Reviewerly/exHarmony huggingface dataset:
Performance Assessment of Face Analysis Algorithms with Occluded Faces

In retail environments, it is important to acquire information about customers entering in a selling area, by counting them, but also by understanding stable traits (such as gender, age, or ethnicity) and temporary feelings (such as the emotion). Anyway, in the last year, due to the COVID-19 pandemic, it is becoming mandatory to wear a mask, covering at least half of the face, thus making the above mentioned face analysis tasks definitely more challenging. In this paper, we evaluate the drop in the performance of these analytics when the face is partially covered by a mask, in order to evaluate how existing face analysis applications can perform with occluded faces. According to our knowledge, this is the first time a similar analysis has been performed. Furthermore, we also propose two new datasets, designed as extensions with masked faces of the widely adopted VGG-Face and RAF-DB datasets, that we make publicly available for benchmarking purposes. The analysis we conducted demonstrates that, except for gender and ethnicity recognition whose accuracy drop is quite limited (less than \(10\%\)), further investigations are necessary for increasing the performance of methods for age estimation (MAE drop between 4 and 10 years) and emotion recognition (accuracy decrease between \(45\%\) and \(55\%\)).
Towards alignment strategies in human-agent interactions based on measures of lexical repetitions

Alignment of communicative behaviour is an important feature of Human–Human interaction that directly affects the collaboration and the social connection of conversational partners. With the aim of improving the communicative abilities of a virtual agent, and in particular its strategies related to (lexical) verbal alignment, this article focuses on the alignment of linguistic productions of dialogue participants in task-oriented dialogues. We propose a new framework to quantify both the lexical alignment and the self-repetition behaviours of dialogue participants from dyadic dialogue transcripts. The framework involves easily computable measures based on repetition of lexical patterns automatically extracted via a sequential pattern mining approach. These measures allow the characterisation of the nature of these processes by addressing various informative aspects such as their variety, complexity, and strength. This framework is implemented in the freely available and open-source software dialign. Using these measures, we present a contrastive study between Human–Human and Human–Agent dialogues on various corpora that reveals major differences in the lexical alignment and self-repetition behaviours. Lastly, we address the challenge of integrating lexical alignment capabilities in artificial agents. To this end, we describe guidelines and we discuss the integration of the proposed framework in a real-time dialogue system.
Estimating PV models using multi-group salp swarm algorithm

&lt;span id="docs-internal-guid-ea798321-7fff-3e0c-24d7-776c9b1165b3"&gt;&lt;span&gt;In this paper, a multi-group salp swarm algorithm (MGSSA) is presented for estimating the photovoltaic (PV) solar cell models. The SSA is a metaheuristic technique that mimics the social behavior of the salp. The salps work in a group that follow a certain leader. The leader approaches the food source and the rest follows it, hence resulting in slow convergence of SSA toward the solution. For several groups, the searching mechanism is going to be improved. In this work, a recently developed algorithm based on several salp groups is implemented to estimate the single-, double-, triple-, Quadruple-, and Quintuple-diode models of a PV solar cell. Six versions of MGSSA algorithms are developed with different chain numbers; one, two, four, six, eight and half number of the salps. The results are compared to the regular particle swarm optimization (PSO) and some of its newly developed forms. The results show that MGSSA has a faster convergence rate, and shorter settling time than SSA. Similar to the inspired actual salp chain, the leader is the most important member in the chain; the rest has less significant effect on the algorithm. Therefore, it is highly recommended to increase the number of leaders and reduce the chain length. Increasing the number of leaders (number of groups) can reduce the root mean squared error (RMSE) and maximum absolute error (MAE) by 50% of its value.&lt;/span&gt;&lt;/span&gt;
Multi-Cloud Adoption Challenges for the Cloud-Native Era

With the ready availability of appropriate technologies and tools for crafting hybrid clouds, the move towards employing multiple clouds for hosting and running various business workloads is garnering subtle attention. The concept of cloud-native computing is gaining prominence with the faster proliferation of microservices and containers. The faster stability and maturity of container orchestration platforms also greatly contribute towards the cloud-native era. This paper guarantees the following contributions: 1) It describes the key motivations for multi-cloud concept and implementations. 2) It also highlights various key drivers of the multi-cloud paradigm. 3) It presents a brew of challenges that are likely to occur while setting up multi-cloud. 4) It elaborates the proven and potential solution approaches to solve the challenges. The technology-inspired and tool-enabled solution approaches significantly simplify and speed up the adoption of the fast-emerging and evolving multi-cloud concept in the cloud-native era.
A Constructive DIREST Security Threat Modeling for Drone as a Service

The technology used in drones is similar or identical across drone types and components, with many common risks and opportunities.The purpose of this study is to enhance the risk assessment procedures for Drone as a Service (DaaS) capabilities.STRIDE is an acronym that includes the following security risks: Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, and Elevation of Privileges.The paper presents a modified STRIDE threat model and prioritizes its desired properties (i.e., authenticity, integrity, non-reputability, confidentiality, availability, and authorization) to generate an appropriate DaaS threat model.To this end, the proposed DIREST threat model better meets the overall security assessment needs of DaaS.Moreover, this paper discusses the security risks of drones, identifies best practices for security assessment, and proposes a novel software update mechanism for drones during their operations.We explore the best practices related to drone penetration testing, including an effective methodology to maintain the continuity of drone operations, particularly drones used for emergency, safety, and rescue operations.Moreover, this research raises awareness of DaaS and drone operation in general as well as in the forensic science community due to its focus on the importance of securely operated drones for first responders.Furthermore, we address various aspects of security concerns, including data transmission, software restrictions, and embedded system-related events.In order to propose a security assessment for drones, we incorporate digital forensics and penetration testing techniques related to drone operations.Our results show that the proposed threat model enhances the security of flying devices and provides consistency in digital forensic procedures.This work introduces modifications to the STRIDE threat model based on the current literature, drone images provided by the NIST program, and a firmware static analysis of a zino hubsan brand drone.
The development and application of new media technology in news communication industry

Many new media technologies have emerged in modern society. The application of new media technologies has impacted traditional TV news media, which not only faces great challenges, but also brings some lessons for the development of TV news media. New media technology relies on powerful information processing technology and data storage technology to develop and grow continuously. Compared with traditional news, new media technology has more powerful information storage capacity and dissemination capacity. Firstly, this paper briefly introduces the concept of new media technology, summarizes the typical characteristics of new media technology, and analyzes the existing problems in the application of new media technology in the news communication industry based on the necessity of applying new media technology. Finally, some Suggestions are put forward based on this, hoping to provide some reference for the development of news communication industry.
FNG-IE: an improved graph-based method for keyword extraction from scholarly big-data

Keyword extraction is essential in determining influenced keywords from huge documents as the research repositories are becoming massive in volume day by day. The research community is drowning in data and starving for information. The keywords are the words that describe the theme of the whole document in a precise way by consisting of just a few words. Furthermore, many state-of-the-art approaches are available for keyword extraction from a huge collection of documents and are classified into three types, the statistical approaches, machine learning, and graph-based methods. The machine learning approaches require a large training dataset that needs to be developed manually by domain experts, which sometimes is difficult to produce while determining influenced keywords. However, this research focused on enhancing state-of-the-art graph-based methods to extract keywords when the training dataset is unavailable. This research first converted the handcrafted dataset, collected from impact factor journals into n -grams combinations, ranging from unigram to pentagram and also enhanced traditional graph-based approaches. The experiment was conducted on a handcrafted dataset, and all methods were applied on it. Domain experts performed the user study to evaluate the results. The results were observed from every method and were evaluated with the user study using precision, recall and f-measure as evaluation matrices. The results showed that the proposed method (FNG-IE) performed well and scored near the machine learning approaches score.
Three‐Mode Squeezing of Simultaneous and Ordinal Cascaded Four‐Wave Mixing Processes in Rubidium Vapor

Abstract Multipartite quantum correlation plays a vital role in potential applications of quantum technologies. In this paper, using energy‐level cascaded four‐wave mixing (EC‐FWM) process, a scheme to produce quantum correlated three‐mode light beams within a single device of hot atomic medium of rubidium is proposed. Two‐ and three‐mode amplitude, phase quadrature squeezing and intensity difference squeezing using energy‐level simultaneous cascaded FWM (ESC‐FWM) method and energy‐level ordinal cascaded FWM (EOC‐FWM) method, respectively, is theoretically reported. Via investigating their covariance matrix properties, the comparison has been made between the two methods, which shows the ESC‐FWM method has a more stable mode structure than EOC‐FWM method. Moreover, versatile gain dependent squeezing via EC‐FWM is studied and analytical expressions are given. These results show a simplified and efficient experimental scheme producing multipartite quantum correlation among multiple spatially separated beams.
Unconstrained Text Detection in Manga: A New Dataset and Baseline

The detection and recognition of unconstrained text is an open problem in research. Text in comic books has unusual styles that raise many challenges for text detection. This work aims to binarize text in a comic genre with highly sophisticated text styles: Japanese manga. To overcome the lack of a manga dataset with text annotations at a pixel level, we create our own. To improve the evaluation and search of an optimal model, in addition to standard metrics in binarization, we implement other special metrics. Using these resources, we designed and evaluated a deep network model, outperforming current methods for text binarization in manga in most metrics.
RETRACTED: Extreme learning machine for stock price prediction

Stock market performance prediction has always been a hit research topic and is attractive due to its strong potential to generate financial profit. Being able to predict future stock price in a relatively accurate way forms a significant task of stock market analysis. Different mechanisms from fundamental analysis to statistical modeling have been deployed to study stock market performance and various factors from fundamental factors, technical factors to market sentiments are also incorporated in the stock price prediction task. However, due to the chaotic stock market performance, which is close to random walk, and the difficulty in discerning influential factors, predicting stock price faces a lot of challenges. In recent years, fast development in fields such as machine learning has offered new ways to look at this task. In this paper, we employ Extreme Learning Machine (ELM) algorithm, a recent modification of traditional feedforward neural network with single hidden layer, whose learning speed is greatly improved based on solid mathematical background and capability to circumvent problems such as local minimum is also enhanced, to construct an ELM combination model to study stock market performance and predict stock price. A comparison between the predicted output and the real data is carried out to test the feasibility of applying ELM model to stock market analysis. The result indicates that ELM model is desirable for predicting stock price variation trend while some inaccuracy exists in the prediction of peak values, which may require further model modification. Overall, by applying the machine learning model ELM to predict stock price and generating desirable outcome, this paper both contributes to offering a new way to investigate stock market performance and enlarging the field deployment of ELM model as well.
Neural Dialogue Generation Methods in Open Domain: A Survey

Open-Domain Dialogue Generation (human-computer interaction) is an important issue in the field of Natural Language Processing (NLP).Because of the improvement of deep learning techniques, a large number of neural dialogue generative methods were proposed to generate better responses.In this survey, we elaborated the research history of these existing generative methods, and then roughly divided them into six categories, i.e., Encoder-Decoder framework-based methods, Hierarchical Recurrent Encoder-Decoder (HRED)-based methods, Variational Autoencoder (VAE)-based methods, Reinforcement Learning (RL)-based methods, Generative Adversarial Network (GAN)-based methods, and pretraining-model-based methods.We dived into the methods of each category and gave the detailed discussions of these methods.After that, we presented a comparison among the different categories of methods and analyzed their advantages and disadvantages.We enumerated some open access public datasets and some commonly used automatic evaluating metrics.Finally, we discuss some possible research directions that can take the research of neural dialogue generation into a new frontier in the future.
Fake and dishonest participant location scheme in secret image sharing

&lt;abstract&gt;&lt;p&gt;A $ (k, n) $ threshold secret image sharing (SIS) scheme divides a secret image into $ n $ shadows. One can reconstruct the secret image only when holding $ k $ or more than $ k $ shadows but cannot know any information on the secret from fewer than $ k $ shadows. Based on this characteristic, SIS has been widely used in access control, information hiding, distributed storage and other areas. Verifiable SIS aims to prevent malicious behaviour by attackers through verifying the authenticity of shadows and previous works did not solve this problem well. Our contribution is that we proposed a verifiable SIS scheme which combined CRT-based SIS and $ (2, n+1) $ threshold visual secret sharing(VSS). Our scheme is applicable no matter whether there exists a third party dealer. And it is worth mentioning that when the dealer is involved, our scheme can not only detect fake participants, but also locate dishonest participants. In general, loose screening criterion and efficient encoding and decoding rate of CRT-based SIS guarantee high-efficiency shadows generation and low recovery computation complexity. The uncertainty of the bits used for screening prevents malicious behavior by dishonest participants. In addition, our scheme has the advantages of lossless recovery, no pixel expansion and precise detection.&lt;/p&gt;&lt;/abstract&gt;
Toward Bias Analysis Using Tweets and Natural Language Processing

Whether intentionally or not, many individuals in society express some form of prejudice or bias in their thinking and communications. Prejudice can be expressed in a multitude of ways and may be directed towards anything in which people can have an opinion. What makes prejudiced/biased thinking different and potentially more dangerous than having an opinion is that they are not always based on one's personal experiences and therefore may be misinformed. With the rise of social media platforms in the last 20 years or so, people have more ways than ever to express their opinions and share their voices. This paper defines an approach to using Twitter, one of the most popular modern-day social media platforms, to analyze an individual's tweets for the purposes of identifying potential biases. Using sentiment analysis along with other natural language processing tools, a score of potential bias of an individual user is calculated. This score can provide insight to the degree and/or frequency at which a person can be expected to exhibit biased behavior or make biased statements and is only meant to serve as a reference.
Is There Any Hope for Developing Automated Translation Technology for Sign Languages?

This article discusses the prerequisites for the machine translation of sign languages. The topic is complex, including questions relating to technology, interaction design, linguistics and culture. At the moment, despite the affordances provided by the technology, automated translation between signed and spoken languages – or between sign languages – is not possible. The very need of such translation and its associated technology can also be questioned. Yet, we believe that contributing to the improvement of sign language detection, processing and even sign language translation to spoken languages in the future is a matter that should not be abandoned. However, we argue that this work should focus on all necessary aspects of sign languages and sign language user communities. Thus, a more diverse and critical perspective towards these issues is needed in order to avoid generalisations and bias that is often manifested within dominant research paradigms particularly in the fields of spoken language research and speech community.
MVP U-Net: Multi-View Pointwise U-Net for Brain Tumor Segmentation

It is a challenging task to segment brain tumors from multi-modality MRI scans. How to segment and reconstruct brain tumors more accurately and faster remains an open question. The key is to effectively model spatial-temporal information that resides in the input volumetric data. In this paper, we propose Multi-View Pointwise U-Net (MVP U-Net) for brain tumor segmentation. Our segmentation approach follows encoder-decoder based 3D U-Net architecture, among which, the 3D convolution is replaced by three 2D multi-view convolutions in three orthogonal views (axial, sagittal, coronal) of the input data to learn spatial features and one pointwise convolution to learn channel features. Further, we modify the Squeeze-and-Excitation (SE) block properly and introduce it into our original MVP U-Net after the concatenation section. In this way, the generalization ability of the model can be improved while the number of parameters can be reduced. In BraTS 2020 testing dataset, the mean Dice scores of the proposed method were 0.715, 0.839, and 0.768 for enhanced tumor, whole tumor, and tumor core, respectively. The results show the effectiveness of the proposed MVP U-Net with the SE block for multi-modal brain tumor segmentation.
How much is too much: the difficulties of social media content moderation

Recent events, including the 2020 Presidential Election and the Insurrection of the U.S. Capitol, have shown us that social media can be used for darker purposes. Hate speech, fake news, and content inciting violence have become the unfortunate norm when scrolling through one’s newsfeed. Platforms have had to face the issue of dealing with objectionable content such as this. Should they leave it there? Should they get rid of it? How do they differentiate between what’s acceptable and what’s not? Are these decisions made consistently and accurately? The bigger questions have become whether social media platforms are removing enough material or removing too much. This Article address the two major methods that social media platforms have used to moderate objectionable content, including the many flaws associated with each. External legal factors including Section 230 and FOSTA-SESTA are discussed as potential motivators for the evolving social media moderating techniques. Additionally, this Article discusses the strengthening hold that app markets such as Apple, Amazon, and Google have over social media platforms and how these relationships directly influence how platforms police content. Finally, alternative methods of moderation are proposed and discussed in relation to the current moderating norms.
Hybrid Classification for Handwritten Character Recognition of a Subset of the Tamil Alphabet

In this paper, we present a hybrid classification approach using a weighted contribution of feature point-based and convolutional neural network (CNN) methods to improve classification and recognition of handwritten Tamil vowels. We explore different feature point methods, including BRISK, ORB, and KAZE, such that we featurize input images with a visual bag-of-words representation before applying standard machine learning classifiers including a multi-layer perceptron. We also consider different CNN configurations for this classification task. We find that since feature point methods and CNNs learn different representations of underlying input images, our hybrid classifier outperforms the individual feature point and CNN classifiers with respect to accuracy, precision, recall, and F1 score.
Descent-to-Delete: Gradient-Based Methods for Machine Unlearning

We study the data deletion problem for convex models. By leveraging techniques from convex optimization and reservoir sampling, we give the first data deletion algorithms that are able to handle an arbitrarily long sequence of adversarial updates while promising both per-deletion run-time and steady-state error that do not grow with the length of the update sequence. We also introduce several new conceptual distinctions: for example, we can ask that after a deletion, the entire state maintained by the optimization algorithm is statistically indistinguishable from the state that would have resulted had we retrained, or we can ask for the weaker condition that only the observable output is statistically indistinguishable from the observable output that would have resulted from retraining. We are able to give more efficient deletion algorithms under this weaker deletion criterion.