Reviewerly/exHarmony huggingface dataset:
Querying Heterogeneous Property Graph Data Sources based on a Unified Conceptual View

With the great success of NoSQL (Not only SQL) databases, data becomes more heterogeneous than ever. Hence, it is essential to provide solutions to integrate such heterogeneous databases and facilitate accessing them. This paper proposes an Ontology Based Data Access (OBDA) approach to query NoSQL property graph databases using SPARQL language. This allows users to access diverse data sources with the same query through a unified conceptual view (ontology). The proposed work goes through an offline phase to map the property graph data model to a unified ontology model. This is done by proposing hybrid relational graph algebra expressions. Then, an online phase starts when the user submits a SPARQL query. The submitted query is translated into the property graph declarative query language, Cypher. The translated query is then executed on the target property graph database and query results are presented to users in a unified format.
Reasoning About Causation

Despite the importance of effective causal reasoning, people still have trouble answering questions like: Does this chemical cause cancer? Was this economic policy the cause of economic growth? Does this drug prevent COVID-19? Did Thomas Crittendon cause the death of Jesse James? This essay aims to provide tools to help people reason effectively about causation. We use the killing of Jesse James to discuss naturalistic causal analysis, then introduce Pearl’s causal ladder and counterfactual reasoning. Methods, such as random assignment and the kind of observational methods used in epidemiology are discussed in the context of assessing evidence relative to the potential for causal relationships. We take an in-depth look at the evidence and methods that were used to establish a causal relationship between smoking and lung cancer and describe the psychological heuristics that can interfere with effective causal analysis.
Asymptotic and Approximate Discrete Distributions for the Length of the Ewens Sampling Formula

The Ewens sampling formula is well known as the probability for a partition of a positive integer. Here, we discuss the asymptotic and approximate discrete distributions of the length of the formula. We give a sufficient condition for the length to converge in distribution to the shifted Poisson distribution. This condition is proved using two methods: One is based on the sum of independent Bernoulli random variables, and the other is based on an expression of the length that is not the sum of independent random variables. As discrete approximations of the length, we give those based on the Poisson distribution and the binomial distribution. The results show that the first two moments of the approximation based on the binomial distribution are almost equal to those of the length. Two applications of this approximation are given.
Institutionalization of C2G New Communication Forms: Trends and Dynamic in Saint Petersburg

Research has been proposed to determine trends and dynamic of citizen’s trust in new C2G communication forms. The survey sheds a light to trust concept and its evidences in IT performance in public sector. A representative survey was conducted using a SCOT approach. From the 800 respondents to this survey, the sampling error was no higher than 4%, the level of reliability reached 95%. The questionnaire contained variables to evaluate the experience of use and the level of trust in new technologies in the interaction with the government. The study revealed an increase in citizens’ trust in communicating with government authorities via the Internet and in receiving state and municipal services in electronic form.
Domain Private and Agnostic Feature for Modality Adaptive Face Recognition

Heterogeneous face recognition is a challenging task due to the large modality discrepancy and insufficient cross-modal samples. Most existing works focus on discriminative feature transformation, metric learning and cross-modal face synthesis. However, the fact that cross-modal faces are always coupled by domain (modality) and identity information has received little attention. Therefore, how to learn and utilize the domain-private feature and domain-agnostic feature for modality adaptive face recognition is the focus of this work. Specifically, this paper proposes a Feature Aggregation Network (FAN), which includes disentangled representation module (DRM), feature fusion module (FFM) and adaptive penalty metric (APM) learning session. First, in DRM, two subnetworks, i.e. domain-private network and domain-agnostic network are specially designed for learning modality features and identity features, respectively. Second, in FFM, the identity features are fused with domain features to achieve cross-modal bidirectional identity feature transformation, which, to a large extent, further disentangles the modality information and identity information. Third, considering that the distribution imbalance between easy and hard pairs exists in cross-modal datasets, which increases the risk of model bias, the identity preserving guided metric learning with adaptive hard pairs penalization is proposed in our FAN. The proposed APM also guarantees the cross-modality intra-class compactness and inter-class separation. Extensive experiments on benchmark cross-modal face datasets show that our FAN outperforms SOTA methods.
Fuzzy rules based efficient event-driven simulation of blockchain-based applications

Decentralized application (DAPP), replacing traditional business logic and data access layer with block chain, is a new form of Internet service. Testing DAPP requires large-scale distributed systems. Performing experiments in a real system is costly and difficult. This article carefully analyses the process of block generation and synchronization and explains the reasons for the low efficiency of block chain system simulation. We incorporate fuzzy rule based model for enhancing the logging system in blockchain. Rules based on fuzzy are utilized inside system of fuzzy logic to obtain outcome on basis of input variables. The data of Ethereum and Bitcoin proves that the block generation interval conforms to the exponential distribution, and the real PoW calculation can be replaced with random numbers. Both block verification and network propagation processes have latency, which can be simulated with asynchronous messaging. Based on the above analysis, this article proposes a high-performance simulation method based on event-driven model, which is suitable for describing the communication and synchronization behave our of block chain networks. The method can effectively describe the block generation, the synchronization process between nodes, and supports different equity proof forms. Using this method, the performance of the PoW systemis tested. Under the ecs.c6.xlargeinstance,the simulation running speed reaches 782 times of actual system. Further experiments show that this method can be efficiently used in larger-scale networks and is an effective tool for DAPP developing and testing.
Triplet Network with Multi-level Feature Fusion for Object Tracking

In recent years, Siamese network-based trackers have received increasing interest because of the balanced accuracy and speed. However, these tracking methods only extract the high-level features as target representations and merely utilize the first frame as the exemplar branch, which are less discriminative to distinguish similar distractors and are vulnerable to background clutter. To address these issues, we propose a novel Triplet network with a multi-level feature fusion structure (TripMFF) to leverage the merits of Triplet network and fuse multi-layer features for robust object tracking. Firstly, the Triplet network is adopted as our backbone architecture to take full advantage of the correlation among frames in the video sequence and make the exemplar branches have more useful information. Secondly, in order to capture more abundant features, a multilevel feature fusion structure is put forward to combine the low-level fine-grained and high-level abstract information, which improves the discriminative capability and stability of the tracker. Experimental results on tracking benchmarks prove that our proposed method achieves competitive performance and real-time tracking speed compared with other state-of-the-art trackers Contribution-We propose a tracker TripMFF which adopts the Triplet network as backbone architecture and employs a multi-level feature fusion structure for robust object tracking.
Motion Imitation for Monocular Videos

This work represents a simple method for motion transfer (i.e., given a source video of a subject [person] performing some movements or in motion, that movement/motion is transferred to amateur target in different motion). The pose is used as an intermediate representation to perform this translation. To transfer the motion of the source subject to the target subject, the pose is extracted from the source subject, and then the target subject is generated by applying the learned pose to-appearance mapping. To perform this translation, the video is considered as a set of images consisting of all the frames. Generative adversarial networks (GANs) are used to transfer the motion from source subject to the target subject. GANs are an evolving field of deep learning.
Enhanced Hybrid Segmentation with Non Local Block and Deep Residual Networks

The use of convolutional neural networks (CNNs) has increased in the edge devices due to its successful performance. Various such applications includes semantic segmentation which is one of the most challenging tasks due to the involvement of tremendous model size and parameters. In this paper, an enhanced hybrid segmentation with non-local block and deep residual networks is introduced for pixel level semantic segmentation. A light weight model is developed to facilitate deployment on edge devices. Skip connection is applied to fire layer in encoder and decoder block of segmentation model and non-local block is inserted in between encoder and decoder. Due to such amendments, the proposed network has optimized the number of parameters to only 2 Million whereas SegNet-Basic architecture required 5 Million. The performance is validated on the Camvid dataset and 87.5% accuracy is achieved.
An Ontology-based Approach for Semantic Interoperability in Air Traffic Information Management

With the advancement of information and communication technologies, to improve the interoperability between heterogeneous information systems by regularizing the syntax for information exchange is essential to achieve global seamless air traffic management operation. However, the current point-to-point aviation related information exchange among different systems and operators cannot satisfy the requirement for interoperability. The concept of System Wide Information Management (SWIM) has been promoted by the International Civil Aviation Organization (ICAO) to implement interoperability and harmonization in a global operation. In the SWIM environment, all the related stakeholders need to efficiently obtain the necessary ATM data with situational awareness from various information domains. However, this is difficult to realize in the current system, as different data are structured based on different data models. In this study, we construct domain ontologies based on flight, aeronautical, and weather information exchange models. Moreover, for semantic interoperability in the SWIM environment, we develop an upper ontology-based reference ontology that enables common situational awareness of spatiotemporal concepts. Furthermore, we propose a methodology for mapping heterogeneous domain ontologies to the reference ontology with the manual refinement. Finally, we apply the proposed ontologies to a SWIM test system. The applicability and scalability of the proposed ontologies are demonstrated through a case study in the SWIM environment.
Implementing Planning KL-Divergence

Variants of accuracy and precision are the gold-standard by which the computer vision community measures progress of perception algorithms. One reason for the ubiquity of these metrics is that they are largely task-agnostic; we in general seek to detect zero false negatives or positives. The downside of these metrics is that, at worst, they penalize all incorrect detections equally without conditioning on the task or scene, and at best, heuristics need to be chosen to ensure that different mistakes count differently. In this paper, we revisit "Planning KL-Divergence" , a principled metric for 3D object detection specifically for the task of self-driving. The core idea behind PKL is to isolate the task of object detection and measure the impact the produced detections would induce on the downstream task of driving. We summarize functionality provided by our python package planning-centric-metrics that implements PKL. nuScenes is in the process of incorporating PKL into their detection leaderboard and we hope that the convenience of our implementation encourages other leaderboards to follow suit.
Nuclei Segmentation in Hematoxylin and Eosin (H&amp;E)-Stained Histopathological Images Using a Deep Neural Network

Breast cancer, being the second most frequently seen cancer globally, is the most common cancer among women. Early and accurate diagnosis is dependent on the correct identification of nuclei in histopathological images, which is a very tedious task when it is done manually. The proposed method is a deep learning model using U-Net architecture supplied with various pre- and post-processing techniques. The model was trained and tested on a unique dataset. Our model performed the nuclei segmentation task with a Dice score of 0.84, a true negative rate (TNR) of 0.95, a true positive rate (TPR) of 0.84, and an accuracy rate of 0.93. To enhance the performance of our model, our planned future work includes increasing the number of histopathological images in our dataset.
Cloud-Based Multimedia Content Protection System

We propose a new design for large-scale multimedia content protection systems. Our design leverages cloud infrastructures to provide cost efficiency, rapid deployment, scalability, and elasticity to accommodate varying workloads. The proposed system can be used to protect different multimedia content types, including videos, images, audio clips, songs, and music clips. The system can be deployed on private and/or public clouds. Our system has two novel components: (i) method to create signatures of videos, and (ii) distributed matching engine for multimedia objects. The signature method creates robust and representative signatures of videos that capture the depth signals in these videos and it is computationally efficient to compute and compare as well as it requires small storage. The distributed matching engine achieves high scalability and it is designed to support different multimedia objects. We implemented the proposed system and deployed it on two clouds: Amazon cloud and our private cloud. Our experiments with more than 11,000 videos and 1 million images show the high accuracy and scalability of the proposed system. In addition, we compared our system to the protection system used by YouTube and our results show that the YouTube protection system fails to detect most copies of videos, while our system detects more than 98% of them.
Some properties of the classification algorithms using ensemble kernels

Abstract In this paper, we study the properties of the KCCE algorithm (Kernel-based Classification with Cluster Ensemble) proposed in our previous publication. Different strategies for choosing the ensemble weights are investigated from the point of view of their influence on the quality and speed of the algorithm. We perform numerical experiments with the algorithm on real datasets from the UCI repository under conditions of artificially added noise at different levels.
음악줄넘기 운동이 ADHD 아동의 신경전달물질 및 뇌파에 미치는 영향

The purpose of this study was to investigate the effect of music jump rope exercise on neurotransmitter and EEG in ADHD children. ADHD children in music jump rope exercise program for 12 weeks. They were training for three times per week. Statistical Data was processed by SPSS(Windows Version 12.0) using descriptive statistics regarding the physical characteristics of subjects as well as each variables(neurotransmitter, EEG). The difference of within and between in two group was analyzed repeated measures ANOVA, significant level was set a=.05. The results were as follows: Neurotransmitter, there was significant difference according to the measurement, EEG, there was significant difference according to the measurement. As a result, the 12 weeks of music jump rope exercises brought the positive effects on the changes in relieving symptoms of ADHD children.
Joint Embedding of Words and Labels for Sentiment Classification

Due to the fast growth of social networks, sentiment analysis on the web has been a popular research topic. Recently, word embedding-based sentiment analysis methods have reached outstanding performance compared to traditional methods. However, word embeddings always ignore information from dataset's labels. Inspired by LEAM model proposed by Wang [1], we propose a method that jointly learns information of words and sentiment labels, which can improve the performance of the label embedding model. We defined a set of sentiment lexicons and used it to represent sentiment labels in the proposed method. We finally conducted experiments on Yelp dataset, which reached 64.99% accuracy.
Quantification of Multimillion Offers in ‘Next-of-Kin’ Unsolicited Bulk Emails

Unsolicited Bulk Email (UBE) still continues to successfully pass through the keyword-based and Artificial Intelligence (AI)-based filters due to the smart use of ‘word-salad’, ‘slang’ and ‘leet’ by the spammers to dilute as well as pollute the email text. Many research works account for the wastage of bandwidth and time of users as well as loss of money due to victimization to ‘next-of-kin’ type of UBE. This is the first formal attempt to present the quantified results on the calculations of averages for transaction amount offered, offered share of total amount, duration of transaction completion and total amount mentioned in such UBE. A peep is also provided on the identification of most frequently used first-names and surnames by the spammers. It is emphasized that this type of UBE does not ask for bank details and asks for money directly, unlike the many other types of UBE often confused with this type. On the side lines, the paper presents an interplay of scam and spam while providing useful statistical information for design of more robust spam email filters and blockers. A corpus of more than 1500 specific and only ‘next-of-kin’ type of spam emails has been used. It has been found that the average, respectively, for amount and share offered by the spammers is 10.64 million USD and 39.43% of the total amount. Only 5.57, 2.49 and 1.86% of the total number of spam emails mention the offered share, duration of transaction completion and total amount, respectively. The average transaction duration was 9 days while the average of total amount was found to be 134 million USD. ‘Aisha’ and ‘Kabore’ were the most frequent first-name and surname respectively.
Have Fun Storming the Castle(s)!

In recent years, large-scale datasets, each typically tailored to a particular problem, have become a critical factor towards fueling rapid progress in the field of computer vision. This paper describes a valuable new dataset that should accelerate research efforts on problems such as fine-grained classification, instance recognition and retrieval, and geolocalization. The dataset, comprised of more than 2400 individual castles, palaces and fortresses from more than 90 countries, contains more than 770K images in total. This paper details the dataset's construction process, the characteristics including annotations such as location (geotagged latlong and country label), construction date, Google Maps link and estimated per-class and per-image difficulty. An experimental section provides baseline experiments for important vision tasks including classification, instance retrieval and geolocalization (estimating global location from an image's visual appearance). The dataset is publicly available at vision.cs.byu.edu/castles.
Towards Ontology-based Domain Specific Language for Internet of Things

Development of Internet of Things (IoT) applications is considered as a complex task. It requires knowledge in the different software layers starting from the low level perception layer to the high level application layer. The domain expert should be involved from the start of the project to its end, to ensure that the delivered system satisfies the user needs. Such involvement results from the continuous need for the domain knowledge throughout the software development lifecycle. Such long development time along with the high cost of IoT applications, cause a slow progress in the IoT development. In this paper, a Domain Specific Language (DSL), called OntIoT, is proposed that contributes in reducing the complexity of IoT application development through providing the needed domain knowledge in an automated manner. OntIoT is an ontology-based DSL that utilizes the Semantic Sensor Network (SSN) ontology to catch the IoT domain concepts and constraints.
Intelligent Edge Computing: State-of-the-art Techniques and Applications

To enable intelligent decisions at the network edge, supervised and unsupervised machine learning techniques and their variations are highly utilized in recent research studies. These include techniques and the corresponding applications such as detecting manufacturing faults in a smart factory setting, monitoring patient activities and health problems in smart health systems, detecting security attacks on the Internet of Things devices, and finding the rare events in the audio signals. In this paper, we present an extensive review of state-of-the-art techniques and applications of intelligent edge computing and provide classification and discussion of various approaches in this field.
An Evaluation on Effectiveness of Deep Learning in Detecting Small Object Within a Large Image

Multiple Deep Learning (DL) algorithms have been developed recently and are shown to be achieving very high accuracy in object detection. However, challenges have been reported in detecting small objects within a large image (e.g. > 2000 by 2000 in resolution). Various methods have been proposed using different detection algorithms in order to detect small objects. However, these approaches require high computational resources and are not suitable for edge computing devices that are used for practical applications such as pedestrian traffic light detection. We explored two different methods of detection to evaluate which method is best at detecting small objects. The first method is a two—part procedure with the first step being image processing and the second step, a R-CNN based detection using Edge Boxes algorithm for the extraction of region proposals. The second method is solely Faster R-CNN Object Detection with Instance Segmentation, termed as Mask R-CNN. A total of 4000 streets images of Singapore with pedestrian traffic lights were used as training data. The dimensions of the images range from 1200 by 900 to 4000 by 3000. The small object to be detected is the green or red man within pedestrian traffic lights. We evaluated these methods based on training time required, detection time, accuracy as well as suitability for deployment in edge computing devices. From the results, it is shown that the HSV + R-CNN approach is preferred as it achieves an accuracy of 95.5% and can be deployed in edge devices.