Reviewerly/exHarmony huggingface dataset:
Scalable Suffix Sorting on a Multicore Machine

A number of methods have been proposed for suffix sorting on internal memory of RAM and external memory of hard disks. The current best results for suffix sorting on internal or external memory are achieved by several algorithms using the induced sorting (IS) method in various ways. While these algorithms are efficient, the internal ones are much different from those external in terms of the algorithm designs. A scalable IS method that can be applied for suffix sorting on both internal and external memory is highly desired. This article proposes a blockwise IS method to facilitate pipelined access on internal memory and sequential I/Os on external memory. The detailed algorithm of using this method for a 4-stage pipeline with multiple threads is described, where multiple threads are applied to parallelize not only the pipelined stages of consecutive blocks but also the tasks within each stage wherever possible. This algorithm is evaluated by our experiments on a set of realistic and artificial datasets to achieve better overall time and space performance than the existing best results from pSACAK, pDSS and pKS. Beside sorting suffixes on internal memory in linear time, the proposed method can be ported to external memory for sorting massive suffixes in linear I/O complexity.
Modeling Memory Faults in Signature and Authenticated Encryption Schemes

Memory fault attacks, inducing errors in computations, have been an ever-evolving threat to cryptographic schemes since their discovery for cryptography by Boneh et al. (Eurocrypt 1997). Initially requiring physical tampering with hardware, the software-based rowhammer attack put forward by Kim et al. (ISCA 2014) enabled fault attacks also through malicious software running on the same host machine. This led to concerning novel attack vectors, for example on deterministic signature schemes, whose approach to avoid dependency on (good) randomness renders them vulnerable to fault attacks. This has been demonstrated in realistic adversarial settings in a series of recent works. However, a unified formalism of different memory fault attacks, enabling also to argue the security of countermeasures, is missing yet. In this work, we suggest a generic extension for existing security models that enables a game-based treatment of cryptographic fault resilience. Our modeling specifies exemplary memory fault attack types of different strength, ranging from random bit-flip faults to differential (rowhammer-style) faults to full adversarial control on indicated memory variables. We apply our model first to deterministic signatures to revisit known fault attacks as well as to establish provable guarantees of fault resilience for proposed fault-attack countermeasures. In a second application to nonce-misuse resistant authenticated encryption, we provide the first fault-attack treatment of the SIV mode of operation and give a provably secure fault-resilient variant.
Aqueous alteration mapping in Rishabdev ultramafic complex using imaging spectroscopy

Hyperspectral remote sensing/imaging spectroscopy has enabled precise identification and mapping of hydrothermal alteration mineral assemblages based on diagnostic absorption features of minerals. In the present study, we use Airborne Visible InfraRed Imaging Spectrometer-Next Generation (AVIRIS-NG) datasets acquired over Rishabdev ultramafic suite to derive surficial mineral map using least square based spectral shape matching in wavelength range of diagnostic absorption features of minerals. Resulting mineral map revealed presence of hydrothermally altered serpentine group of minerals and associated alteration products (talc and dolomite) along with clays and phyllosilicates. Mineral maps are validated using field spectral measurements and published geological map. Involvement of low temperature (<350 °C) hydrothermal fluid in serpentinization of ultramafic rocks in the region is inferred from analysis of deepest absorption features of muscovites at 2.20 μm, spectral abundance of lizardite and absence of prenhite-pumpyllite facies mineral assemblages. Talc was found to be the most common alteration product of serpentines followed by dolomites. Intense alteration of serpentines to talc along the fracture zone is attributed to the circulation of carbon dioxide rich hydrothermal fluids along these conduits. Kaolinite and halloysite are primarily associated with granites and are the result of hydrothermal alteration of plagioclase feldspar in granites while muscovite and illites are generally associated with phyllites and quartzites . The study demonstrates the potential of imaging spectroscopy for mapping hydrothermal alteration mineral assemblages in ultramafic complex.
Stereo System based Distance Calculation of an Object in Image

One of the great interest in science and industry is the ability for a machine to capture 3D information from the real world which can duplicate the ability of human vision. Many research has been conducted in stereo vision to duplicate the real world environment for a machine to adapt the ability of human vision. The distance of an object from an observer taken in an image is calculated using a stereo camera. Camera calibration is performed to obtain the stereo parameters of both the cameras, the images from both the camera are rectified so that they are in the same plane. The disparity between the left and the right images is calculated, using the disparity map and the stereo parameter 3-Dimensional(3D) scene is constructed. The distance of the object is calculated by determining the centroid of the bounding box and calculating the Euclidean distance between the centroid of the bounding box and the camera. Results show the output of the object being placed at the distance between 10 to 3 meters. The average error in the distance calculation is 2.08.
Text Encryption using Extended Bit Circular Shift Cipher

Various cryptographic algorithms have been evolved to improve data security. One of development in cryptographic techniques, the especially symmetric key type is Shift Cipher which is very simple method and only changes the position of the plaintext character. This research proposed a super encryption method created from the development of a shift method with double shift ciphers that can carry out two operations at once, namely shift operations that function to scramble positions and shift operations that can change values, such that encryption substitution occurs. By using this method, the results of encryption are far better than the shift cipher method, even better than several methods developed in research that in recent years. To test the quality of encryption several tests were carried out, namely the avalanche effect (AE), bit error rate (BER), time taken and character error rate (CER). The test results showed that the proposed method produces good performance.
A convex single image dehazing model via sparse dark channel prior

In this paper, we present a convex model for single image dehazing via a sparse dark channel prior. Our work is based on an observation that the number of bright pixels is very small in the dark channel of a haze-free image, but significantly increases in that of a hazy image due to the existence of bright atmosphere light. Since the dehazing problem is inherently ambiguous, we first reformulate the degradation model of hazy images into an equivalent form where the transmission and the image variable are decoupled. With the above formulation and observation, we propose the convex model to recover the haze-free image whose dark channel is assumed to be sparse. The proposed objective function consists of four terms: a data-fitting term, an l1 regularization term for the dark channel of the haze-free image, and two total variation regularization terms for both the haze-free image and the transmission map. We develop an efficient alternating direction method of multipliers (ADMM) to tackle the proposed convex model. Extensive experiments on real hazy images illustrate that our method outperforms the state-of-the-art methods.
Environmental monitoring data storage system based on consortium blockchains

In traditional environmental monitoring system, the monitoring data adopts centralized storage management, and there is a possibility that the server is maliciously damaged or data is tampered with, and security and authenticity of the data cannot be guaranteed.In order to solve the problem that traditional environmental monitoring system cannot guarantee security and authenticity of the monitoring data, using characteristics of decoupling, trust and non-tampering of blockchain technology, a data storage system based on the consortium blockchain is proposed to achieve secure storage of monitoring data. And design interactive web pages, providing a visual web interface for operators to access consortium blockchain system, simplifying the operation process.
Ontology-Based Knowledge Modelling of Power Grid Dispatching Regulations

In the integrated mode of power grid dispatching and control centralized monitoring, the dispatchers need to prioritize events, determine fault types and propose solutions timely and accurately, which puts forward the high requirements on the operational ability and the response level of the dispatchers. To relieve the working pressure of the dispatchers, a grid dispatching ontology knowledge base based on the ontology theory and the dispatching rules is built in this paper, which realizes information search and query with conciseness, preciseness, and rapidity. The instantiation of the power grid dispatching knowledge ontology can provide decision-making assistance, information query and verification, emergency handling and other functions.
Challenges of blockchain technologies in the future

Nowadays, blockchain technologies based on peerto-peer networks [1] are generating a new multi-billion dollar economy that presents new challenges for humanity. This paper aims to examine blockchain technology, comment on its problems, and propose a new algorithm that will help solve some of the problems associated with the regulation of this economy and the use of cryptocurrencies as a means of payment.
On the Logic of Belief and Propositional Quantification

Abstract We consider extending the modal logic KD45, commonly taken as the baseline system for belief, with propositional quantifiers that can be used to formalize natural language sentences such as “everything I believe is true” or “there is something that I neither believe nor disbelieve.” Our main results are axiomatizations of the logics with propositional quantifiers of natural classes of complete Boolean algebras with an operator (BAOs) validating KD45. Among them is the class of complete, atomic, and completely multiplicative BAOs validating KD45. Hence, by duality, we also cover the usual method of adding propositional quantifiers to normal modal logics by considering their classes of Kripke frames. In addition, we obtain decidability for all the concrete logics we discuss.
AGV multi-objective path planning method based on improved cuckoo algorithm

In order to save time and reduce the energy loss of the AGV when transporting the objects, the discrete cuckoo algorithm is used to solve the path planning problem of multi targets points . Firstly, the cuckoo algorithm is applied to the path planning of the AGV single target point based on the grip map, and the optimal path of the AGV to each object and the optimal path of each object to the specified position are obtained. Secondly, discretizing the cuckoo algorithm, and in the process of discretization, multiple operators such as reverse sequence, insertion, substitution and double bridge are combined into different neighborhood structures, which will used to find the optimal solution through Levi flight mechanism. Finally, the discrete cuckoo algorithm is used to generate the global optimal path for AGV to transport multiple objects. Simulation results show that the proposed algorithm successfully solves the multi-target point path planning problem of AGV. Compared with other algorithms, the proposed algorithm can avoid partial optimality and achieve fast convergence speed.
A Novel Method to Distinguish Photorealistic Computer Generated Images from Photographic Images

The computer graphics rendering software can be used to create computer generated images so photorealistic that it becomes almost impossible to discriminate computer generated images from photographic images (generated by digital camera) by human visual system. The use of photorealistic computer generated images has brought the revolutionary changes in the film industry and video games. On the other hand, peoples with malicious intentions are using the photorealistic computer generated images in various fields such as fake journalism, forgery in academic research etc. Therefore it becomes necessary to distinguish photorealistic computer generated images from the real photographic images. In this paper a method has been proposed to distinguish photorealistic computer generated images from the real photographic images based on the Tetrolet transform and Neuro-fuzzy classifier. Based on the experimental findings it is observed that the proposed method is more effective in terms of the classification accuracy than the state-of-the-art techniques.
Implementation of the Park Schema on User Authentication Services Using Password-Based Web Codeigniter Library to Overcome Man in the Middle Attack

Park Scheme is an OTP based on a hash chain that can be used to overcome password weaknesses by changing the password value in the database every time a user logs in. Changing password values makes this scheme resistance to MITM attacks because passwords obtained from an authentication process cannot be used for next login sessions. This method can provide better security for authentication services. This research will implement the Park scheme with the SHA-256 hash algorithm, AES-128, and the Codeigniter Web framework. The research method uses Design Research Methodology (DRM), while the software development method uses Secure Software Development Life Cycle (SSDLC). Authentication modules that have been created have been declared to have passed unit testing, system testing, integration testing, and security testing. Based on the results of the validation, it was concluded that the resulting library module succeeded in implementing user authentication services using web-based Codeigniter passwords in the form of libraries and it also proves that the Park library produced was resistant to MITM attacks.
CSAT: A User-interactive Cyber Security Architecture Tool based on NIST-compliance Security Controls for Risk Management

Security risk management is a vital part of any system development, including e-commerce and other information systems that need security. Notably, NIST has developed cyber security and privacy controls, such as SP-800-53, to facilitate risk management for federal information systems. By integrating such NIST-compliance security controls, our CSAT is innovative to offer a user-interactive software tool for effectively facilitating the robust and secure architecture development of information systems in the way of enhancing overall risk management. It specifically promotes the enhancement of risk management by composing reports/graphs in different NIST defined do-mains/controls/capabilities specification effectively. This helps to reduce development cost, time, and manpower by using the tool to quickly define information system security standards based on NIST's security and privacy guidelines. The development of such a tool is of importance for risk management, e.g., security evaluation, risk assessment, controls implementation, system security planning). It can be used to optimize the risk management in the information system architecture in the lowest cost, while increasing the security robustness by systemically providing NIST guideline and risk management in the information system development level.
Implementation of a specified face recognition system based on video

Face recognition has been an important research direction in computer vision for a long time. The algorithms proposed in related fields are endless, and the accuracy that can be achieved is higher than ever. However, it is still quite difficult to apply face recognition technology. This paper combines the algorithms of face detection and face recognition to build a video-based face recognition system for efficient and accurate recognition of the faces of specified characters in the video. The system extracts the image of the person to be detected through MTCNN, and then uses Facenet to extract its features. Finally, the images are classified by SVM, thereby detecting the person to be detected appearing in the video. Experiments show that our method can still achieve good recognition results when the target is lacking data and the picture quality of the video is unstable. The accuracy of our method in self-built dataset in this paper can reach 94.9%.
Experience Selection in Multi-agent Deep Reinforcement Learning

Experience replay is a crucial technology for off-policy deep reinforcement learning, which uses a portion of memory as the replay buffer to store previous experience samples for the later policy update. Since each experience sample can be used multiple times, experience replay drastically improves the utilization rate of experience samples. However, how to effectively combine experience replay with multi-agent reinforcement learning is still an open challenge. In multi-agent reinforcement learning, the decision of the agent needs to consider the dynamic information of the environment as well as the behavior of other agents. If the policies of other agents change, updating the current policy with previous experience samples may deteriorate the agents' subsequent decisions. Some methods use a small-capacity replay buffer to store recent experience samples. Although this avoids the problem that the experience sample in the replay buffer is not compatible with the current policy, it will reduce the diversity of experience samples in the replay buffer, that resulting in agents unable to learn the optimal strategy. This paper eases this conflict by enhancing the experience selection mechanism: 1) we use the reservoir retention algorithm to increase the diversity of experience samples in the replay buffer; 2) we use prioritized experience replay to alleviate the problem that the experience sample in the replay buffer is not compatible with the current policy. The experimental results on the covert communication problem confirm the effectiveness of our proposed method.
Analysis of Critical Success Factors from ERP System Implementation in Pharmaceutical Fields by Information System Success Model

The implementation of ERP system in this era is important because it accelerates the achievement of company goals. The company in this study is engaged in pharmacy using the SAP system, but the implementation of the SAP system has not run optimally in the company, therefore it requires an analysis of the success factors of ERP in order to minimize the negative impact and failure of ERP implementation. The analysis critical success factors of the ERP system implementation in the pharmaceutical company is based on the success model of information systems by DeLone and Mclean. This type of research is explanatory research using a quantitative approach, the quantitative data obtained by using a survey in the form of a questionnaire. The results of the study indicate that the critical success factors of ERP implementation that has a significant effect is the use of net benefits, user satisfaction of net benefits, and system quality of use.
Modified U-Net Architecture for Ischemic Stroke Lesion Segmentation and Detection

In this paper, to improve the accuracy of detection and segmentation, we modify U-Net architecture to address ischemic stroke segmentation and detection, from ISLES 2018 dataset. In this dataset, CT images (in five modalities) and corresponding ground truth created by combining manual annotations are provided. We use shortcut connections in the architecture, which performs as a residual block. In the meantime, to reduce the overfitting caused by the scarcity of training data, we use elementwise-sum and concatenation in the network. We also use the dice coefficient and the Jaccard index to assess our model. Our architecture can be applied to ischemic segmentation and detection of CT images easily by choosing suitable hyperparameters. Experiment results show that our model can segment ischemic stroke accurately, with the dice coefficient between the segmentation given by our network and ground truth is about 0.77 while the dice coefficient of U-Net is about 0.74.
Proactive chat in research databases: Inviting new and different questions

This study analyzes over 4500 chat interactions to understand how proactivity and research context affect use of proactive chat. Quantitative and qualitative analyses show patrons ask questions that are both more complex and more likely to relate to a research need when proactively approached in research databases. The availability of proactive chat in databases results in a greater frequency and greater proportion of research related questions. Results suggest that proactive chat in databases represents an important area for expanding library reference services.
A Pointer Meter Detection Method based on Optimal SSD network

Pointer meter detection is very useful for intelligent reading of pointer meter. This paper presents an optimal SSD network for pointer meter detection. The contributions includes: 1) the LBP image is introduced into the input layer of SSD network to enhance the adaptability of features to the environment; 2) the basic network of SSD is modified to extract multi-convolution fusion features through increasing a Fusing layer, and replacing the maxpooling method with S3Pool method, to enhance feature distinguishing ability for pointer meter detection. Experiments show that the new method has high TPR (True Positive Rate) and low FPR (False Positive Rate) for pointer meter detection.
A Fast UAV Aerial Image Mosaic Method Based on Improved KAZE

This paper presents a novel image mosaic method for the unmanned aerial vehicle (UAV) aerial image based on the improved KAZE algorithm. For practical implementation and cost efficiency, the matching speed of KAZE algorithm is not desirable and random sample consensus (RANSAC) algorithm takes plenty of time to screen the correct feature pairs. In addition, the UAV aerial image is susceptible to environmental and other factors, such as scale, noise and brightness change. To overcome these problems, the novel image mosaic method is proposed. In particular, the non-linear scale space is constructed using the fast-explicit diffusion (FED) algorithm Then, the feature points are described by the improved fast retina key-point (FREAK) feature descriptor. Further, we adopt the Hamming algorithm for a rough match of these feature points, and select the improved progressive sample consensus (PROSAC) algorithm to exact correct matches. Finally, the image is stitched by the weighted average algorithm Results from numerical experiments show that compared with the scale invariant feature transform (SIFT), speed-up robust features (SURF), oriented FAST and rotated BRIEF (ORB) it as well as the KAZE algorithm, the proposed algorithm has better performance on feature extraction speed, feature matching speed, as well as matching accuracy.