Reviewerly/exHarmony huggingface dataset:
Analysis of Compound Stained Cervical Cell Images Using Multi-Spectral Imaging

Multi-spectral imaging technique plays an important role in real-world applications such as medicine and medical detections. This paper proposes a cervical cancer cell screening method to simultaneously adopt TBS classification and DNA quantitative analysis for a single cell smear. Through using compound staining on a smear, the cytoplasm is stained by Papanicolauo and the nucleus is stained by Feulgen. The main evaluation parameter is the DNA content of the nucleus, not the subjective description of cell morphology, which is more objective than the TBS classification method and reduces the chances of missing a diagnosis due to subjective factors. Each nucleus has its own DI value and color image of the whole cell, which is convenient for doctors as it allows them to review and confirm the morphology of cells with a nucleus DI of over 2.5. Mouse liver smears and cervical cases are utilized as the measuring specimens to evaluate the performance of the microscope multi-spectral imaging system; illustrative results demonstrate that the proposed system qualifies, with high accuracy and reliability, and further presents wide application prospects in the early diagnosis of cervical cancer.
An Optimal Path-planning Algorithm for Unmanned Rollers with Constraints on Roller Attitude

The unmanned roller (UR) is promising in improving the compaction quality and efficiency simultaneously. When transiting from on work site to another, a well-designed path-planning algorithm is essential for the efficient operation of rollers between two sites. Existing path planning algorithms barely consider the vehicle attitude at the destination and the kinematic constraints, limiting the efficiency in the initial phase of operation in a new work site. Therefore, in this paper, an improved RRT* algorithm is proposed. By combining Dubins curve with RRT* algorithm, the algorithm enforces the roller to maintain an optimal attitude (orientation) when approaching the entrance of the next work site, by manipulating the target path. In addition, a smooth and achievable path is generated, by setting-up the upper bound of the curvature based on kinematic and steering dynamic models then fitting the target path using the B-spline. The proposed algorithm is tested in both simulation and experiments, confirming its effectiveness.
ProBGP: Progressive Visual Analytics of Live BGP Updates

Abstract The global routing network is the backbone of the Internet. However, it is quite vulnerable to attacks that cause major disruptions or routing manipulations. Prior related works have visualized routing path changes with node link diagrams, but it requires strong domain expertise to understand if a routing change between autonomous systems is suspicious. Geographic visualization has an advantage over conventional node‐link diagrams by helping uncover such suspicious routes as the user can immediately see if a path is the shortest path to the target or an unreasonable detour. In this paper, we present ProBGP, a web‐based progressive approach to visually analyze BGP update routes. We created a novel progressive data processing algorithm for the geographic approximation of autonomous systems and combined it with a progressively updating visualization. While the newest log data is continuously loaded, our approach also allows querying the entire log recordings since 1999. We present the usefulness of our approach with a real use case of a major route leak from June 2019. We report on multiple interviews with domain experts throughout the development. Finally, we evaluated our algorithm quantitatively against a public peering database and qualitatively against AS network maps.
Face Spoof Detection: An Experimental Framework

Face recognition has recently become widespread in security applications. Although advancing technology has improved the performance of these systems, they are still prone to various attacks, including spoofing. The inherent feature extraction capability of machine learning techniques and deep neural networks has facilitated more accurate performance in spoofing detection. However, challenges still remain in the generalisation of these methods. One significant challenge is training dataset limitation in terms of size and variance. This paper investigates how different train/test ratios and variance in training data affect model performance with the NUAA dataset for spoofing detection. We show how using different splits of this dataset results in different models with different performances. We also open up new research directions by demonstrating how the problem of generalisation can be neatly demonstrated with an existing manageable dataset.
Defining the information flows for DLT of a transport company in the mining industry according to the criteria for sustainable development

The application of information technologies leads to the improvement of the companies’ production parameters in each sector according to the criteria for sustainable development. Naturally, in order to achieve efficiency, they must be tailored to the specifics of the industry, in this case the mining industry. The article proposes a methodology for the introduction of Distributed Ledger Technology (DLT) for the transport information flow at a mining company. Based on the chosen organizational structure, the participating actors and the data they share, the information channels are determined. According to the necessary rights of the participants to modify the transactions and the number of channels of the transport information flow, a consensus mechanism Practical Byzantine Fault Tolerant and the so-called smart contract have been chosen. Different DLT platforms are analyzed. Hyperledger Fabric was selected as an appropriate platform in order to ensure the continuity of the system, the asynchronous control of the various channels and the ability to include different actors.
Bringing Cloud-Native Storage to SAP IQ

In this paper, we describe our journey of transforming SAP IQ into a relational database management system (RDBMS) that utilizes cheap, elastically scalable object stores on the cloud. SAP IQ is a three-decade old, disk-based, columnar RDBMS that is optimized for complex online analytical processing (OLAP) workloads. Traditionally, SAP IQ has been designed to operate on shared storage devices with strong consistency guarantees (e.g., high-caliber storage area network devices). Therefore, deploying SAP IQ on the cloud, as is, would have meant utilizing storage solutions such as NetApp or AWS EFS that provide a POSIX compliant file interface and strong consistency guarantees, but at a much higher monetary cost. These costs can accumulate easily to diminish the economies of scale that one would expect on the cloud, which can be undesirable. Instead, we have enhanced the design of SAP IQ to operate on cloud object stores such as AWS S3 and Azure Blob Storage. Object stores rely on a weaker consistency model, and potentially have higher latency; however, because of these design trade-offs, they are able to offer (i) better pricing, (ii) enhanced durability, (iii) improved elasticity, and (iv) higher throughput. By enhancing SAP IQ to operate under these design trade-offs, we have unlocked many of the opportunities offered by object stores. More specifically, we have extended SAP IQ's buffer manager and transaction manager, and have introduced a new caching layer that utilizes instance storage on AWS EC2. Experiments using the TPC-H benchmark demonstrate that we can gain an order of magnitude reduction in data-at rest storage costs while improving query and load performance.
LAGraph: Linear Algebra, Network Analysis Libraries, and the Study of Graph Algorithms

Graph algorithms can be expressed in terms of linear algebra. GraphBLAS is a library of low-level building blocks for such algorithms that targets algorithm developers. LAGraph builds on top of the GraphBLAS to target users of graph algorithms with high-level algorithms common in network analysis. In this paper, we describe the first release of the LAGraph library, the design decisions behind the library, and performance using the GAP benchmark suite. LAGraph, however, is much more than a library. It is also a project to document and analyze the full range of algorithms enabled by the GraphBLAS. To that end, we have developed a compact and intuitive notation for describing these algorithms. In this paper, we present that notation with examples from the GAP benchmark suite.
Fast Detection of Heterogeneous Parallel Steganography for Streaming Voice

Heterogeneous parallel steganography (HPS) has become a new trend of current streaming media voice steganography, which hides secret information in the frames of streaming media with multiple kinds of orthogonal steganography. Because of the complexity and imperceptibility of HPS, detecting its existence is a challenge for previous steganalysis methods, especially in the case of short sliding window length and low embedding rate. In order to improve the situation, we design a fast and efficient detection method named the key feature extraction and fusion network (KFEF) based on attention mechanism. The proposed model is able to effectively extract the key characteristic of the exceptions due to steganography and fuse the extracted features for different steganographic algorithms used in HPS. Experimental results show that the proposed method significantly improves the classification accuracy in detecting both low embedding rate samples and short segment samples. In addition, the detection time consumption is shorter than other methods and meets real-time requirements. Finally, with the help of attention we can predict the approximate locations of secret information which may bring new ideas to further steganalysis.
GT-STORM

Decentralized nonconvex optimization has received increasing attention in recent years in machine learning due to its advantages in system robustness, data privacy, and implementation simplicity. However, three fundamental challenges in designing decentralized optimization algorithms are how to reduce their sample, communication, and memory complexities. In this paper, we propose a gradient-tracking-based stochastic recursive momentum (GT-STORM) algorithm for efficiently solving nonconvex optimization problems. We show that to reach an ϵ2-stationary solution, the total number of sample evaluations of our algorithm is Õ(m1/2ϵ-3) and the number of communication rounds is Õ(m1/2ϵ-3), which improve the O(ϵ-4) costs of sample evaluations and communications for the existing decentralized stochastic gradient algorithms. We conduct extensive experiments with a variety of learning models, including non-convex logistical regression and convolutional neural networks, to verify our theoretical findings. Collectively, our results contribute to the state of the art of theories and algorithms for decentralized network optimization.
Seek and Reflect: A Mobile Scavenger Hunt to Develop Community Engagement

Fostering a sense of connection to a place and building relations between residents are goals of community engagement. Engagement doesn’t have to be serious, rather it can be playful and casual. Periodic local festivals bring communities together and provide context for introducing information and communication technology (ICT) for community engagement. We report on a mobile scavenger hunt app released during a local festival, to understand how it facilitated community engagement. We interviewed 15 app users about their community experiences and how the app supported them to engage with various aspects of the festival and host community. We found that the app augmented community engagement through increased place attachment, an awareness of neighbors, seeking out new experiences, and providing opportunities for reflecting on historic events. We suggest that mobile ICTs for community engagement be entertaining and flexible, and leverage both online and offline activities to get people engaged in their community.
End-to-End Learned Image Compression with Augmented Normalizing Flows

This paper presents a new attempt at using augmented normalizing flows (ANF) for lossy image compression. ANF is a specific type of normalizing flow models that augment the input with an independent noise, allowing a smoother transformation from the augmented input space to the latent space. Inspired by the fact that ANF can offer greater expressivity by stacking multiple variational autoencoders (VAE), we generalize the popular VAE-based compression framework by the autoencoding transforms of ANF. When evaluated on Kodak dataset, our ANF-based model provides 3.4% higher BD-rate saving as compared with a VAE-based baseline that implements hyper-prior with mean prediction. Interestingly, it benefits even more from the incorporation of a post-processing network, showing 11.8% rate saving as compared to 6.0% with the baseline plus post-processing.
Nested Grassmanns for Dimensionality Reduction with Applications to Shape Analysis

Grassmann manifolds have been widely used to represent the geometry of feature spaces in a variety of problems in medical imaging and computer vision including but not limited to shape analysis, action recognition, subspace clustering and motion segmentation. For these problems, the features usually lie in a very high-dimensional Grassmann manifold and hence an appropriate dimensionality reduction technique is called for in order to curtail the computational burden. To this end, the Principal Geodesic Analysis (PGA), a nonlinear extension of the well known principal component analysis, is applicable as a general tool to many Riemannian manifolds. In this paper, we propose a novel framework for dimensionality reduction of data in Riemannian homogeneous spaces and then focus on the Grassman manifold which is an example of a homogeneous space. Our framework explicitly exploits the geometry of the homogeneous space yielding reduced dimensional nested sub-manifolds that need not be geodesic submanifolds and thus are more expressive. Specifically, we project points in a Grassmann manifold to an embedded lower dimensional Grassmann manifold. A salient feature of our method is that it leads to higher expressed variance compared to PGA which we demonstrate via synthetic and real data experiments.
ATTICA: A Dataset for Arabic Text-Based Traffic Panels Detection

Detection and recognition of traffic panels and their textual information are important applications of advanced driving assistance systems (ADAS).They can significantly contribute in enhancing road safety by optimizing the driving experience.However, these tasks might face two major challenges.First, the lack of suitable and good quality datasets.Second, the in-feasibility of global standardization of traffic panels in terms of shape, color and language of the written text.Present research is intensively directed toward Latin text-based panels, while other scripts such as Arabic remain quiet undervalued.In this paper, we address this issue by introducing ATTICA, a new open-source multi-task dataset, consisting of two main sub-datasets: ATTICA_Sign for traffic signs/panels detection and ATTICA_Text for Arabic text extraction/recognition.Our dataset gathers 1215 images with 3173 traffic panel boxes, 870 traffic sign boxes and 7293 Arabic text boxes.In order to examine the utility and advantages of our dataset, we adopt state-ofthe-art deep learning based approaches.The conducted experiments show promising results confirming the valuable addition of our dataset in this field of research.
A Taxonomy of Web Security Vulnerabilities

the rapid evolution in web applications and software development has not witnessed a similar pace in development opposite what has taken place in the security and cybersecurity arenas. Web applications security has become a significant issue since insecure applications and software undermine various areas including finance, health care, defense, and other mission critical infrastructures. Web application vulnerability results from misconfiguration, flaws in the design, implementation, operation, or management at the different levels of a web application (input side, output side, or both sides). This taxonomy paper studies web vulnerabilities, their impact on users' infrastructures, and their detection tools (static, dynamic, or hybrid) and mitigation mechanisms at different software architecture levels.
Flaw and amendment of a two-party authenticated key agreement protocol for post-quantum environments

Recent advances in quantum computers challenge the security of key agreement protocol that are designed with the intractability assumptions of discrete logarithmic problem and integer factorization problem. Hence, motivated to develop the key agreement protocol that is secure in post-quantum era, recently Islam proposed a provably secure two-party authenticated key agreement (2PAKA) protocol. The protocol is based on the intractability assumption of the famous Ring Learning With Error (RLWE) problem. In this work, we show that Islam’s two-party key agreement is vulnerable to the modified version of the signal leakage attack (SLA) which is termed as improved-signal leakage attack (i-SLA) in this article. Using i-SLA, the attacker can successfully recover the long term private key of the honest user by instantiating utmost q number of key exchange sessions with the honest user using q number of malformed public keys. To overcome the attack, we provide a countermeasure without changing the original design of the protocol.
Ensemble of Pruned Networks for Reliable Classifiers

Convolutional Neural Networks (CNNs) have been widely used for image recognition or natural language processing. When CNNs are used in safety-critical applications, their reliability becomes a priority and in particular their tolerance to soft errors. Unfortunately, traditional fault tolerant techniques based on modular redundancy introduce a large overhead, which is not acceptable in many resource-limited embedded systems given the complexity of CNNs. To reduce the cost of protecting CNNs, the use of an ensemble of smaller CNNs has been recently proposed. The idea is that the ensemble cannot only improve the classification accuracy but also protect against errors. To reduce the cost, pruning the CNNs is widely used. In this paper, the combination of both ideas is explored, namely ensembles of CNNs that are built using different pruned versions of the same CNN are analyzed. To evaluate the effectiveness of the idea, VGG16 is used as a case study. Small networks with different sizes are generated by pruning the original VGG16, and a combiner was designed with the ability to detect a faulty base learner. The reliability of different ensembles of the pruned networks is evaluated in PyTorch on Cifar-10. The reliability evaluation results reveal that the ensemble of pruned networks could achieve higher reliability than the Triple Modular Redundancy (TMR) protected VGG16 networks with an overhead lower than double modular redundancy. Complementarity analysis between different pruned VGG nets has been performed to explain the reliability improvement.
Revisiting Data Prefetching for Database Systems with Machine Learning Techniques

Among diverse parts in database systems, database prefetching, which aims at predicting future page access patterns and fetching pages to be accessed ahead of time to mitigate blocked I/O operations, plays a crucial role in the overall performance tuning. Existing approaches just use simple heuristic-based methods and suffer from the low hit rate and extra I/O overhead. Recently, with the emerging success of machine learning in different applications, attempts using learning-based models to augment or improve components for database systems have shed some light on this tough problem. Impressed by the enormous potential of machine learning in data management, we present an end-to-end deep learning-based framework to predict page access patterns. We model the prediction of page access as a classification problem and evaluate several variants of neural networks on the accuracy of prediction. On the basis of it, we propose a new Multi-Model framework to construct an accurate model for prefetching. On a suite of real-world database benchmarks, our neural network based prefetching model consistently outperforms existing widely used solutions in real-world database systems.
Application of Artificial Intelligence System in Libraries through Data Mining and Content Filtering Methods

Abstract With the rapid advancement of artificial intelligence theory, this paper adopts a multi-intelligent agent collaboration method and derives through data mining. In combination with content filtering methods and intelligent agent learning optimization, it improves the high performance by using a personalized information service system architecture. The performance of the library system of vocational colleges. According to the difference of readers’ interest, it matches the results of traditional document retrieval, effectively filtering out readers’ demand information, reducing the time for readers to search for required information, improving reader retrieval efficiency, realizing information push of similar users, and realizing “information looking for people”.
Work-in-Progress–—Is Virtual Reality Simulation Ineffective for Skill Acquisition Training?

The purpose of this work-in-progress paper is to examine the effect of levels of tactility with the presence and absence of a performance guide as formative feedback in an immersive virtual reality-based simulation (VRS). The experimental group will have four levels of tactile implementation, and the control group will have a non-virtual power tool. The experimental group will receive more specific feedback based on the data measured through virtual reality simulations. We will measure perceived task load, virtual presence, usability, and performance time and accuracy during training for all groups.
Neural Cryptosystem for Textual Message with Plasticity and Secret Dimensions

Current asymmetric cryptography methods are based on trapdoor functions like modulo computing with large primes. The robustness of security depends upon the guessing time of keys used to compute the remainder. To enhance the robustness, a non-linear function in a high dimensional space such as the neural network can be used to encrypt and decrypt a message. Differing from weight synchronization between the sender and receiver and weight representation of the encrypted message, this paper proposes another neural cryptography approach based on secret dimensions and a plasticity neural network with security analysis. The analysis of time and space complexities as well as robustness for encryption and decryption are also discussed.
Enhancing Education System with a Q&amp;A Chatbot: A Case Based on Open edX Platform

The emergence of MOOCs (Massive Open Online Course) increased the need for personalized student support. Question and Answering solution can reduce the dropout rate and improve learning. In this paper, we explore the potential of using chatbots to enhance the learning experience for students and how to take advantage of Deep Learning. A review of chatbots models suggests that task-oriented ones are more convenient in the educational context. To address this issue, we propose an integrated chatbot in Open edX platform. We study architecture and proposed technical options. The model is developed to perform a question-answering task. The trainings were carried out using TensorFlow and NTLK. The paper also presents the various steps in the conception and implementation. The chatbot performs well and contributes to the improvement of the e-learning systems.