Reviewerly/exHarmony huggingface dataset:
Using Freivalds’ Algorithm to Accelerate Lattice-Based Signature Verifications

We present a novel computational technique to check whether a matrix-vector product is correct with a relatively high probability. While the idea could be related to verifiable delegated computations, most of the literature in this line of work focuses on provably secure functional aspects and do not provide clear computational techniques to verify whether a product $$xA = y$$ is correct where x, A and y are not given nor computed by the party which requires validity checking: this is typically the case for some cryptographic lattice-based signature schemes. This paper focuses on the computational aspects and the improvement on both speed and memory when implementing such a verifier, and use a practical example: the Diagonal Reduction Signature (DRS) scheme as it was one of the candidates in the recent National Institute of Standards and Technology Post-Quantum Cryptography Standardization Calls for Proposals competition. We show that in the case of DRS, we can gain a factor of 20 in verification speed.
A general framework in single and multi-modality registration for lung imaging analysis using statistical prior shapes

Background and Objective: A fusion of multi-slice computed tomography (MSCT) and single photon emission computed tomography (SPECT) represents a powerful tool for chronic obstructive pulmonary disease (COPD) analysis. In this paper, a novel and high-performance MSCT/SPECT non-rigid registration algorithm is proposed to accurately map the lung lobe information onto the functional imaging. Such a fusion can then be used to guide lung volume reduction surgery. Methods: The multi-modality fusion method proposed here is developed by a multi-channel technique which performs registration from MSCT scan to ventilation and perfusion SPECT scans simultaneously. Furthermore, a novel function with less parameters is also proposed to avoid the adjustment of the weighting parameter and to achieve a better performance in comparison with the exisitng methods in the literature. Results: A lung imaging dataset from a hospital and a synthetic dataset created by software are employed to validate single- and multi-modality registration results. Our method is demonstrated to achieve the improvements in terms of registration accuracy and stability by up to 23% and 54% respectively. Our multi-channel technique proposed here is also proved to obtain improved registration accuracy in comparison with single-channel method. Conclusions: The fusion of lung lobes onto SPECT imaging is achievable by accurate MSCT/SPECT alignment. It can also be used to perform lobar lung activity analysis for COPD diagnosis and treatment.
Field cancerization in the understanding of parenchymal analysis of mammograms for breast cancer risk assessment

In recent years, mammographic image analysis has shown great potential for breast cancer risk assessment. The aim of risk assessment is to predict how likely a woman is to develop breast cancer in the future. Several studies suggest that computerized parenchymal analysis of mammograms can be utilized as an independent imaging biomarker of breast cancer. Parenchymal analysis consists of the quantitative assessment of visual texture patterns in mammograms to infer the level of risk. In spite of substantial evidence of the association between parenchymal patterns and breast cancer risk, its biological foundations remain poorly understood. In this work, we draw a hypothesis that links the field cancerization (FC) with breast cancer risk assessment based on the parenchymal analysis. In the literature, the FC is interpreted as a biochemical anomaly amplification in otherwise healthy cells due to the effect of pre-cancerous transformed cells in surrounding regions. Our hypothesis is that these biochemical anomaly amplifications change the cellular micro-environment which, in turn, alter tissue responses to X-ray radiation. As a result, it is reasonable to think that these changes influence the interaction of X-rays with parenchymal – the functional – breast tissue thus enabling cancer prediction by analyzing X-ray images of the breast. We believe that our hypothesis provides an actionable explanation as to how computerized parenchymal analysis of apparently normal mammograms can be successfully utilized for the stratification of breast cancer risk.
Semi-Automated Sleep EEG Scoring with Active Learning and HMM-Based Deletion of Ambiguous Instances

Sleep scoring is an important tool for physicians. Assigning of segments of long biomedical signal into sleep stages is, however, a very time consuming, tedious and expensive task which is performed by an expert. Automatic sleep scoring is not well accepted in clinical practice because of low interactivity and unacceptable error, which is often caused by inter-patient variability. This is solved by proposing a semi-automatic approach, where parts of the signal are selected for manual labeling by active learning and the resulting classifier is used for automatic labeling of the remaining signal. The active learning is disturbed by noisy ambiguous data instances caused by continuous character of the sleep stage transitions and a removal of such transitional instances from the training set prior to active learning can improve the efficiency of the method. This paper proposes to use the hidden Markov model for the detection of the transitional instances. It shows experimentally on 35 sleep EEG recordings that such a method significantly improves the semi-automatic method. A complete methodology for semi-automatic sleep scoring is proposed and evaluated, which can be better accepted as a decision support tool for sleep scoring experts.
The new management of recordkeeping

Abstract This article focuses on the emerging applications of blockchain technology for recordkeeping and data management requirements. Blockchain—the technology supporting Bitcoin and other cryptocurrencies—is a shared ledger that maintains transactions and documents in digital form. In a time when businesses face challenges in managing data security, there is a compelling case for developing a digital‐based recordkeeping system to address social problems on a global scale. Improving food supply chain safety, reducing the conflict diamond trade, and assisting refugees in preserving identity and academic credential records are just a few of the solutions offered by blockchains. Our global economy depends on the ability to create, store, and share accurate records. While implementation challenges remain, blockchain technology's ability to store and distribute immutable documents has the potential to improve data management security for industries worldwide.
HCAR: Human continuous activity recognition using latent structure features

Abstract With the rapid development of the Internet of Things and the improvement of computing power, edge‐computing becomes an emergency computing paradigm that communicates between the terminal and cloud. One of the most representative works of edge‐computing is to achieve human‐activity recognition at the edge side, as it has lower latency and it could reduce transmission costs, compared with processing at the cloud side. However, existing approaches have many drawbacks: (1) they can merely recognize separated actions as it is incompetence for continuous activity recognition; and (2) they are not robust to action transformation and environmental noise due to the value‐feature‐based matching strategy. In this paper, we propose HCAR , a structure‐feature–based human continuous activity recognition system, which is insensitive to action transformation and environmental noise. Firstly, we leverage word2vec to embed the CSI sequences to CSI value space. Secondly, we select representative features from the embedded vectors and use HMM‐LDA to cluster them into different action categories. Lastly, for each new coming sequence, we calculate the Hellinger distance and bi‐modality coefficient to different categories and then identify the corresponding action(s). We implement HCAR by Intel 5300 NIC to evaluate the activity recognition precision in different cases. The experiments show that HCAR can recognize actions corresponding to the unsegmented CSI sequence with high accuracy, ie, &gt;90%.
A Fuzzy Rule Based Visual Human Tracking System for Drones

Drone sales are on the rise with each passing year and more and more people are embracing the idea of owning these devices. These unmanned aerial vehicles are now finding more practical and innovative solutions about daily problems. And also, body detection and face recognition tasks are becoming a new trend in the security authentication systems. In the light of these facts, a real time tracker algorithm is developed to track a specified human face with DJI Phantom 3 drone. After recognizing the human face, his body is detected and it is tracked. In order to improve the tracking accuracy in outdoor environments a Kalman Filter is used. When the drone misses the target, the Filter will track him for a certain time. After that a Fuzzy Logic algorithm is used to maneuver the drone towards to the human in real time. Results show that, the developed tracking system can be used to detect a human for all of the programmable drones.
Education-based augmented reality applications for heat learning media content lesson in physics

Abstract This study aims to create applications that can be used as a medium of physics learning with sub-material heat. Application-shaped learning media created using Augmented Reality-based technology Android. The application created is named Mecalor. Mecalor application process using the Waterfall method. This Waterfall method provides a sequential approach to software lifecycle. Testing the feasibility of the application is done through four characteristics of the ISO 25010 standard which Suitability Functional, Performance Efficiency, Portability, and Usability. The test was conducted in Garut - Indonesia Vocational High School with a total of 30 students and two software experts. Data analysis techniques use a Likert scale and Guttman scale. The test results on the characteristics of Functional Suitability applications, Performance Efficiency, and Portability is valid with a percentage of 100% so it can be said that the application is good to use. Likewise, characteristics Usability test results also suggested a value of 3.9 so that applications can Mecalor declared fit for use.
Automatic syntax error reporting and recovery in parsing expression grammars

Error recovery is an essential feature for a parser that should be plugged in Integrated Development Environments (IDEs), which must build Abstract Syntax Trees (ASTs) even for syntactically invalid programs in order to offer features such as automated refactoring and code completion. Parsing Expressions Grammars (PEGs) are a formalism that naturally describes recursive top-down parsers using a restricted form of backtracking. Labeled failures are a conservative extension of PEGs that adds an error reporting mechanism for PEG parsers, and these labels can also be associated with recovery expressions to provide an error recovery mechanism. These expressions can use the full expressivity of PEGs to recover from syntactic errors. Manually annotating a large grammar with labels and recovery expressions can be difficult. In this work, we present two approaches, Standard and Unique, to automatically annotate a PEG with labels, and to build their corresponding recovery expressions. The Standard approach annotates a grammar in a way similar to manual annotation, but it may insert labels incorrectly, while the Unique approach is more conservative to annotate a grammar and does not insert labels incorrectly. We evaluate both approaches by using them to generate error recovering parsers for four programming languages: Titan, C, Pascal and Java. In our evaluation, the parsers produced using the Standard approach, after a manual intervention to remove the labels incorrectly added, gave an acceptable recovery for at least 70% of the files in each language. By it turn, the acceptable recovery rate of the parsers produced via the Unique approach, without the need of manual intervention, ranged from 41% to 76%.
Dual incremental fuzzy schemes for frequent itemsets discovery in streaming numeric data

Discovering frequent itemsets is essential for finding association rules, yet too computational expensive using existing algorithms. It is even more challenging to find frequent itemsets upon streaming numeric data. The streaming characteristic leads to a challenge that streaming numeric data cannot be scanned repetitively. The numeric characteristic requires that streaming numeric data should be pre-processed into itemsets, e.g., fuzzy-set methods can transform numeric data into itemsets with non-integer membership values. This leads to a challenge that the frequency of itemsets are usually not integer. To overcome such challenges, fast methods and stream processing methods have been applied. However, the existing algorithms usually either still need to re-visit some previous data multiple times, or cannot count non-integer frequencies. Those existing algorithms re-visiting some previous data have to sacrifice large memory spaces to cache those previous data to avoid repetitive scanning. When dealing with big streaming data nowadays, such large-memory requirement often goes beyond the capacity of many computers. Those existing algorithms unable to count non-integer frequencies would be very inaccurate in estimating the non-integer frequencies of frequent itemsets if used with integer approximation of frequency-counting. To solve the aforementioned issues, in this paper we propose two incremental schemes for frequent itemsets discovery that are capable to work efficiently with streaming numeric data. In particular, they are able to count non-integer frequency without re-visiting any previous data. The key of our schemes to the benefits in efficiency is to extract essential statistics that would occupy much less memory than the raw data do for the ongoing streaming data. This grants the advantages of our schemes 1) allowing non-integer counting and thus natural integration with a fuzzy-set discretization method to boost robustness and anti-noise capability for numeric data, 2) enabling the design of a decay ratio for different data distributions, which can be adapted for three general stream models: landmark, damped and sliding windows, and 3) achieving highly-accurate fuzzy-item-sets discovery with efficient stream-processing. Experimental studies demonstrate the efficiency and effectiveness of our dual schemes with both synthetic and real-world datasets.
Quantifying and Analyzing Information Security Risk from Incident Data

Multiple cybersecurity risk assessment and root cause analysis methods propose incident data as a source of information. However, it is not a straightforward matter to apply incident data in risk assessments. The paper trail of incident data is often incomplete, ambiguous, and dependent on the incident handlers routines for keeping records. Current incident classification approaches classify incidents as one specific type, for example, "Data spillage," "Compromised information," or "Hacking." Through incident analysis, we found that the current classification schemes are ambiguous and that most incident consists of additional components. This paper builds on previous work on incident classifications and proposes a method for quantifying and risk analyzing incident data for improving decision-making. The applied approach uses a set of incident data to derive the causes, outcomes, and frequencies of risk events. The data in this paper was gathered from a year of incident handling from a Scandinavian university's security operations center (SOC), and consists of 550 handled incidents from November 2016 to October 2017. By applying the proposed method, this paper offers empirical insight into the risk frequencies of the University during the period. We demonstrate the utility of the approach by deducting the properties of the most frequent risks and creating graphical representations of risks using a bow-tie diagram. The primary contribution of this paper is the highlighting of the ambiguity of existing incident classification methods and how to address it in risk quantification. Additionally, we apply the data in risk analysis to provide insight into common cyber risks faced by the University during the period. A fundamental limitation is that this study only defines adverse outcomes and does not include consequence estimates.
Semantic Frame Induction as a Community Detection Problem

Resources such as FrameNet provide semantic information that is important for multiple tasks. However, they are expensive to build and, consequently, are unavailable for many languages and domains. Thus, approaches able to induce semantic frames in an unsupervised manner are highly valuable. In this paper we approach that task from a network perspective as a community detection problem that targets the identification of groups of verb instances that evoke the same semantic frame. To do so, we apply a graph-clustering algorithm to a graph with contextualized representations of verb instances as nodes connected by an edge if the distance between them is below a threshold that defines the granularity of the induced frames. By applying this approach to the benchmark dataset defined in the context of the SemEval shared task we outperformed all the previous approaches to the task.
Study on the Orbit Prediction Errors of Space Objects Based on Historical TLE Data

Two line element (TLE) released by the North American Aerospace Defense Command (NORAD) is widely used by aerospace workers, and the matched SGP4/SDP4 (Simplified General Perturbation Version 4/Simplified Deep-space Perturbation Version 4) model is used to propagate it. Nevertheless, no corresponding information about its accuracy and covariance is clearly given, thus the application of the TLE data is greatly restricted. In this paper, the determined and predicted orbits are compared to generate the orbit error data, based on the historical TLE data obtained from the Space-Track website and the SGP4/SDP4 model. By dividing different time bins, the fitting coefficients of the variation of orbit prediction error with time are given for each space object, and the characteristics of the error evolution are further discussed for the different types of orbits. The mean analytic model of the orbit prediction error evolution with time is given respectively for the four orbit types of space objects, which provides a valuable reference for extending the application of the TLE data.
Using Short Texts and Emojis to Predict the Gender of a Texter in Turkish

With the advancement of technology and the spread of social media, people express their ideas, wishes and thoughts in digital environments through texts, which have all but replaced telephone conversations. These texts have also become a very important resource for data science. For example, it is possible to extract a great deal of useful information such as the gender of an author, especially in areas like e-commerce and social media. Online messaging platforms make it easy for users to falsify their name, age, gender and location in order to hide their true identity. It is not uncommon for some to hide their gender while texting. We think that this can lead to some undesirable consequences in terms of both social life and security. Some features, such as vocabulary and syntax, offer clues about a person's gender. The aim of this study is to predict the gender of a Turkish-language texter using text classification techniques in machine learning. Since emojis are an important component of text messaging, they are included in the prediction process. We achieved 67.22%, 74.31% and 68.66% accuracy rates for gender prediction based on text-only, emoji-only and text-and-emoji features respectively.
Position Paper: Knowledge Sharing and Distances in Collaborative Modeling

To develop systems effectively, a shared system understanding is required. Collaborative modeling is one way to capture this shared understanding. Increasingly, in large systems engineering projects different distances lead to social barriers between stakeholders. These barriers affect the quantity and quality of knowledge that is shared between stakeholders, thus reducing the quality of the resulting product. While it has been proposed to limit modeling activities to co-located teams, this might not always be possible or feasible. We argue that, despite the technological advances in collaborative modeling, effective collaboration can only be achieved if we understand how to account for social barriers. We propose to study, in depth, how these barriers affect modeling, and how their effects can be reduced. By understanding the effects of social barriers and accounting for them, we can maximize the benefits of collaborative modeling.
Accelerated Disaster Reconnaissance Using Automatic Traffic Sign Detection with UAV and AI

Because of frequent extreme weather conditions, accelerated disaster reconnaissance has become extremely important. In particular, surveying traffic sign damage and conditions has become essential for determining and prioritizing necessary repair/replacement. Under the research project sponsored by the National Academy of Sciences NCHRP-IDEA program, a conventional sign detection algorithm based on color, shape, and texture has been developed to process the images of signs. The developed algorithm has been enhanced by digital image processing and deep-learning methods, such as convolutional neural networks (CNN), generative adversarial networks (GAN), and region-based convolutional neural networks (RCNN). In this paper, a method is designed to process the images obtained using unmanned aerial vehicles (UAV), employing a model UAV, to further develop of our current research. The preliminary test shows that it is promising to use a UAV and machine learning to develop an expedited infrastructure condition evaluation following natural disasters because of its automatic and non-contact nature. The preliminary outcomes show the detection rates have satisfying FN rates and very low FP rates. Besides, the designed algorithm and data-gathering method provides a real-time and on-site computation capability that reduces the quantity of data to be stored by filtering out unnecessary data instantly.
Video Quality Assessment in the DASH Technique

The paper presents video quality assessment issues that include different methods. A subjective video quality assessment of the MPEG-4 / H.264 encoded clips, at the selected bitrates and picture resolutions, is shown. The authors underline the necessity of using a subjective approach to complement objective methods and propose the measurement scenario and the assessment procedure in a specially designed test environment, based on the methods presented by the International Telecommunication Union. The research shows a high correlation between results obtained while using objective and subjective quality assessment methods. The paper presents also the differences between the subjective assessment grades depending on the type of the user equipment, i.e. personal computer and mobile phone. The authors propose a statistical analysis of the results as a method of selecting significant differences between formats of video chunks that may be chosen in video transmission using adaptive streaming techniques over HTTP.