Reviewerly/exHarmony huggingface dataset:
AVR: attention based salient visual relationship detection

Visual relationship detection aims to locate objects in images and recognize the relationships between objects. Traditional methods treat all observed relationships in an image equally, which causes a relatively poor performance in the detection tasks on complex images with abundant visual objects and various relationships. To address this problem, we propose an attention based model, namely AVR, to achieve salient visual relationships based on both local and global context of the relationships. Specifically, AVR recognizes relationships and measures the attention on the relationships in the local context of an input image by fusing the visual features, semantic and spatial information of the relationships. AVR then applies the attention to assign important relationships with larger salient weights for effective information filtering. Furthermore, AVR is integrated with the priori knowledge in the global context of image datasets to improve the precision of relationship prediction, where the context is modeled as a heterogeneous graph to measure the priori probability of relationships based on the random walk algorithm. Comprehensive experiments are conducted to demonstrate the effectiveness of AVR in several real-world image datasets, and the results show that AVR outperforms state-of-the-art visual relationship detection methods significantly by up to 87.5% in terms of recall.
Pseudo-random Number Sequence Generator Based on Chaotic Logistic-Tent System

In this paper, a new scheme of designing pseudo-random number generator (PRNG) based on a 1D discrete chaotic system is presented. Firstly, a new 1D compound discrete chaotic system, Logistic-Tent map, is proposed. The Logistic-Tent system emerges chaos phenomenon in a wider range of parameters. Moreover, the chaotic array produced with this Logistic-Tent map has higher randomness. Secondly, a pseudo-random number generating algorithm is proposed. The pseudo-random numbers geberated by this algorithm are uniformly distributed and can successfully pass the randomness tests with NIST SP800-22 software package. Furthermore, histogram test, information entropy analysis, and sensitivity test confirmed that the proposed approach can generate pseudo-random number sequences with good cryptographic performance.
Small Robots With Big Tasks

A significant portion of the over 65 population experience a fall at least once a year in their home environment. This puts a faller under significant health risks and adds to the financial burden of the care services. This Late Breaking Report explores a proof of concept implementation of a fall alert system that uses MiRo (a small mobile social robot) in the home environment. We take advantage of MiRo's pet-like characteristics, small size, mobility, and array of sensors to implement a system where a person who has fallen can interact with it and summon help if needed. The initial aim of this proof of concept system described here was to act as a demonstration tool for health professionals and housing association representatives, gauging their needs and requirements, driving this research forward.
Construction of information network vulnerability threat assessment model for CPS risk assessment

Cyber physical systems (CPS) has been hailed as the next future of computing by integrating information technology into physical systems to increase their capabilities. Information network is an organic part of CPS, and vulnerability threat assessment of information network is one of the important bases of CPS risk assessment. In view of the limitation of general information network vulnerability threat assessment technology, this paper proposes an information network vulnerability threat assessment model based on dynamic attack and defence game. Firstly, a risk assessment model suitable for CPS is constructed based on the actual control scheduling of information system. At the same time, considering the constraint conditions of both sides, including the quantity of available resources, rational allocation of resources, cost-effectiveness and the number of attacked nodes, a more perfect information network vulnerability threat assessment model is established. Then, on this basis, the dynamic attack and defence game function and game solution steps are designed to realize the quantitative assessment of information network vulnerability threat of CPS risk assessment. Finally, a simulation test based on IEEE 34-node information network system is carried out to verify the effectiveness of the proposed method.
Enriching the library subject headings with folksonomy

Purpose The purpose of this paper is to explore the potential of enriching the library subject headings with folksonomy for enhancing the visibility and usability of the library subject headings. Design/methodology/approach The WorldCat-million data set and SocialBM0311 are preprocessing and over 210,000 library catalog records and 124,482 non-repeating tags were adopted to construct the matrix to observe the semantic relation between library subject headings and folksonomy. The proposed system is compared with the state-of-the-art methods and the parameters are fixed to obtain effective performance. Findings The results demonstrate that by integrating different semantic relations from library subject headings and folksonomy, the system’s performance can be improved compared to the benchmark methods. The evaluation results also show that the folksonomy can enrich library subject headings through the semantic relationship. Originality/value The proposed method simultaneous weighted matrix factorization can integrate the semantic relation from the library subject headings and folksonomy into one semantic space. The observation of the semantic relation between library subject headings and social tags from folksonomy can help enriching the library subject headings and improving the visibility of the library subject headings.
Leakage-Resilient Authenticated Encryption from Leakage-Resilient Pseudorandom Functions

In this work we study the leakage resilience of authenticated encryption schemes. We show that, if one settles for non-adaptive leakage, leakage-resilient authenticated encryption schemes can be built from leakage-resilient pseudorandom functions. Degabriele et al. (ASIACRYPT 2019) introduce the $${ \textsc {FGHF}}^{\prime }$$ construction which allows to build leakage-resilient authenticated encryption schemes from functions which, under leakage, retain both pseudorandomness and unpredictability. We revisit their construction and show the following. First, pseudorandomness and unpredictability do not imply one another in the leakage setting. Unfortunately, this entails that any instantiation of the $${ \textsc {FGHF}}^{\prime }$$ construction indeed seems to require a function that is proven both pseudorandom and unpredictable under leakage. Second, however, we show that the unpredictability requirement is an artefact that stems from the underlying composition theorem of the $${ \textsc {N2}}$$ construction given by Barwell et al. (ASIACRYPT 2017). By recasting this composition theorem, we show that the unpredictability requirement is unnecessary for the $${ \textsc {FGHF}}^{\prime }$$ construction. Thus, leakage-resilient AEAD schemes can be obtained by instantiating the $${ \textsc {FGHF}}^{\prime }$$ construction with functions that are solely pseudorandom under leakage.
Enhanced Knowledge Distillation for Face Recognition

Face recognition have been widely used in different industries due to the advancement of deep convolutional neural networks. Although deep learning has greatly promoted the development of face recognition technology, its computing-intensive and memory-intensive features make it difficult to deploy the model on some embedded devices or mobile computing platforms. Many solutions which include Knowledge Distillation have been proposed to increase the calculation speed of model and reduce the storage space required for calculations, in this paper, we propose a novel Two Stage Knowledge Distillation which enhances the performance of knowledge distillation in face recognition and low resolution face recognition. After experimenting on the several major face datasets, our method turns out to have better results compared to the traditional optimization methods.
The Power of Many Samples in Query Complexity

The randomized query complexity 𝖱(f) of a boolean function f: {0,1}ⁿ → {0,1} is famously characterized (via Yao’s minimax) by the least number of queries needed to distinguish a distribution 𝒟₀ over 0-inputs from a distribution 𝒟₁ over 1-inputs, maximized over all pairs (𝒟₀,𝒟₁). We ask: Does this task become easier if we allow query access to infinitely many samples from either 𝒟₀ or 𝒟₁? We show the answer is no: There exists a hard pair (𝒟₀,𝒟₁) such that distinguishing 𝒟₀^∞ from 𝒟₁^∞ requires Θ(𝖱(f)) many queries. As an application, we show that for any composed function f∘g we have 𝖱(f∘g) ≥ Ω(fbs(f)𝖱(g)) where fbs denotes fractional block sensitivity.
Enfoque desde una perspectiva bibliométrica de la aplicación educativa de la realidad virtual en personas con Trastorno del Espectro Autista

Technology is an integrative element that can contribute to the inclusion of people with Autism Spectrum Disorder (ASD), being Virtual Reality (VR) the one that best adapts to their learning style. In this sense, the purpose of this study is to make an analysis of the scientific production on VR and ASD. The sample consisted of 248 documents belonging to the main collection of the Web of Science being analyzed according to 9 indicators. The results indicate that the United States is the largest producer on the subject and has the largest production centers, although it has a high index of isolation. Likewise, the longest period has been 2014-2017, with developmental psychology and rehabilitation being the areas of the Web of Science most present. Finally, the vast majority of journals are present in Q1, where the authors publish the most on this subject.
Self-Adaptive Resource Management Framework for Software Services in Cloud

The development of cloud computing makes it possible to dynamically adjust resources for software services on demand. However, different software services have different QoS requirements, and their external environment is changing all the time. Thus, self-adaptive ability is needed for software services because engineers manage is difficult in the cloud resources. This paper proposes a self-adaptive resource management framework for software services in Cloud. First, for a given software services, its iterative QoS model is trained on historical data, which is capable to predict a QoS value of one management operation by using the information on current running workload, allocated resources, real QoS value and an operation of resource allocation. Then, we employ PSO-based runtime decision algorithm together with the predicted QoS value to determine future resource allocation operations. Last, a feedback control loop is introduced to gradually allocate resource through feedback and iteration. The loops iterate until the PSO-based algorithm suggests no further improvement over the current resource allocation. Such self-adaptive management programs can adapt to the change in the outer environments and allocate cloud resource effectively.
Task Allocation Model for Optimal System Cost Using Fuzzy C-Means Clustering Technique in Distributed System

The task scheduling is an important activity in distributed system environment to divide the proper load among the available processors.The requirement of efficient task scheduling technique is an important issue in distributed computing systems, which can balance the load in such a way, so that no processor remains idle.Further, it can provide proper utilization of available resources and minimize the response time and system cost, with the maximum system reliability.In this paper the novel task allocation technique is being proposed with the aim of minimizing the response time and system cost.The method of clustering is used for the proper distribution of tasks on the processors.The proposed technique uses Fuzzy C-Means clustering technique and Hungarian method for task allocations.The performance of the algorithm is evaluated through examples and the results are compared with some existing models.
The usability and digitalization of healthcare

As interaction researchers strive to make sense of the forest, they should not lose sight of the trees.
The Stochastic Matching Problem with (Very) Few Queries

Motivated by an application in kidney exchange , we study the following stochastic matching problem: We are given a graph G ( V, E ) (not necessarily bipartite), where each edge in E is realized with some constant probability p &gt; 0, and the goal is to find a maximum matching in the realized graph. An algorithm in this setting is allowed to make queries to edges in E to determine whether or not they are realized. We design an adaptive algorithm for this problem that, for any graph G , computes a (1−ε)-approximate maximum matching in the realized graph G p with high probability, while making O (log (1/ε p )/ε p ) queries per vertex, where the edges to query are chosen adaptively in O (log (1/ε p )/ε p ) rounds. We further present a non-adaptive algorithm that makes O (log (1/ε p )/ε p ) queries per vertex and computes a (1/2−ε)-approximate maximum matching in G p with high probability. Both our adaptive and non-adaptive algorithms achieve the same approximation factor as the previous best algorithms of Blum et al. (EC 2015) for this problem, while requiring an exponentially smaller number of per-vertex queries (and rounds of adaptive queries for the adaptive algorithm). Our results settle an open problem raised by Blum et al. by achieving only a polynomial dependency on both ε and p . Moreover, the approximation guarantee of our algorithms is instance-wise as opposed to only being competitive in expectation as is the case for Blum et al. This is of particular relevance to the key application of stochastic matching in kidney exchange. We obtain our results via two main techniques—namely, matching-covers and vertex sparsification —that may be of independent interest.
Panic Detection in Crowded Scenes

A crowd is a gathering of a huge number of individuals in a confined area. Early identification and detection of unusual behaviors in terms of panic occurring in crowded scenes are very important. Panic detection comprises of formulating normal scene behaviors and detecting and identifying non-matching behaviors. However, panic detection and recognition is a very difficult problem, especially when considering diverse scenes. Many methods proposed to cope with these problems have limited robustness as the density of the crowd varies. In order to handle this challenge, this paper proposes the integration of different features into a unified model. Discriminant binary patterns and neighborhood information are used to model complex and unique motion patterns in order to characterize different levels of features for diverse types of crowd scenes, focusing in particular on the detection of panic and non-pedestrian entities. The proposed method was evaluated considering two benchmark datasets and outperformed five existing methods.
Application of Clustering and VARIMA for Rainfall Prediction

Abstract Global warming causes climate change throughout the world which has an impact on changes in erratic rainfall. For fishermen, the estimated rainfall is an important thing to know before sailing. VARIMA is divided into VAR, VMA, VARMA, and VARIMA. This study uses a rainfall dataset consisting of rainfall (mm), temperature (°C), humidity (%) and wind speed (km/hour) with observations per day for one year in 2018. Training data is data that starts from 1 January 2018 until 30 November 2018 and the testing data starts on 1 December 2018 until 31 December 2018. Clustering was carried out in the area around Lampulo Pelabuhan Perikanan Samudera (PPS) which are Bandar Raya, Lampineung, Kuta Radja, and Kuta Alam. The best model obtained is VARIMA (1,1,2), which is based on the smallest AIC and BIC values, which are 8.5104 and 9.0581. The VARIMA (1,1,2) model also fulfills the white noise test, so it can be concluded that the data does not have autocorrelation and model can be used for predictions. Estimated values of rainfall, temperature, humidity and wind speed are not much different from actual values, with MAPE of all variables are relatively low.
Deep Deblurring Correlation Filter for Object Tracking

Motion blur is a quite tricky issue in object tracking community. In recent years, many trackers have been explored to address this issue without sensational performance. This paper proposes a novel correlation tracking framework with Recurrent Neural Network (RNN) deblurnet and proposal detection to handle motion blur and heavy occlusion in object tracking tasks. We take advantage of high efficient Kernelized Correlation Filter (KCF) tracker, a typical method that exploits the circulant structure and the kernel trick to enhance the performance, and furthermore incorporate two regression methods in it. We employ RNN as our baseline of deblurnet, and introduce residual block and ConvLSTM in our deblur network to improve the result of deblurring. In addition, we suggest an edge information based rectification system to overcome the challenge of target occlusion. Finally, we update the model adaptively in term of the feedback from high-confidence tracking results to avoid the model degradation. Extensive experimental results demonstrate our tracker outperforms several state-of-the-art trackers on the OTB-2015 and VOT-2016 datasets.
Science plan for improving three-dimensional seismic velocity models in the San Francisco Bay region, 2019–24

First posted April 16, 2020 For additional information, contact: Director, Geologic Hazards Science CenterU.S. Geological SurveyMS 966, Box 25046Denver, CO 80225 This five-year science plan outlines short-term and long-term goals for improving three-dimensional seismic velocity models in the greater San Francisco Bay region as well as how to foster a community effort in reaching those goals. The short-term goals focus on improving the current U.S. Geological Survey San Francisco Bay region geologic and seismic velocity model using existing data. The long-term goals focus on acquiring new data and leveraging better analytic tools to improve the model and characterize the uncertainty. The plan describes opportunities for contributions by members of the community to develop these seismic velocity models, provides current and potential users with general information on where efforts will likely be focused to improve these models and how new versions of the models will be released, and outlines funding needs and obstacles for improving and maintaining such models. Several aspects of this plan, including how to foster a community effort, are independent of the geographic region and apply to other similar efforts.
Structural damage assessment of MAV flapping wings using DIC-wavelet technique

DIC-Wavelet technique for vibration based structural damage identification in dragonfly wing structure is reported. A bio-mimicked dragonfly wing, stiffened and unstiffened plate made of MWCNT-PP nanocomposite wing skin and Carbon-fibre epoxy composite stiffener are developed. Modal frequencies and vibration mode shapes of intact samples are obtained using well known Digital image correlation technique. Artificial cracks are created in plates and wings at stigma and nodus. Wavelet analysis is applied on displacement data obtained at discretized wing surface using DIC technique. Symlet function of order 4 is selected as mother wavelet function. The spikes in amplitude of wavelet coefficients corresponds to the damage region. Effect of scaling function in wavelet transform is studied for efficient identification of damages. The study indicates that the proposed DIC-Wavelet technique is efficient in predicting the damage zones in plates and wings.
A Survey: Cryptographic Hash Functions for Digital Stamping

The current study aims to examine a general overview of the application of hash functions in cryptography and study the relationships between cryptographic hash functions and uses of the digital signature. Functions of the cryptographic hash are an important tool applied in several sections of data security, and application of hash function is common and used for various purposes such as File Integrity Verification, Key Derivation, Time stamping, Password Hashing, Rootkit Detection and Digital Signature. Digital Signature is a code that is linked electronically with the document including the sender's identity. Therefore, the digital signature is of high value in verifying digital messages or documents. Cryptographic hash functions do not present without mathematics. The success of computer science is attributed to mathematics; in other words, it is because of mathematical science, that computer science was understood and could be explained to all. The study aims to teach the reader hash functions and its applications such as digital signature and to show in details some hash functions and their designing.
Electric field correlations in quantum radar and the quantum advantage

In this paper, we derive the electric field covariance matrix of the signal and idler beams from an entangled source for applications involving quantum radar. We also derive the corresponding covariance matrix for a classical matched filtering remote sensing system and compare to the quantum result. We use this comparison to derive an expression for the quantum enhancement factor as a function of the mean photon number per mode, N<sub>s</sub>. This result is significant because it allows one to exactly calculate the predicted quantum enhancement as a function of transmit power, rather than only having an upper bound. Additionally, we look into previous analog correlation techniques using an optical parametric amplifier (OPA) and show that immediately detecting the idler produces the same cross correlation terms. However, the actual measurements needed to harness these correlations is enhanced when one immediately detects the idler because it minimizes the added noise caused by the additional length of the idler path in the conventional method. Finally, our results also show that one does not need to count photons to harness these correlations, but rather, perform electric field measurements.
A Hybrid Encryption Approach for Secured Authentication and Enhancement in Confidentiality of Data

Currently, data security issues are remaining as a major concern during digital communication. A large amount of crucial data is transmitted through the communication channel. There are many cryptographic algorithms available, which are used for providing data security during communication and storage process. However, the data needs to be decrypted for performing operations, which may lead to elevation of the privilege of data. The pin or passwords used for decryption of data can be easily identified using a brute force attack. This leads to losing the confidentiality of crucial data to an unauthorized user. In the proposed system, a combination of Homomorphic and Honey encryption is used to improve data confidentiality and user authentication problems. Thus, the system provides better data security for the issues related to outsourced databases.