Reviewerly/exHarmony huggingface dataset:
Enabling data security in data using vertical split with parallel feature selection using meta heuristic algorithms

Summary Big data is the emerging trend in modern science that deals with datasets larger and more complex that cannot be dealt by the traditional data processing techniques. This seems to be the core of current technology and business. In practice, many criteria should be considered in the implementation of this technique. The way of the search space for finding potential subsets of features and prediction performance of classifiers are major important issue. To solve this issue, feature selection methods are introduced in the recent work. In the feature selection algorithm, Non‐deterministic Polynomial (NP) Hard, and searching the space has been becomes more difficult task. To solve this problem, this work provides a new approach toward feature selection based on Vertical Split Group FireFly (VSGFF) algorithm. FF algorithm gets its inspiration from social aspects of real fireflies. At the same time, VSGFF is proposed with the principle of multiple clusters to avoid privacy problem. Finally, Naïve Bayes (NB), K Nearest Neighbor (KNN), and Multi‐Layer Perceptron Neural Network (MLPNN) classification algorithms are proposed for big data classification. Experimental outcomes depicts that proposed technique improves classification accuracy by 4% compared to traditional vertical split firefly algorithm.
Literature review on Big Data and Its Application Fields

In recent years, due to information technology such as computer, Internet and the development of sensor technology, make modern life appeared “everything can be a digital” thinking, as a complex system test, test and condition monitoring of data series in growth, big data is becoming a hot research topic in the field of all kinds of industrial. This paper will start from the background of the era of big data, discuss the features of big data and its application in reality, and elaborate the trend of big data development from the perspective of data science, industry 4.0 and information physics system, and analyze the challenges of big data development.
Improving the programming skills of students in multiagent systems master courses

Abstract Multiagent systems (MASs) courses are nowadays present in most master's degrees in Artificial Intelligence. In such courses, students have to cope with the gap between some high‐level theoretical concepts and techniques that allow agents to reach agreements in order successfully cooperate (the so‐called Agreement Technologies) and their corresponding implementations to be used in real programs. If the learning outcomes of such courses include the development of actual (although reasonably simple) MASs, the incorporation of some strategy to make students enhance their initial programming skills becomes crucial for their success. In this context, this article presents both a tool called SimFleet and a teaching methodology that have been designed together to guide students throughout a typical MASs course and, specifically, to enhance their programming skills in order learn how to develop actual MASs.
Topic model for graph mining based on hierarchical Dirichlet process

In this paper, a nonparametric Bayesian graph topic model (GTM) based on hierarchical Dirichlet process (HDP) is proposed. The HDP makes the number of topics selected flexibly, which breaks the limitation that the number of topics need to be given in advance. Moreover, the GTM releases the assumption of ‘bag of words’ and considers the graph structure of the text. The combination of HDP and GTM takes advantage of both which is named as HDP–GTM. The variational inference algorithm is used for the posterior inference and the convergence of the algorithm is analysed. We apply the proposed model in text categorisation, comparing to three related topic models, latent Dirichlet allocation (LDA), GTM and HDP.
Towards Generating Spatial Referring Expressions in a Social Robot: Dynamic vs Non-Ambiguous

We present in this paper our work towards a new dynamic method of generating spatial referring expressions. While people are generally ambiguous in their description of locations, previous methods of artificial generation mostly considered non-ambiguous descriptions. However, to increase the naturalness of interaction and share workload in the communication, robots should be able to generate language in a more dynamic way. Our method initially produces ambiguous spatial referring expressions followed by dynamically generating repair statements. We built a classifier using data from 18 participants as they described locations to each other. We perform a preliminary analysis on this method using two further pilot studies.
Think Global, Act Global: Collection Development in STEM Across an International Academic Institution

In an age where more and more universities are expanding their campuses to other countries, libraries must attempt to deliver a consistent experience in their services, policies, and access to information resources. Within New York University’s Division of Libraries, collection development and electronic resources librarians along with subject specialists across the STEM disciplines regularly meet to procure information resources for the benefit of all of their respective scholarly communities. The librarians and specialists liaise with the schools of engineering, mathematics, medicine, nursing, and the arts & sciences across campuses in Manhattan, Brooklyn, Abu Dhabi, and Shanghai. Although this approach is similar to how state university systems act in consortia to share licenses, the librarians in NYU’s virtual science consortium accomplish this on a global scale—supporting the institution’s growing scholarly communities and spirit of interdisciplinary collaboration. This commentary reflects on the challenges and successes that have accompanied building a global library collection spanning over a dozen global sites and two portal campuses. It will address familiar issues around rising publishing costs, resource sharing/licensing, and the weeding/vetting of electronic resources from a global perspective. Finally, the members of the consortium will share their forecasts for the future of collection development and libraries in globalized higher education.
Active Learning for Network Intrusion Detection

Network operators are generally aware of common attack vectors that they defend against. For most networks, the vast majority of traffic is legitimate. However, new attack vectors are continually designed and attempted by bad actors which bypass detection and go unnoticed due to low volume. One strategy for finding such activity is to look for anomalous behavior. Investigating anomalous behavior requires significant time and resources. Collecting a large number of labeled examples for training supervised models is both prohibitively expensive and subject to obsoletion as new attacks surface. A purely unsupervised methodology is ideal; however, research has shown that even a very small number of labeled examples can significantly improve the quality of anomaly detection. A methodology that minimizes the number of required labels while maximizing the quality of detection is desirable. False positives in this context result in wasted effort or blockage of legitimate traffic, and false negatives translate to undetected attacks. We propose a general active learning framework and experiment with different choices of learners and sampling strategies.
Application of Augmented Reality in Product Package with Quick Response Code

Users understand a product usually need to open its packaging, control product manuals to understand the function and usage of the product. However, through the use of Augmented Reality (AR) technology and some computer 3D modelling techniques into Quick Response Code (QR Code) on product packaging, a virtual product model can be constructed by scanning a QR Code with a mobile phone without disassembling the package can understand the appearance, function and use of the product. This article aims to study the market value of the technology and its technical means. Meanwhile, its advantages are discussed in packing.
Sustainable Physical Structure Design of AR Solution for Golf Applications

One of the applications of virtual and augmented reality (VR/AR) is the social sphere, such as communication among individuals and also sports activities. It is important to select a sustainable technical solution when designing the AR application. At present, AR smart glasses, which are offered by several manufacturers on the market, can be considered the most suitable. The author’s article discusses possible options for assessing the sustainability of the proposed hardware, based on the methods of system dynamics simulation.
Paintball – Automated Wordnet Expansion Algorithm based on Distributional Semantics and Information Spreading

plWordNet has been consequently built on the basis of the corpus-based wordnet development method.As plWordNet construction had started from scratch it was necessary to find a way to reduce the amount of work required, and not to reduce the quality.In the paper we discuss the gained experience in applying different tools based on Distributional Semantics methods to support the work of lexicographers.A special attention is given to the Paintball algorithm for semiautomated wordnet expansion and its application in the WordnetWeaver system.
Mobile Applications and Their Use in Language Learning

It is undeniable that the world is taking a turn for the digital. Banking, shopping, working, education, entertainment, everything has its own digital equivalent or means thereof. This includes language learning. Nowadays, there is a myriad of (mobile and digital) possibilities to study, learn and improve a person’s knowledge of virtually any language. Thus, there should be potential benefits in their use in academic level (foreign) language learning, but also in an individual “self-learning” process. In this study, we choose to tackle mobile apps and their impact in the teaching/learning of languages, the perception of their usefulness and their impact (positive or otherwise) on the subject. To achieve this, a literature review on the subject was conducted, in addition to twenty interviews performed with students of the University of Aveiro. Supported by our literature review, we can safely state that language learning is just one of the many segments of both life and academia that can only be positively affected by digitalisation, and that we will continue seeing a rising trend towards that goal.
Energy bounds for entangled states

This paper shows which are the extremal values of the local energy of an arbitrary finite bipartite system having a given amount of entanglement. The authors also identify families of states reaching these energy bounds. These extremal states are shown to be typically rare and they appear to have connections to different areas of physics such as quantum information, quantum thermodynamics, quantum optics, and many-body physics
Towards an emotionally-aware smart wheelchair

In this abstract and the poster associated with it, we present preliminary results on people's emotional perception of assisted driving interventions as a first step in the development of emotionally-aware smart wheelchairs. Current approaches to collaborative wheelchair navigation are designed to handle objective and functional information (such as goals and system states) but little subjective information (such as the user's feeling at the introduction of an intervention). We explore user affect as a potential communication channel through which users could communicate more richly with their smart mobility device.
Autonomous Navigation and Nonlinear Control for Quadrotors in a Structured Environment

This paper deals with the autonomous flight problem for a quadrotor. The quadrotor must reach its mission waypoints to complete a package delivery task autonomously while avoiding obstructing obstacles. A new linear matrix inequality–based dynamic gain adaptive robust control approach is proposed for the attitude and position controller of the quadrotor. The transient and steady-state tracking performances are guaranteed under bounded uncertain parameters of the quadrotor and external disturbances. To realize a higher level of automation for the quadrotor, an autonomous navigation strategy that consists of a trajectory generation approach and a sampling-based multicriteria local waypoint selection algorithm is presented. The trajectory generation approach is used to realize waypoint-based navigation with the proposed controller for the quadrotor. The sampling-based multicriteria local waypoint selection approach is proposed based on a deterministic sampling process and solved by a multicriteria optimization problem to balance path safety and speed in a structured environment. This autonomous navigation strategy enables the avoidance of path/trajectory planning, which could be time consuming in a cluttered environment. The simulation results demonstrate that the proposed controller and autonomous navigation strategy are useful for a variety of quadrotor tasks, including precise trajectory tracking and autonomous navigation in an unknown obstacle-laden environment.
Recognising Summary Articles

Online content providers process massive streams of texts to supply topics and entities of interest to their customers. In this process, they face several information overload problems. Apart from identifying topically relevant articles, this includes identifying duplicates as well as filtering summary articles that comprise of disparate topical sections. Such summary articles would be treated as noise from a media monitoring perspective, an end user might however be interested in just those articles. In this paper, we introduce the recognition of summary articles as a novel task and present theoretical and experimental work towards addressing the problem. Rather than treating this as a single-step binary classification task, we propose a framework to tackle it as a two-step approach of boundary detection followed by classification. Boundary detection is achieved with a bi-directional LSTM sequence learner. Structural features are then extracted using the boundaries and clusters devised with the output of this LSTM. A range of classifiers are applied for ensuing summary recognition including a convolutional neural network (CNN) where we treat articles as 1-dimensional structural 'images'. A corpus of natural summary articles is collected for evaluation using the Signal 1M news dataset. To assess the generalisation properties of our framework, we also investigate its performance on synthetic summaries. We show that our structural features sustain their performance on generalisation in comparison to baseline bag-of-words and word2vec classifiers.
Logging Integrity with Blockchain Structures

In developed countries, it is frequent for family members do not have the time, knowledge, or live in a close distance of their senior loved ones, so that many institutions offer their services to provide a good quality of life of older adults. To enable distributed local support, there is the need of digital platforms to allow the exchange of information. These platforms need to create trustful environments and to guarantee the integrity of the information exchanged. In this paper, it is presented a solution for a Logging Service that was developed for the SOCIAL platform, based on FHIR, which aims to solve the interoperability and data integrity of the platform user’s activity logs.
Towards a Structural Proof Theory of Probabilistic $$\mu $$ -Calculi

Abstract We present a structural proof system, based on the machinery of hypersequent calculi, for a simple probabilistic modal logic underlying very expressive probabilistic $$\mu $$ -calculi. We prove the soundness and completeness of the proof system with respect to an equational axiomatisation and the fundamental cut-elimination theorem.
Implementation of RFID as tracking for hajj pilgrims connected with GPS (IM RFID SECURE GPS)

This is a research of identification system and position of the carrier model ID card. Unique number is using as identifier based on work principle of RFID tag and reader, utilizing radio waves. The method of experimental by tracking the unique number of ID card model carried by pilgrims. Before the models gets away from the group as soon as possible it detected at a certain position. This distance limitation is to guarantee the safety of the pilgrim and as a guarantee for the services of hajj bureau. Hajj service is prioritizes the safety and comfortable of every pilgrim’s member. So it is necessary to monitor the position of pilgrims who are away or separated from their group. The solution is by designing a tool / an identification system for tracking the position of hajj pilgrims. Position viewer media utilizes Google Maps on a smartphone. The purpose of this research is to design a system for anticipate the disappearance of hajj pilgrims. The identify is designed with concept of identification RFID tag-reader through radio waves 2.45 GHz. The detection system is adopted by utilizing two NodeMCU ESP8266 communications, each as an RFID tag and reader. The NodeMCU reader/master has identified as a unique number of the tag/client. The samples are consisting of two clients have been detected by Reader in NodeMCU according to unique number according to their position. Connectivity NodeMCU and GSM Module enables to send SMS for Tag users via smartphone/non smartphone. The message is “Wait, you get lost, so we will pick you up” and coordinate from Google Maps. The tracking system of the Tag model position by the reader has been tested at a radius of < 15 meter.
On the Linear Complexity of Binary Generalized Cyclotomic Sequences of Period 2&lt;i&gt;p&lt;/i&gt;&lt;sup&gt;&lt;i&gt;m&lt;/i&gt;+1&lt;/sup&gt;&lt;i&gt;q&lt;/i&gt;&lt;sup&gt;&lt;i&gt;n&lt;/i&gt;+1&lt;/sup&gt;

In this paper, new classes of binary generalized cyclotomic sequences of period 2pm+1qn+1 are constructed. These sequences are balanced. We calculate the linear complexity of the constructed sequences with a simple method. The results show that the linear complexity of such sequences attains the maximum.
Design of Public-Key Algorithms Based on Partial Homomorphic Encryptions

With the rapid development of cloud computing, which has become a key aspect to maintain the security of user information that may be highly confidential and maintained during transport and storage process. The reliance on traditional algorithms that are used to encrypt data are not secure enough because we cannot process the data only after decrypt. In this article is proposed the use of homomorphic encryption to solve this problem because it can deal with encrypted data without the decryption, which can lead to ensuring confidentiality of the data. A number of public-key algorithms are explained, which is based on the concept of homomorphic encryption. In this article an algorithm is proposed based on HE and it is similar to Menesez-EC but with one digit as a secret key according to its advantage, whereby reducing the cost of communication, and storage and provides high processing speed when compared with other algorithms. This algorithm provides enough security for a bank's customer information and then compared with ECC, each of RSA and Piallier algorithms as evaluated.
Pro/Con: Neural Detection of Stance in Argumentative Opinions

Accurate information from both sides of the contemporary issues is known to be an ‘antidote in confirmation bias’. While these types of information help the educators to improve their vital skills including critical thinking and open-mindedness, they are relatively rare and hard to find online. With the well-researched argumentative opinions (arguments) on controversial issues shared by Procon.org in a non-partisan format, detecting the stance of arguments is a crucial step to automate organizing such resources. We use a universal pretrained language model with weight-dropped LSTM neural network to leverage the context of an argument for stance detection on the proposed dataset. Experimental results show that the dataset is challenging, however, utilizing the pretrained language model fine-tuned on context information yields a general model that beats the competitive baselines. We also provide analysis to find the informative segments of an argument to our stance detection model and investigate the relationship between the sentiment of an argument with its stance.
Private Shotgun DNA Sequencing: A Structured Approach

DNA sequencing has faced a huge demand since it was first introduced as a service to the public. This service is often offloaded to the sequencing companies who will have access to full knowledge of individuals' sequences, a major violation of privacy. To address this challenge, we propose a solution, which is based on separating the process of reading the fragments of sequences, which is done at a sequencing machine, and assembling the reads, which is done at a trusted local data collector. To confuse the sequencer, in a pooled sequencing scenario, in which multiple sequences are going to be sequenced simultaneously, for each target individual, we add fragments of one non-target individual, with a known DNA sequence at the data collector. Then coverage depth of the individuals, defined as the number of DNA fragments per DNA site, are selected proportional to the powers of two. This layered structured solution allows us to ensure privacy, using only one sequencing machine, in contrast to our previous solution, where we relied on the existence of multiple non-colluding sequencing machines.
Long short-term memory network with external memories for image caption generation

In long short-term memory (LSTM) neural networks, the input gates and output gates control information flowing into and out of memory cells. For sequence-to-sequence learning problems, each element is input into the network only once. If the input gates are closed at a certain step, the information is lost and is not input again. The same problem exists for the output gates. Therefore, the input and output gates do not fully support the roles of gating. An LSTM network with external memories, in which separate memories are installed for the input and output gates, is proposed. Information that is blocked by the input gates is preserved in the input memories, enabling the cells to read these memories when necessary. Similarly, information blocked by the output gates is preserved in the output memories and flows out to hidden units of the network at an appropriate time. In addition, a dynamic attention model is proposed to take into account the attention history. It provides guidance when predicting the attention weights at each step. The proposed model exploits attention-based encoder–decoder architecture to generate image captions. Experiments were conducted on three benchmark datasets, namely Flickr8k, Flickr30k, and MSCOCO, to demonstrate the effectiveness of the proposed approach. Captions generated by the proposed method are longer and more informative than those obtained with the original LSTM network.