Reviewerly/exHarmony huggingface dataset:
Automatic Detection and Classification of Road, Car, and Pedestrian Using Binocular Cameras in Traffic Scenes with a Common Framework

In order to solve the problems of traffic object detection, fuzzification, and simplification in real traffic environment, an automatic detection and classification algorithm for roads, vehicles, and pedestrians with multiple traffic objects under the same framework is proposed. We construct the final V view through a considerate U-V view method, which determines the location of the horizon and the initial contour of the road. Road detection results are obtained through error label reclassification, omitting point reassignment, and so an. We propose a peripheral envelope algorithm to determine sources of vehicles and pedestrians on the road. The initial segmentation results are determined by the regional growth of the source point through the minimum neighbor similarity algorithm. Vehicle detection results on the road are confirmed by combining disparity and color energy minimum algorithms with the object window aspect ratio threshold method. A method of multifeature fusion is presented to obtain the pedestrian target area, and the pedestrian detection results on the road are accurately segmented by combining the disparity neighbor similarity and the minimum energy algorithm. The algorithm is tested in three datasets of Enpeda, KITTI, and Daimler; then, the corresponding results prove the efficiency and accuracy of the proposed approach. Meanwhile, the real-time analysis of the algorithm is performed, and the average time efficiency is 13 pfs, which can realize the real-time performance of the detection process.
Robust Camera Translation Estimation via Rank Enforcement

Camera translation averaging, aiming to recover the global camera locations from a given set of camera translation directions, is a challenging problem for Structure from Motion (SfM) in the field of computer vision, largely due to the fact that the given relative translation directions from a set of noisy essential matrices are generally of low accuracy. To tackle this problem, we first reveal a novel but a simple property of the camera translation matrix consisting of all the pairwise camera translations among an arbitrary set of cameras that the rank of this translation matrix is always smaller or equal to 4. Then, by explicitly enforcing this rank property, a novel translation estimation method for computing global camera locations is proposed, called TERE. Moreover, to further improve the performances of the explored TERE in the two aspects of accuracy and speed, an iterative batch-based translation estimation method is proposed, called B-TERE, where a small-scale batch of cameras is selected without replacement from the given set of cameras according to a simple camera selection strategy at each iterative step, and the locations of the selected cameras are estimated by the proposed TERE accordingly. Extensive experimental results on various datasets demonstrate that our proposed methods could achieve better performances in comparison to several state-of-the-art methods.
Extraction of Threat Actions from Threat-related Articles using Multi-Label Machine Learning Classification Method

With the rapid development of open source threat intelligence, researchers are sharing threat-related articles through blog articles or reports. The shared information, like IoC and threat action, can promote defense ability against the potential threats. However, with high columns of threat-related articles published, extracting threat-related information from unstructured context, especially threat action, has become a challenge. To overcome this problem, we present a method to extract threat action from threat-related articles. Firstly, topics are indexed with latent semantic indexing method. Next, with ATT&CK as taxonomy, semantic similarities are computed as classification features. Finally, a multi-label classification model extracts threat actions from threat-related articles. To evaluate our method, a labelled APT group-related dataset is collected and shared. The result shows, the maximum precision, recall ratio and F-1 measure for multi-label classification are 59.50%, 69.86% and 56.96%. Our method can help researcher understand network situation and make proactive defense measures against potential threats.
Design of Learning Media in Mixed Reality for Lao Education

To improve and develop education systems, the communication between instructors and learners in a class during the learning process is of utmost importance. Currently the presentations of 3D models using mixed reality (MR) technology can be used to avoid misinterpretations of oral and 2D model presentations. As an independent concept and MR applications, MR combines the excellent of each virtual reality (VR) and augmented reality (AR). This work aims to present the descriptions of MR systems, which include its devices, applications, and literature reviews and proposes computer vision tracking using the AR Toolkit Tracking Library. The focus of this work will be on creating 3D models and implementing in Unity 3D using the Vuforia SDK platform to develop VR and AR applications for architectural presentations.
DigGCN: Learning Compact Graph Convolutional Networks via Diffusion Aggregation

Recent interests in graph neural networks (GNNs) have received increasing concerns due to their superior ability in the network embedding field. The GNNs typically follow a message passing scheme and represent nodes by aggregating features from neighbors. However, the current aggregation methods assume that the network structure is static and define the local receptive fields under visible connections, which consequently fails to consider latent or high-order structures. Besides, the aggregation methods are known to have a depth dilemma due to the over-smoothness issues. To solve the above shortcomings, we present in this article a compact graph convolutional network framework which defines the graph receptive fields based on diffusion paths and explicitly compresses the neural networks with sparsity regularization. The proposed model seeks to learn from invisible connections and recover the latent proximity. First, we infer the high-order proximity and construct diffusion paths by diffusion samplings. Compared with random walk samplings, the diffusion samplings are based on regions instead of paths. The network inference then obtains accurate weights that can be leveraged to build small but informative receptive fields with salient neighbors. Second, to utilize the deep information while avoiding overfitting, we propose learning a lightweight model by introducing a nonconvex regularizer. Numerical comparisons with the existing network embedding methods under unsupervised feature learning and supervised classification show the effectiveness of our model.
Blockchain and the Resurrection of Consumer Sovereignty in a Sustainable Food Economy

First paragraphs: Consumption is the sole end and purpose of all pro­duction; and the interest of the producer ought to be attended to, only so far as it may be necessary for pro­moting that of the consumer. The maxim is so perfectly self-evident, that it would be absurd to attempt to prove it. —Adam Smith, An Inquiry into the Nature and Cause of the Wealth of Nations (1776) Introduction In today’s global food system, where the concen­tration of both economic and political power is self-evident, the maxim of consumer sovereignty is in great need of proof. In Montana, where we live, we have the great fortune to buy grass-finished cer­tified organic beef from a rancher almost literally in our own backyard. We know the supplier of our food not only as a producer, but as a friend. This rancher can easily garner from us, and his other costumers, our preferences. In a sense, we drive the rancher’s production methods and pricing. Even though we insist on organic certification, it is largely on the basis of trust and friendship that we return to purchase from him over and over for our family’s beef supply. . . . See the press release for this article.&nbsp
Detection of Skin Cancer Image by Feature Selection Methods Using New Buzzard Optimization (BUZO) Algorithm

Feature selection is used in machine learning as well as in statistical pattern recognition.This is important in many applications, such as classification.There are so many extracted features in these applications which are either useless or do not have much information.If not removing these features, make raises the computational burden for the main application.In different methods of feature selection, a subset is selected as the answer, which can optimize the value of an evaluation function.In this study, a new algorithm for classification of Dermoscopy images into two types of malignant and benign are presented.To develop the general skin cancer detection system, at first a pre-processing step is applied to enhance image quality.Then the lesion area is removed from the healthy areas using the Otsu threshold method.Nine shape feature and nine color features are extracted from the segmented image using different optimization schema.At the end of the operation, classification was done by SVM, KNN and Decision Tree methods.The results show that combination of buzzard optimization algorithm for feature extraction and SVM classifier accuracy is 94.3%.This result shows the high potential of buzzard optimization algorithm for feature extraction.
Modern Federated Database Systems: An Overview

Usually, modern applications manipulate datasets with diverse models, usages, and storages. “One size fits all” approaches are not sufficient for heterogeneous data, storages, and schemes. The rise of new kinds of data stores and processing, like NoSQL data stores, distributed file systems, and new data processing frameworks, brought new possibilities to meet this scenario’s requirements. However, semantic, schema and storage heterogeneity, autonomy, and distributed processing are still among the main concerns when building data-driven applications. This work surveys the literature aiming at giving an overview of the state of the art of modern federated database systems. It presents the background, characterizes existing tools, depicts guidelines one should follow when creating solutions, and points out research challenges to consider in future work. This work gives fundamentals for researchers and practitioners in the area.
Threshold Visual Cryptography Schemes With Tagged Shares

The appearance of shares encoded by traditional visual cryptography is not easy to distinguish and meaningless.It means the shares cannot be managed efficiently nor user-friendly.A tagged visual cryptography scheme (TVCS) provides additional tag images for the shares and decodes the tag images by folding up a single tagged share.A TVCS allows users to efficiently manage shares and greatly increases the user-friendliness of the shares.Previous researches have focused on codebook-based and random-grid-based TVCS approaches, but the visual qualities of recovered secret images and tag images can still be improved.Therefore, this study aims to provide better visual quality of recovered images by improving on previous methods.Moreover, the proposed approach supports adjustable visual qualities of recovered secret images and tag images.In this study, we theoretically analyze the performances of the proposed approach and then demonstrate implementation of the approach.
A Blockchain-Based Computing Architecture for Mobile Ad Hoc Cloud

Mobile ad-hoc cloud can exploit the computing resources (e.g., smartphones, vehicles, and unmanned systems) scattered in the mobile environment to form a self-organized ad-hoc local resource pool for providing opportunistic computing services. However, the highly dynamic and distributed characteristics of the mobile ad-hoc network environment bring great challenges in privacy and security in such opportunistic resource sharing. In this article, we first discuss the attractive features of blockchain for providing such resource sharing services in the mobile ad-hoc network environment in a secure and trustful way and then discuss the problems caused when using existing consensus protocols in such an environment. We accordingly devise a blockchain based trustful mobile ad-hoc cloud architecture, AdChain cloud. We describe the functions at different layers in this architecture, including the network layer, blockchain layer, and smart contract layer. To adapt to the high dynamics of the mobile ad-hoc network environment, we design a stability-aware consensus protocol at the blockchain layer. Simulation results show that our solution can achieve improved performance as compared with existing work.
Poisson Learning: Graph Based Semi-Supervised Learning At Very Low Label Rates

We propose a new framework, called Poisson learning, for graph based semi-supervised learning at very low label rates. Poisson learning is motivated by the need to address the degeneracy of Laplacian semi-supervised learning in this regime. The method replaces the assignment of label values at training points with the placement of sources and sinks, and solves the resulting Poisson equation on the graph. The outcomes are provably more stable and informative than those of Laplacian learning. Poisson learning is efficient and simple to implement, and we present numerical experiments showing the method is superior to other recent approaches to semi-supervised learning at low label rates on MNIST, FashionMNIST, and Cifar-10. We also propose a graph-cut enhancement of Poisson learning, called Poisson MBO, that gives higher accuracy and can incorporate prior knowledge of relative class sizes.
Quantifying 60 Years of Gender Bias in Biomedical Research with Word Embeddings

Gender bias in biomedical research can have an adverse impact on the health of real people.For example, there is evidence that heart disease-related funded research generally focuses on men.Health disparities can form between men and at-risk groups of women (i.e., elderly and low-income) if there is not an equal number of heart disease-related studies for both genders.In this paper, we study temporal bias in biomedical research articles by measuring gender differences in word embeddings.Specifically, we address multiple questions, including, How has gender bias changed over time in biomedical research, and what health-related concepts are the most biased?Overall, we find that traditional gender stereotypes have reduced over time.However, we also find that the embeddings of many medical conditions are as biased today as they were 60 years ago (e.g., concepts related to drug addiction and body dysmorphia).
Uncloneable Quantum Encryption via Oracles

Quantum information is well known to achieve cryptographic feats that are unattainable using classical information alone. Here, we add to this repertoire by introducing a new cryptographic functionality called uncloneable encryption. This functionality allows the encryption of a classical message such that two collaborating but isolated adversaries are prevented from simultaneously recovering the message, even when the encryption key is revealed. Clearly, such functionality is unattainable using classical information alone.We formally define uncloneable encryption, and show how to achieve it using Wiesner’s conjugate coding, combined with a quantum-secure pseudorandom function (qPRF). Modelling the qPRF as an oracle, we show security by adapting techniques from the quantum one-way-to-hiding lemma, as well as using bounds from quantum monogamy-of-entanglement games.
New Developments for Robert – Assisting Novice Users Even Better in DIY Projects

Do-It-Yourself (DIY) home improvement projects require a combination of specific knowledge and practical abilities. Novice users often lack both and thus tend to fail or be frightful of performing DIY projects – even though they would like to. By providing suitable and individualised assistance in the form of step-by-step instructions, the assistant Robert allows even novice users to successfully complete their DIY projects. Simultaneously, Robert allows its users to learn how to perform these steps themselves and thus enables them to become more independent in the future. In this paper, we report on the latest progress with Robert. Compared to earlier versions, Robert is now able to adaptively change its instructions based on the wishes and preferences of the user. Further, Robert is now able to use connected tools – i.e. tools that are able to sense and communicate their status – to check whether the user is performing the project's steps correctly and to provide further assistance in the case of failure. Lastly, we present the results of an empirical study conducted to show Robert's effectiveness.
The benefits of target relations: A comparison of multitask extensions and classifier chains

Multitask (multi-target or multi-output) learning (MTL) deals with simultaneous prediction of several outputs. MTL approaches rely on the optimization of a joint score function over the targets. However, defining a joint score in global models is problematic when the target scales are different. To address such problems, single target (i.e. local) learning strategies are commonly employed. Here we propose alternative tree-based learning strategies to handle the issue with target scaling in global models, and to identify the learning order for chaining operations in local models. In the first proposal, the problems with target scaling are resolved using alternative splitting strategies which consider the learning tasks in a multi-objective optimization framework. The second proposal deals with the problem of ordering in the chaining strategies. We introduce an alternative estimation strategy, minimum error chain policy, that gradually expands the input space using the estimations that approximate to true characteristics of outputs, namely out-of-bag estimations in tree-based ensemble framework. Our experiments on benchmark datasets illustrate the success of the proposed multitask extension of trees compared to the decision trees with de facto design especially for datasets with large number of targets. In line with that, minimum error chain policy improves the performance of the state-of-the-art chaining policies.
A Polarity Capturing Sphere for Word to Vector Representation

Embedding words from a dictionary as vectors in a space has become an active research field, due to its many uses in several natural language processing applications. Distances between the vectors should reflect the relatedness between the corresponding words. The problem with existing word embedding methods is that they often fail to distinguish between synonymous, antonymous, and unrelated word pairs. Meanwhile, polarity detection is crucial for applications such as sentiment analysis. In this work we propose an embedding approach that is designed to capture the polarity issue. The approach is based on embedding the word vectors into a sphere, whereby the dot product between any vectors represents the similarity. Vectors corresponding to synonymous words would be close to each other on the sphere, while a word and its antonym would lie at opposite poles of the sphere. The approach used to design the vectors is a simple relaxation algorithm. The proposed word embedding is successful in distinguishing between synonyms, antonyms, and unrelated word pairs. It achieves results that are better than those of some of the state-of-the-art techniques and competes well with the others.
The microelement composition of caustobioliths and oil generation processes – from the D.I. Mendeleev’s hypothesis to the present day

The past 2019 is the year of the 150th anniversary of the Periodic Law discovery by D.I. Mendeleev. The international community has recognized it as the International Year of the Periodic Table of Chemical Elements. This initiative was made by the Russian Academy of Sciences, which was supported by UNESCO and the UN General Assembly. The brilliant law of D.I. Mendeleev is an interdisciplinary phenomenon, the universal language of communication between scientists – not only chemists, but also doctors, biologists, physicists, geochemists, geologists, and probably many other specialties. The article presents the features of microelements (ME) distribution in various classes of caustobiolites in connection with the Periodic Table Mendeleev. Their comparative assessment was carried out. MEs were identified that are concentrated in oils and shales in increased ore concentrations. Theories of oil generation developed by D.I. Mendeleev and other scientists, possible sources of ME in oils, features of the correlation dependencies of ME of the composition of oils and the earth’s crust at different levels. The polygenic source of ME in oils is substantiated, which is associated both with sedimentary rocks and organic matter (OM) buried in them, and the occurrence of ME in oils introduced from deep zones of the earth’s crust. A comparative assessment of ME composition of oil and gas basins and the composition of various geological substances indicate that the bulk of MEs are inherited by oils from OM. High enrichment by moving elements is associated with the migration activity during the formation of oil fields, while the correlation of ME oils with the chemical composition of the lower crust indicates the involvement of lower crustal fluids in this process. Scientific heritage of D.I. Mendeleev continues to develop and is currently at a higher level of scientific knowledge.
Bag of biterms modeling for short texts

Analyzing texts from social media encounters many challenges due to their unique characteristics of shortness, massiveness, and dynamic. Short texts do not provide enough context information, causing the failure of the traditional statistical models. Furthermore, many applications often face with massive and dynamic short texts, causing various computational challenges to the current batch learning algorithms. This paper presents a novel framework, namely bag of biterms modeling (BBM), for modeling massive, dynamic, and short text collections. BBM comprises of two main ingredients: (1) the concept of bag of biterms (BoB) for representing documents, and (2) a simple way to help statistical models to include BoB. Our framework can be easily deployed for a large class of probabilistic models, and we demonstrate its usefulness with two well-known models: latent Dirichlet allocation (LDA) and hierarchical Dirichlet process (HDP). By exploiting both terms (words) and biterms (pairs of words), the major advantages of BBM are: (1) it enhances the length of the documents and makes the context more coherent by emphasizing the word connotation and co-occurrence via bag of biterms, and (2) it inherits inference and learning algorithms from the primitive to make it straightforward to design online and streaming algorithms for short texts. Extensive experiments suggest that BBM outperforms several state-of-the-art models. We also point out that the BoB representation performs better than the traditional representations (e.g., bag of words, tf-idf) even for normal texts.
Packet Chasing: Spying on Network Packets over a Cache Side-Channel

This paper presents Packet Chasing, an attack on the network that does not require access to the network, and works regardless of the privilege level of the process receiving the packets. A spy process can easily probe and discover the exact cache location of each buffer used by the network driver. Even more useful, it can discover the exact sequence in which those buffers are used to receive packets. This then enables packet frequency and packet sizes to be monitored through cache side channels. This allows both covert channels between a sender and a remote spy with no access to the network, as well as direct attacks that can identify, among other things, the web page access patterns of a victim on the network. In addition to identifying the potential attack, this work proposes a software-based short-term mitigation as well as a light-weight, adaptive, cache partitioning mitigation that blocks the interference of I/O and CPU requests in the last-level cache.