Reviewerly/exHarmony huggingface dataset:
Comparison between Different Approaches to Sentiment Analysis in the Context of the Portuguese Language

Sentiment analysis aims to extract subjective information, such as opinions and feelings, from natural language texts. This paper presents the comparison of different approaches to sentiment analysis in the context of the Portuguese language in order to make these results available in the literature to assist researchers in their future work. As an object of application of this study, tweets related to the volleyball theme were used, and a database was organized that has 2,330 tweets divided into 1,032 negatives and 1,298 positives. Lexical, committee and machine learning approaches were used. The committee approach in the Stacking model formed by the Decision Tree, Naive Bayes and Support Vector Machine algorithms as level-0 and the Logistic Regression algorithm as level-1 obtained the best performance with approximately 82% Accuracy, Precision, Recall and F1 Score in prediction of texts.
Using Connectives in Implicit Discourse Relation Recognition

Discourse relation recognition is an important branch of NLP, which is helpful to solve many NLP downstream tasks. In particular, implicit discourse relation recognition(IDRR) is the focus of many research, but they all ignore the role of connectives in IDRR. In this paper, we propose two methods for using connectives to help IDRR task and achieve better results.
Research on Hotspot Mining Method of Twitter News Report Based on LDA and Sentiment Analysis

Nowadays, media from various countries have published a large number of report tweets on international hot topics. The rapid spread of news events on twitter has become increasingly popular. For hotspot mining of news events, topic division and sentiment analysis are two indispensable factors. In this Paper, we use topic segmentation and sentiment analysis to conduct hot mining of social media news for the US media and Chinese media tweets in Huawei-related news in 2019. First, we apply LDA to media tweets to divide topics and obtain related topic words. Then we devised improved methods for effective sentiment analysis on media tweets and influencer comments respectively. What's more, we draw some valid conclusions about news hotspot mining in social media tweets.
Research on Effectiveness Evaluation Method of Weapon System Based on Cloud Model

Abstract The evaluation of weapon system effectiveness can provide scientific basis for improving its own and operational effectiveness, and is of great significance to enrich the theory of weapon construction. Aiming at the complexity and uncertainty of weapon system, a comprehensive evaluation method based on cloud model and AHP is proposed to evaluate the effectiveness of weapon system. Firstly, the specific steps of cloud barycenter evaluation method based on cloud model are given; then, based on the requirement analysis of weapon system, the evaluation index system is established by using analytic hierarchy process; finally, the feasibility of the method is verified by a case. The proposed method provides a new idea for weapon system effectiveness evaluation.
The Guardian Slippers: Designing an IoT Device to Enhance Safety for the Elderly in the Nursing Home

The rapid growth in the global elderly population and the declining number of the newborn brings about the burden of family caregiving for the elderly, resulting in the rising needs for the elderly to stay in a nursing home. Staying in the nursing home elevates the safety for the elderly. However, the caregivers in the nursing home are not able to pay full-time attention to every elderly due to the shortages of staff and their daily heavy work. It might lead to unexpected accidents happening to the elderly out of the caregivers’ sight and cause a delay for assistance. In addition, the family members of the elderly would like to be aware of the elderly’s health status to ensure the condition and living quality. In this paper, we present the design of The Guardian Slippers as an Internet connected slippers with pressure sensor and Bluetooth modules to estimate the elderly’s health status and report their current locations in the nursing home. We describe in detail how design is applied in relation to IoT, and how the Guardian Slippers support to construct a better caring environment between the elderly, caregivers, and family members. We discuss the subtleness of this design application, its practical usage in the indoor environment of the nursing home, and its challenges on the design aspect. Based on these discussions, we provide future designers a design direction on designing nursing home caring systems for the elderly.
Catch the Bus: Probing Other-Than-Human Perspectives in Design Research

Prompted by Catch the Bus, an experimental street game design project with and for autonomous buses, this study explores strategies to substantiate the speculation about other-than-human perspectives. It builds on philosophical arguments about the role of species similarity in grasping nonhuman experience and applies these arguments to thing perspectives. Gameplay and props from Catch the Bus instantiate a kind of similarity between human players and autonomous buses that emerges through the adoption of certain choreographies and sensing capabilities. The study contributes theoretical arguments to the debate of other-than-human perspectives in more-than-human design.
Training Aggregation in Federated Learning

Federated learning is a new machine learning paradigm for distributed data. It enables multi-party cooperation to train global models without sharing their private data. In the classic federated learning protocol, the model parameters are the interaction information between the client and the server. The client can update the local model according to the global model parameters, and the server can aggregate the updated model parameters of each client to obtain a new aggregation model. However, in the actual federated learning scenario, there are still privacy problems caused by model stealing attack in collaborative learning using model parameters as interactive information. Therefore, we use knowledge distillation technology to avoid the model stealing attack in federated learning, and on this basis, we propose a novel aggregation scheme, which can make the output distribution of each customer refine the aggregation results through model training. Experiments show that the scheme can achieve normal convergence while ensuring privacy security, and reduce the number of interactions between client and server, thus reducing the resource consumption of each client participating in federated learning.
Robustness of purity of quantum channels

Abstract Quantifying purity is a basic problem in quantum information theory. When a quantum state passes through a quantum channel, its purity may be changed. In order to explore what role the quantum channel plays in this process, it is necessary to study the purity of quantum channels. In this paper, we formulate the quantum resource theory for channel purity. There are two basic elements in the quantum resource theory for channel purity: free channels and free superchannels. The free channels are completely randomizing channels which neither store nor output purity. The free superchannels are the uniformity preserving superchannels which do not generate resource channels from free ones. We also provide a method for the realization of a uniformity preserving superchannel. We quantify the purity of quantum channels by the robustness and show that the robustness of purity of a quantum channel (RPC) is a purity monotone. We obtain the efficient numerical calculation of RPC through semidefinite programming. Moreover, we find that the RPC has operational significance as the best advantage that one can obtain in the ensemble of quantum state discrimination games. As an application, we also study the RPC for some single-qubit channels.
Player behaviour metrics for adjusting content in VR games: the case of fear

Video games are complex multidisciplinary artefacts, that more and more include sophisticated narrative content able to leverage emotions in players. Due to their interactive nature, they should meet the expectation of a diversified audience. Till now the problem of automatically adapting content to players have focused mainly on gameplay-related aspects, and too few attentions have been devoted to track players emotions to adjust coherently the narrative of the game. This issue becomes more evident in Virtual Reality (VR) based games, in which the immersivity is drastically emphasized, and for specific game-genres, such as horror games. We have tackled this issue by designing and developing a system that tracks the behaviour of the players to adapt the content of a horror VR game, increasing or decreasing drastically its frightening elements according to how much a specific player “likes to be scared”. We have then tested our proposed solution with a sample of players, obtaining some preliminary encouraging results.
Competent of Feature Selection Methods to Classify Big Data Using Social Internet of Things (SIoT)

SIoT discusses about the research activities undergoing in integrating the social networking with the concepts of Internet of Things (IoT). Let us get into the insight of the various feature selection algorithms used to enhance the networking services in IoT in the big data environment. Many research papers in this topic have been done using either filter method, multi-filter multi-wrapper (MFMW) or hybrid method for classification process. The Internet of Things (IoT) is a system of connected things like objects, people or animals that are provided with UIDs and the ability to transmit the data without human to human and human to machine interaction. SIoT gives a social structure of Internet of Things (IoT). SIoT gives the social relationships between objects.
Multi-core Aware Virtual Machine Placement for Cloud Data Centers with Constraint Programming

Creation of a Virtual Machine (VM) in a suitable Physical Machine (PM) is the critical requirement of a Cloud Service Provider in cloud data center. Mapping the VM to an appropriate PM is called VM Placement. The VMP decision is required at different stages of a cloud data center. Finding the best PM to place/create a VM is popularly known as Virtual Machine Placement Problem (VMPP). Efficient VMP techniques improve the resource utilization, power consumption and number of VM migrations in a cloud data center. Due to the significant and intrinsic difficulty, many models and algorithms have been proposed for VMPP. VMP algorithm is executed by considering the resources like CPU, RAM, Disk, etc. of VMs and PMs. Many researchers have designed VMP algorithms by considering the CPU capacity of a PM without considering the number of physical cores (pCPU) available in a PM and physical core capacity of a PM. i.e. VMs are mapped onto PMs, if and only if the sum of their CPU capacity does not exceed the CPU capacity of a PM. Such allocation results in pCPU overload which leads to performance degradation and violation of Service Level Agreement. However, in the real scenario, PM and VM consist of multiple physical cores. So, to place VM on PM, the CPU capacity of VM must be mapped to pCPUcapacity of a PM. i.e. the virtual core (vCPU) of a VM should be mapped to a pCPU of a PM. So, VM to PM mapping must be done based on core to core mapping and not on machine to machine mapping. Also, VMP using the total computational capacity of the PM leads to increase the number of core overloads and thus the host overload. More number of host overload leads to more number of VM migration. So, we have designed a VMP algorithm which will eradicate all the above mentioned problems of core overload, less resource utilization, more number of host overloads and more number of VM migrations. In our proposed work, we have considered a multi-core aware VMP by considering the core capacity of a VM and PM. In multi-core aware VMP, the CPU capacity of VM is checked against the pCPU capacity of PM, so that, the pCPU of a PM not get overloaded. So, that the overall performance is improved when compared to non-core aware virtual machine placement algorithms.
MoScan: a model-based vulnerability scanner for web single sign-on services

Various third-party single sign-on (SSO) services (e.g., Facebook Login and Twitter Login) are widely deployed by web applications to facilitate their authentication and authorization processes. Nevertheless, integrating these services in a secure manner remains challenging, such that security issues are continually reported in recent years. In this work, we develop MoScan, a model-based scanner that can be used by software testers and security analysts for detecting and reporting security vulnerabilities in SSO implementations. MoScan takes as input a state machine built based on an SSO standard and our empirical study to represent participants' states and transitions during the login process. In the testing process, it analyzes network traces captured during the execution of SSO services, and increments the state machine which is then used to generate payloads to test the protocol participants. We evaluate MoScan with 23 real-world websites which integrate the Facebook SSO service to test its capability of identifying security vulnerabilities. To show the adaptability of MoScan's state machine, we also test it on Twitter and LinkedIn’s SSO services, and Github's authentication plugin in Jenkins. It detects three known weaknesses and one new logic fault from them, showing a new perspective in testing stateful protocol implementations like SSO services. Our demonstration and the source code of MoScan are available at https://github.com/baigd/moscan.
Automobile indexation from 3D point clouds of urban scenarios

In this paper, we introduce a methodology for the detection and segmentation of automobiles in urban scenarios. We use the LiDAR Velodyne HDL-64E to scan the surroundings. The method is comprised of three steps: (1) remove facades, ground plan, and unstructured objects, (2) smoothing data using robust principal component analysis (RPCA), and finally, (3) unstructured objects model and indexing. The dataset is partitioned into training with 4500 objects and test with 3000 objects. Mean Shift thresholds, the filter, the Delaunay parameters, and the histogram modelling are optimized via ROC analysis. It is observed that the car scan quality affects our method to a lesser degree when compared with state-of-the-art methods.
A Survey on Sentiment Analysis

Natural language processing (NLP) is a booming field in this era of data, where almost all businesses and organizations have access to many review sites, social media, and e-commerce websites. Recently, deep learning models have shown state-of-the-art results in NLP tasks. With the help of complex models like long-short term memory, various problems such as vanishing gradient problem have been diminished and new models like the attention model or aspect embedding increases accuracy. These made a drastic change in the field of sentiment analysis and made it more business-oriented, like most of the big business organizations, for example, Amazon and Flipkart, where it is used for analyzing details about their customer review. Some researchers have shown us a way to not even using complex models like LSTM we can do so, even better with adding gating mechanism to our well-known CNN. Watching all of these, we are going to do a brief review of many technologies discovered by many scientists across the world and focus on some of the state-of-the-art works done in the domain of sentiment analysis.
Critical Tools for Machine Learning: Situating, Figuring, Diffracting, Fabulating Machine Learning Systems Design

This workshop draws on feminist and other critical methodologies to construct interdisciplinary interventions in the design of machine learning systems. Theoretical concepts of “figuration”, “situating/situated knowledge”, “critical fabulation/speculation,” and “diffraction” are explored through hands-on experimentation to imagine and design machine learning systems in a more situated, inclusive, contextualize and accountable way. Through this “theory turned practice” approach the workshop aims to address systemic socio-cultural biases and develop more socially responsible frameworks of design. The workshop provides space for building a network for future research on interdisciplinary machine learning systems design.
Lion Swarm Optimization by Reinforcement Pattern Search

Lion swarm optimization (LSO) is a swarm intelligence algorithm that simulates lion king guarding, lioness hunting, and cub following. However, there are problems that lions are easily out of bounds when the range of activity is large and the position update formulas are not universal, which affect the performance of LSO. Aiming at above problems, a swarm intelligence algorithm, lion swarm optimization by reinforcement pattern search (RPSLSO) is proposed. The algorithm is based on the proposed modified lion swarm optimization (MLSO) and reinforcement pattern search (RPS) algorithm. The former solves above two problems, and the latter enhances the local search capability of MLSO, making the search more directional. In order to test the performance of RPSLSO, RPSLSO was compared with MLSO, LSO and the other two algorithms on the CEC2013 test function set. The experimental results show that the performance of RPSLSO is better, and the modifications to LSO and the proposed RPS in this paper are also effective.
Users’ Content Memorization in Multi-User Interactive Public Displays

In this study, we investigate users’ content memorization in interactive public displays that can be simultaneously used by multiple persons. We designed a system in which interaction would enhance users’ content memorization, and that allows multiple users to collaborate on interaction. We installed the system in a university campus and conducted a field study on passersby. We interviewed the users who continued interacting with the system to reach or pass a certain phase, and evaluated users’ content memorization from the accuracy of displayed information that was unconsciously memorized. As a result, the users who interacted with the display had a significantly higher correct answer rate for detailed questions about the displayed information than those who only watched a video on the display, which suggests that our interactive public display resulted in better content memorization. It is also shown that displayed information was efficiently delivered to many users by enabling multiple persons to interact with the display.
Empirical analysis of variance for genetic programming based symbolic regression

Genetic programming (GP) based symbolic regression is a stochastic, high-variance algorithm. Its sensitivity to changes in training data is a drawback for practical applications.
Vision: What Johnny learns about Password Security from Videos posted on YouTube

The text password is the most pervasive authentication scheme and is unlikely to disappear soon. Companies employ password awareness and training campaigns to reduce the risk of insecure password management endangering these companies. While larger companies may buy measures for those campaigns externally or develop their own ones, small and medium-sized companies (SMEs) are likely to turn to freely available material – most likely videos – as they don’t have budget and own experience to handle it like larger companies. We analysed such freely available videos and show their shortcomings. To that end, we aggregated requirements from the existing literature and applied these to a body of 32 freely available YouTube videos using search terms informed by Google Trends. The contributions of this work are two-fold. Firstly, the findings of our analysis show that the best video covers only about half of the requirements, which raises serious concerns regarding the quality of available videos and their suitability for usage in awareness campaigns by SMEs. Secondly, our list of aggregated requirements can inform the design of future videos, which is planned as a follow-up to this work to remedy the concerns uncovered in our analysis.
Revolution and stability in the study of the human factor in the security of information systems field : A systematic literature review over 30 years of publication

Human factor is widely recognized as the first threat to the security of information systems (ISS). ISS research thus points to the problem of user behavior, which is overwhelmingly represented as a fallibility that would be part of its nature. Companies would therefore have no choice but to anticipate these behaviors in order to reinforce the security of the information system. However, despite all the collective legitimacy contributing to the "normal" evolution of this field of research, could we think differently this problem? We therefore conducted a critical review of the literature on the human factor in information system security publications over 31 years (between 1989 and 2020). Our results draw the details of a normal science that has developed and deepened our knowledge of human behavior to protect an information system. We discovered that this main knowledge production shares structural epistemic and moral assumptions. These researchers' choices are problematic since they are implicit and consequently raise concern about a very partial and simplifying representation of user's contribution to a good security. We advocate for the development of alternative epistemic and moral choices to nourish the evolution of the current paradigmatic consensus. This alternative agenda is likely to improve recommendations for practice while showing a greater objectivity.
Dual-Path Model for Person Re-Identification Under Cloth Changing

Most of the existing person re-identification (ReID) methods relies heavily on a person's clothes since clothing information is the clear and remarkable visual feature when the face of a person is unclear. However, in reality, people does not always wear the same cloth across camera views. Even worse, an adversary may change the clothes aiming to evade the identification. Some studies confirms that clothes changing downgrades the existing ReID methods significantly. The current ReID method considering clothes-changing does not fully utilize the person discriminant features, which may reduce its accuracy. This paper presents a dual-path model to learn the robust features under clothes changing and also the discriminant features for ReID from a RGB image and its contour sketch image respectively. The appearance and shape features of a person extracted by the two branches of our model are then combined to make a decision. The clothing information is eliminated from the appearance features by encouraging the similarity between the learned appearance and shape features. The experimental results on the PRCC dataset demonstrate that our model achieves higher performance under clothes changing compared to state-of-the-art ReID methods.
Slovene and Croatian word embeddings in terms of gender occupational analogies

In recent years, the use of deep neural networks and dense vector embeddings for text representation have led to excellent results in the field of computational understanding of natural language. It has also been shown that word embeddings often capture gender, racial and other types of bias. The article focuses on evaluating Slovene and Croatian word embeddings in terms of gender bias using word analogy calculations. We compiled a list of masculine and feminine nouns for occupations in Slovene and evaluated the gender bias of fastText, word2vec and ELMo embeddings with different configurations and different approaches to analogy calculations. The lowest occupational gender bias was observed with the fastText embeddings. Similarly, we compared different fastText embeddings on Croatian occupational analogies.