Reviewerly/exHarmony huggingface dataset:
Efficient Bit-Parallel Multiplier for All Trinomials Based on <i>n</i>-Term Karatsuba Algorithm

Recently, hybrid multiplication schemes over the binary extension field GF(2m) based on nterm Karatsuba algorithm (KA) have been proposed for irreducible trinomials. Their complexities depend on a decomposition of m and the choice of a generation polynomial. However, these multipliers have some limitations on a decomposition of m or generation polynomial x <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> + x <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sup> + 1 such that m ≥ 2k. In this paper, we loosen such limited conditions. We present a new hybrid bit-parallel multiplier based on n-term KA for any irreducible trinomial x <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> + x <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sup> + 1 (0 k m), where m is decomposed as m = nm <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> + r with 0 r m0 and 1 n. (Here, various values for n, m <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> and r may be chosen.) To this end, we generalize the previously proposed multiplication scheme for x <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nm0+1</sup> +x <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sup> +1 into x <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nm0+r</sup> +x <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sup> +1. We evaluate the explicit complexity of the proposed multiplier. Specific comparisons show that the proposed multiplier achieves the lowest space complexity with the same or lower time complexity among hybrid multipliers. Compared to the fastest multipliers, the time complexity of the proposed multiplier costs only TX higher while its space complexity is much lower (it has roughly 40% reduced space complexity), where TX is the delay of one 2-input XOR gate.
Question Answering When Knowledge Bases are Incomplete

While systems for question answering over knowledge bases (KB) continue to progress, real world usage requires systems that are robust to incomplete KBs. Dependence on the closed world assumption is highly problematic, as in many practical cases the information is constantly evolving and KBs cannot keep up. In this paper we formalize a typology of missing information in knowledge bases, and present a dataset based on the Spider KB question answering dataset, where we deliberately remove information from several knowledge bases, in this case implemented as relational databases (The dataset and the code to reproduce experiments are available at https://github.com/camillepradel/IDK.). Our dataset, called IDK (Incomplete Data in Knowledge base question answering), allows to perform studies on how to detect and recover from such cases. The analysis shows that simple baselines fail to detect most of the unanswerable questions.
Deployment of an IoT Platform for Activity Recognition at the UAL’s Smart Home

Currently, most smart homes are aimed at user comfort or even energy efficiency. However, there are many cases in which Ambient Assisted Living is being used, to control the health of the elderly people, or people with disabilities. In this paper, a proposal for an IoT system for activity recognition in a smart home will be shown. Specifically, various low-cost sensors are incorporated into a home that send data to the cloud. In addition, an activity recognition algorithm has been included to classify the information from the sensors and to determine which activity has been carried out. Results are also displayed in a web system, allowing the user to validate them or correct them. This web system allows the visualization of the data generated by the sensors of the smart home and help to easily monitor the activities carried out, and to alert to the doctors or the user’s family when bad habits or any problem in the behaviour are detected.
Estimating Uncertainties of Recurrent Neural Networks in Application to Multitarget Tracking

In multitarget tracking, finding an association between the new measurements and the known targets is a crucial challenge. By considering both the uncertainties of all the predictions and measurements, the most likely association can be determined. While Kalman filters inherently provide the predicted uncertainties, they require a predefined model. In contrast, neural networks offer data-driven possibilities, but provide only deterministic predictions. We therefore compare two common approaches for uncertainty estimation in neural networks applied to LSTMs using our multitarget tracking benchmark for optical belt sorting. As a result, we show that the estimation of measurement uncertainties improves the tracking results of LSTMs, posing them as a viable alternative to manual motion modeling.
Dynamics of two coupled qubits interacting with two-photon transitions via a nondegenerate parametric amplifier: nonlocal correlations under intrinsic decoherence

In this study, an analytical solution for a novel intrinsic noise model represented by two coupled qubits inside a cavity, the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">S</mml:mi> <mml:mi mathvariant="normal">u</mml:mi> </mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:mn>1</mml:mn> <mml:mo>,</mml:mo> <mml:mn>1</mml:mn> <mml:mo stretchy="false">)</mml:mo> </mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">S</mml:mi> <mml:mi mathvariant="normal">u</mml:mi> </mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:mn>2</mml:mn> <mml:mo stretchy="false">)</mml:mo> </mml:math> Lie group, is investigated. Each qubit interacts with a two-mode parametric amplifier through a nondegenerate two-photon process when the two-mode system is initially in a superposition of a generalized Barut–Girardello coherent state. The nonlinearity of the interaction and the initial two-mode fields lead to the generation of different quantum correlations (QCs), which are measured by log-negativity, uncertainty-induced nonlocality, and local quantum uncertainty (LQU). The generated QC of the interaction depends not only on the two-qubit coupling but also on the intrinsic noise and the initial coherent intensity. Our results show that the ability of the two-qubit coupling to protect and enhance the robustness and generation of the QCs depends on the superposition and the coherent intensity of the initial <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">S</mml:mi> <mml:mi mathvariant="normal">u</mml:mi> </mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:mn>1</mml:mn> <mml:mo>,</mml:mo> <mml:mn>1</mml:mn> <mml:mo stretchy="false">)</mml:mo> </mml:math> state. Furthermore, the sudden birth and death of the log-negativity and the sudden variations of the LQU depend on the intrinsic noise and the two-qubit coupling.
How do Students Experience and Judge Software Comprehension Techniques?

Today, there is a wide range of techniques to support software comprehension. However, we do not fully understand yet what techniques really help novices, to comprehend a software system. In this paper, we present a master level project course on software evolution, which has a large focus on software comprehension. We collected data about student's experience with diverse comprehension techniques during focus group discussions over the course of two years. Our results indicate that systematic code reading can be supported by additional techniques to guiding reading efforts. Most techniques are considered valuable for gaining an overview and some techniques are judged to be helpful only in later stages of software comprehension efforts.
On the Effectiveness of Higher-Order Logic Programming in Language-Oriented Programming

In previous work we have presented , a functional language-oriented programming language with languages as first-class-citizens. Language definitions can be bound to variables, passed to and returned by functions, and can be modified at run-time before being used. programs are compiled and executed in the higher-order logic programming language $$\lambda $$ Prolog. In this paper, we describe our compilation methods, which highlight how the distinctive features of higher-order logic programming are a great fit in implementing a language-oriented programming language.
Comparative Analysis of the Kruskal and Boruvka Algorithms in Solving Minimum Spanning Tree on Complete Graph

The problem that is often encountered in daily life is connecting all points in one work domain with a low optimization value, for example, the most economical cost required to connect a water pipe to each house in an area. To solve this problem, a system that can find a path that connects all points in one work domain with the lowest optimization is needed. In this study, the system was built using two algorithms, namely, Kruskal and Boruvka algorithms, and a complete graph is used as a modeling of the problem. Using these two algorithms, the system will find the optimum path that connects all points in the complete graph; then, the system also displays a comparison between the two algorithms in finding the optimum route. The data used is dynamic, meaning the users can enter and change the value of the side of the complete graph as needed. From the tests that have been done, it is found that the Kruskal algorithm is more effective than the Boruvka to find the minimum spanning tree in a complete graph with some nodes, and sides are 15 points and 105 sides.
A Frontier-Based Coverage Path Planning Algorithm for Robot Exploration in Unknown Environment

This paper presents a path planning algorithm for robot exploration in unknown large-scale environments. The proposed algorithm employs a finite state machine to iteratively derive the action that the robot takes to explore its environment when no prior map is available. The finite state machine contains two states, that is zigzag state and travel state. In the zigzag state, the robot explores the current cell using a zigzag pattern and adds frontier nodes. In the travel state, the robot moves to another cell by going to the nearest frontier node. Compared with previous studies, the proposed algorithm can realize complete exploration while maintaining efficiency. Simulation results demonstrate that the efficiency and robustness of the proposed algorithm for exploring the unknown large-scale environments as compared with two other state-of-art algorithms.
An Efficient Blockchain-based Firmware Update Framework for IoT Environment

Recently, massive deployment of Internet of Things (IoT) devices has accelerated digital transformation of modern society in various aspects. Naturally, how to ensure security of deployed IoT devices has become an important and practical issue. Several successful large-scale DDoS attacks were utilized vulnerabilities of firmware on IoT devices in the past couple of years. Therefore, effective and efficient firmware update solution on IoT devices is in demand. In this study, an automatic real-time firmware update framework based on blockchain technology and MQTT protocol is proposed. To support automatic real-time firmware update operation on deployed IoT devices, multiple MQTT servers are installed in corresponding blockchain nodes to execute firmware patch delivery operation by the design of our framework. Blockchain technology is used to record the original manufacturer of a published firmware patch for a dedicated IoT device and smart contract is adopted to discover MQTT service nodes in a blockchain network. Data integrity of a newly generated firmware patch is preserved by distributing the patch with its hashed value. A framework prototype is constructed and experiments are conducted. Based on the experimental results, the proposed framework can efficiently and securely deliver firmware patches to targeted gateways in real-time scale.
Poster: The Use of a Virtual Personal Assistant (FENNChat) as a Platform for Providing Automated Responses to ODL Students’ Queries at UNISA

Virtual personal assistants (VPAs) are increasingly becoming and exponentially colonising our everyday lives. In this instance, VPAs have different names, two examples of which are conversational agents and intelligent personal assistants (Bohouta and Këpuska 2018; Garcia Brustenga et al. 2018; Knote et al. 2019; Krassmann et al. 2018). Traditionally, they serve either as text-based or voice-based dialogue (conversational) systems, or as a combination of both. Against this backdrop, the current paper focuses on one prototype of a VPA, called FENNChat, that is intended to offer automated responses to students enrolled in a module, English for Academic Purposes (ENN103F). The latter is an undergraduate module offered by the Department of English Studies at the University of South Africa (UNISA), which is an open and distance e-learning (ODeL) university. This module is one of the modules whose enrolment figure is often above 10,000 students. As a prototype, FENNChat is intended to provide responses to three types of student queries: module administrative queries, module content queries, and frequently asked questions (FAQs).
Fully-phase encryption using transport of intensity equation

A new method of optical image encryption based on transport of intensity equation has been proposed. The main drawback of the conventional double random phase encoding based optical encryption is to record complex information, which can be alleviated with transport of intensity equation.
Fusion of Deep Learning and Image Processing Techniques for Breast Cancer Diagnosis

Deep learning has the capacity to gain great accuracy of diagnosing of numerous types of cancers, along with lung, cervical, colon, and breast cancer. It builds an efficient algorithm based on multiple processing (hidden) layers of neurons. Manual assessment of Cancer using Medical Image (CT images) requires expensive human labors and can easily cause the misdiagnose of any type of cancer. The Researcher focus on automatically diagnosing cancer by using the deep learning technique. Breast cancer is a particularly common sickness among women and maximum associated cause of female mortality. The survival rate of breast cancer patients can be expanded with the aid of powerful treatment, which can initiate upon early prognosis of the disease. This chapter introduces Deep Learning under medical image processing to analysis and diagnosis of Cancer (Ehteshami Bejnordi et al., in Deep learning-based assessment of tumor-associated stroma for diagnosing breast cancer in histopathology images, pp. 929–932, 2017 [1]). Identification of most cancer might facilitate in sparing a massive wide variety of lives over the globe community and deep neural networks may be correctly used for intelligent image analysis. The essential structure of how this deep learning takes a shot at medical image processing (Litjens et al. in A survey on deep learning in medical image analysis, 2017, [2]; Rezaeilouyeh et al. in J Med Imaging 3(4):044501, 2016 [3]) is furnished in this research, i.e., pre-processing, image segmentation and post-processing. The following piece of this part depicts the rudiments of the field of disease conclusion, which incorporates steps of malignant growth determination followed by the regular arrangement strategies utilized by specialists, giving a verifiable thought of disease grouping methods to the readers. Next an attempt has been made to classify the extracted features from mammograms as benign or malignant by using Convolutional neural network (CNN) (Cireşan in Mitosis detection in breast cancer histology images with deep neural networks. Springer, Berlin, pp. 411–418, 2013 [4]; LeCun et al. in International symposium on circuits and systems, pp. 253–256, 2010 [5]; Huynh et al. in J Med Imaging 3(3):034501, 2016 [6]) is applied to classify cancer using optimal features obtained from cell segmented images. Performance improvised of the approaches by varying various parameters is studied.
Improved SegMitos framework for mitosis detection in breast cancer histopathology images

Mitotic cell counting is the strongest predictor of tumor aggressiveness in breast cancer prognosis. Since the manual annotation of mitotic cells by pathologists is extremely hard and time-consuming, automatic mitosis detection systems are highly required in pathology laboratories. In this paper, we propose a mitosis detection system inspired by the state-of-the-art SegMitos framework for which we substitute the segmentation network by the more effective DeepLabv3+ semantic segmentation model to achieve better mitosis detection performance. The improved SegMitos model consists of a downsampling path that can capture rich contextual information at multiple scales and an upsampling path that can gradually recover the image objects boundaries. Experimental results on the 2012 ICPR MITOSIS dataset and the AMIDA13 dataset demonstrate the effectiveness of our improved SegMitos system that yields better results than the original SegMitos framework and other state-of-the-art approaches with F-scores of 0.820 and 0.695 respectively.
Automatic Schema Generation for Document-Oriented Systems

Popular document-oriented systems store JSON-like data (e.g. MongoDB). Such data formats combine the flexibility of semi-structured models and traditional data structures like records and arrays. This allows numerous structuring possibilities even for simple data. The data structure choice is important as it impacts many aspects such as memory footprint, data access performances and programming complexity. Our work aims at helping users in selecting data structuring from a set of automatically generated alternatives. These alternatives can be analyzed considering complexity metrics, query requirements and best practices using such "schemaless" databases. Our approach for "schema" generation has been inspired from Software Product Lines strategies based on feature models. From a UML class diagram that represents user's data, we generate automatically a feature model that implicitly contains the structure alternatives with their variations and common points. This feature model satisfies document-oriented constraints so as user constraints reflecting good practices or particular needs. It leads to a set of data structuring alternatives to be considered by the user for his operational choices.
Comparison of deep learning models for predictive maintenance

Abstract There is a clear intersection between the Internet of Things (IoT) and Artificial Intelligence (AI). IoT is about connecting machines and making use of the data generated from those machines. AI is about simulating intelligent behaviour in machines of all kinds. As IoT devices will generate vast amounts of data, then AI will be functionally necessary to deal with these huge volumes if we’re to have any chance of making sense of the data. AI is beneficial for both real-time and post event processing: Post event processing – identifying patterns in data sets and running predictive analytics, e.g. the correlation between traffic congestion, air pollution and chronic respiratory illnesses within a city centre. Real-time processing – responding quickly to conditions and building up knowledge of decisions about those events, e.g. remote video camera reading license plates for parking payments.
A Hybrid Recommendation Method Integrating the Social Trust Network and Local Social Influence of Users

Recommendation system plays an indispensable role in helping users make decisions in different application scenarios. The issue about how to improve the accuracy of a recommendation system has gained widespread concern in both academic and industry fields. To solve this problem, many models have been proposed, but most of them usually focus on a single perspective. Different from the existing work, we propose a hybrid recommendation method based on the users’ social trust network in this study. The proposed method has several advantages over conventional recommendation solutions. First, it offers a reliable two-step way of determining reference users by employing direct trust between users in the social trust network and setting a similarity threshold. Second, it improves the traditional collaborative filtering (CF) method based on a Pearson Correlation Coefficient (PCC) to reduce extreme values in prediction. Third, it introduces a personalized local social influence (LSI) factor into the improved CF method to further enhance the prediction accuracy. Seventy-one groups of random experiments based on the real dataset Epinions in social networks verify the proposed method. The experimental results demonstrate its feasibility, effectiveness, and accuracy in improving recommendation performance.
A New Automatic Tool Searching for Impossible Differential of NIST Candidate ACE

The ACE algorithm is a candidate of the Lightweight Cryptography standardization process started by the National Institute of Standards and Technology (NIST) of the USA that passed the first round and successfully entered the second round. It is designed to achieve a balance between hardware cost and software efficiency for both authenticated encryption with associated data (AEAD) and hashing functionalities. This paper focuses on the impossible differential attack against the ACE permutation, which is the core component of the ACE algorithm. Based on the method of characteristic matrix, we build an automatic searching algorithm that can be used to search for structural impossible differentials and give the optimal permutation for ACE permutation and other SPN ciphers. We prove that there is no impossible differential of ACE permutation longer than 9 steps and construct two 8-step impossible differentials. In the end, we give the optimal word permutation against impossible differential cryptanalysis, which is π′=(2,4,1,0,3), and a safer word XOR structure of ACE permutation.
Feature Matching Algorithm Based on SURF and Lowes Algorithm

Feature extraction and matching of images is a key step in 3D reconstruction, and its accuracy directly affects the accuracy of 3D reconstruction. In this paper, aiming at the mismatch caused by the high similarity between screws, proposes a feature matching algorithm based on median filtering, Lowes algorithm and scale-invariant feature transform (SURF), called M-L-SURF algorithm. First, the median filtering is performed on the screw image to remove noise, then the SURF algorithm is used for feature extraction and matching, and finally, the Lowe's algorithm is used to filter the matching results. The results of experiments show that the M-L-SURF algorithm can achieve a 97.4% correct rate of screw image matching. The matching results obtained in this paper can be better applied to the subsequent work of 3D reconstruction.