Reviewerly/exHarmony huggingface dataset:
Behavior Recognition Based on Improved Faster RCNN

We divide the recognition process into “object detection” and “behavior prediction”. Firstly, all objects in the image are detected, and then the detection results are used as the input of the behavior recognition part to predict the interaction actions between objects. In the process of feature extraction, we add extra parameters to the sampling point of each convolution kernel to give the characteristic of convolution kernel deformation, so that the network has better adaptability to complex scenes. In the detection of target, the attention mechanism is combined with ResNet network, and the network structure is changed from “post-activation” to “pre-activation”, which makes the suggestion box have certain screening ability and avoids the phenomenon of overfitting. In action prediction, the network takes the instance object in the feature map as the center, the interactive objects around which are detected according to the appearance characteristics and attention weight of the object, and the action scores between them are predicted. Finally, our network is trained on the enhanced COCO dataset. Compared to traditional methods. The proposed method can well detect the actions in the image, and the mAP reaches 67.2%, an increase of nearly 14 percentage points, which is of high experimental value.
Hierarchical Deterministic protocol for the defragmentation of identity in a blockchain-based framework

In the present identity management system, there is the problem of digital identity differentiation, centralization system ruling the user’s data and fragmentation of identity. A framework using smart contract in a blockchain has been constructed that generates an identifier that is unique to solve the differentiation of user identities. An authorization mechanism has been constructed related to the Hierarchical Deterministic algorithm which is used to fix the problem of fragmentation in the identity information. Including to this, trust-worthy information verifying method is proposed to brief, by improving the integrity of the digital identity. There are novel methods proposed for recovery of identity and authorization of networking services. The final deployment of the system shows the self-sovereign identity, that describes that the user is a controlling authority of their own identity and also the evidencing factor of the digital identity.
Smart Disaster Risk Reduction and Emergency Management in the Built Environment

Smart technologies such as artificial intelligence, the Internet of Things, and other cyber-physical systems are often associated to Industry 4.0 given their potential for transforming current manufacturing and industrial practices. In particular, the significant potential of these technologies for increasing automation, improving communication and self-monitoring, and optimizing production overall for industries is well known. However, the influential power of these technologies is not bounded by these applications and has significant potential for fields such as disaster risk reduction and emergency management. In this context, the proposed chapter discusses several applications of digital technologies and innovations from Industry 4.0 in these fields, such as big data, the Internet of Things and machine learning techniques for big data analytics. Additionally, research and governance needs in this context are highlighted, as well as certain challenges to widespread and mainstream the reliable use of these technologies in disaster management.
Direct measurement of the polynomial invariant of degree 2

One of the basic scientific researches that lies at the heart of quantum information is quantum entanglement. Growing interest in experimental applications of quantum entanglement led to wide study in this field. We propose a realistic protocol for measuring directly the polynomial invariant of degree 2 (PID-2) of an even N-qubit pure state , as long as two copies and some operations are used. To clarify the matter, we apply the same protocol to a pure four-qubit system and provide a quantum circuit to implement this entanglement measure.
Social Media as a Driver of Innovative Service Delivery in Academic Libraries

As technology continues to impact the delivery of library and information services, traditional academic library systems have continued to experience changes. Social media are changing and becoming more sophisticated, and librarians need to adopt them for innovative services to their users. Library staff skills and library services have to shift from book-eccentric to user-eccentric. As the academic library continues to redefine its role in the digital environment, it needs to leverage its strengths and create responsive and convenient services. The chapter discusses how social media can be adopted and utilized for delivering innovative services and the various innovative ways social media can be utilized in effective service delivery, the challenges associated with social media and innovative service delivery, and the strategies to enhance the use of social media for innovative service delivery in academic libraries. The chapter serves as guide to students and practitioners on the various ways to use social media for effective service delivery in academic libraries.
Integration of Color and Shape Features for Household Object Recognition

Intelligent robots such as domestic service robots (DSR), office robots are required to be able to interact with dynamic and complex environments. In order to carry out the tasks given in such environments, the ability to interact with the objects becomes prevalent. In particular, the DSR need to interact with a household object that is normally being lied in arbitrary positions at the home. To accomplish such a challenging task, the robot has to be able to recognize the object. As human does, a visual-based recognition is most common and natural for intelligent robots. To realize such ability the use of visual information captured from a visual sensor is necessary. Thanks to the second version of Microsoft Kinect (Kinect V2), visual information such as color, depth, and near-infrared information can be acquired. In this study, the captured visual information is then processed for object extraction and object recognition. To solve the problems, we propose a method that exploits multiple features such as color and shape features. The proposed method has incorporated the results of each classifier such as k-nearest neighbor (kNN) using a simple probabilistic method to obtain robust recognition results of household objects. To validate the proposed method, we have conducted several experiments. The results reveal that our method can achieve an accuracy of (84.02 ± 18.85) % for the recognition of household objects with extreme conditions.
Information and Communication Support for Engineering Students and Faculty Collaboration

Engineering education for the 21 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> century requires students to have a broader foundation experience for life-long success, to develop skills that can help them become competitive on the international market. Having surveyed 102 students with their majors in engineering and 87 members of academic staff we found out that the existing system of engineering education has several weaknesses. Some of the weaknesses are lack of effective student-faculty collaboration, lack of information resources for students on their scientific work, low level of English for taking part in research activities. To strengthen student-faculty cooperation we created an academic website on the Tilda platform that provides the basis for student-faculty collaborative activities that can ensure up-to-date information support, enhanced communication and efficient self-study management. The results of the feedback survey show the efficiency of the website use for student-faculty academic communication.
GDSim: Benchmarking Geo-Distributed Data Center Schedulers

As cloud providers scale up their data centers and distribute them around the world to meet demand, proposing new job schedulers that take into account data center geographical distribution have been receiving considerable attention from the data center management research and practitioner community. However, testing and benchmarking new schedulers for geo-distributed data centers is complicated by the lack of a common, easily extensible experimental platform. To address this gap, we propose GDSim, an open-source job scheduling simulation environment for geo-distributed data centers that aims at facilitating development, testing, and evaluation of new geo-distributed schedulers. We showcase GDSim by using it to reproduce experiments and results for recently proposed geodistributed job schedulers, as well as testing those schedulers under new conditions which can reveal trends that have not been previously uncovered.
Pedogenic-weathering evolution and soil discrimination by sensor fusion combined with machine-learning-based spectral modeling

Loess deposits are important records of the evolution of the soil environment and pedogenic weathering. Changes in pedogenic weathering conditions through space and time, as well as discrimination/sourcing of loess-derived soils, are important scientific issues in the soil (paleosol) community. Here, 502 soil samples from four loess chronosequences representing different climatic zones of China were investigated using portable X-ray fluorescence (PXRF) and visible to near-infrared reflectance (VNIR) in combination with previously published mineralogic and magnetic datasets. A spectral modeling approach was employed to discriminate loess-derived soils from different regions of China. We developed a three-dimensional model to fingerprint loess-derived soils using principal component analysis (PCA), showing that soils accumulating under varying climatic conditions were effectively discriminated with sensor fusion data. Six key soil mineralogic and magnetic attributes serving as pedogenic-weathering proxies were analyzed and predicted with conventional methods and chemometric models. Predictive models were constructed with machine learning methods, including partial least squares regression (PLSR), random forest (RF), and Cubist algorithms. The results indicate that the Cubist algorithm works better than PLSR and RF in predicting pedogenic-weathering proxies. The cross-validation results indicate that, although models derived from single sensors (i.e., PXRF or VNIR) work well in predicting pedogenic-weathering proxies, the sensor fusion approach is superior with regard to accuracy and robustness of results in most cases. We suggest that the combined elemental and secondary-mineral information provided by the fused PXRF-VNIR datasets can yield high-accuracy models in soil (paleosol) investigations. The sensor fusion models reveal that pedogenic processes in the loess chronosequences are diversified both spatially and temporally in different climate zones of China. Our results suggest that spectral modeling can be an alternative to geochemical, mineralogic and magnetic pedogenic-weathering proxies, and that it has great potential for investigating soil-forming conditions and pedogenic-weathering evolution, especially when large-area and/or high-resolution analysis is required.
Modeling and Simulation of Scalable Cloud Environment using iDR

The Cloud Environment is an integration of mixed-type workflow with heterogeneous computing resources. Experiment with a real cloud environment is required for testing, capacity planning, and performance evaluation for cloud hosting applications. However, some limitations exist on experimental studies, i.e., actual cloud experiments are too expensive, not covered in all situations, do not cover all functions. The iDR simulation tool is an alternate of actual cloud experiments. Simulation using iDR is a method to estimate cloud resources based on synthetic cloud workload. The iDR is a toolkit of methods and processes to simulate a cloud environment with several dimensions, i.e., performance, costing, energy consumption, environmental impact, and reliability in a single GUI platform. In this paper, we illustrate iDR simulation toolkit that enables modeling and simulation of Cloud Computing Environments.
VoFSQ: An Efficient File-Sharing Interactive Verification Protocol

This paper explores the possibility of improving the verification of file-sharing qualification in Blockchain-based File-Sharing system. Previous work, such as Filecoin, proposes an interactive protocol for file storage scenario. It enables file-holders to prove that the file is stored in independent storage without disclosing the content of the file. However, it uses a heavy cryptographic machinery zk-SNARKs and needs a trusted third party to generate public parameters. This paper proposes an efficient file-sharing verification protocol: Verifications of File-Sharing Qualification (VoFSQ). VoFSQ allows file-holders to verify with each other whether the shared-file has been preserved for a period of time. Users who compete for file-sharing qualification need to spend a certain amount of computing and storage resources to generate the shared-files based evidence files. Take into the cost into account, users will prefer using idle hard disk to preserve the evidence file continuously other than delete and regenerate it. Compared to the protocol of Filecoin, VoFSQ is more efficient in the prover and verifier proof phases. Meanwhile, VoFSQ does not disclose the private information of shared-file, and does not rely on trusted third parties. This paper also implements and tests VoFSQ, experimental results show that protocol can be executed efficiently.
Incorporating Ghost Module into RCAN for Super-Resolution of Satellite Images

With the explosion of amount of low cost satellites, satellite images have been widely used for many non-military applications, such as agriculture, landscape, and recognition of environment. Improving the image resolution to mine useful information becomes one of the immediate problems. Therefore, it is expected to improve the recognition accuracy by increasing the resolution of satellite images. Recently, deep learning technique has been proposed to increase the resolution of images. However it requires a large number of learning parameters, which results in huge computational cost. To overcome this problem, we develop a new deep learning model based on ghost module to reduce the parameters while maintaining the quality of results. We utilized Google Earth Pro satellite imagery for the network training and testing. Comparing to the classical convolutional neural network module based methods, the number of parameters used in our model was reduced 49.31 % but keeping the same level of Peak Signal-to- Noise Ratio (24.1578) and Structural Similarity (0.7174).
Search-Based Local Black-Box Deobfuscation

This presentation is based on the paper "Search-based Local Blackbox Deobfuscation: Understand Improve and Mitigate'' from the same authors, which has been accepted for publication at ACM CCS 2021.
Acceleration of Deflate Encoding and Decoding with GPU implementations

Deflate coding is a very popular lossless data compression method used in zlib, GNU zip (gzip), and zip, which performs LZSS compression algorithm with Huffman coding. However, Deflate coding has not been used in GPU computing, because Deflate encoding and decoding involve sequential operations and their acceleration using a GPU is quite hard. The main purpose of this paper is to present GPU implementations for encoding and decoding of Deflate coding. For efficient GPU implementations of Deflate coding, we have used multiple small hash tables for finding matching subsequences in the dictionary by multiple threads in parallel and applied the Single Kernel Soft Synchronization (SKSS) technique to fully utilize GPU computing resources. We have also generated gap arrays in Huffman encoding to accelerate parallel Huffman decoding. We have evaluated the performance of our GPU implementations using an NVIDIA Tesla V100 GPU and compared with that of pigz, a library for parallel Deflate coding designed for Intel X86 multicore CPUs. Our experimental results for Deflate encoding show that our GPU implementation is up to 1.28x times faster than pigz running on four Intel Xeon E7-8870 CPUs (2.10GHz) with 20 physical cores each. Further, our GPU decoding for the same 9 files is up to 36.49x times faster than pigz decoding. Thus, our GPU implementations for Deflate coding are promising tools for GPU computations operating large lossless data.
Emotion Intelligent Application for Independent Wellbeing Management using Certainty Factor

According to the Indonesian National Research Master Plan, Indonesia is currently facing strong challenges in the field of public health, particularly mental health. Especially during the Covid-19 pandemic where there are many problems with emotional disorders that can threaten physical and behavioral health. Emotion health during a pandemic need to be maintained. The problem is that the lack of access and restrictions in consulting a psychologist makes it difficult for people to understand and recognize their emotions which will adversely affect their life and their decision making. To overcome this problem, in this study an expert system-based Certainty Factor (CF) method was built to recognize emotions. The method used the belief value and knowledge-based acquired from reference books as well as a psychologist expert. Implemented as a mobile application, made it easier for user to access it through their smart phones. The CF method was able to run in accordance with functional requirements and produced expected results. Based on the test results, the expert system to recognize emotions using the Certainty Factor method has worked with an accuracy rate of 92.31% on the test data.
A Case Study on Five Maturity Levels of A Kubernetes Operator

Deploying distributed applications using their Operators in a containerized platform on the state-of-art cloud orchestration tooling, such as Kubernetes, has truly become widely accepted. However, the quality of an Operator has a significant impact on a few core metrics of the application, such as its availability, consistency, and quality of service. This paper introduces the Kubernetes Operator maturity model and its five maturity levels, and then gives a demonstration on how a demo Kubernetes Operator is capable of reaching all the five levels respectively by using an example Operator named New Visitors Site Operator. Finally, an experiment illustrating the capability of the example Operator&#x2019;s auto-scaling functions to improve the application performance is presented. This example Operator will enable developers and researchers to design containerized applications with more enhanced features. The code is available at https://github.com/ringdrx/visitors-operator.
Ukrainian Text Preprocessing in GRAC

GRAC is a large reference corpus of Ukrainian. Some texts come from OCR-ed documents and contain errors that must be fixed before inclusion in the corpus. Ukrainian texts also contain foreign-language (mostly Russian) fragments that need to be identified and either excised or left in place, depending on their length. To address these key issues, the GRAC team has developed algorithms and tools and applied them to corpus texts. A discussion of the specifics is provided and potential for further development is outlined. The developed tools have been made available online to the NLP and CL community.
Training and exercises for Critical Infrastructure – A Hellenic computer-assisted exercise use case analysis

This paper aims to highlight the importance of emerging tools and technologies in the exercises and training of Critical Infrastructure (CI) operators. Computer-assisted exercises (CAX) may offer multiple advantages in designing and conducting exercises (e.g. cost-effectiveness, distance/remote learning), yet, customized platforms need to be designed and employed to meet the particular needs of CI protection. In this context, the paper analyses training and exercises of the Hellenic program for Critical Infrastructure Protection (CIP). A brief description of the current framework for the protection of CI in Greece encompassing the European directive is also presented. A CAX use case analysis is described with the pioneer implementation of the Joint Research Centre (JRC - E.2 Unit), Platform-based Operational System Events and Injects Distribution Online (POSEIDON) web-based IT platform, that was customised for the needs of the exercise under the code name EYZS-3, focusing on the transportation sector. Advanced training approaches, such as EYZS-3, seem to gain ground, as opposed to typical discussion-based exercises (tabletop), indicating valuable findings for future exploitation for planning training and exercises in CI protection.
Smart Badminton Detection System Based on Scaled-YOLOv4

In recent years, artificial intelligence applied to computer vision is growing popularly. Also, the smart sport system is now a flourishing research topic that more and more researchers are studying in this area. In this paper, we used scaled-YOLOv4 [1], which is now the state-of-the-art deep learning model on object detection, as our deep learning network to build a smart badminton detection system. We proposed the modified scaled-YOLOv4 by adding residual attention modules to detect the positions of badminton players and the shuttlecock and an image projection method to calculate the moving distance of the players.
Lightweight, Secure, Similar-Document Retrieval over Encrypted Data

Applications for document similarity detection are widespread in diverse communities, including institutions and corporations. However, currently available detection systems fail to take into account the private nature of material or documents that have been outsourced to remote servers. None of the existing solutions can be described as lightweight techniques that are compatible with lightweight client implementation, and this deficiency can limit the effectiveness of these systems. For instance, the discovery of similarity between two conferences or journals must maintain the privacy of the submitted papers in a lightweight manner to ensure that the security and application requirements for limited-resource devices are fulfilled. This paper considers the problem of lightweight similarity detection between document sets while preserving the privacy of the material. The proposed solution permits documents to be compared without disclosing the content to untrusted servers. The fingerprint set for each document is determined in an efficient manner, also developing an inverted index that uses the whole set of fingerprints. Before being uploaded to the untrusted server, this index is secured by the Paillier cryptosystem. This study develops a secure, yet efficient method for scalable encrypted document comparison. To evaluate the computational performance of this method, this paper carries out several comparative assessments against other major approaches.
Face Recognition from Partial Face Data

During the spread of the Corona epidemic, everyone started wearing masks as protection in public places. Therefore, this causes a major challenge in authentication and safety systems, such as face recognition systems in railway stations, airports, and payment systems based on facial recognition technologies. Face recognition systems are safer than touch-based biometric systems. However, the face recognition systems are ineffective in the presence of a face with a mask. Therefore, we developed an efficient algorithm using the MTCNN and VGGF model to improve the efficacy of face recognition systems in partially occluded face images. The proposed approach produced 90% accuracy in the top half of the facial images.