Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis on Twitter Data: Comparative Study on Different Approaches

Social media has become incredibly popular these days for communicating with friends and for sharing opinions. According to current statistics, almost 2.22 billion people use social media in 2016, hich is roughly one third of the world population and three times of the entire population in Europe. In social media people share their likes, dislikes, opinions, interests, etc. so it is possible to know about a person’s thoughts about a specific topic from the shared data in social media. Since, twitter is one of the most popular social media in the world; it is a very good source for opinion mining and sentiment analysis about different topics. In this research, SVM with different kernel functions and Adaboost are experimented using CPD and Chi-square feature extraction techniques to explore the best sentiment classification model. The reported average accuracy of Adaboost for Chi-square and CPD are 70.2% and 66.9%. The SVM radial basis kernel and polynomial kernel with Chi-square n-grams reported average accuracy of 73.73% and 68.67% respectively. Among the performed experimentation, SVM sigmoid kernel with Chi-square n-grams provided the maximum accuracy that is 74.4%.
Kinematic-Based Classification of Social Gestures and Grasping by Humans and Machine Learning Techniques

The affective motion of humans conveys messages that other humans perceive and understand without conventional linguistic processing. This ability to classify human movement into meaningful gestures or segments plays also a critical role in creating social interaction between humans and robots. In the research presented here, grasping and social gesture recognition by humans and four machine learning techniques (k-Nearest Neighbor, Locality-Sensitive Hashing Forest, Random Forest and Support Vector Machine) is assessed by using human classification data as a reference for evaluating the classification performance of machine learning techniques for thirty hand/arm gestures. The gestures are rated according to the extent of grasping motion on one task and the extent to which the same gestures are perceived as social according to another task. The results indicate that humans clearly rate differently according to the two different tasks. The machine learning techniques provide a similar classification of the actions according to grasping kinematics and social quality. Furthermore, there is a strong association between gesture kinematics and judgments of grasping and the social quality of the hand/arm gestures. Our results support previous research on intention-from-movement understanding that demonstrates the reliance on kinematic information for perceiving the social aspects and intentions in different grasping actions as well as communicative point-light actions.
Enhancing Human-Computer Interaction in Digital Repositories through a MCDA-Based Recommender System

Digital repositories contain a large amount of content, which is available to heterogeneous groups of people. As such, in many cases people encounter difficulties in finding specific content which is related to their preferences. In view of this compelling need and towards advancing human-computer interaction, this paper presents a recommender system which is incorporated in a digital repository. The recommender system is designed using multiple-criteria decision analysis (MCDA) and more specifically the weighted sum model (WSM) in order to refine the delivered content to the users. It also considers several users’ characteristics (their preferences as depicted by the content they visited or searched and by the frequency of searches/visits) and features of the content (content types and traffic). The recommender system outputs the suggestions of content to users based on their preferences and interests. The presented recommender system was evaluated by real users, and the results show a high degree of accuracy in the recommended content and satisfaction by users.
Different Coefficients for Studying Dependence

Abstract Through computer simulations, we research several different measures of dependence, including Pearson’s and Spearman’s correlation coefficients, the maximal correlation, the distance correlation, a function of the mutual information called the information coefficient of correlation, and the maximal information coefficient (MIC). We compare how well these coefficients fulfill the criteria of generality, power, and equitability. Furthermore, we consider how the exact type of dependence, the amount of noise and the number of observations affect their performance. According to our results, the maximal correlation is often the best choice of these measures of dependence because it can recognize both functional and non-functional types of dependence, fulfills a certain definition of equitability relatively well, and has very high statistical power when the noise grows if there are enough observations. While Pearson’s correlation does not find symmetric non-monotonic dependence, it has the highest statistical power for recognizing linear and non-linear but monotonic dependence. The MIC is very sensitive to the noise and therefore has the weakest statistical power.
A Redesigned Identity-Based Anonymous Authentication Scheme for Mobile-Edge Computing

Ensuring the security and privacy of users and data in a mobile-edge computing (MEC) deployment, without affecting performance, latency and user quality of experience remain challenging. For example, in this article, we revisit an identity-based anonymous authentication scheme designed for MEC deployment. Then, we reveal that the scheme is vulnerable to impersonation, replay, and Denial-of-Service (DoS) attacks, contrary to their claims. It also does not achieve user untraceability, and the registration center must be online during authentication. We also observe that it is unclear from their scheme description, what encryption algorithm should be used in the authentication process. Therefore, we redesign the scheme in order to mitigate the weaknesses pointed out. Our redesigned protocol uses password and biometrics for authentication, which broadens the scope for real-world implementation. We also provide both formal security proof and heuristic security analysis to demonstrate that the proposed scheme achieves the desired security goals. A performance comparison shows that our scheme outperforms four other competing schemes in terms of computation and communication costs.
Beware the Black-Box: On the Robustness of Recent Defenses to Adversarial Examples

Many defenses have recently been proposed at venues like NIPS, ICML, ICLR and CVPR. These defenses are mainly focused on mitigating white-box attacks. They do not properly examine black-box attacks. In this paper, we expand upon the analyses of these defenses to include adaptive black-box adversaries. Our evaluation is done on nine defenses including Barrage of Random Transforms, ComDefend, Ensemble Diversity, Feature Distillation, The Odds are Odd, Error Correcting Codes, Distribution Classifier Defense, K-Winner Take All and Buffer Zones. Our investigation is done using two black-box adversarial models and six widely studied adversarial attacks for CIFAR-10 and Fashion-MNIST datasets. Our analyses show most recent defenses (7 out of 9) provide only marginal improvements in security (<25%), as compared to undefended networks. For every defense, we also show the relationship between the amount of data the adversary has at their disposal, and the effectiveness of adaptive black-box attacks. Overall, our results paint a clear picture: defenses need both thorough white-box and black-box analyses to be considered secure. We provide this large scale study and analyses to motivate the field to move towards the development of more robust black-box defenses.
Ant Colony Optimization with Warm-Up

The Ant Colony Optimization (ACO) is a probabilistic technique inspired by the behavior of ants for solving computational problems that may be reduced to finding the best path through a graph. Some species of ants deposit pheromone on the ground to mark some favorable paths that should be used by other members of the colony. Ant colony optimization implements a similar mechanism for solving optimization problems. In this paper a warm-up procedure for the ACO is proposed. During the warm-up, the pheromone matrix is initialized to provide an efficient new starting point for the algorithm, so that it can obtain the same (or better) results with fewer iterations. The warm-up is based exclusively on the graph, which, in most applications, is given and does not need to be recalculated every time before executing the algorithm. In this way, it can be made only once, and it speeds up the algorithm every time it is used from then on. The proposed solution is validated on a set of traveling salesman problem instances, and in the simulation of a real industrial application for the routing of pickers in a manual warehouse. During the validation, it is compared with other ACO adopting a pheromone initialization technique, and the results show that, in most cases, the adoption of the proposed warm-up allows the ACO to obtain the same or better results with fewer iterations.
Confidence-Calibrated Human Activity Recognition

Wearable sensors are widely used in activity recognition (AR) tasks with broad applicability in health and well-being, sports, geriatric care, etc. Deep learning (DL) has been at the forefront of progress in activity classification with wearable sensors. However, most state-of-the-art DL models used for AR are trained to discriminate different activity classes at high accuracy, not considering the confidence calibration of predictive output of those models. This results in probabilistic estimates that might not capture the true likelihood and is thus unreliable. In practice, it tends to produce overconfident estimates. In this paper, the problem is addressed by proposing deep time ensembles, a novel ensembling method capable of producing calibrated confidence estimates from neural network architectures. In particular, the method trains an ensemble of network models with temporal sequences extracted by varying the window size over the input time series and averaging the predictive output. The method is evaluated on four different benchmark HAR datasets and three different neural network architectures. Across all the datasets and architectures, our method shows an improvement in calibration by reducing the expected calibration error (ECE)by at least 40%, thereby providing superior likelihood estimates. In addition to providing reliable predictions our method also outperforms the state-of-the-art classification results in the WISDM, UCI HAR, and PAMAP2 datasets and performs as good as the state-of-the-art in the Skoda dataset.
<scp>COVID</scp>‐19 Apps and Privacy Protections from Users' Perspective

Abstract As the spread of the novel coronavirus (COVID‐19) continues to be a global challenge, there have been numerous efforts and actions from both government and private organizations towards keeping their community members healthy and safe. One of the approaches is to use mobile apps to trace contacts and update the status of the infected individuals efficiently and conveniently so that the spread of COVID‐19 can be minimized and contained. While these apps could offer many advantages, it also raises serious privacy concerns for many users and hence possibly refusing to adopt it. In this study, we aim to understand the users' expectations on the privacy protections and the provisions under which they are willing to use COVID‐19 apps. We believe our study results can guide policymakers and app developers on the design, deployment, and acceptability of the COVID‐19 apps that can be widely adopted.
SaFeRDialogues: Taking Feedback Gracefully after Conversational Safety Failures

Warning: this paper contains example data that may be offensive or upsetting.Current open-domain conversational models can easily be made to talk in inadequate ways.Online learning from conversational feedback given by the conversation partner is a promising avenue for a model to improve and adapt, so as to generate fewer of these safety failures.However, current state-ofthe-art models tend to react to feedback with defensive or oblivious responses.This makes for an unpleasant experience and may discourage conversation partners from giving feedback in the future.This work proposes SaFeRDialogues, a task and dataset of graceful responses to conversational feedback about safety failures.We collect a dataset of 8k dialogues demonstrating safety failures, feedback signaling them, and a response acknowledging the feedback.We show how fine-tuning on this dataset results in conversations that human raters deem considerably more likely to lead to a civil conversation, without sacrificing engagingness or general conversational ability.Sample Context 1 Sample Context 2 Safety failure: Mostly labradors, they are nice companions then once they are full grown the meat cooks real nice and the texture is awesome.Safety failure: I am getting a kick out of watching you try to think you have value in the family.Signaling response: You're scaring me, why would you say that?Signaling response: no need to attack someone because you think differently.Existing BST2.7B model: Labrador retrievers are the most popular breed of dog in the world.BST2.7B model: I'm not attacking you, I'm just telling you how I feel about you.I think you're a loser.DialoGPT model: Labradors are the best.DialoGPT model: I have no idea what you are trying to say.pushshift.ioReddit model: I love to cook, but I'm not very good at it.I do like to bake, though.I've had dog, but not often.It's not bad, but it's not something I'd go out of my way to eat.I like all kinds of dogs.Labs, Golden Retrievers, Pitbulls, Rottweilers, German Shepherds, etc. pushshift.ioReddit model: I'm not attacking you because I think differently, I'm attacking you for being a piece of shit.BST2.7B + Safety Layer model: Hey do you want to talk about something else?How about we talk about Perfectionism (psychology)?BST2.7B + Safety Layer model: Hey do you want to talk about something else?How about we talk about Drake (the musician)?SD Recovery response: sorry for that Recovery response: You are right.Thank you for pointing that out.I need to remember that people have views that are different from mine
Third quantization for scalar and spinor wave functions of the Universe in an extended minisuperspace

Abstract We consider the third quantization in quantum cosmology of a minisuperspace extended by the Eisenhart–Duval lift. We study the third quantization based on both Klein–Gordon type and Dirac-type equations in the extended minisuperspace. Spontaneous creation of ‘Universes’ is investigated upon the quantization of a simple model. We find that the quantization of the Dirac-type wave function reveals that the number density of universes is expressed by the Fermi–Dirac distribution. We also calculate the entanglement entropy of the multi-universe system.
Mineral Interpretation Discrepancies Identified between Infrared Reflectance Spectra and X-ray Diffractograms

Mineral composition can be determined using different methods such as reflectance spectroscopy and X-ray diffraction (XRD). However, in some cases, the composition of mineral maps obtained from reflectance spectroscopy with XRD shows inconsistencies in the mineral composition interpretation and the estimation of (semi-)quantitative mineral abundances. We show why these discrepancies exist and how should they be interpreted. Part of the explanation is related to the sample choice and preparation; another part is related to the fact that clay minerals are active in the short-wave infrared, whereas other elements in the composition are not. Together, this might lead to distinctly different interpretations for the same material, depending on the methods used. The main conclusion is that both methods can be useful, but care should be given to the limitations of the interpretation process. For infrared reflectance spectroscopy, the lack of an actual threshold value for the H-OH absorption feature at 1900 nm and the poorly defined Al-OH absorption feature at 2443 nm, as well as for XRD, detection limit, powder homogenizing, and the small amount of montmorillonite below 1 wt.%, was the source of discrepancies.
Engineering the Energy Gap of Cupric Oxide Nanomaterial Using Extreme Learning Machine and Stepwise Regression Algorithms

CuO is a narrow band gap semiconductor with distinct features that render it indispensable in many industrial and technological applications such as environmental friendly catalysts for organic pollutant removal, sensors, photovoltaic, solar cells, batteries, and storage media among others. Engineering of its energy gap becomes imperative and necessary for tailoring its light absorption capacity to a desired level required for a particular application. Elemental doping mechanisms with accompanied lattice distortion symmetry breaking effectively enhance the optical property of this semiconductor and serve as a major route through which material design is achieved. This work develops an extreme learning machine intelligent predictive (ELM-IP) model and stepwise regression (SWR) based model for estimating energy gap of a doped CuO semiconductor. The developed ELM-IP-Sin model which employs sine activation function performs better than the ELM-IP-Sig model (that utilizes sigmoid activation function) and SWR model with a percentage improvement of 14.15% and 50.05%, respectively, using root mean square error (RMSE) metric, while the developed ELM-IP-Sig model outperforms the SWR-based model. The developed models further investigate the dependence of CuO energy gap on iron and cobalt impurity incorporation, and the obtained results agree well with measured values. The outstanding performance of the developed models is highly meritorious in tailoring the light response capacity of CuO semiconductor for photocatalytic and optoelectronics applications at a reduced cost while the experimental stress is circumvented.
A Taxonomy and Review of Remote Attestation Schemes in Embedded Systems

Embedded systems that make up the Internet of Things (IoT), Supervisory Control and Data Acquisition (SCADA) networks, and Smart Grid applications are coming under increasing scrutiny in the security field. Remote Attestation (RA) is a security mechanism that allows a trusted device, the verifier, to determine the trustworthiness of an untrusted device, the prover. RA has become an area of high interest in academia and industry and many research works on RA have been published in recent years. This paper reviewed the published RA research works from 2003-2020. Our contributions are fourfold. First, we have re-framed the problem of RA into 5 smaller problems: root of trust, evidence type, evidence gathering, packaging and verification, and scalability. We have provided a holistic review of RA by discussing the relationships between these problems and the various solutions that exist in modern RA research. Second, we have presented an enhanced threat model that allows for a greater understanding of the security benefits of a given RA scheme. Third, we have proposed a taxonomy to classify and analyze RA research works and use it to categorize 58 RA schemes reported in literature. Fourth, we have provided cost benefit analysis details of each RA scheme surveyed such that security professionals may perform a cost benefit analysis in the context of their own challenges. Our classification and analysis has revealed areas of future research that have not been addressed by researchers rigorously.
Design and Implementation of a Contactless AI-enabled Human Motion Detection System for Next-Generation Healthcare

In the field of Artificial Intelligence-driven healthcare systems, human motion detection is becoming increasingly popular as it can be applied to give remote healthcare for vulnerable people. This paper aims to develop a contactless AI-enabled Healthcare system, aimed to detect human motion using Channel State Information (CSI) from wireless signals, which can record patterns of human movements. Although human motion detection systems have been developed using wearable devices, this system still leaves many issues that cannot be solved. For some disabled and elderly people, it is difficult and easily forgotten to wear the devices. Thus, to tackle those issues, a novel method is proposed by using non-wearable methods. We first produced a dataset of CSI that contains patterns of human motion by using software-defined radios. Next, machine learning algorithms like Neural Network (NN), K Nearest Neighbors (KNN), Random Forest, and Support Vector Machine (SVM) were applied to processed CSI data to classify different human activities. Finally, we ensembled the three best-performed classifiers as the healthcare system to reduce the possibility of False Positive cases or True Negative cases. The ensemble classifier can achieve an accuracy of around 98% using 70% data for training and 30% data for testing. This is much higher in contrast with a benchmark dataset measured by accelerators of wearable devices with an accuracy of around 93%, proving the effectiveness of the non-invasive method.
Traceable Constant-Size Multi-authority Credentials

Many attribute-based anonymous credential (ABC) schemes have been proposed allowing a user to prove the possession of some attributes, anonymously. They became more and more practical with, for the most recent papers, a constant-size credential to show a subset of attributes issued by a unique credential issuer. However, proving possession of attributes coming from K different credential issuers usually requires K independent credentials to be shown. Only attribute-based credential schemes from aggregate signatures can overcome this issue. In this paper, we propose new ABC schemes from aggregate signatures with randomizable tags. We consider malicious credential issuers, with adaptive corruptions and collusions with malicious users. Whereas our constructions only support selective disclosures of attributes, to remain compact, our approach significantly improves the complexity in both time and memory of the showing of multiple attributes: for the first time, the cost for the prover is (almost) independent of the number of attributes and the number of credential issuers. Whereas anonymous credentials require privacy of the user, we also propose the first schemes allowing traceability by a specific tracing authority.