobenius norm of the exact solution is large or the noise is large. On the other hand, the bound of the residual error of EAVGN is always smaller than that of FP-RNN. Simulation results verify that when different types of noises exist, the proposed EAVGN owns better anti-noise property compared with the state-of-the-art methods. In addition, a practical application is presented to illustrate the implementation process and the practical benefits of the EAVGN.
Blockchain as a project management platform

The construction industry will continue to be a key driver of economic growth for any country.It is one of the biggest industries in the world which contributes heavily to the economic development of a country.However, the productivity and the effectiveness of the industry have often been called into a question.Therefore, a number of different modelling tools and software have introduced to upgrade the standards of the construction industry.This review seeks to identify how blockchain can address the project management perspectives of the construction industry with respect to the guidelines mentioned in the Project Management Body of Knowledge.Five major criterions namely purchase management, contract management, asset and inventory management, finance management and subcontractor management were selected for the analysis using the PMBOK guidelines.For that, literature review using articles in ScienceDirect which appeared the context "blockchain in construction", "blockchain and project management", "application of blockchain" were referred.It is identified that the blockchain technology can assist financial management without involving third parties, subcontractor management by linking derivable and payment schemes, contract administration by using smart contracts, inventory and asset management by tracking and tracing material movements and purchase management by linking key stakeholders in supply chains.