Reviewerly/exHarmony huggingface dataset:
Randomly Elected Blockchain System based on Grouping Verifiers for Efficiency and Security

Many systems have been built for transactions without a central manager by introducing blockchain in various fields such as industry, public sector, and trading economy. The blockchain system is also contributing to the advancement of technology to enable reliable transactions in distributed networks like P2P or D2D. Consensus algorithm for each operating environment is used to verify whether a transaction is valid. Transactions are verified generally with Proof-of-work (PoW) in public blockchain and Practice Byzantine Fault Tolerance (PBFT) in private blockchain. The PBFT is only available to a group of limited participants and is used for small groups. However, these small groups can be compromised by the collusion of malicious users, making it impossible to trade or forge. In addition, the blockchain does not define any security structure on users, devices, transaction history accessibility, etc., which causes issues with system permission. Therefore, this paper analyzes the problem of the conventional blockchain in terms of participant-collusion, authentication, and confidentiality, and proposes a Randomly Elected Blockchain (REBC) to solve each problem. In the first participant-collusion, the private blockchain enables the system to operate without any cost for verification. Thereby solving the problem of grouping nodes for malicious collusion, the proposed mechanism is to randomly elect the nodes in the unit group, and then conduct consensus algorithms. Besides, many participants are able to participate in the consensus experiment without degraded performance. In the second problem, a smart manager system provides authentication of users and devices. The solution to the third problem related to confidentiality is to analyze the current RSA encryption method and supplement it with a proxy re-encryption method.
Automatic Encoder Combined with Nasnet in Histopathologic Cancer Detection

Computer vision is a major branch of artificial intelligence algorithm. The algorithm of computer vision mainly consists of the processing of image and video, including image recognition and image detection etc. Practice has proved that computer vision is scientific and practical to a certain extent. In pace with the development of in-depth learning, computer vision has already been put to use well in all walks of life. However, it is still in exploring stage in the medical field, because the medical data is sensitive, which requires high accuracy of the algorithm. In this paper, images of PCam [1], [2] medical electron microscope are put to use for tumor detection, which is an task of image recognition and an automatic encoder is used to lower the dimensions of the data into low-dimensional vectors which are used as features in training. Then the vectors are added as features to the training, and the model is trained together with the original data set as the training features of NASnet. Because detection algorithms in the medical field pay more importance to the true positive rate and false positive rate. When the output is positive, it is necessary to be revalidated by SVM model trained by encoder. As a result, ROC curve is 0.98, which is 0.03 higher than Baseline.
Proposal of Profile and Event Sharing by Agent Communication

Finding the valuable data among huge amount of data is too laborious due to the human limitation on perception capability. Reversely, pushed data about a service must miss the point without understanding user's demand. So, our base idea is to collect profiles of users and providers, and to match the user's demand with the providers' service. In this paper, we propose a communication framework for agents to find friend-agents and to share events in place of the client. Concretely we propose two-stage protocols, that is, profile sharing and event sharing. Thereby, only significant events can be shared in real time since friend-agents already have logical linkage. We verify that the latency to convey the valuable event to the receiver can be reduced. Finally, we discuss the structure of the profile data, and shows that required load for word matching can be saved by the hierarchical structure.
On the Security of a Key Agreement and Key Protection Scheme

We point out that the key agreement and key protection scheme (published in IEEE Transactions on Information Forensics and Security, doi: 10.1109/TIFS.2018.2850299) fails to achieve the two-factor security. We demonstrate that in the scheme, if an adversary can control the master device of a target user, he can impersonate the user to pass the server's authentication.
What did your adversary believeƒ Optimal Filtering and Smoothing in Counter-Adversarial Autonomous Systems

We consider fixed-interval smoothing problems for counter-adversarial autonomous systems. An adversary deploys an autonomous filtering and control system that i) measures our current state via a noisy sensor, ii) computes a posterior estimate (belief) and iii) takes an action that we can observe. Based on such observed actions and our knowledge of our state sequence, we aim to estimate the adversary's past and current beliefs - this forms a foundation for predicting, and counteracting against, future actions. We derive the optimal smoother for the adversary's beliefs (we treat the problem in a Bayesian framework). Moreover, we demonstrate how the smoother can be computed for discrete systems even though the corresponding backward variables do not admit a finite-dimensional characterization. Finally, we illustrate our results in numerical simulations.
Deep Multi-Region Hashing

Hashing has been widely used for large-scale approximate nearest neighbors retrieval own to its high efficiency. In the existing hashing methods, deep supervised hashing methods have achieved the best performance by utilizing the semantic labels on data with deep learning. However, most of these methods only consider the semantics of whole image but ignore the local information which contains much more semantic details. Evidently, the semantic details are beneficial for hash learning. To address this issue, in this paper, we proposed a novel Deep Multi-Region Hashing (DMRH) method to fully utilize the semantic details, which uses overlapping N × N regions of an image to learn N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> hash codes for getting a final hash code. Extensive experimental results with three datasets show that DMRH can achieve state-of-the-art performance.
Unsupervised Person Re-Identification Using Multi-Branch Feature Compensation Network and Link-Based Cluster Dissimilarity Metric

Feature extraction and label estimation are critical in unsupervised person re-identification (re-ID). Most previous works focus on acquiring high-layer semantic features and reckon without the lower-layer details lost in the learning process, which causes the extracted features to be less comprehensive and may degrade re-ID performance. Therefore in this paper, a Multi-branch Feature Compensation Network (MFC-Net) is developed in which the significant parts of lower-layer features are learned and fused with high-layer feature as compensation. Moreover, to accurately conduct label estimation through applying hierarchical clustering on individual samples, a Link-based Cluster Dissimilarity Metric (LCDM) is proposed to discover the inner correlation between clusters. These two methods are later integrated as a MFC-LCDM scheme to improve unsupervised re-ID performance. Extensive experiments on large-scale image-based and video-based datasets ulteriorly demonstrate the superiority and effectiveness of MFC-LCDM.
Visual Auxiliary Solutions to Analyse Social Media Data for Improving Marketing &amp; Business

According to various sources, around 85% of all social media data has not been analysed. How can companies use some of this data to make better, more informed marketing decisions? We conducted a review of the analytical techniques and novel visualisations being applied, and we considered various papers across various domains that are focused on extracting information from social media data. We aimed to describe various analytical approaches used on social media data that could be used to improve marketing. For each approach, we also discussed the visualisations that can be used to describe and summarise the results of the analytical approaches. These visualisations could potentially be used by marketing managers to gain invaluable insight into the success, or otherwise, of marketing campaigns, and sentiment towards their products. Finally, we have developed a prototype solution using various analytical approaches to visualise social media data effectively.
Analysis of Methods for Hiding Information in PDF Documents and Opportunities for Their Progress

Notwithstanding the popularity of multimedia files for steganography purposes, other files, whether binary data files, executables or text based files, can also be used to hide information. The widespread use of PDF files can make its use for this purpose an interesting and practical solution. In the article, an analysis was made of the structures of PDF documents, the use of steganography capabilities and their detection. A study of steganography algorithms from open sources was conducted and based on the analysis, an algorithm for detecting steganography was proposed. For this purpose, an integrated approach was developed.
The Features of Security of Transfer and Storage Data for the Internet of Things in Cloud Database

The article discusses the massager queue telemetry transport protocol of the Internet of Things and sensor networks, its features, application options, and specific procedures. The publisher-subscriber principle is considered. We analyzed the information elements and message. This article covers topics such as the secure storage of Internet of Things data, the provision of this stored data through secure of application programming interface from mobile and web applications. This article discusses all the key tools provided by the protocol of transport layer security to protect information.
Graph-based Recommendation Meets Bayes and Similarity Measures

Graph-based approaches provide an effective memory-based alternative to latent factor models for collaborative recommendation. Modern approaches rely on either sampling short walks or enumerating short paths starting from the target user in a user-item bipartite graph. While the effectiveness of random walk sampling heavily depends on the underlying path sampling strategy, path enumeration is sensitive to the strategy adopted for scoring each individual path. In this article, we demonstrate how both strategies can be improved through Bayesian reasoning. In particular, we propose to improve random walk sampling by exploiting distributional aspects of items’ ratings on the sampled paths. Likewise, we extend existing path enumeration approaches to leverage categorical ratings and to scale the score of each path proportionally to the affinity of pairs of users and pairs of items on the path. Experiments on several publicly available datasets demonstrate the effectiveness of our proposed approaches compared to state-of-the-art graph-based recommenders.
Vulnerability of Hardware Neural Networks to Dynamic Operation Point Variations

Editor's notes: This article studies the impacts of physical variations on neural networks. The proposed studies reveal an important observation that both multiple layer perceptron (MLP) and convolutional neural network (CNN) may fail to operate appropriately even with small variations (e.g., voltage droops as small as 20 mV). Robust neural network architectures, including the binarized neural network (BNN) and the local binary pattern network (LBPNet), are explored to address this variability issue that has become a major bottleneck for practical applications. -Xin Li, Duke University.
On Predicting the PUE with Gated Recurrent Unit in Data Centers

The huge energy consumption of data centers has brought great pressure to operating enterprises, power plants, and the environment. How to effectively reduce energy consumption has aroused wide attention. Prediction of data center PUE (Power Usage Effectiveness) or energy consumption is a promising way to reduce data center energy consumption. It will provide many ideas for data center to reduce energy consumption if the PUE prediction can be accurately predicted. However, in the previous researches on predicting PUE or energy consumption, there are some shortcomings such as the factors to PUE are not fully considered and lack of consideration of the time series information of energy related data. In this paper, we investigate how to predict the PUE value of data center by fully exploiting the knowledge energy consumption related historical data over time. To this end, we first collect more than 50,000 data samples with 144 energy related variables. Then, these data samples are preprocessed for normalization and feature selection. After that, considering the temporal property of the energy consumption related data, a GRU based neural network model is designed as the algorithm to train the data for generating the model for PUE prediction. Finally, extensive experiments are conducted based on the real data trace to evaluate the performance of the GRU model. The results demonstrate that our proposed model is efficient in accurately predicting the PUE value, and outperforms the baseline schemes with respect to MAE, MSE, and R-Squared.
A Temperature-based Omnidirectional Solar Tracking System for IoT Application

Nowadays, electricity is one of the major sources that should use by all citizens in the world to live a daily life. The electricity is produced primarily from burning limited fossil fuel resources that have great consequences for the environment. By conventional power generation, renewable energy sources and among them solar panels are widely used. The solar panel reacts to sunlight by transforming into electricity. To optimize the maximum power from sunlight, the tracking system was built to get the perfect angle at the time. Lithium-ion polymer battery is responsible to supply the energy to power up the IoT device. Internet of Things (IoT) device has the ability to transfer data over a network without needing interaction by human-to-computer or human-to-human. The IoT device has been designed to detect the temperature, transmit to the cloud and the data will be collected by the MQTT lens application. This is for plant field and their workers to get the information about the temperature early and for plan ahead in the future. The paper here is presented the solar-tracking the IoT device.
Spatio‐temporal context based recurrent visual attention model for lymph node detection

Abstract False‐positive reduction is one of the most crucial components in an automated lymph nodes (LNs) detection task in volumetric computed tomography (CT) scans, which is a highly sought goal for cancer diagnosis and early treatment. In this article, treating the three‐dimensional (3D) LN detection task as object detection on sequence problem, we propose a novel spatio‐temporal context‐based recurrent visual attention model (STRAM) for the LNs false positive reduction. We firstly extract the deep spatial features maps for two‐dimensional LN patches from pre‐trained Inception‐V3 model. A new Gaussian kernel‐based spatial attention method is then presented to extract the most discriminating spatial features for the corresponding center slices. Additionally, to combine the temporal information between 3D CT slices, we devise a novel “Siamese” mixture density networks which can learn to adaptively focus on the most relevant parts of the CT slices. Considering the lesion areas always locate around the centroid of the 3D CT scans, a hard constraint is imposed on the predicted attention locations with batch normalization technique and the Siamese architecture. The proposed model is a fully differentiable unit that can be optimized end‐to‐end by using stochastic gradient descent. The effectiveness of our method is verified on LN dataset: 388 mediastinal LNs labeled by radiologists in 90 patient CT scans, and 595 abdominal LNs in 86 patient CT scans. Our method demonstrates sensitivities of about 87%/82% at 3 FP/vol. and 93%/89% at 6 FP/vol. for mediastinum and abdomen, respectively, which compares favorably to previous methods.
Alzh-care: - Knowledge - driven intelligent system to assist Alzheimer Patients

Alzh-care is an Alzheimer patients assistive system. This system assists the patients by recognizing their intention on the activity and guides them to the completion of that task. It helps the patients in completing their day-today tasks by recognizing their intent of the task at the early stage. In this work, Intention recognition is achieved through the sensor based activity recognition process. These kinds of systems have far reaching applications in the domains like assisted living; healthcare monitoring and they offer lots of open challenges to solve in the arena of research and development. This paper discusses the implementation of the Alzh-care assistive system using sensors for the day-to-day activities like eating meals and taking medicines etc., We have used knowledge driven approach for representing the domain knowledge on the day-to-day activities is as ontology. This in turn is referred to know the intentions of the patients and voice assist them in completing the intended task. We have tried state machine approach to infer and assist on the task. Knowledge driven approach has proven to perform better than the data driven approaches as the latter suffers from the cold start and reliability issues.We have tabulated the trials and noted the success rate of recognition of the intent for each of the activity experimented. This system is proven to be 85% to 90% accurate for predicting the intentions in the finite disambiguous tasks, owing the other 5% to 10% of the failure caused by the sensors used.
Domain Robust, Fast, and Compact Neural Language Models

Despite advances in neural language modeling, obtaining a good model on a large scale multi-domain dataset still remains a difficult task. We propose training methods for building neural language models for such a task, which are not only domain robust, but reasonable in model size and fast for evaluation. We combine knowledge distillation from pretrained domain expert language models with the noise contrastive estimation (NCE) loss. Knowledge distillation allows to train a single student model which is both compact and domain robust, while the use of NCE loss makes the model self-normalized, which enables fast evaluation. We conduct experiments on a large English multi-domain speech recognition dataset provided by AppTek. The resulting student model is of the size of one domain expert, while it gives similar perplexities as various teacher models on their expert domain; the model is self-normalized, allowing for 30% faster first pass decoding than the naive models which require the full soft- max computation, and finally it gives improvements of more than 8% relative in terms of word error rate over a large multidomain 4-gram count model trained on more than 10 B words.
Laplacian state transfer in edge coronas

Characterizing which graphs have Laplaican perfect state transfer or Laplacian pretty good state transfer is an interesting and desirable problem. In this paper, we investigate the existence of Laplacian perfect state transfer and Laplacian pretty good state transfer in edge coronas. We give sufficient conditions for edge coronas to not have Laplacian perfect state transfer as well as sufficient conditions for edge coronas to have Laplacian perfect state transfer. Additionally, we also give sufficient conditions for edge coronas to have Laplacian pretty good state transfer.
A Comprehensive Survey on Multi Object Tracking Under Occlusion in Aerial Image Sequences

Object detection and tracking is a one of the major research topics in the field of computer vision. It is a major component in applications like video surveillance, people monitoring, traffic monitoring etc. Object tracking algorithms typically estimate the location of the target in each frame in a sequence, given initial location of the target as a bounding box. In spite of extensive research reported on tracking, object tracking under occlusion remains a challenge still. There are many practical challenges in object tracking that have to be addressed. This survey paper aims to compile the various techniques available for object tracking under occlusion. Details about the various performance measures available to evaluate the tracking performance under occlusion is also discussed. This paper also points to a list of publicly available datasets that are used to test the tracking algorithms.
Review of Various Sentiment Analysis Approaches

In this new world of social media, the trend of sharing views or opinions on several web portals such as blogs, Instagram pages, Facebook pages, Twitter, YouTube is becoming so popular and common. The data on these sites is humongous which attract the researchers to focus on sentiment analysis. Sentiment analysis is a method of determining positive, negative, neutral, sarcastic, ironical comments. Researchers tend to develop a system or a valid method for accurate prediction of opinions of users. There are various techniques that are used to detect the emotions from the online data such as supervised learning approaches, unsupervised, and many more. In this paper, we present a study of the methods used in the assessment of sentiments.
High Performance SqueezeNext for CIFAR-10

CNNs is the foundation for deep learning and computer vision domain enabling applications such as autonomous driving, face recognition, automatic radiology image reading, etc.But, CNN is a algorithm which is memory and computationally intensive.DSE of neural networks and compression techniques have made convolution neural networks memory and computationally efficient.It improved the CNN architectures and made it more suitable to implement on real-time embedded systems.This paper proposes an efficient and a compact CNN to ameliorate the performance of existing CNN architectures.The intuition behind this proposed architecture is to supplant convolution layers with a more sophisticated block module and to develop a compact architecture with a competitive accuracy.Further, explores the bottleneck module and squeezenext basic block structure.The state-of-the-art squeezenext baseline architecture is used as a foundation to recreate and propose a high performance squeezenext architecture.The proposed architecture is further trained on the CIFAR-10 dataset from scratch.All the training and testing results are visualized with live loss and accuracy graphs.Focus of this paper is to make an adaptable and a flexible model for efficient CNN performance which can perform better with the minimum tradeoff between model accuracy, size, and speed.Finally, the conclusion is made that the performance of CNN can be improved by developing an architecture for a specific dataset.The purpose of this paper is to introduce and propose high performance squeezenext for CIFAR-10.