Reviewerly/exHarmony huggingface dataset:
On the Approximation Ratio of LZ-End to LZ77

A family of Lempel-Ziv factorizations is a well-studied string structure. The LZ-End factorization is a member of the family that achieved faster extraction of any substrings (Kreft & Navarro, TCS 2013). One of the interests for LZ-End factorizations is the possible difference between the size of LZ-End and LZ77 factorizations. They also showed families of strings where the approximation ratio of the number of LZ-End phrases to the number of LZ77 phrases asymptotically approaches 2. However, the alphabet size of these strings is unbounded. In this paper, we analyze the LZ-End factorization of the period-doubling sequence. We also show that the approximation ratio for the period-doubling sequence asymptotically approaches 2 for the binary alphabet.
Robust Chromatic Adaptation Based Color Correction Technology for Underwater Images

Recovering correct or at least realistic colors of underwater scenes is a challenging issue for image processing due to the unknown imaging conditions including the optical water type, scene location, illumination, and camera settings. With the assumption that the illumination of the scene is uniform, a chromatic adaptation-based color correction technology is proposed in this paper to remove the color cast using a single underwater image without any other information. First, the underwater RGB image is first linearized to make its pixel values proportional to the light intensities arrived at the pixels. Second, the illumination is estimated in a uniform chromatic space based on the white-patch hypothesis. Third, the chromatic adaptation transform is implemented in the device-independent XYZ color space. Qualitative and quantitative evaluations both show that the proposed method outperforms the other test methods in terms of color restoration, especially for the images with severe color cast. The proposed method is simple yet effective and robust, which is helpful in obtaining the in-air images of underwater scenes.
Overview of GFO 2.0 Functions: An ontology module for representing teleological knowledge

Teleological knowledge and functional representation are present in numerous scientific and engineering disciplines and are key aspects addressed by a broad spectrum of design and modeling frameworks. The current paper summarizes the results obtained in the area of functional modeling achieved under the umbrella of the second release of the General Formal Ontology (GFO 2.0), which is a modular top-level ontological framework actively developed by the Onto-Med Research Group. This paper presents and discusses the Function Module of GFO 2.0, which serves three objectives. (1) It supports the representation of teleological components, let it be a function or a goal-oriented process, by a blueprint of structured specifications. (2) It provides a classification of relationships enabling the construction of functional decomposition models. (3) The module enables the description of the elements of a domain in functional terms by means of a family of function ascription constructs, which handle the modeling of not only function-to-object assignments, but also of malfunctions.
Optimizing Flow Completion Time via Adaptive Buffer Management in Data Center Networks

The traffic of modern data centers exhibits long-tail distribution, in which massive delay-sensitive short flows and a small number of bandwidth-hungry long flows co-exist. These two types of flows could share same bottleneck links in the data center networks but request different or even opposite network requirements. Existing solutions try to realize a trade-off between the requirements of different flows by either prioritizing short flows or limiting the buffer used by long flows at switches or end-hosts. However, they do not consider the dynamic traffic change and suffer from performance degradation, resulted from severe queueing delay and massive packet drops for short flows under current First-In-First- Out (FIFO) queueing mechanism. In this paper, we propose a novel buffer management scheme at switches, called Cut-in Queue (CQ), to achieve both low latency for short flows and high throughput for long flows. Based on network status in real time, CQ prioritizes short flows by dynamically cutting the short flows' packets into the head of long flows or evicting some enqueued long flows' packets and enables high throughput for long flows in most of the cases. Evaluation of both DPDK testbed and NS2 simulations show that CQ outperforms state-of-the-art buffer management schemes by reducing flow completion time by up to 73%.
Access Control beyond Authentication

Nowadays, the Zero Trust model has become one of the standard security models. This paradigm stipulates as mandatory the protection of each endpoint, looking for providing security to all the network. To meet this end, it is necessary to guarantee the integrity of the access control systems. One possibility for bringing security to the different endpoints is continuous authentication, as an access control system. Continuous authentication is the set of technologies capable of determining if a user’s identity remains in time; whether he is the legitimate user (i.e., the only one who should know the secret credentials) or the identity has been impersonated by someone else after the authentication’s process was completed. Continuous authentication does not require the active participation of the user. Aiming to identify the different technologies involved in continuous authentication’s implementations, evaluation methods, and its use cases, this paper presents a systematic review that synthesizes the state of the art. This review is conducted to get a picture about which data sources could allow continuous authentication, in which systems it has been successfully implemented, and which are the most adequate ways to process the data. This review also identifies the defining dimensions of continuous authentication systems.
Emoji as method

This chapter seeks to understand how emotional responses to changing historic places can be captured using emoji-based methods. These methods bring both emoji and visual methods into conversation to help to understand why historic places matter emotionally. The chapter first outlines the rise of emoji in popular culture before bringing in emerging work on emoji as method across academic disciplines. The chapter then outlines the approach taken by describing four emoji-based methods and discussing how this approach helps us to develop a better understanding of the reasons why people respond emotionally to changing historic places. The chapter concludes with some reflections on the future development of emoji-based research in the context of heritage theory and practice.
A Novel Hybrid Approach to Improve Neural Machine Translation Decoding using Phrase-Based Statistical Machine Translation

Phrase-based models are among the best performing statistical machine translation (SMT) systems. These systems make translations phrase-by-phrase at a time. The decoding process is done locally in these systems. In addition, neural machine translation (NMT) systems have become very popular for the past four or five years with essential features such as more fluent translations. However, sometimes NMT systems give up accuracy for fluent translations due to the nature of the decoding technique they use. In this study, we aim to develop a hybrid system by guiding NMT decoding using the output sentences of the phrase-based SMT systems. According to the two-way translation experiments, German-to-English and English-to-German, and the results obtained in terms of two popular machine translation evaluation metrics: BLEU and METEOR, our method improves the quality of NMT system translations.
Bentonite clay minerals mapping using ASTER and field mineralogical data: A case study from the eastern Rif belt, Morocco

Remote sensing and mineralogical data have been integrated to map the spatial distribution of bentonite clay minerals in the Zeghanghane area, located in the eastern Rif belt of Morocco. Numerous bentonite deposits of substantial economic potential are recognized in this region and their acceptability is still restricted. These clay minerals are directly derived from different weathering types of Neogene volcanic outcrops. For this purpose, different remote sensing processing techniques have been applied to the Advanced Spaceborne Thermal Emission and Reflection Radiometer satellite data, including Relative absorption band-depth, Minimum noise fraction, Pixel purity index, and ND visualization. The obtained results allowed identifying three clay minerals, namely kaolinite/smectite, illite, and montmorillonite which typically characterize the bentonite clay minerals. They are mainly concentrated in the Neogene volcanic massifs such as Gourougou, Tidiennit and Amjar. Field work, petrographic and X-ray diffraction analyses permitted the verification and validation of the obtained results. A new bentonite alteration zones have been discovered and the majority of pre-existing ore deposits have been confirmed. The established bentonite map will serve as a guide for future mining exploration in the Zeghanghane area.
Using Digital Scholarship Methods and Tools to Enhance and Transform Learning during COVID-19 at a Liberal Arts College

This article describes how the libraries at Lafayette College used digital scholarship methodologies to enhance student engagement and learning during the transition to remote learning due to the COVID-19 crisis. The article argues that digital scholarship tools and methods enhanced active learning during the shift to all-remote instruction by allowing students to engage directly with digital tools for collecting, analysing, organising and presenting information from wherever they were. We discuss how active learning using these tools enabled students to participate more fully in their own learning process and construct their own forms of knowledge and understanding; and provided an effective way to keep students engaged at a time when the unexpected transition to online instruction created a sense of dislocation for many. For institutions returning to in-person instruction these methods can provide enhanced engagement in the traditional classroom setting, and offer increased flexibility for the delivery of future programs.
New complexity estimation on the Rainbow-Band-Separation attack

Multivariate public key cryptography is a candidate for post-quantum cryptography, and it allows generating particularly short signatures and fast verification. The Rainbow signature scheme proposed by Ding and Schmidt is such a multivariate cryptosystem, and it is considered secure against all known attacks. The Rainbow-Band-Separation attack recovers a secret key of Rainbow by solving certain systems of quadratic equations, and its complexity is estimated by the well-known theoretical value called the degree of regularity. However, the degree of regularity is generally larger than the solving degree in experiments, and an accurate estimation cannot be obtained. In this article, we propose a new theoretical value for the complexity of the Rainbow-Band-Separation attack using a Gröbner basis algorithm, which provides a more precise estimation compared to that using the degree of regularity. This theoretical value is deduced by the two-variable power series∏i=1m(1−t1di1t2di2)(1−t1)n1(1−t2)n2. Since the two-variable power series coincides with the one-variable power series at t1=t2 deriving the degree of regularity, the theoretical value is less than or equal to the degree of regularity under a certain condition. Moreover, we show a relation between the Rainbow-Band-Separation attack using the hybrid approach and the HighRank attack. By considering this relation and our theoretical value, we obtain a new complexity estimation for the Rainbow-Band-Separation attack. Furthermore, applying our theoretical value to the complexity formula used in the NIST PQC 2nd round, we show that a slight modification of the proposed Rainbow parameter sets is required. Consequently, we provide a new theoretical value for generally estimating the solving degree of a bi-graded polynomial system, which can influence the parameter selection of Rainbow in the NIST PQC standardization project.
MMF: Multi-task Multi-structure Fusion for Hierarchical Image Classification

Hierarchical classification is significant for complex tasks by providing multi-granular predictions and encouraging better mistakes. As the label structure decides its performance, many existing approaches attempt to construct an excellent label structure for promoting the classification results. In this paper, we consider that different label structures provide a variety of prior knowledge for category recognition, thus fusing them is helpful to achieve better hierarchical classification results. Furthermore, we propose a multi-task multi-structure fusion model to integrate different label structures. It contains two kinds of branches: one is the traditional classification branch to classify the common subclasses, the other is responsible for identifying the heterogeneous superclasses defined by different label structures. Besides the effect of multiple label structures, we also explore the architecture of the deep model for better hierachical classification and adjust the hierarchical evaluation metrics for multiple label structures. Experimental results on CIFAR100 and Car196 show that our method obtains significantly better results than using a flat classifier or a hierarchical classifier with any single label structure.
SpBiSeq: An Early-Stage Fingerprint Generation Method with High Robustness for Encrypted Mobile Application Traffic

Early-stage encrypted traffic classification refers to the identification of the application to which the flow belongs within a limited number of transmission packets or bytes after the network flow is connected and before the end of the data transfer cycle. Accurate identification is an important foundation for quality of service (QoS), network management, policy-based routing, and traffic accounting. With the widespread use of common encryption protocols such as Transport Layer Security Protocol (TLS) and Quick UDP Internet Connection (QUIC), traffic is showing a trend of being fully encrypted, in addition, the large-scale traffic in the mobile core network, traffic asymmetry, packet loss and disorder caused by complex network topology present enormous challenges to the early-stage classification of mobile application traffic. In this paper, we propose a highly robust early-stage classification fingerprint generation method for encrypted mobile traffic named SpBiSeq (Simple Bi-Directional Sequence), which has better classification performance, better anti packet loss ability than previous studies. Furthermore, SpBiSeq has low time and space complexity, which makes it well adapted to high-speed network environments.
Application of Artificial Immune Recognition System for Monitoring the Brake System Using Vibration-Based Statistical Learning

Infotainment system is one of the standard elements used in all ranges of motor vehicles. These elements act as driver assistance systems that offer numerous advantages to the drivers to ensure safe driving. The infotainment systems indicate the monitoring information based on which the preventive measures can be initiated. In this study, we made an attempt to display the faults that occur in a brake system using vibration analysis through an artificial intelligence technique called artificial immune recognition system (AIRS). The vibration signals captured under various brake faults were processed and the statistical information was extracted. Three versions of the AIRS were used for the classification. Compared to AIRS 1 and AIRS 2 parallel, the AIRS 2 version produced the maximum accuracy of 97.82%. Based on the outcome, an electronics-based diagnostic module is proposed for categorizing and displaying the nature of brake faults in the infotainment system.
Written Conversation Technique and its Necessity in Improving Speaking and Writing Proficiencies for EFL Students

This study aims to investigate the effect of using new strategy (written conversation) in teaching speaking and writing English as a foreign language. Speaking and Writing are the most commonly used and effective means of self- expression in all fields of human life, whether personal, social or professional. This study is not only an empirical examination of speaking and writing methods but also an attempt to understand the concept of speaking and writing as essential proficiencies in learning English. By reviewing the definitions of speaking and writing and their teaching methods, the study presents the writing and speaking proficiencies in a new frame theoretically and practically.The written conversation technique is intended to improve the students' proficiencies in Writing and speaking through practicing them with their teachers and peers. A test is designed to determine the students' level in speaking and writing and identify the impact of using the new technique (written conversation) on learning the writing and speaking. The experimental design of the study consists of two groups, namely, control group and experimental group, with 40 female students for each.After analyzing the data, the experimental group shows better performance than that of the control one. This indicates that teaching the writing and speaking proficiencies by using written conversation technique is more effective than teaching by traditional method.
False Positive Suppression in Cervical Cell Screening via Attention-Guided Semi-supervised Learning

Cervical cancer is one of the primary factors that endanger women's health, and Thinprep cytologic test (TCT) is the common testing tool for the early diagnosis of cervical cancer. However, it is tedious and time-consuming for pathologists to assess and find abnormal cells in many TCT samples. Thus, automatic detection of abnormal cervical cells is highly demanded. Nevertheless, false positive cells are inevitable after automatic detection. It is still a burden for the pathologist if the false positive rate is high. To this end, here we propose a semi-supervised cervical cell diagnosis method that can significantly reduce the false positive rate. First, we incorporate a detection network to localize the suspicious abnormal cervical cells. Then, we design a semi-supervised classification network to identify whether the cervical cells are truly abnormal or not. To boost the performance of the semi-supervised classification network, and make full use of the localizing information derived from the detection network, we use the predicted bounding boxes of the detection network as an additional constraint for the attention masks from the classification network. Besides, we also develop a novel consistency constraint between the teacher and student models to guarantee the robustness of the network. Our experimental results show that our network can achieve satisfactory classification accuracy using only a limited number of labeled cells, and also greatly reduce the false positive rate in cervical cell detection.
Towards Integration of Blockchain and Machine Learning Technologies for Security in Smart Cities

Wadhwa, Shivani Gupta, Divya Moudgil, Aditi Rani, ShalliToday, the evolution of the Internet of things (IoT) technology has proved a great support to various applications. The communication among heterogeneous network devices in huge traffic environments always deals with security and privacy concerns. The traditional security approaches are not suitable for these IoT devices. The emerging technologies (such as artificial intelligence, blockchain, machine learning, fog computing) may provide efficient security solutions when embedded with any particular IoT application. In this paper, the authors first discussed various security solutions proposed by different research groups to provide security in IoT networks using blockchain and machine learning technologies. Further, the background of these technologies provides more insight into the working of these technologies. The security in smart cities (an IoT application) with the integration of blockchain and machine learning is presented using a use case scenario of a web trading platform. At last, the open research challenges in the implementation of this integration are explained.
Identifying the recurrence of sleep apnea using a harmonic hidden Markov model

We propose to model time-varying periodic and oscillatory processes by means of a hidden Markov model where the states are defined through the spectral properties of a periodic regime. The number of states is unknown along with the relevant periodicities, the role and number of which may vary across states. We address this inference problem by a Bayesian nonparametric hidden Markov model assuming a sticky hierarchical Dirichlet process for the switching dynamics between different states while the periodicities characterizing each state are explored by means of a trans-dimensional Markov chain Monte Carlo sampling step. We develop the full Bayesian inference algorithm and illustrate the use of our proposed methodology for different simulation studies as well as an application related to respiratory research which focuses on the detection of apnea instances in human breathing traces.
Exploring HW/SW Co-Optimizations for Accelerating Large-scale Texture Identification on Distributed GPUs

Texture identification has been developed recently to support one-to-one verification and one-to-many search, which provides much broader support than texture classification in real-life applications. It has demonstrated great potentials to enable product traceability by identifying the unique texture information on the surface of the targeted objects. However, existing hardware acceleration schemes are not enough to support a large-scale texture identification, especially for the search task, where the number of texture images being searched can reach millions, creating enormous compute and memory demands and making real-time texture identification infeasible. To address these problems, we propose a comprehensive toolset with jointly optimization strategies from both hardware and software to deliver optimized GPU acceleration and leverage large-scale texture identification with real-time responses. Novel technologies include: 1) a highly-optimized cuBLAS implementation for efficiently running 2-nearest neighbors algorithm; 2) a hybrid cache design to incorporate host memory for streaming data toward GPUs, which delivers a 5 × larger memory capacity while running the targeted workloads; 3) a batch process to fully exploit the data reuse opportunities by considering available compute resources and memory bandwidth constraints. 4) an asymmetric local feature extraction to reduce the memory footprint for keeping feature matrices of reference texture images. To the best of our knowledge, this work is the first implementation to provide real-time large-scale texture identification on GPUs. By exploring the co-optimizations from both hardware and software, we can deliver 31 × faster search and 20 × larger feature cache capacity compared to a conventional CUDA implementation. We also demonstrate our proposed designs by proposing a distributed texture identification system with 14 Nvidia Tesla P100 GPUs which can complete 872,984 texture similarity comparisons in just one second.