Reviewerly/exHarmony huggingface dataset:
The Necessity of Geometrical Representation for Deep Graph Analysis

Spatial convolution layer which is widely used in Graph Neural Networks (GNNs) aggregates the feature vector of each node with the feature vectors of its neighboring nodes. The GNN is not aware of the locations of the nodes in the global structure of the graph and when the local structures corresponding to different nodes are similar to each other, the convolution layer maps all those nodes to similar or same feature vectors in the continuous feature space. Therefore, the GNN cannot distinguish two graphs if their difference is not in their local structures. In addition, when the nodes are not labeled/attributed the convolution layers can fail to distinguish even different local structures. In this paper, we propose an effective solution to address this problem. The proposed approach leverages a spatial representation of the graph which makes the neural network aware of the differences between the nodes and also their locations in the graph. The spatial representation which is equivalent to a point-cloud representation of the graph is obtained by a graph embedding method. Using the proposed approach, the local feature extractor of the GNN distinguishes similar local structures in different locations of the graph and the GNN infers the topological structure of the graph from the spatial distribution of the locally extracted feature vectors. Moreover, the spatial representation is utilized to simplify the graph downsampling problem. A new graph pooling method is proposed and it is shown that the proposed pooling method achieves competitive or better results in comparison with the existing methods.
HAWKS: Evolving Challenging Benchmark Sets for Cluster Analysis

Comprehensive benchmarking of clustering algorithms is rendered difficult by two key factors: (i)~the elusiveness of a unique mathematical definition of this unsupervised learning approach and (ii)~dependencies between the generating models or clustering criteria adopted by some clustering algorithms and indices for internal cluster validation. Consequently, there is no consensus regarding the best practice for rigorous benchmarking, and whether this is possible at all outside the context of a given application. Here, we argue that synthetic datasets must continue to play an important role in the evaluation of clustering algorithms, but that this necessitates constructing benchmarks that appropriately cover the diverse set of properties that impact clustering algorithm performance. Through our framework, HAWKS, we demonstrate the important role evolutionary algorithms play to support flexible generation of such benchmarks, allowing simple modification and extension. We illustrate two possible uses of our framework: (i)~the evolution of benchmark data consistent with a set of hand-derived properties and (ii)~the generation of datasets that tease out performance differences between a given pair of algorithms. Our work has implications for the design of clustering benchmarks that sufficiently challenge a broad range of algorithms, and for furthering insight into the strengths and weaknesses of specific approaches.
Towards a Lightweight Blockchain Platform for Critical Infrastructure Protection

Critical Infrastructures are one of the vital systems that support modern societies. The adoption of technologies like Industry 4.0, Industrial Internet of Things (IIoT) in critical infrastructures have made it a lucrative target for cyberattackers. Protecting critical infrastructure is of paramount importance due to the sensitive nature of the data coupled with the resource-constrained nature of the devices. The advent of blockchains can be a significant enabler for protecting critical infrastructure through the use of immutable ledger for storing the operations. However, blockchains are computationally expensive, have limited scalability and incur significant delays in processing transactions thus necessitating the development of a lightweight platform while retaining the functionality. This paper develops a lightweight blockchain based framework for protecting critical infrastructure by leveraging its hierarchical nature. Evaluation using embedded devices shows that our proposed framework minimizes the execution time of blockchain operations thus making it suitable for protecting critical infrastructure. Finally, our proposed framework is generic, in that it can be applied to any of the domains operating in the critical infrastructure.
Robust Template Adjustment Siamese Network for Object Visual Tracking

Most of the existing trackers address the visual tracking problem by extracting an appearance template from the first frame, which is used to localize the target in the current frame. Unfortunately, they typically face the model degeneration challenge, which easily results in model drift and target loss. To address this issue, a novel Template Adjustment Siamese Network (TA-Siam) is proposed in this paper. The proposed framework TA-Siam consists of two simple subnetworks: The template adjustment subnetwork for feature extraction and the classification-regression subnetwork for bounding box prediction. The template adjustment module adaptively uses the feature of subsequent frames to adjust the current template. It makes the template adapt to the target appearance variation of long-term sequence and effectively overcomes model drift problem of Siamese networks. In order to reduce classification errors, the rhombus labels are proposed in our TA-Siam. For more efficient learning and faster convergence, our proposed tracker uses a more effective regression loss in the training process. Extensive experiments and comparisons with trackers are conducted on the challenging benchmarks including VOT2016, VOT2018, OTB50, OTB100, GOT-10K, and LaSOT. Our TA-Siam achieves state-of-the-art performance at the speed of 45 FPS.
Federated Learning of Neural Network Models with Heterogeneous Structures

Federated learning trains a model on a centralized server using datasets distributed over a large number of edge devices. Applying federated learning ensures data privacy because it does not transfer local data from edge devices to the server. Existing federated learning algorithms assume that all deployed models share the same structure. However, it is often infeasible to distribute the same model to every edge device because of hardware limitations such as computing performance and storage space. This paper proposes a novel federated learning algorithm to aggregate information from multiple heterogeneous models. The proposed method uses weighted average ensemble to combine the outputs from each model. The weight for the ensemble is optimized using black box optimization methods. We evaluated the proposed method using diverse models and datasets and found that it can achieve comparable performance to conventional training using centralized datasets. Furthermore, we compared six different optimization methods to tune the weights for the weighted average ensemble and found that tree parzen estimator achieves the highest accuracy among the alternatives.
Adaptation of the TOSCA standard model for the Kubernetes container environment

The main difference between container-based and virtualization-based cloud environments is the way of the virtualization methods for OS and auxiliary resources. Software applications can be both deployed in a cloud on virtual machines or in local data centres on physical machines. Containers are based on OS-level virtualization. So, container applications can be deployed on a virtual machine in the cloud too. To deal with the growth of various, but functionally similar cloud environments, the TOSCA [1] (Topology and Orchestration for Cloud Applications) standard was created. It allows to describe deployment resources in a TOSCA Domain-specific language that does not depend on the provider. Kubernetes [8] is an open-source system for deployment automation, scaling, and management of containerized applications. TOSCA standard was created to describe cloud applications, but the model may be suitable for describing container applications that Kubernetes orchestrate. This paper considers Kubernetes as a cloud provider and examines the applicability of the TOSCA standard for unifying deployment and orchestration not only of resources provided by virtualization-based clouds but also of containerized ones.
Evaluating the K-Means Analysis in Clustering Area Based on Estates Productivity in Tana Luwu Using Silhouette Index

Abstract Tana Luwu is an area in South Sulawesi Province that consists of 4 districts or city, namely Luwu, East Luwu, North Luwu, and Palopo. Those regions have abundant natural resources, one of which is plantation crops. In this manuscript, the first objective was to group the 45 sub-districts in those districts into 2 to 5 clusters based on their productivity on providing plantation crops using K-Means Algorithm. Variables used in this study are coconut, palm, coffee, pepper, cocoa, cloves, and land area. Secondly, after the clusters were formed, those were evaluated using Silhouette Index to find out the most optimum number of clusters. As the result, it was found that the optimum number was two with Silhouette Index 0,8068 while the value of 3 clusters is 0,6468. For the values of 4 clusters and 5 clusters are 0,5029 and 0,5509 respectively.
Impact of Articulation Agreements on Student Transfer between Higher Education Institutions: A Case Study of a Cybersecurity Program

(2021). Impact of Articulation Agreements on Student Transfer between Higher Education Institutions: A Case Study of a Cybersecurity Program. Community College Journal of Research and Practice. Ahead of Print.
Uncertainty-aware Self-supervised 3D Data Association

3D object trackers usually require training on large amounts of annotated data that is expensive and time-consuming to collect. Instead, we propose leveraging vast unlabeled datasets by self-supervised metric learning of 3D object trackers, with a focus on data association. Large scale annotations for unlabeled data are cheaply obtained by automatic object detection and association across frames. We show how these self-supervised annotations can be used in a principled manner to learn point-cloud embeddings that are effective for 3D tracking. We estimate and incorporate uncertainty in self-supervised tracking to learn more robust embeddings, without needing any labeled data. We design embeddings to differentiate objects across frames, and learn them using uncertainty-aware self-supervised training. Finally, we demonstrate their ability to perform accurate data association across frames, towards effective and accurate 3D tracking. Project videos and code are at https://jianrenw.github.io/Self-Supervised-3D-Data-Association/.
Blockchain Technology Based Information Classification Management Service

Hyper-connectivity in Industry 4.0 has resulted in not only a rapid increase in the amount of information, but also the expansion of areas and assets to be protected. In terms of information security, it has led to an enormous economic cost due to the various and numerous security solutions used in protecting the increased assets. Also, it has caused difficulties in managing those issues due to reasons such as mutual interference, countless security events and logs’ data, etc. Within this security environment, an organization should identify and classify assets based on the value of data and their security perspective, and then apply appropriate protection measures according to the assets’ security classification for effective security management. But there are still difficulties stemming from the need to manage numerous security solutions in order to protect the classified assets. In this paper, we propose an information classification management service based on blockchain, which presents and uses a model of the value of data and the security perspective. It records transactions of classifying assets and managing assets by each class in a distributed ledger of blockchain. The proposed service reduces assets to be protected and security solutions to be applied, and provides security measures at the platform level rather than individual security solutions, by using blockchain. In the rapidly changing security environment of Industry 4.0, this proposed service enables economic security, provides a new integrated security platform, and demonstrates service value.
Cryptanalysis of a Chaos-Based Block Cryptosystem Using Multiple Samples Correlation Power Analysis

Multiple Samples Correlation Power Analysis (MSCPA) is proposed to attack the chaos-based block cipher which is based on Feistel structure. This is the first time to analyze the security of Feistel-based chaotic block cipher from hardware prospective. Firstly, attack points of the cipher are comprehensively analyzed by the proposed MSCPA. The outputs of Cubic map and addition operation generate relatively small correlation coefficients for incorrect keys, therefore they are selected as the attack points. Then correlation coefficients between the keys and power samples are calculated and analysed to find interesting points of power traces. These interesting points are combined together to obtain the correct key by the MSCPA. The encryption algorithm based on Feistel structure is implemented on an Atmel XMEGA microcontroller. Results show that although the Feistel-based chaotic block cipher has a good performance under conventional statistical analyses, it still can be attacked by MSCPA. Moreover compared to CPA, MSCPA has a better performance in accurate key differentiating level.
Depth estimation for underwater images from single view image

Underwater images often undergo distortions from scattering, absorption, colour loss, diffraction, polarisation, and varying attenuation depending on the light frequency, due to the water medium. This study answers problems associated with the recovery of underwater images, by developing novel depth map estimation which may be used as an intermediary step for underwater image restoration. The depth map is an important factor for the recovery of the underwater image, as it has been shown that proper estimation of depth, results in better restoration of the underwater image. The proposed algorithm estimates a depth map from a single view image, using blurriness and lighting information, obtained using a simple background neutralisation method. Results from the algorithm on selected raw underwater image data set have been compared with other algorithms and it has been shown that the proposed method gives Pearson coefficients in the range of 0.75–0.95, which are considerably higher and better log root mean square value compared to other methods in the literature. This suggests a better estimation of depth by the proposed method.
Analysis of Decision Support System with Analytical Hierarchy Process Method

Abstract Many factors can increase work motivation besides income and profits. The leadership style in a company also greatly influences work motivation in the work environment. This study aims to analyze the leadership style that most influences work motivation by utilizing decision support systems (DSS) techniques. Sources of data were obtained by observing, interviewing and giving random questionnaires at the Tirtauli regional water company (PDAM) in Pematangsiantar. The DSS technique uses the Analytical Hierarchy Process (AHP) method. The criterion variables used in the study were Directing Style (K1), Participation Style (K2), Supporting Style (K3), Achievement Style (K4) and Delegation Style (K5). The results of the AHP method analysis show that the most influential leadership style in motivating work is the delegation style of a leader (K5) with a final value of 0.6326 and a directing style (K1) with a final value of 0.4861. The results of the research can be used as input to the leader in applying a leadership style so as to increase motivation in the work environment to achieve company goals.
ARPA: Armenian Paraphrase Detection Corpus and Models

In this work, we employ a semi-automatic method based on back translation to generate a sentential paraphrase corpus for the Armenian language. The initial collection of sentences is translated from Armenian to English and back twice, resulting in pairs of lexically distant but semantically similar sentences. The generated paraphrases are then manually reviewed and annotated. Using the method train and test datasets are created, containing 2360 paraphrases in total. In addition, the datasets are used to train and evaluate BERT-based models for detecting paraphrase in Armenian, achieving results comparable to the state-of-the-art of other languages.
Resource Networks of Pet Scam Websites

The pet scam is a form of online fraud in which scammers leverage victims’ emotional attachment to fictitious pets as a means for extorting money. Both fraudulent pet seller sites and fraudulent delivery sites are involved in the scam. When sites of either kind are taken down, scammers create new sites, often reusing effective content from previous scams.We explore connections within the largest current collection of pet scam websites, examining four distinct types of resource sharing that are indicative of shared authorship. We find that 90% of all accessible sites share at least one form of connection to another known site, including many identifiable links between seller and delivery sites, and that some scam authors could be behind hundreds of individual scam websites. We partially validate our linkage methods using domain registration data, and discuss the implications of using different connection types to analyse online fraud more generally.
Factors impacting patent applications in European countries

Abstract The paper investigates the gap in the number of patent applications by European countries in the period 2010–2018. During the observed period, World Intellectual Property Organization (WIPO) received an average of 1.9 applications per million inhabitants per year from the countries of the Western Balkans (WB‐5), which is as much as 98% less than the average for the whole of Europe. The purpose of the paper is to create a model for increasing the number of patent applications in developing countries, which should lead to a higher number of granted patents. The author analyses variables that have an impact on the number of the patent applications (functional literacy, human development index, university/industry research collaboration, and gross domestic spending on R&amp;D) in the developed (EU‐28) and developing (WB‐5) countries. The WB‐5 firms, especially small and medium enterprises (SMEs), do not use of the opportunities offered by intellectual property (IP) protection. They find the system too costly, complex, and difficult to manage. Based on the results of statistical analyses and conclusions from recent literature that tackles the usage of IP, the author builds a model for increasing the number of patent applications among European SMEs that is based on the support of international business support organizations (BSOs). Moreover, the author recognizes the positive impact of international IP support programmes (such as EU IPR Helpdesk ambassador scheme) on the work of local BSOs that deal with IP rights.
Dopnet: Densely Oriented Pooling Network For Medical Image Segmentation

Since manual annotation of medical images is time consuming for clinical experts, reliable automatic segmentation would be the ideal way to handle large medical datasets. Deep learning-based models have been the dominant approach, achieving remarkable performance on various medical segmentation tasks. There can be a significant variation in the size of the feature being segmented out of a medical image relative to the other features in the image, which can be challenging. In this paper, we propose a Densely Oriented Pooling Network (DOPNet) to capture variation in feature size in medical images and preserve spatial interconnection. DOPNet is based on two interdependent ideas: the dense connectivity and the pooling oriented layer. When tested on three publicly available medical image segmentation datasets, the proposed model achieves leading performance.
Vision Only 3-D Shape Estimation for Autonomous Driving

We present a probabilistic framework for detailed 3-D shape estimation and tracking using only vision measurements. Vision detections are processed via a bird's eye view representation, creating accurate detections at far ranges. A probabilistic model of the vision based point cloud measurements is learned and used in the framework. A 3-D shape model is developed by fusing a set of point cloud detections via a recursive Best Linear Unbiased Estimator (BLUE). The point cloud fusion accounts for noisy and inaccurate measurements, as well as minimizing growth of points in the 3-D shape. The use of a tracking algorithm and sensor pose enables 3-D shape estimation of dynamic objects from a moving car. Results are analyzed on experimental data, demonstrating the ability of our approach to produce more accurate and cleaner shape estimates.
Massive Parallelization for Finding Shortest Lattice Vectors Based on Ubiquity Generator Framework

Lattice-based cryptography has received attention as a next-generation encryption technique, because it is believed to be secure against attacks by classical and quantum computers. Its essential security depends on the hardness of solving the shortest vector problem (SVP). In the cryptography, to determine security levels, it is becoming significantly more important to estimate the hardness of the SVP by high-performance computing. In this study, we develop the world’s first distributed and asynchronous parallel SVP solver, the MAssively Parallel solver for SVP (MAP-SVP). It can parallelize algorithms for solving the SVP by applying the Ubiquity Generator framework, which is a generic framework for branch-and-bound algorithms. The MAP-SVP is suitable for massive-scale parallelization, owing to its small memory footprint, low communication overhead, and rapid checkpoint and restart mechanisms. We demonstrate its performance and scalability of the MAP-SVP by using up to 100,032 cores to solve instances of the Darmstadt SVP Challenge.
CCA-Secure Attribute-Based Encryption Supporting Dynamic Membership in the Standard Model

Attribute-based encryption (ABE) is an access control mechanism where a sender encrypts messages according to an attribute set for multiple receivers. With fine-grained access control, it has been widely applied to cloud storage and file sharing systems. In such a mechanism, it is a challenge to achieve the revocation efficiently on a specific user since different users may share common attributes. Thus, dynamic membership is a critical issue to discuss. On the other hand, most works on LSSS-based ABE do not address the situation about threshold on the access structure, and it lowers the diversity of access policies. This manuscript presents an efficient attribute-based encryption scheme with dynamic membership by using LSSS. The proposed scheme can implement threshold gates in the access structure. Furthermore, it is the first ABE supporting complete dynamic membership that achieves the CCA security in the standard model, i.e. without the assumption of random oracles.
Image Emotion Caption Based on Visual Attention Mechanisms

Nowadays, most image automatic descriptions are based on objective descriptions and not contain subjective emotions. However, when people observe images, they will have certain subjective feelings. This paper proposes an image emotion description system based on attention mechanism, which automatically generates captions with sentiments. In the encoding stage, we use CNN to extract image features. In the decoding description phase, we use two same direction parallel LSTMs- - one represents factual descriptions; another specializes in descriptions with emotion. When attention mechanism detects different regions of the image, whether emotional words need to be output is selected by a switching mechanism. This system makes the image description more vivid and ensures the accuracy of the description.