Reviewerly/exHarmony huggingface dataset:
A Detour Strategy for Visiting Phishing URLs Based on Dynamic DNS Response Policy Zone

Email based Uniform Resource Locator (URL) distribution is one of the popular ways for starting phishing attacks. Conventional anti-phishing solutions rely on security facilities and investigate all incoming emails. This makes the security facilities get overloaded and cause consequences of upgrades or new deployments even with no better options. This paper presents a novel detour strategy for the traffic of visiting potential phishing URLs based on dynamic Domain Name System (DNS) Response Policy Zone (RPZ) in order to mitigate the overloads on security facilities. In the strategy, the URLs included in the incoming emails will be extracted and the corresponding Fully Qualified Domain Name (FQDN) will be registered in the RPZ of the local DNS cache server with mapping the IP address of a special Hypertext Transfer Protocol (HTTP) proxy. The contribution of the approach is to avoid heavy investigations on all incoming emails and mitigate the overloads on security facilities by directing the traffic to phishing URLs to the special HTTP proxy connected with a set of security facilities conducting various inspections. The evaluation results on the prototype system showed that the URL extraction and FQDN registration were finished before the emails had been delivered and accesses to the URLs were successfully directed to the special HTTP proxy. The results of overhead measurements also confirmed that the proposed strategy only affected the internal email server with 11% of performance decrease on the prototype system.
Trace elements in the spring waters of Moscow

Data on the content of major ions (Na + , K + , Mg 2+ , Ca 2+ , Cl –, SO 4 2– , HCO 3 – ) and dissolved trace elements (Rb, Cs, Be, Sr, Ba, B, Si, P min , V, Cr, Ge, As, Mo, W, Sb, Te, Mn, Fe, Co, Ni, Cu, Zn, Cd, Ag, Sn, Pb, Al, Ga, Ti, Zr, U) in the waters of 30 springs in Moscow during the winter low water period are presented. The geometric mean values and ranges of concentration of the studied components, as well as correlations between them, were established. The presence of hydrochemical anomalies of dissolved iron and manganese in the north-east of Moscow with an excess of maximum allowable concentration (MAC) for these elements was revealed, whereas in the rest of the city their concentration almost everywhere correspond to the natural background.
Fiat-Shamir with Aborts: From Identification Schemes to Linkable Ring Signatures

Fiat-Shamir with aborts is a technique to transform a lattice-based identification scheme to a signature scheme introduced by Lyubashevsky (in Asiacrypt 2009). The scheme is also provably secure based on some standard lattice problems. In this paper, we show how to generically transform a signature scheme, obtained by Fiat-Shamir transformation from the ring learning with errors problem (RLWE), to a ring signature. The ring signature obtained with this transformation possesses standard security notions like unforgeability and anonymity. We also show how to achieve a linkable ring signature from the ring signature using a collision-resistant hash function. Linkable ring signatures are an important cryptographic tool as it protects signer anonymity and link signatures from the same signer. The linkable ring signature obtained from this transformation performs at par with the other lattice-based solutions for linkable ring signature, which does not require high-end zero-knowledge proofs.
Random Number Generator Based on Skew-tent Map and Chaotic Sampling

In this paper a novel random number generator is introduced and it is based on the Skew-tent discrete-time chaotic map. The RNG presented in this paper is made using the discrete-time chaotic map and chaotic sampling of regular waveform method together to increase the throughput and statistical quality of the output sequence. An explanation of the arithmetic model for the proposed design is given in this paper with an algebra confirmation for the generated bit stream that shows how it passes the primary four tests of the FIPS-140-2 test suit successfully. Finally the bit stream resulting from the hardware implementation of the circuit in a similar method has been confirmed to pass all NIST-800-22 test with no post processing. A presentation of the experimentally obtained results is given therefor proving the the circuit's usefulness. The proposed RNG can be built with the integrated circuit.
Opening the NLP Blackbox - Analysis and Evaluation of NLP Models

Although Rapid progress in NLP Research has seen a swift translation to real world commercial deployment. While a number of NLP applications have emerged, failures of translating scientific progress in NLP to real-world software have also been considerable. Evaluation of NLP models is often limited to held out test set accuracy on a handful of datasets. Lack of rigorous evaluation leads to over-estimation of generalization performance of the built model. A lack of understanding of the inner workings of the model results in ‘Clever Hans’ models which fail in real world deployments. Of late there has been considerable research interest into analysis methods for NLP models, and evaluation techniques going beyond test set performance metrics. However, this area of work is still not widely disseminated through the NLP community. This tutorial aims to address this gap, by providing a detailed overview of NLP model analysis and evaluation methods, discuss their strengths and weaknesses and also point towards future research directions in this area.
Hathi: An MCDM-based Approach to Capacity Planning for Cloud-hosted DBMS

The evolution of distributed Database Management Systems (DBMSs) has led to heterogeneity in DBMS technologies. Particularly DBMSs applying a shared-nothing approach enable distributed operation and support fine-grained configurations of distribution characteristics such as replication degree and consistency. Overall, the operation of such DBMSs on IaaS clouds leads to a large configuration space involving different cloud providers, cloud resources and pricing models.The selection of a specific configuration impacts nonfunctional features such as performance, availability, consistency, but also costs of the deployment. In consequence, these need to be traded-off against each other and a suitable configuration needs to be found, satisfying technical and operational aspects. Yet, due to the strong interdependency between different non-functional features as well as the large number of DBMSs, configuration options, and cloud providers, a manual analysis and comparison is not possible.In this paper, we present Hathi, an evaluation-driven Multi Criteria Decision Making (MCDM) framework for planning of cloud-hosted distributed DBMS. By specifying DBMS configurations, workloads, and cloud offers, Hathi automatically performs experiments and evaluates their results. These are then matched against a list of user-defined preferences using an MCDM algorithm.Our evaluation shows that Hathi is able of performing largescale evaluation scenarios involving multiple DBMS in various cluster sizes, cloud providers, and cloud offers. Hathi can weight the resulting data and derives deployment recommendations with respect to throughput, latency, cost, consistency, availability, and stability.
ArrivieW: An Application for General-Purpose Information Presentation by Using Position and Azimuth Sensors

In this study, we are trying to develop an Android application for general-purpose information presentation by using position and azimuth sensors. A researcher at Waseda University identified where all the 766 photos taken at the beginning of excavation in Tonozuka and Himezuka ancient tombs in Shibayama Town in 1956. In our laboratory, ARTonoHime was developed that took advantage of its feature. It is an Android application by using an augmented reality (AR) technology that presents the photos, descriptions, and reading voices at the time of excavation at the exact location where the photos were taken. Later, by using GPS data and azimuth data, the system was expanded to GPS-TonoHime which does not need markers. Currently, GPS-TonoHime directly embeds content such as photos and data such as location information in the application. In addition, since GPS-TonoHime does not show the current position on the map, it is necessary to directly instruct users where to proceed. Therefore, we aim to build a system that allows anyone to easily present information by adding a file selection function on the application. In addition, the system displays the current position on the guide map so that anyone can easily understand how and in which direction to reach the visiting point. Therefore, this system can be used anywhere and is useful as teaching materials in various subjects in school, commercial applications, etc.
Bi-Directional Self-Attention with Relative Positional Encoding for Video Summarization

Video summarization technique is a promising approach to process large-scale video by shortening the video content into a compact version. Most previous methods used recurrent networks such as Long Short-Term Memory (LSTM) even combined with attention mechanism to achieve state-of-the-art results. However, these networks are complex to implement and cannot be easily parallelized in computation. In this paper, we propose a novel deep summarization framework named Bi-Directional Self-Attention with Relative Positional Encoding for Video Summarization (BiDAVS) that can be highly parallelized. Our proposed BiDAVS considers position information of input sequence and effectively capture long-range temporal dependencies of sequential frames by computing bi-directional attention. Extensive experiments on two popular benchmark datasets, i.e., SumMe and TVSum, show that our proposed model outperforms state-of-the-art approaches.
A Signal Theory Model for Continuous Monitoring and Intelligence Systems

ABSTRACT Continuous monitoring and intelligence systems analyze data and text from various sources and use that information to monitor risk, controls, reputations, companies, people, opportunities, competition, and other concerns. We summarize some of the previous literature for such systems and the capabilities of existing continuous monitoring systems. Unfortunately, there has been limited theoretical development for such systems. We view the monitored information sources as providing information signals relating to events, activities, issues, etc. Systems must choose what information sources to monitor and gather information from. Unfortunately, choice of such source systems is not a straightforward problem, but must account for time, cost, redundancy, reliability, relevance, information asymmetries, weak signals, and other issues. Further, analytics must be generated to monitor signals created from the information sources to gather the information needed for the system. Accordingly, this paper generates and applies a signal theory model to address these issues.
HemoSmart: A Non-invasive, Machine Learning Based Device and Mobile App for Anemia Detection

This paper presents a non-invasive method to detect Anemia (a low level of Hemoglobin) easily. The Hemoglobin concentration in human blood is an important substance to health condition determination. With the results which are obtained from Hemoglobin test, a condition which is called as Anemia can be revealed. Traditionally the Hemoglobin test is done using blood samples which are taken using needles. The non-invasive Hemoglobin measurement system, discussed in this paper, describes a better idea about the hemoglobin concentration in the human blood. The images of the finger- tip of the different hemoglobin level patients which are taken using a camera is used to develop the neural network-based algorithm. The pre-mentioned algorithm is used in the developed noninvasive device to display the Hemoglobin level. Before doing the above procedure, an account is created in the mobile app and a questionnaire is given to answer by the patient. Finally, both the results which are obtained from the mobile app and the device are run through a machine learning algorithm to get the final output. According to the result patient would be able to detect anemia at an early stage.
Comparison of Evolutionary Strategies for Reinforcement Learning in a Swarm Aggregation Behaviour

This article studies the performance of different evolutionary strategies for deep reinforcement learning policy optimization. The policy will be centred in an important swarm robotic task: the aggregation of simple robots in the environment. The main inspiration for robotic swarm comes from the observation of social animals. Ants, bees, birds, and fish are some examples of how simple individuals can succeed when they gather in groups. In addition, is important to highlight that aggregation may be considered as a previous requirement to tackle another tasks.
How to Improve Optical Character Recognition of Historical Finnish Newspapers Using Open Source Tesseract OCR Engine – Final Notes on Development and Evaluation

The current paper presents work that has been carried out in the National Library of Finland (NLF) to improve optical character recognition (OCR) quality of the historical Finnish newspaper collection 1771–1910. Evaluation results reported in the paper are based mainly on a 500 000 word sample of the Finnish language part of the whole collection. The sample has three different parallel parts: a manually corrected ground truth version, original OCR with ABBYY FineReader v. 7 or v. 8, and an ABBYY FineReader v. 11 re-OCRed version for comparison with Tesseract’s OCR. Using this sample and its page image originals we have developed a re-OCRing procedure using the open source software package Tesseract v. 3.04.01. Our method achieved initially 27.48% improvement vs. ABBYY FineReader 7 or 8 and 9.16% improvement vs. ABBYY FineReader 11 on document level. On word level our method achieved 36.25% improvement vs. ABBYY FineReader 7 or 8 and 20.14% improvement vs. ABBYY FineReader 11. Our final precision and recall results on word level show clear improvement in the quality: recall is 76.0 and precision 92.0 in comparison to GT OCR. Other measures, such as recognizability of words with a morphological analyzer and character accuracy rate, show also steady improvement after re-OCRing.
Exact Passive Aggressive Algorithm for Multiclass Classification Using Partial Labels

Complete information about the class label is not given in many real-world classification problems. For example in a multiclass setting, instead of the ground-truth label, we can be given a set of candidate labels, assuming that the true label belongs to this set. This type of setting is called learning under partial labels. In this paper, we propose exact passive-aggressive online algorithms for multiclass classification using only partial labels. For updating the weights, we find the exact solution of a quadratic optimization problem under multiple class separability constraints. We obtain this by finding the active constraints using KKT conditions of the optimization problem. The set of support classes for which the weight vector is to be updated is determined by these constraints. The proposed algorithms are called PA, PA-I, and PA-II. We also provide a thorough theoretical analysis of the proposed algorithms including regret bounds. We provide extensive simulation results to show the effectiveness of the proposed approaches.
Partition function and coherent states for the quantum multiverse

Motivated by a wider acceptance of the quantum cosmology and the idea of multiverse, we follow the second and third quantization formalism for the conceptual comprehension of quantum multiverse. The other motivating feature to pursue the present study is that at the different levels of quantization the equation of quantum state is similar to that of harmonic oscillator, which encourages us to give a glancing taste of the coherent states in the domain of quantum cosmology. Third quantization enables one to model the universe as a particle in a quantum field theory of the minisuperspace and the wave description prompts one to recognize interactions among the universes of the multiverse. This picture of the system further instigates us to investigate the partition function and the thermodynamic equations of the system. The path integral formalism with proper mathematical extensions are employed to find the partition function followed by the usual techniques of statistical mechanics to examine the thermodynamic properties.
Neural network-based traffic sign recognition in 360° images for semi-automatic road maintenance inventory

Traffic sign inventory is an important part of road safety and traffic management. Road maintenance services perform this task with periodical on-site inspections, monitoring the physical presence and integrity of each object. Due to the increasing complexity of road infrastructure and the high number of objects, this task is usually time-consuming. Object detection based on artificial neural networks and state-of-the-art 360° camera equipment may be used to enhance the work of road maintenance services. By automatically capturing images during mandatory inspection drives, detecting traffic signs in those images and linking them with corresponding database entries, semi-automatic inventory and off-site inspections will be possible. This paper describes a system for semi-automatic traffic sign inventory that, in contrast to other approaches, automatically detects and classifies various traffic sign types in 360° images. It then references those signs in an existing inventory database, using GPS and distance estimation and allows for a virtual reality like off-site inspections with a 360° view.
Privacy and Security in the Digitalisation Era

The IT-enabled organisational transformation has been one of the widely studied topics since the introduction of IT in the business process of organisations, regardless of size and industry. Digital transformation, a relatively new but related phenomenon, has recently dominated the debates in the information systems (IS) research domain. Even though there is little disagreement in the role of new digital technologies in enabling innovation and providing a competitive advantage for organisations, researchers and practitioners have also voiced their concerns on the challenges associated with this technology-induced change. Among others, the volume of data collected and stored to make the digital transformation possible has brought the issue of security and privacy to the fore. A systematic literature review of recent studies was conducted to provide an overview of the developments on how organisations attempt to maintain privacy and security while undertaking digital transformation. The results suggest that the adoption of new technologies is contingent on the perception of different stakeholders on how privacy and security are maintained. Besides technical measures in place, organisational culture, organisational structure, HR practices, privacy and security policy, as well as senior leaders' commitment and IT leadership skills are essential to maintain acceptable privacy and security measures that support digital transformation. Implications for research and practice, as well as opportunities for future research, are also presented.
Towards Prioritizing Vulnerability Testing

Vulnerability assessment is the process of identifying and prioritizing the vulnerabilities in a system. Vulnerability scanners can, for example, scan a website for known vulnerabilities by running a repository of security tests, each of which is designed to reveal a known vulnerability. As the security tests need to be executed on each and every web page encountered, it may take quite a while for these scanners to report vulnerabilities. In this work, we present an approach for revealing the vulnerabilities faster by prioritizing the executions of the security tests on a per web page basis. The approach is based on a simple conjecture that “similar” web pages may possess “similar” vulnerabilities and that identifying these similarities can help prioritize the security tests. The results of the experiments we carried out by using 2927 distinct web pages (collected from 80 web sites), support our basic hypothesis; the percentages of the times the actual vulnerabilities appear in the top 8 and 15 predicted vulnerabilities were 86.9% and 98.4 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> , respectively.
Speaker-Adaptive Neural Vocoders for Parametric Speech Synthesis Systems

This paper proposes speaker-adaptive neural vocoders for parametric text-to-speech (TTS) systems. Recently proposed WaveNet-based neural vocoding systems successfully generate a time sequence of speech signal with an autoregressive framework. However, it remains a challenge to synthesize high-quality speech when the amount of a target speaker's training data is insufficient. To generate more natural speech signals with the constraint of limited training data, we propose a speaker adaptation task with an effective variation of neural vocoding models. In the proposed method, a speaker-independent training method is applied to capture universal attributes embedded in multiple speakers, and the trained model is then optimized to represent the specific characteristics of the target speaker. Experimental results verify that the proposed TTS systems with speaker-adaptive neural vocoders outperform those with traditional source-filter model-based vocoders and those with WaveNet vocoders, trained either speaker-dependently or speaker-independently. In particular, our TTS system achieves 3.80 and 3.77 MOS for the Korean male and Korean female speakers, respectively, even though we use only ten minutes' speech corpus for training the model.
A Rule-based Service Pattern Convergence Framework for Crossover Service

The convergence of the Internet and traditional industries gives the birth to the crossover services, which break through the boundaries of domains, enterprises, and businesses. The design of the service pattern is one of the key points to the success of crossover services. Existing works are mainly aimed at resource and process convergence, unable to guide the design of whole crossover service. To address this issue, we propose a rule-based service pattern convergence framework for crossover service, which consists of participant convergence, resource convergence and service process convergence. In addition to summarizing the general rules of convergence, we introduce semantic similarity to promote deep pattern convergence. Finally, a case study is presented to prove the operability of this framework.
Rollover Detection of Infants Using Posture Estimation Model

In predicting the posture of an infant during sleep, images taken by a surveillance camera are useful. The posture estimation of infants is designed to be used in a home environment and uses monocular camera images rather than special cameras such as distance cameras. In this paper, we compare the accuracy of infant posture estimation by two posture estimation models, OpenPose and Cascaded Pyramid Network(CPN). We also introduce a system for estimating infant's sleep turn using the posture estimation results. The experimental results show that CPN can detect infants' posture estimation with higher accuracy than OpenPose. In addition, the system is successfully used to detect the infant's turning with high accuracy.