Reviewerly/exHarmony huggingface dataset:
Blockchain-based Multi-Party Authorization for Accessing IPFS Encrypted Data

Multi-party authorization (MPA) typically involves multiple parties to control and grant access to shared data. MPA is used to solve the insider’s attack problem by ensuring that a single authority or party is not acting alone. Currently, almost all existing implementations of MPA are centralized and fall short in providing logs and events related to provenance of granting permissions in a trusted, secure, immutable, auditable, and decentralized manner. Moreover, for sharing data, proxy re-encryption algorithms are often used to give secure access to encrypted shared data. These schemes and algorithms are also centralized and cannot be trusted. In this paper, we propose a fully decentralized blockchain-based solution in which MPA is implemented using Ethereum smart contracts, and proxy re-encryption algorithms (which are computationally expensive) are implemented using multiple oracles to give access to encrypted shared data stored on a public and decentralized storage platform, such as the Interplanetary File Systems (IPFS). The smart contracts help to validate results based on the majority of encrypted results determined by the oracles. For this, we incorporate reputation mechanisms in the proposed smart contracts to rate the oracles based on their malicious and non-malicious behaviors. We present algorithms along with their full implementation, testing, and validation details. We evaluate the proposed system in terms of security, cost, and generalization to show its reliability and practicality. We make the smart contract source code publicly available on Github.
Robust Morph-Detection at Automated Border Control Gate using Deep Decomposed 3D Shape &amp; Diffuse Reflectance

Face recognition is widely employed in Automated Border Control (ABC) gates, which verify the face image on passport or electronic Machine Readable Travel Document (eMTRD) against the captured image to confirm the identity of the passport holder. In this paper, we present a robust morph detection algorithm that is based on differential morph detection. The proposed method decomposes the bona fide image captured from the ABC gate and the digital face image extracted from the eMRTD into the diffuse reconstructed image and a quantized normal map. The extracted features are further used to learn a linear classifier (SVM) to detect a morphing attack based on the assessment of differences between the bona fide image from the ABC gate and the digital face image extracted from the passport. Owing to the availability of multiple cameras within an ABC gate, we extend the proposed method to fuse the classification scores to generate the final decision on morph-attack-detection. To validate our proposed algorithm, we create a morph attack database with overall 588 images, where bona fide are captured in an indoor lighting environment with a Canon DSLR Camera with one sample per subject and correspondingly images from ABC gates. We benchmark our proposed method with the existing state-of-the-art and can state that the new approach significantly outperforms previous approaches in the ABC gate scenario.
The Perseus Catalog: of FRBR, Finding Aids, Linked Data, and Open Greek and Latin

Plans for the Perseus Catalog were first developed in 2005 and it has been the product of continuous data creation since that time. Various efforts to bring the catalog online resulted in the current Blacklight instance, first released in 2013. Currently, both the XML data behind the Perseus Catalog and the digital infrastructure used to support it are undergoing a significant revision, with a focus on finally making the bibliographic data available as Linked Open Data (LOD). In addition, work is underway to develop a digital infrastructure that is not just open source but that is more easily extensible and better supports navigating the complex relationships found in that data. This article describes the history of the Perseus Catalog, its use of open metadata standards for bibliographic data, and the different open source technologies used in building and putting it online. It also documents the challenges inherent in the creation of open bibliographic data and ends with a discussion of the move towards LOD and other planned future directions.
The Exact Security of PMAC with Two Powering-Up Masks

PMAC is a rate-1, parallelizable, block-cipher-based message authentication code (MAC), proposed by Black and Rogaway (EUROCRYPT 2002). Improving the security bound is a main research topic for PMAC. In particular, showing a tight bound is the primary goal of the research, since Luykx et al.’s paper (EUROCRYPT 2016). Regarding the pseudo-random-function (PRF) security of PMAC, a collision of the hash function, or the difference between a random permutation and a random function offers the lower bound Ω(q2/2n) for q queries and the block cipher size n. Regarding the MAC security (unforgeability), a hash collision for MAC queries, or guessing a tag offers the lower bound Ω(q2m /2n + qv/2n) for qm MAC queries and qv verification queries (forgery attempts). The tight upper bound of the PRF-security O(q2/2n) of PMAC was given by Gaži et el. (ToSC 2017, Issue 1), but their proof requires a 4-wise independent masking scheme that uses 4 n-bit random values. Open problems from their work are: (1) find a masking scheme with three or less random values with which PMAC has the tight upper bound for PRF-security; (2) find a masking scheme with which PMAC has the tight upper bound for MAC-security.In this paper, we consider PMAC with two powering-up masks that uses two random values for the masking scheme. Using the structure of the powering-up masking scheme, we show that the PMAC has the tight upper bound O(q2/2n) for PRF-security, which answers the open problem (1), and the tight upper bound O(q2m /2n + qv/2n) for MAC-security, which answers the open problem (2). Note that these results deal with two-key PMACs, thus showing tight upper bounds of PMACs with single-key and/or with one powering-up mask are open problems.
A Proposed Supply Chain Model of Blockchain Technology-Based in Automotive Component Industry

The automotive industry has rapidly developed and overrun the market for the last decade. In this context, automotive components or parts are essential factors to manufacture automotive products. Undoubtedly, the supplier that provides the automotive components or inbound logistics becomes a critical party of Supply Chain Management activity. In this frame, the failure of inbound logistics potentially creates a severe implication to the automotive industry in terms of the financial effects, company image to damage the customer. Arguably, the supplier activities encompass the continuity of automotive components delivery, on-time delivery schedule, supplier and customer relationship maintenance, and financial operation. In this point, the problem identifies the distribution of automotive components and stock maintenance. Therefore, this qualitative research is essential to explore a proposed Blockchain Technology model in Supply Chain Management (BlcSCM), especially in the automotive component industry. On that basis, design science research methodology facilitates to create a proposed model, Leavitt Diamond. In this model, the essential factors (people, process, technology, and organization) merges with supply chain management. Finally, it refers to the Blockchain Technology model in Supply Chain Management.
Content Based Image Retrieval using Collaborative Color, Texture and Shape Features

Selection of feature extraction method is incredibly recondite task in Content Based Image Retrieval (CBIR). In this paper, CBIR is implemented using collaboration of color; texture and shape attribute to improve the feature discriminating property. The implementation is divided in to three steps such as preprocessing, features extraction, classification. We have proposed color histogram features for color feature extraction, Local Binary Pattern (LBP) for texture feature extraction, and Histogram of oriented gradients (HOG) for shape attribute extraction. For the classification support vector machine classifier is applied. Experimental results show that combination of all three features outperforms the individual feature or combination of two feature extraction techniques.
One-class Text Classification with Multi-modal Deep Support Vector Data Description

This work presents multi-modal deep SVDD (mSVDD) for one-class text classification. By extending the uni-modal SVDD to a multiple modal one, we build mSVDD with multiple hyperspheres, that enable us to build a much better description for target one-class data. Additionally, the end-to-end architecture of mSVDD can jointly handle neural feature learning and one-class text learning. We also introduce a mechanism for incorporating negative supervision in the absence of real negative data, which can be beneficial to one-class text models including mSVDD model. We conduct experiments on Reuters, 20 Newsgroup, and TREC datasets, and the experimental results demonstrate that mSVDD outperforms uni-modal SVDD and mSVDD can get further improvements when negative supervision is incorporated.
Exploring the Sufficiency of Undergraduate Students’ Cybersecurity Knowledge Within Top Universitie’ Entrepreneurship Programs

Small businesses using technology are at risk of cyberattacks and often do not have adequate cybersecurity knowledge, budgets, or dedicated security staff. Attackers know small businesses are accordingly vulnerable. An attack can result in severe losses or the closure of business, making this knowledge critical. Businesses ownership can originate with newly graduated entrepreneurship students, so that sample is selected for this study to determine if cybersecurity knowledge is gained through undergraduate curriculum. The preliminary findings of the study imply that entrepreneurship education might be enhanced with coursework that would help future small businesses avoid becoming victims of cyberattacks.
Color Image Encryption Based on Cross 2d Hyperchaotic Map Using Combined Cycle Shift Scrambling and Selecting Diffusion

Abstract A novel color image encryption algorithm based on a cross 2D hyperchaotic map is proposed in this paper. The cross 2D hyperchaotic map is constructed using one nonlinear function and two chaotic maps with cross structure. Chaotic behaviors are illustrated using bifurcation diagrams, Lyapunov exponent spectra and phase portraits, etc. In the color image encryption algorithm, the keys are generated using hash function SHA-512 and the information of plain color image. First, the color plain image is converted to a combined bit-level matrix and permuted by the chaos based row and column combined cycle shift scrambling method. Then the scrambled integer matrix is diffused according to the selecting sequence which depends on the chaotic sequence. Last, the cipher color image is obtained by decomposed the diffused matrix. Simulation results show that the algorithm can encrypt the color image effectively and has good security.
High-precision ID-TIMS Cassiterite U-Pb systematics using a low-contamination hydrothermal decomposition: implications for LA-ICP-MS and ore deposit geochronology

Abstract. Cassiterite (SnO2) is the most common ore phase of Sn. Typically containing 1–100 µg/g U and relatively low concentrations of common Pb the mineral has been increasingly targeted for U-Pb geochronology, principally via micro-beam methods, to understand the timing and durations of granite related magmatic-hydrothermal systems throughout geological time. However, due to the extreme resistance of cassiterite to most forms of acid digestion, to date, there has been no published method permitting the complete, closed system decomposition of cassiterite under conditions where the basic necessities of measurement by isotope dilution can be met, leading to a paucity of reference, and validation materials. To address this a new low blank (
Detecting Spam Messages in Twitter Data by Machine learning Algorithms using Cross Validation

Now a day’s human relations are maintained by social media networks. Traditional relationships now days are obsolete. To maintain in association, sharing ideas, exchange knowledge between we use social media networking sites. Social media networking sites like Twitter, Facebook, LinkedIn etc are available in the communication environment. Through Twitter media users share their opinions, interests, knowledge to others by messages. At the same time some of the user’s misguide the genuine users. These genuine users are also called solicited users and the users who misguidance are called spammers. These spammers post unwanted information to the non spam users. The non spammers may retweet them to others and they follow the spammers. To avoid this spam messages we propose a methodology by us using machine learning algorithms. To develop our approach used a set of content based features. In spam detection model we used Support vector machine algorithm(SVM) and Naive bayes classification algorithm. To measure the performance of our model we used precision, recall and F measure metrics.
Classifying White Blood Cells in Blood Smear Images using a Convolutional Neural Network

We have tried to automate the classification task of white blood cells by using a Convolutional Neural Network. We have divided white blood cell classification in two types of problems, a binary class problem and a 4-classification problem. In binary class problem we classify white blood cell as either mononuclear or Grenrecules. In 4-classification problem where cells are classified into their subtypes (monocytes, lymphocytes, neutrophils, basophils and eosinophils). In our experiment we were able to achieve validation accuracy of 100% in binary classification and 98.40 in multiple classifications.
Congestion Control early warning system using Deep Learning

A new approach is proposed to analyze the live crowd and to provide an alert at the time of congestion, over-crowding and sudden gathering of pedestrians in a particular region. This paper proposes a completely software-oriented approach using MATLAB where it uses object detection and object tracking using Faster R- CNN (Region Based Convolutional Neural Network) algorithm where inception model of Google is used as CNN model which is pre-trained. This proposed method gives significant result on proposed dataset and the crowd congestion using Faster R-CNN approach which gives an accuracy of 93.503% at the rate 28 frames per second and the crowd detected video frames are uploaded to cloud storage.
On the arithmetic of theorem proving

We present the arithmetization of strings in order to be deployed as an alternative model for automatic theorem proving.
Links between Division Property and Other Cube Attack Variants

A theoretically reliable key-recovery attack should evaluate not only the non-randomness for the correct key guess but also the randomness for the wrong ones as well. The former has always been the main focus but the absence of the latter can also cause self-contradicted results. In fact, the theoretic discussion of wrong key guesses is overlooked in quite some existing key-recovery attacks, especially the previous cube attack variants based on pure experiments. In this paper, we draw links between the division property and several variants of the cube attack. In addition to the zero-sum property, we further prove that the bias phenomenon, the non-randomness widely utilized in dynamic cube attacks and cube testers, can also be reflected by the division property. Based on such links, we are able to provide several results: Firstly, we give a dynamic cube key-recovery attack on full Grain-128. Compared with Dinur et al.’s original one, this attack is supported by a theoretical analysis of the bias based on a more elaborate assumption. Our attack can recover 3 key bits with a complexity 297.86 and evaluated success probability 99.83%. Thus, the overall complexity for recovering full 128 key bits is 2125. Secondly, now that the bias phenomenon can be efficiently and elaborately evaluated, we further derive new secure bounds for Grain-like primitives (namely Grain-128, Grain-128a, Grain-V1, Plantlet) against both the zero-sum and bias cube testers. Our secure bounds indicate that 256 initialization rounds are not able to guarantee Grain-128 to resist bias-based cube testers. This is an efficient tool for newly designed stream ciphers for determining the number of initialization rounds. Thirdly, we improve Wang et al.’s relaxed term enumeration technique proposed in CRYPTO 2018 and extend their results on Kreyvium and ACORN by 1 and 13 rounds (reaching 892 and 763 rounds) with complexities 2121.19 and 2125.54 respectively. To our knowledge, our results are the current best key-recovery attacks on these two primitives.
Dynamic Data Analysis and Decision Making on Twitter Data

It became a tedious task for the data analysts to make decisions on social networks. The existing approaches are not adequate to perform data pre-processing, analysis and decision making on the data dynamically. Therefore, this research aims to propose an approach to data analysis and decision making. The proposed approach emphasis on extracting tweets form twitter API (Application Program Interface), pre-processing the tweets by following seven pre-processing steps. The processed tweets are trained by NLTK (Natural Language Toolkit) and Text Blob are given to the sentiment analysis. Classification is done using the Naive Bayes algorithm to make a decision on processed tweets. The tweets which are related to “MeToo Movement” are considered primarily for decision making and satisfactory results are obtained. It is been observed that the proposed approach is accurate when compared to other approaches.
The interaction of financial openness and economic growth

Introduction.In the period 1980-2000, a number of factors contributed to the increase in the level of financial openness of world economies.The spread of globalization, the long ascending phase of the economic cycle, financial liberalization as one of the main recommendations of international development institutions and numerous scientific studies confirming that the foreign capital inflows stimulate catch-up effect, all served as an exogenous stimulus to financial deregulation.On the other hand, the objective needs of attracting FDI,
Design and Research on Amphibious Robot

Robots plays a major role in industrial automation and in wide range of applications such as manufacturing, surgery, or handling of hazardous materials. The aim of this research project is to design two wheeled amphibious robot which can travel on both land and water. In this project, a bot is designed with proper balancing with good structured buoyancy effects to make the bot to float without problem. Due to the mobility in water, the robot has been designed with insulation and water proof materials.
Object Detection and Identification

Object Detection systems have been growing in the last few years for various applications. Since the hardware can not detect the smallest objects. Many algorithms are used for object detection like Yolo, R-CNN, Fast R-CNN, Faster R-CNN, etc. object detection using YOLO is faster than other algorithms and the YOLO scans the whole image completely at one time. Object detection, which is based on Convolutional Neural Networks (CNNs) and it's based on classification and localization. An object is detected by extracting the features of an object like the color of the object, the texture of the object or shape, or some other features. Then based on these features, objects are classified into many classes and each class is assigned a label. When we subsequently provide an image to the model, it will output many objects it detects, the location of a bounding box that contains every object with their label and score indicates the confidence. Text-To-Speech (TTS) conversion is a computer-based systemthat requires for the label are converted text-to-speech. The main motive is that the smallest amount of objects can be detected object and labeling the object with voice for real-time object detection. The final model architecture proposed is more accurate and provides the fast result of object detection with voice as compared to previous researches
Diagonal unitary and orthogonal symmetries in quantum theory: II. Evolution operators

We study bipartite unitary operators which stay invariant under the local actions of diagonal unitary and orthogonal groups. We investigate structural properties of these operators, arguing that the diagonal symmetry makes them suitable for analytical study. As a first application, we construct large new families of dual unitary gates in arbitrary finite dimensions, which are important toy models for entanglement spreading in quantum circuits. We then analyze the non-local nature of these invariant operators, both in discrete (operator Schmidt rank) and continuous (entangling power) settings. Our scrutiny reveals that these operators can be used to simulate any bipartite unitary gate via stochastic local operations and classical communication. Furthermore, we establish a one-to-one connection between the set of local diagonal unitary invariant dual unitary operators with maximum entangling power and the set of complex Hadamard matrices. Finally, we discuss distinguishability of unitary operators in the setting of the stated diagonal symmetry.
A channel state information based virtual MAC spoofing detector

Physical layer security has attracted lots of attention with the expansion of wireless devices to the edge networks in recent years. Due to limited authentication mechanisms, MAC spoofing attack, also known as the identity attack, threatens wireless systems. In this paper, we study a new type of MAC spoofing attack, the virtual MAC spoofing attack, in a tight environment with strong spatial similarities, which can create multiple counterfeits entities powered by the virtualization technologies to interrupt regular services. We develop a system to effectively detect such virtual MAC spoofing attacks via the deep learning method as a countermeasure. A deep convolutional neural network is constructed to analyze signal level information extracted from Channel State Information (CSI) between the communication peers to provide additional authentication protection at the physical layer. A significant merit of the proposed detection system is that this system can distinguish two different devices even at the same location, which was not well addressed by the existing approaches. Our extensive experimental results demonstrate the effectiveness of the system with an average detection accuracy of 95%, even when devices are co-located.