Reviewerly/exHarmony huggingface dataset:
A Multi-Layer Big Data Value Chain Approach for Security Issues

Big Data systems generate a lot of data from different sources, sometimes are less reliable. Also, business ecosystems are highly interconnected, through Big Data Value Chains (BDVC) either internally or with partners, making their data assets and processes more vulnerable to multiple cyber-attacks. However, this kind of sensitive exposition and data workflows requires specific protection and security management. In this contribution, we highlight the importance of coupling BDVC and Big Data security as well as existing contributions addressing these topics. Also, we propose a multi-dimensional model aiming to show cybersecurity milestones and reduce the gap between cyber-risks and how organizations manage their data. For this goal, we suggest a multi-layered security framework to deal with security issues along BDVC. This framework, which is a generic view adaptable to different domains, allows protecting organizations' sensitive data assets as well as privacy concerns. Furthermore, this multi-layer projection ensures a sustainable cyber-ecosystem.
Study and Performance Evaluation Binary Robust Invariant Scalable Keypoints (BRISK) for Underwater Image Stitching

Abstract In this paper, performance evaluation of Binary Robust Invariant Scalable Key points (BRISK) for underwater image stitching is presented. Underwater image stitching is quite challenging because usually images produced by underwater cameras have few features and noise such as motion blur and underexposure. In this paper, we proposed a robust algorithm for the underwater image stitching which consists of several stages: taking an image frame, getting a key point using BRISK, feature matching using Random Sample Consensus (RANSAC), homography estimation, and perspective warping. The experimental result shows that the proposed algorithm can be implemented for underwater image and achieve better matching results even the less detected keypoints.
Fuzzy Elliptic Curve Cryptography based Cipher Text Policy Attribute based Encryption for Cloud Security

Cipher Text Policy Attribute Based Encryption which is a form of Public Key Encryption has become a renowned approach as a Data access control scheme for data security and confidentiality. It not only provides the flexibility and scalability in the access control mechanisms but also enhances security by fuzzy fined-grained access control. However, schemes are there which for more security increases the key size which ultimately leads to high encryption and decryption time. Also, there is no provision for handling the middle man attacks during data transfer. In this paper, a light-weight and more scalable encryption mechanism is provided which not only uses fewer resources for encoding and decoding but also improves the security along with faster encryption and decryption time. Moreover, this scheme provides an efficient key sharing mechanism for providing secure transfer to avoid any man-in-the-middle attacks. Also, due to fuzzy policies inclusion, chances are there to get approximation of user attributes available which makes the process fast and reliable and improves the performance of legitimate users.
Squircular-CPP: A Smooth Coverage Path Planning Algorithm based on Squircular Fitting and Spiral Path

Coverage path planning (CPP) is essential for applications such as robotic floor cleaning and high-pressure cleaning of surfaces. Smooth CPP algorithms have several benefits including smoother motion of the robot and the reduction of aggressive accelerations and decelerations resulting from sharp turns. In this paper, a novel smooth CPP algorithm is presented which is named Squircular-CPP. This algorithm proposes a squircular shape, which is an intermediate shape between the circle and the square, to fit a target area. Squircular-CPP can also fit a shape between the ellipse and the rectangle. The shape fitting is simple, fast, and analytical and doesn't require a preselection of the shape (i.e., square, circle, ellipse or rectangle). It enables and complements the creation of a smooth spiral path within the fitted shape. Several case studies are presented to demonstrate the effectiveness of the algorithm and to compare it against the popular boustrophedon-based coverage approach and the Deformable Spiral CPP (DSCPP) algorithm.
Towards a Circular Economy of Industrial Software

Software has become an indispensable part of industrial production and thus influences the life cycle of manufacturing systems, as many of these systems have to be replaced or evolved due to changing requirements. Software adaptation through continuous evolution extends the service time of these systems and thus saves valuable resources. In this paper we present an interdisciplinary methodology for reengineering legacy software to increase the productive lifetime. Our proposed approach systematically reuses implicit domain knowledge to evolve, validate and commission new software from legacy code. The approach is evaluated on a CNC machine as a special type of industrial system.
An Efficient Bar/QR Code Recognition System for Consumer Service Applications

In recent years, consumer electronics for providing better customer service has sought a significant growth. For production, storage and supply of goods to consumers, it is essential to have correct information, recognize and store the information efficiently in computers. Therefore, it is essential to have an efficient and handy bar code recognition system. This paper presents an efficient method for bar code and QR code recognition together. The method automatically detects the Bar code QR code and displays the complete information of the product. The method is developed in python environment using OpenCV library. However, OpenCV does not have any dedicated modules that can be used to read and decode Bar codes and QR codes. The database is developed at the authors' Institute where bar codes and QR codes are separately assigned to more than 100 items such as books, sofa, tables and chairs. The image of the bar code or QR code is captured in real-time and further processed using the proposed method. The code is being decoded, compared with the Data frame of the stored product and finally, displays the result i.e, the complete information about the product. The execution time of the proposed method is 0.25 seconds. The proposed method can be further prototyped on micro-controllers to develop an efficient bar code recognition system.
Unlinkable Updatable Databases and Oblivious Transfer with Access Control

An oblivious transfer with access control protocol (OTAC) allows us to protect privacy of accesses to a database while enforcing access control policies. Existing OTAC have several shortcomings. First, their design is not modular. Typically, to create an OTAC, an adaptive oblivious transfer protocol (OT) is extended ad-hoc. Consequently, the security of the OT is reanalyzed when proving security of the OTAC, and it is not possible to instantiate the OTAC with any secure OT. Second, existing OTAC do not allow for policy updates. Finally, in practical applications, many messages share the same policy. However, existing OTAC cannot take advantage of that to improve storage efficiency. We propose an UC-secure OTAC that addresses the aforementioned shortcomings. Our OTAC uses as building blocks the ideal functionalities for OT, for zero-knowledge (ZK) and for an unlinkable updatable database ( $$\mathrm {UUD}$$ ), which we define and construct. $$\mathrm {UUD}$$ is a protocol between an updater $$\mathcal {U} $$ and multiple readers $$\mathcal {R} _k$$ . $$\mathcal {U} $$ sets up a database and updates it. $$\mathcal {R} _k$$ can read the database by computing UC ZK proofs of an entry in the database, without disclosing what entry is read. In our OTAC, $$\mathrm {UUD}$$ is used to store and read the policies. We construct an $$\mathrm {UUD}$$ based on subvector commitments (SVC). We extend the definition of SVC with update algorithms for commitments and openings, and we provide an UC ZK proof of a subvector. Our efficiency analysis shows that our $$\mathrm {UUD}$$ is practical.
Measurement of Employee Information Security Awareness Using the Human Aspects of Information Security Questionnaire (HAIS-Q): Case Study at PT. PQS

Human error is one of information security breaches that plays a significant role, with employee identified as the largest source of compromise. The purpose of this study is to measure information awareness at PT. PQS by using Human Aspects of Information Security Questionnaire (HAIS- Q). HAIS-Q is an instrument used to assess the extent to which a company's organizational system is vulnerable to threats caused by employee behavior. HAIS-Q is distributed to 51 employees with 45 valid questionnaires. Valid questionnaire results were then carried out with a level of internal consistency using Cronbach's Alpha. Based on the measurement results in each sub-area using descriptive statistics, the lowest respondent's score are downloading files, accessing dubious files, and clicking links on emails from known senders. The recommendations we provide for increasing that sub-areas are by improving information-related policies and procedures, conducting security awareness programs, and improving technology aspects.
A Formally Verified Static Analysis Framework for Compositional Contracts

A (commercial or financial) contract is a mutual agreement to exchange resources such as money, goods and services amongst multiple parties. It expresses which actions may, must and must not be performed by its parties at which time, location and under which other conditions. We present a general framework for statically analyzing digital contracts, formal specifications of contracts, expressed in Contract Specification Language (CSL). Semantically, a CSL contract classifies traces of events into compliant (complete and successful) and noncompliant (incomplete or manifestly breached) ones. Our analysis framework is based on compositional abstract interpretation, which soundly approximates the set of traces a contract denotes by an abstract value in a lattice. The framework is parameterized by a lattice and an interpretation of contract primitives and combinators, satisfying certain requirements. It treats recursion by unrestricted unfolding. Employing Schmidt’s natural semantics approach, we interpret our inference system coinductively to account for infinite derivation trees, and prove their abstract interpretation sound. Finally, we show some example applications: participation analysis (who is possibly involved in a transfer to whom; who does definitely participate in a contract) and fairness analysis (bounds on how much is gained by each participant under any compliant execution of the contract). The semantics of CSL, the abstract interpretation framework and its correctness theorem, and the example analyses as instances of the abstract interpretation framework have all been mechanized in the Coq proof assistant.
Sea Area Monitoring and Analysis of Fishing Vessels Activity: The i4sea Big Data Platform

The i4sea research project provides effective and efficient big data integration, processing and analysis technologies to deliver both real-time and historical operational snapshots of fishing vessels activity in national sea areas. This paper presents the architecture of the i4sea big data platform for sea area monitoring and analysis of fishing vessels activity and demonstrates the operation of some use-case pilot scenarios.
Prototype of E-Document Application Based on Digital Signatures to Support Digital Document Authentication

Abstract The purpose of this research is to develop a prototype of a web-based application that can be used to provide digital signatures on files or digital documents. This prototype uses a digital signature mechanism issued by the Ministry of Communication and Information of the Republic of Indonesia (KOMINFO). The results of the research conducted by using the alpha test showed that all functionalities in the application have been able to run well. While the authentication test shows that any changes made to a document that has a digital signature will invalidate the digital signature, which means that there are attempts to falsify documents and their authenticity can be identified. This is because in documents that have the digital signature will have a certain hash value which if the document is changed or modified, then the hash value will change. This is what determines whether a document is still original or has been modified its contents, namely by comparing the hash value contained in the document.
GRLC

Convolutional neural networks (CNNs) require a huge amount of off-chip DRAM access, which accounts for most of its energy consumption. Compression of feature maps can reduce the energy consumption of DRAM access. However, previous compression methods show poor compression ratio if the feature maps are either extremely sparse or dense. To improve the compression ratio efficiently, we have exploited the spatial correlation and the distribution of non-zero activations in output feature maps. In this work, we propose a grid-based run-length compression (GRLC) and have implemented a hardware for the GRLC. Compared with a previous compression method [1], GRLC reduces 11% of the DRAM access and 5% of the energy consumption on average in VGG-16, ExtractionNet and ResNet-18.
How do Graduating Students Evaluate Software Design Diagrams?

An important skill graduating computing students require is to evaluate a given software design and ensure that it satisfies the intended requirements. Prior work has shown that while working with software designs, experts think deeply about the design and simulate scenarios where the design does not satisfy the requirements. In this paper, we examine how students evaluate a given set of software design diagrams (UML class and sequence diagrams) against the given requirements.
Dynamic Consensus: Increasing Blockchain Adaptability to Enterprise Applications

Decentralization powered by blockchain is validated for its capability to build trust like no other computational system before. The evolution of blockchain models has opened new use-cases that are becoming operational in many industry fields such as: energy, healthcare, banking, cross-border trade, aerospace, supply chain, and others. The core component of a decentralized architecture is the consensus algorithm - the set of rules that ensures an automated and fair agreement between the actors in the same network. Classic consensus algorithms are tailored to solve specific problems, but in an open ecosystem, each business case is unique and needs a certain level of customization. This paper introduces a new meta-consensus model called Dynamic Consensus, an architecture extension that allows multiple, complementary, consensus algorithms to run on the same platform. While classic consensus mechanisms are more appropriate for public or private systems (narrow set of rules), a dynamic approach would fit better for federated business consortiums (more rules and higher need for adaptability). The model is illustrated and analyzed as an ongoing experimental feature that can be added to enterprise blockchains designed to operate in cross-domain environments.
Weighted-capsule routing via a fuzzy gaussian model

Capsule network (CapsNet) is a novel architecture that takes into account the hierarchical pose relationships between object parts, which had achieved desirable results on image classification. EM-Routing (EM-R) used in CapsNet is the process of assigning child capsules (parts) to each parent capsule (objects) based on a level of agreement, which is similar to the fuzzy clustering process. However, CapsNet still struggles with backgrounds and the presence of noise. In this paper, a new routing algorithm based on a weighted capsule fuzzy gaussian model (WCFGM-R) and a pose loss function are proposed. The proposed algorithm aims to prohibit atypical child capsules from contaminating the parent capsules by incorporating the activations of capsules in a lower layer as weights that play the role of precision. The pose loss provides the best inter-class separation and improves the ability of pattern classification. Indeed, the experimental analyses demonstrate that CapsNet with WCFGM-R outperforms the CapsNet with EM-R in which it shows excellent results on three datasets (MNIST-bg-img, MNIST-bg-rnd, and CIFAR10).
Traffic-Aware Rate Adaptation for Improving Time-Varying QoE Factors in Mobile Video Streaming

Mobile video has become one of the most valuable services in next-generation heterogeneous networks, and users' quality of experience (QoE) is recognized as its important performance metric. In this paper, we propose an adaptive bitrate (ABR) algorithm to achieve improvement of the timevarying QoE determinants during a mobile video playback. Since watching videos will bring mobile data charges to the users, the traffic consumed by video downloads should be customized to a user-specified amount. To this end, we first analyze the real-world traces, and find the key factors to formulate a continuous QoE model. Then, we introduce a traffic-aware rate adaptation strategy (TARA). Given that users are aware of their real-time capabilities when watching a mobile video, TARA enables robust ABR process which can satisfy users' requirements with traffic constraints. Finally, a centralized reinforcement learning (RL) approach is proposed for the joint optimization of TARA, which aims to maximize the designed QoE metric, and deliver the expected viewing experience to users. Results of experiments driven by both the simulated, and real-world network traces reveal the efficiency of the proposed TARA strategy, and demonstrate its signicant performance improvement as compared to the state-of-the-art ABR algorithms.
Securing Cryptocurrency Exchange: Building up Standard from Huge Failures

After the original publication of the Bitcoin paper, cryptocurrency exchanges emerged to connect the fiat currency world to the cryptocurrency world. Although many users of cryptocurrency exchanges demonstrate confidence in this simple role for an exchange, recent security incidents suggest that a gap exists between the perception of users and the reality. That is, operations, informational assets to be protected, and security postures should be clarified. In this paper, we summarize the results of an investigation of 16 registered and 16 semi-registered cryptocurrency exchanges by Japanese regulators, then analyze the reality of functionalities, implementation, and operations of cryptocurrency exchanges. Then, we propose short-term directions and long term works to fix pitfalls through building technology and management standards to secure the cryptocurrency exchange and custodian, which are essential building blocks toward healthy blockchain ecosystem.
OPS: Optimized Shuffle Management System for Apache Spark

In recent years, distributed computing frameworks, such as Hadoop MapReduce and Spark, are widely used for big data processing. With the explosive growth of the amount of data, companies tend to store intermediate data of the shuffle phase on disk instead of memory. Therefore, intensive network and disk I/O are both involved in the shuffle phase. To optimize the overhead of the shuffle phase, we propose OPS, an open-source distributed computing shuffle management system based on Spark, which provides an independent shuffle service for Spark. By using early-merge and early-shuffle strategy, OPS alleviates the I/O overhead in the shuffle phase and efficiently schedules the I/O and computing resources. OPS also proposes a slot-based scheduling algorithm to predict and calculate the optimal scheduling result of the reduce task. Besides, OPS provides a taint-redo strategy to ensure the fault tolerance of computing jobs. We evaluated the performance of OPS on a 100-node Amazon AWS EC2 cluster. Overall, OPS optimizes the overhead of shuffle by nearly 50%. In the test cases of HiBench, OPS improves end-to-end completion time by nearly 30% on average.
OneByOne (OBO): A Fast Sorting Algorithm

Many sorting algorithms have been developed to enhance the performance of important applications from different domains such as commercial commuting, operational research and scientific simulation. In this paper, we propose a new in-place sorting algorithm called OneByOne (OBO) sort. When compared to selection sort and bubble sort algorithms, OBO demonstrated faster run time at different array sizes. OBO also showed nearly equal performance when compared to the insertion sort algorithm.
Marathi Handwritten Character Recognition Using SVM and KNN Classifier

Marathi handwritten character recognition is the most challenging task in Optical Character Recognition (OCR) research domain. The need for OCR to convert Marathi handwritten documents or scripts to editable text, which can be attained by the proposed work, which will reduce the burden of storage space, the task of data entry in forms in Marathi language and converts degraded historical documents in editable text. Moreover, Handwritten Marathi characters tend to more complicated due to their structure, shape, several strokes, and different writing styles. The character recognition involved four necessary procedures like Pre-Processing on input character images, Segmentation of characters in words, Extraction of features of segmented characters, and Classification, to recognize the Marathi characters with a different style. In this paper, handwritten Marathi single character accepted as input, and the features are extracted using the Histogram Oriented Gradient method (HOG), whereas characters classified using Support Vector Machine (SVM) and K-Nearest Neighbor algorithm (KNN).
A Novel Extension for e-Safety Initiative Based on Developed Fusion of Biometric Traits

Safety is one of the central elements of society functioning. The eSafety aims at increasing road safety through the use of intelligent integrated safety systems. This article presents the concept and PCB implementation of an on-board compact eCall device that can be installed at the owners' request in a used vehicle. The prototype includes accident and rollover detection, passenger detection, and GSM communication with the eCall PSAP service center. Two accident detection algorithms are implemented: the first relates to the collision detection based on exceeding the threshold of acceleration using an accelerometer to measure the dynamic force caused by movement and the gravity force, the second - the rollover detection based on exceeding the threshold of the vehicle angle of inclination using data coming from the accelerometer and a gyroscope. The prototype was tested in eight different test scenarios using laboratory accident simulators as well as during driving. The proposed solution of passenger detection is based on images coming from a built-in 360° view camera. In the authors' approach the obtained images were transformed into linear ones and subject to subsequent operations - initial processing, object detection and redundant detection filtering. The authors utilize biometric methods in object detection such as Viola-Jones and Single Shot MultiBox Detector for face localization, and You Look Only Once for whole body detection. An additional testing mechanism for passenger detection are seat pressure pads that are independent of camera detection. All necessary sensors and communication components verified by practical experiments have been integrated on the custom designed motherboard with the NVIDIA Jetson Nano processing module installed. The tests confirmed the performance is sufficient for rapid accident detection, real-time image analysis and all mandatory eCall communication functions. Results of experiments were promising, providing high accident and passenger detection rates.