Reviewerly/exHarmony huggingface dataset:
User Perceptions of an Intelligent Personal Assistant's Personality

This paper reports on an experimental study that examined user perceptions of Amazon Alexa's personality, as well as influences of stressful or non-stressful interactions on personality perceptions. The study relied on the Five-Factor Model and the Stereotype Content Model personality frameworks. An online data collection instrument was designed to give Alexa's users (N=50) stressful and non-stressful tasks followed by questions related to Alexa's personality. The assumption was that stressful and non-stressful tasks would frame a user's preference for Alexa's personality. Quantitative data of the participants? ratings of Alexa's responses, and qualitative comments about their ratings, experiences, and thoughts about Alexa were collected and analyzed using descriptive statistics, tests of association, and thematic content analysis. The findings indicated that while the majority of users appreciate Alexa's personality as a way of making interactions more personal and enjoyable, some users prefer their conversational agent to be efficient, robotic-like, and devoid of a personality that might cause attachment. The participants described Alexa as competent in terms of information content and usability, and warm in terms of its personality manifestation, but wanted their ideal Alexa to be even more competent and warm. After experiencing stressful and non-stressful experimental conditions, the participants appreciated Alexa's highly competent and highly warm responses, while disparaging low-warmth/high-competence responses. The participants' comments indicated that after stressful tasks, they were more generous in their assessment of Alexa's performance, and valued manifestations of Alexa's warmth higher than after non-stressful tasks. Additionally, after the second group of tasks/towards the end of the study, the participants tended to score low warmth/high competence responses higher, which might indicate a slight preference for competence after longer interactions or a user's fatigued state. The implications of the findings on the design of conversational interfaces and study limitations are discussed.
MuReQua Chain: Multiscale Relativistic Quantum Blockchain

In this paper, we introduce a new approach to fix the validation of a block and the assignment of a new block in a blockchain infrastructure by using a novel negotiation procedure. The block validation and assignment are reached thanks to negotiation procedures based on an extended probability environment. Also, by using a multiscale approach (typical of Complexity Theory) and Quantum and Relativistic Mechanics, the result appears to solve some of the most relevant questions in the Blockchain context, which are the democracy and the randomness of the validator of a block and the assignment of the new one. The selection of actors to mine is invariant concerning the number of addresses, i.e., the coins of owners, which have more chance to be selected generally. This work is the companion of CQKD (Computational Quantum Key Distribution), as we will see in the introduction, where we considered the infrastructural question of the key distribution; also, it is a very effective application of the decision and reasoning in incompleteness or uncertainty conditions as described in the previous and prodromic paper as described in the introduction too.
Dempster–Shafer Theory for Modeling and Treating Uncertainty in IoT Applications Based on Complex Event Processing

The Internet of Things (IoT) has emerged from the proliferation of mobile devices and objects connected, resulting in the acquisition of periodic event flows from different devices and sensors. However, such sensors and devices can be faulty or affected by failures, have poor calibration, and produce inaccurate data and uncertain event flows in IoT applications. A prominent technique for analyzing event flows is Complex Event Processing (CEP). Uncertainty in CEP is usually observed in primitive events (i.e., sensor readings) and rules that derive complex events (i.e., high-level situations). In this paper, we investigate the identification and treatment of uncertainty in CEP-based IoT applications. We propose the DST-CEP, an approach that uses the Dempster–Shafer Theory to treat uncertainties. By using this theory, our solution can combine unreliable sensor data in conflicting situations and detect correct results. DST-CEP has an architectural model for treating uncertainty in events and its propagation to processing rules. We describe a case study using the proposed approach in a multi-sensor fire outbreak detection system. We submit our solution to experiments with a real sensor dataset, and evaluate it using well-known performance metrics. The solution achieves promising results regarding Accuracy, Precision, Recall, F-measure, and ROC Curve, even when combining conflicting sensor readings. DST-CEP demonstrated to be suitable and flexible to deal with uncertainty.
Knowledge-Based Interactive Postmining of User-Preferred Co-Location Patterns Using Ontologies

Co-location pattern mining plays an important role in spatial data mining. With the rapid growth of spatial datasets, the usefulness of co-location patterns is strongly limited by the huge amount of discovered patterns. Although several methods have been proposed to reduce the number of discovered patterns, these statistical algorithms are unable to guarantee that the extracted co-location patterns are user preferred. Therefore, it is crucial to help the decision maker discover his/her preferred co-location patterns via efficient interactive procedures. This article proposes a new interactive approach that enables the user to discover his/her preferred co-location patterns. First, we present a novel and flexible interactive framework to assist the user in discovering his/her preferred co-location patterns. Second, we propose using ontologies to measure the similarity of two co-location patterns. Furthermore, we design a pruning scheme by introducing a pattern filtering model for expressing the user's preference, to reduce the number of the final output. By applying our proposed approach over voluminous sets of co-location patterns, we show that the number of filtered co-location patterns is reduced to several dozen or less and, on average, 80% of the selected co-location patterns are user preferred.
Governing urban infrastructures under pandemic conditions: some thoughts

Urban infrastructure has appeared as a central feature in a range of commentaries on the COVID-19 pandemic. Understanding the imprint of the pandemic on cities and the power-laden processes through which they are being rebuilt requires an attention to the politics and governance of infrastructure. In this intervention, we understand the pandemic as a moment to rethink claims over how infrastructures work and how they might be studied. We focus on three dimensions where COVID-19 has underscored the importance of infrastructure governance: as pandemic condition, as pandemic vulnerability, and as pandemic response. We argue that a strand of future academic work must be attuned to the continued importance of the governance of and by urban infrastructures in a world of cities in which COVID-19 and its associated economic, environmental, and social implications are likely to remain pervasive.
Classification of Breast Cancer using Histology images: Handcrafted and Pre-Trained Features Based Approach

Abstract Breast cancer has become a critical disease in women. The number of patients with breast cancer is quite high in India. It is of paramount importance to detect the disease in advance. Digital histopathology is one of the most advanced techniques for detection using machine learning. Artificial intelligence is going to be like a sunrise in the field of medicine. Deep neural networks have been successfully applied to the problem under consideration in the past. As, we know the feature extraction is one of the essential and crucial steps in case of classification. In this paper, we compare two approaches, first is feature extraction using traditional Handcrafted based and other is Transfer Learning based model (Pre-trained) for multiclass classification of Breast Cancer using Convolutional Neural Network (CNN) as a classifier. The models are trained using handcrafted features like Seeped Up Robust Features (SURF) and Dense Scale Invariant Feature Transform (DSIFT) techniques, later these extracted features are encoded by Locality Constrained Linear Coding method (LLC). In pre-trained model we have used VGG16, VGG19, ResNet50, GoogLeNet for feature extraction. The maximum accuracy for “SURF+CNN” is 92.88% for Handcrafted feature and in case of Pre-trained “GoogLeNet+ CNN” model gives 94%, both for 400X magnification factor.
Invariant Feature Learning for Sensor-based Human Activity Recognition

Wearable sensor-based human activity recognition (HAR) has been a research focus in the field of ubiquitous and mobile computing for years. In recent years, many deep models have been applied to HAR problems. However, deep learning methods typically require a large amount of data for models to generalize well. Significant variances caused by different participants or diverse sensor devices limit the direct application of a pre-trained model to a subject or device that has not been seen before. To address these problems, we present an invariant feature learning framework (IFLF) that extracts common information shared across subjects and devices. IFLF incorporates two learning paradigms: 1) meta-learning to capture robust features across seen domains and adapt to an unseen one with similarity-based data selection; 2) multi-task learning to deal with data shortage and enhance overall performance via knowledge sharing among different subjects. Experiments demonstrated that IFLF is effective in handling both subject and device diversion across popular open datasets and an in-house dataset. It outperforms a baseline model of up to 40 percent in test accuracy.
A Design Framework for Strongly <i>χ</i>²-Private Data Disclosure

In this paper, we study a stochastic disclosure control problem using information-theoretic methods. The useful data to be disclosed depend on private data that should be protected. Thus, we design a privacy mechanism to produce new data which maximizes the disclosed information about the useful data under a strong $\chi^2$-privacy criterion. For sufficiently small leakage, the privacy mechanism design problem can be geometrically studied in the space of probability distributions by a local approximation of the mutual information. By using methods from Euclidean information geometry, the original highly challenging optimization problem can be reduced to a problem of finding the principal right-singular vector of a matrix, which characterizes the optimal privacy mechanism. In two extensions we first consider a scenario where an adversary receives a noisy version of the user's message and then we look for a mechanism which finds $U$ based on observing $X$, maximizing the mutual information between $U$ and $Y$ while satisfying the privacy criterion on $U$ and $Z$ under the Markov chain $(Z,Y)-X-U$.
Intelligent environments for all: a path towards technology-enhanced human well-being

Emerging intelligent environments are considered to offer significant opportunities to positively impact human life, both at an individual and at a societal level, and in particular to provide useful means to support people in their daily life activities and thus improve well-being for everybody, especially for older people and for people with limitations of activities. In this context, accessibility and usability, although necessary, are not sufficient to ensure that applications and services are appropriately designed to satisfy human needs and overcome potential functional limitations in the execution of everyday activities fundamental for well-being. This position paper puts forward the claim that, in order to achieve the above objective, it is necessary that: (i) the design of Assistive Intelligent Environments is centered around the well-being of people, roughly intended as the possibility of executing the (everyday) human activities necessary for living (independently), thus emphasizing usefulness in addition to usability; (ii) the technological environment is orchestrated around such activities and contains knowledge about how they are performed and how people need to be supported to perform them; (iii) the environment makes use of monitoring and reasoning capabilities in order to adapt, fine-tune and evolve over time the type and level of support provided, and this process takes place considering ethical values; (iv) the applications must also support the possibility of contact with other people, who in many cases may be the only effective help. Moving forward from the Design for All paradigm, this paper discusses how the latter can be revisited under the perspective of technology's usefulness and contribution to human well-being. Subsequently, it introduces a practical notion of well-being based on the ICF classification of human functions and activities and discusses how such notion can constitute the starting point and the focus of design approaches targeted to assist people in their everyday life mainly (but not exclusively) in the home environment. As a subsequent step, the need for integrating Artificial Intelligence capabilities in assistive intelligent environments is discussed, based on the complexity of the human problems to be addressed and the diversity of the types of support needed. The proposed approach is exemplified and illustrated through the experience acquired in the development of four applications, addressing vital aspects of human life, namely nutrition, stress management, sleep management and counteracting loneliness. Finally, based on the acquired experience, the need to take into account ethical values in the development of assistive intelligent environments is discussed.
Review of Text Summarization in Indian Regional Languages

Propelled by the advancements in the field of natural language processing, generating summaries of long texts using various NLP tools and techniques has always been a subject of great interest for scientists all over the world. Data is ubiquitous, and a large amount of data is processed every second in the digital space. For these reasons also, there is need of machine learning algorithms that can automatically shorten longer texts or documents and provide the accurate and meaningful summaries. These summaries find their applications in a wide variety of fields like medicine, market review, business analytics, etc. In other languages like English, an ample amount of study and research can be observed. However, in context of the Indian regional languages, the research in text summarization is very limited and is still in the infancy state. This paper tries to explain the research and works that have been performed in the field of text and document summarization in Indian regional languages.
Optimized loss function in deep learning profilometry for improved prediction performance

Abstract Single-shot structured light profilometry (SLP) aims at reconstructing the 3D height map of an object from a single deformed fringe pattern and has long been the ultimate goal in fringe projection profilometry. Recently, deep learning was introduced into SLP setups to replace the task-specific algorithm of fringe demodulation with a dedicated neural network. Research on deep learning-based profilometry has made considerable progress in a short amount of time due to the rapid development of general neural network strategies and to the transferrable nature of deep learning techniques to a wide array of application fields. The selection of the employed loss function has received very little to no attention in the recently reported deep learning-based SLP setups. In this paper, we demonstrate the significant impact of loss function selection on height map prediction accuracy, we evaluate the performance of a range of commonly used loss functions and we propose a new mixed gradient loss function that yields a higher 3D surface reconstruction accuracy than any previously used loss functions.
Telecom traffic pumping analytics via explainable data science

Traffic pumping is a type of fraud committed in several countries, in which small telephone operators inflate the number of incoming calls to their networks, profiting from a higher access charge in relation to the network operator associated with the origin of the call. The identification of traffic pumping is complex due to the lack of labels for performing supervised learning, and the scarce literature on the topic. We propose a decision support system for fraud detection via clustering and decision trees. After data collection and feature engineering, we group the potential fraud cases into various clusters via an unsupervised learning approach. Then, we constructed a decision tree by using the cluster memberships as labels, evolving into the rules of a given variable and a certain label required for filing lawsuits against the suspicious cases. Telecommunication experts validate these rules to seek a legal resource against alleged perpetrators. We present the results of a case study from a Chilean telecommunication provider. All the lawsuits taken by the legal department were granted, confirming our success in dramatically reducing current and future fraud losses for the company.
Learning to Identify Physical Parameters from Video Using Differentiable Physics

Video representation learning has recently attracted attention in computer vision due to its applications for activity and scene forecasting or vision-based planning and control. Video prediction models often learn a latent representation of video which is encoded from input frames and decoded back into images. Even when conditioned on actions, purely deep learning based architectures typically lack a physically interpretable latent space. In this study, we use a differentiable physics engine within an action-conditional video representation network to learn a physical latent representation. We propose supervised and self-supervised learning methods to train our network and identify physical properties. The latter uses spatial transformers to decode physical states back into images. The simulation scenarios in our experiments comprise pushing, sliding and colliding objects, for which we also analyze the observability of the physical properties. In experiments we demonstrate that our network can learn to encode images and identify physical properties like mass and friction from videos and action sequences in the simulated scenarios. We evaluate the accuracy of our supervised and self-supervised methods and compare it with a system identification baseline which directly learns from state trajectories. We also demonstrate the ability of our method to predict future video frames from input images and actions.
Machine Learning Approach Based on Hybrid Features for Detection of Phishing URLs

Phishing attacks are one of the most widespread problems over the internet. A lot of internet users fall into the hands of attackers every day which accounts into millions of dollars of fraud around the globe every day. The availability of the internet among people who don't have the knowledge of cyber-attacks adds more to this problem. Thus, there is a need to employ intelligent algorithms to solve these serious problems. In this paper, we present different ways in which phishing URLs can be detected using machine learning algorithms. The URL based features as well as network-based features were used to feed to the machine learning classifiers. Similarly, other features that might add relevance to our problem are also discussed. The unbalanced dataset is made balanced using various oversampling and undersampling techniques and the performance for the various machine learning algorithms is evaluated for the dataset. The evaluation shows that the machine learning algorithms can show promising results in terms of precision, recall, f- score, and ROC AUC.
Correct Pronunciation Detection of the Arabic Alphabet Using Deep Learning

Automatic speech recognition for Arabic has its unique challenges and there has been relatively slow progress in this domain. Specifically, Classic Arabic has received even less research attention. The correct pronunciation of the Arabic alphabet has significant implications on the meaning of words. In this work, we have designed learning models for the Arabic alphabet classification based on the correct pronunciation of an alphabet. The correct pronunciation classification of the Arabic alphabet is a challenging task for the research community. We divide the problem into two steps, firstly we train the model to recognize an alphabet, namely Arabic alphabet classification. Secondly, we train the model to determine its quality of pronunciation, namely Arabic alphabet pronunciation classification. Due to the less availability of audio data of this kind, we had to collect audio data from the experts, and novices for our model’s training. To train these models, we extract pronunciation features from audio data of the Arabic alphabet using mel-spectrogram. We have employed a deep convolution neural network (DCNN), AlexNet with transfer learning, and bidirectional long short-term memory (BLSTM), a type of recurrent neural network (RNN), for the classification of the audio data. For alphabet classification, DCNN, AlexNet, and BLSTM achieve an accuracy of 95.95%, 98.41%, and 88.32%, respectively. For Arabic alphabet pronunciation classification, DCNN, AlexNet, and BLSTM achieve an accuracy of 97.88%, 99.14%, and 77.71%, respectively.
Analyzing Tweets on New Norm: Work from Home during COVID-19 Outbreak

The COVID-19 pandemic triggered a large-scale work-from-home trend globally in recent months.In this paper, we study the phenomenon of "work-from-home" (WFH) by performing social listening.We propose an analytics pipeline designed to crawl social media data and perform text mining analyzes on textual data from tweets scrapped based on hashtags related to WFH in COVID-19 situation.We apply text mining and NLP techniques to analyze the tweets for extracting the WFH themes and sentiments (positive and negative).Our Twitter theme analysis adds further value by summarizing the common key topics, allowing employers to gain more insights on areas of employee concerns due to pandemic.
TLS Encrypted Application Classification Using Machine Learning with Flow Feature Engineering

Network traffic classification has become increasingly important as the number of devices connected to the Internet is rapidly growing. Proportionally, the amount of encrypted traffic is also increasing, making payload based classification methods obsolete. Consequently, machine learning approaches have become crucial when user privacy is concerned. For this purpose, we propose an accurate, fast, and privacy preserved encrypted traffic classification approach with engineered flow feature extraction and appropriate feature selection. The proposed scheme achieves a 0.92899 macro-average F1 score and a 0.88313 macro-averaged mAP score for the encrypted traffic classification of Audio, Email, Chat, and Video classes derived from the non-vpn2016 dataset. Further experiments on the mixed non-encrypted and encrypted flow dataset with a data augmentation method called Synthetic Minority Over-Sampling Technique are conducted and the results are discussed for TLS-encrypted and mixed flows.