Reviewerly/exHarmony huggingface dataset:
A Multimodal Predictive Agent Model for Human Interaction Generation

Perception and action are inextricably tied together. We propose an agent model which consists of perceptual and proprioceptive pathways. The agent actively samples a sequence of percepts from its environment using the perception-action loop. The model predicts to complete the partial percept and propriocept sequences observed till each sampling instant, and learns where and what to sample from the prediction error, without supervision or reinforcement. The model is implemented using a multimodal variational recurrent neural network. The model is exposed to videos of two-person interactions, where one person is the modeled agent and the other person's actions constitute its visual observation. For each interaction class, the model learns to selectively attend to locations in the other person's body. The proposed attention-based agent is the first of its kind to interact with and learn end-to-end from human interactions, and generate realistic interactions with performance comparable to models without attention and using significantly more computational resources.
Constructing secure and memorable passwords

Purpose Using authentication to secure data and accounts has grown to be a natural part of computing. Even if several authentication methods are in existence, using passwords remain the most common type of authentication. As long and complex passwords are encouraged by research studies and practitioners alike, computer users design passwords using strategies that enable them to remember their passwords. This paper aims to find strategies that allow for the generation of passwords that are both memorable and computationally secure. Design/methodology/approach The study began with a literature review that was used to identify cognitive password creation strategies that facilitate the creation of passwords that are easy to remember. Using an action-based approach, attack models were created for the resulting creation strategies. The attack models were then used to calculate the entropy for passwords created with different strategies and related to a theoretical cracking time. Findings The result of this study suggests that using phrases with four or more words as passwords will generate passwords that are easy to remember and hard to attack. Originality/value This paper considers passwords from a socio-technical approach and provides insight into how passwords that are easy to remember and hard to crack can be generated. The results can be directly used to create password guidelines and training material that enables users to create usable and secure passwords.
Quantum-Limited Squeezed Light Detection with a Camera

We present a technique for squeezed light detection based on direct imaging of the displaced-squeezed-vacuum state using a CCD camera. We show that the squeezing parameter can be accurately estimated using only the first two moments of the recorded pixel-to-pixel photon fluctuation statistics, with accuracy that rivals that of the standard squeezing detection methods such as a balanced homodyne detection. Finally, we numerically simulate the camera operation, reproducing the noisy experimental results with low signal samplings and confirming the theory with high signal samplings.
Small-Scale Face Detection Based on Improved R-FCN

Face detection is an important basic technique for face-related applications, such as face analysis, recognition, and reconstruction. Images in unconstrained scenes may contain many small-scale faces. The features that the detector can extract from small-scale faces are limited, which will cause missed detection and greatly reduce the precision of face detection. Therefore, this study proposes a novel method to detect small-scale faces based on region-based fully convolutional network (R-FCN). First, we propose a novel R-FCN framework with the ability of feature fusion and receptive field adaptation. Second, a bottom-up feature fusion branch is established to enrich the local information of high-layer features. Third, a receptive field adaptation block (RFAB) is proposed to ensure that the receptive field can be adaptively selected to strengthen the expression ability of features. Finally, we improve the anchor setting method and adopt soft non-maximum suppression (SoftNMS) as the selection method of candidate boxes. Experimental results show that average precision for small-scale face detection of R-FCN with feature fusion branch and RFAB (RFAB-f-R-FCN) is improved by 0.8%, 2.9%, and 11% on three subsets of Wider Face compared with that of R-FCN.
The role of norms in information security policy compliance

Purpose The purpose of this paper is to determine which factors influence information system security policy compliance. It examines how different norms influence compliance intention. Design/methodology/approach Based on relevant literature on information system security policy compliance, a research model was developed and validated. An online questionnaire was used to gather data from respondents and partial least square structural equation modelling (PLS-SEM) was used to analyse 432 responses received. Findings The results indicated that attitude towards information security compliance mediates the effects of personal norms on compliance intention. In addition, descriptive and subjective norms are significant predictors of personal norms. Originality/value Though advancement in technology has reached significant heights, it is still inadequate to guaranteed information systems’ security. Researchers have identified humans to be central in ensuring information security. To this effect, this study provides empirical evidence of the role of norms in influence information security behaviour.
SOLOMON: An Automated Framework for Detecting Fault Attack Vulnerabilities in Hardware

Fault attacks are potent physical attacks on crypto-devices. A single fault injected during encryption can reveal the cipher's secret key. In a hardware realization of an encryption algorithm, only a tiny fraction of the gates is exploitable by such an attack. Finding these vulnerable gates has been a manual and tedious task requiring considerable expertise. In this paper, we propose SOLOMON, the first automatic fault attack vulnerability detection framework for hardware designs. Given a cipher implementation, either at RTL or gate-level, SOLOMON uses formal methods to map vulnerable regions in the cipher algorithm to specific locations in the hardware thus enabling targeted countermeasures to be deployed with much lesser overheads. We demonstrate the efficacy of the SOLOMON framework using three ciphers: AES, CLEFIA, and Simon.
Models for circular data from time series spectra

Circular data are those for which the natural support is the unit circle and its toroidal extensions. Numerous constructions have been proposed which can be used to generate models for such data. We propose a new, very general, one based on the normalization of the spectra of complex‐valued stationary processes. As illustrations of the new construction's application, we study models for univariate circular data obtained from the spectra of autoregressive moving average models and relate them to existing models in the literature. We also propose and investigate multivariate circular models obtained from the high‐order spectra of stationary stochastic processes generated using linear filtering with an autoregressive moving average response function. A new family of distributions for a Markov process on the circle is also introduced. Results for asymptotically optimal inference for dependent observations on the circle are presented which provide a new paradigm for inference with circular models. The application of one of the new families of spectra‐generated models is illustrated in an analysis of wind direction data.
Prediction of Depression in Social Network Sites Using Data Mining

Social network Site has captured the attention of the young generation for the last past ten years. Through internet technology, it is becoming convenient and easy to access and interact with these social network sites with another side of the world. So, social network sites are now a source of mining data as large people are interacting and socializing with each other. Immense information relating to the user's state of mind, feeling and negativism can also be extracted. Meanwhile, the Social network sites have been using to seek remedy for their worries not knowing they are having mental problems. Hence, Data mining gives a various way of techniques to retrieve relevant information and knowledge. And can also be used for statistical reports, machine learning and deep learning. These techniques introduce how to handle the retrieved data in the course of data analysis. This study has highlighted and revealed the user's mental health status and condition by analyzing each user's data. Analyzing data involves training each user's data to get an output and also have a test set to get efficiency and accuracy. This study has brought out many depressive indicative words which play an important role to bring this study success. We also proposed some few methods that have been carried out in this study. Analyzation for efficiency has been performed using this proposed method which containing packages of emotional words and can also increase the accuracy, efficiency and the scale of analyzation lapse. Both these technology data mining and machine learning can give us the condition and status of the user's mental health problem.
Learning Cooperation Schemes for Mobile Edge Computing Empowered Internet of Vehicles

Intelligent Transportation System has emerged as a promising paradigm providing efficient traffic management while enabling innovative transport services. The implementation of ITS always demands intensive computation processing under strict delay constraints. Machine Learning empowered Mobile Edge Computing (MEC), which brings intelligent computing service to the proximity of smart vehicles, is a potential approach to meet the processing demands. However, directly offloading and calculating these computation tasks in MEC servers may seriously impair the privacy of end users. To address this problem, we leverage federated learning in MEC empowered internet of vehicles to protect task data privacy. Moreover, we propose optimized learning cooperation schemes, which adaptively take smart vehicles and road side units to act as learning agents, and significantly reduce the learning costs in task execution. Numerical results demonstrate the effectiveness of our schemes.
Self-Awareness for Autonomous Systems

The articles in this month’s special issue cover concepts and fundamentals, architectures and techniques, and applications and case studies in the exciting area of self-awareness in autonomous systems.
DPKI: A Blockchain-Based Decentralized Public Key Infrastructure System

As the technology of Internet-of-Things advances, the potential benefit of compromising such a network increases as well. Conventional security stacks already deployed in proven scenarios, such as public key infrastructure on the world wide web, have been applied to the context of Internet-of-Things, however they fail to address certain issues that are prevalent in these networks, the foremost being a secure methodology of verifying the identities of devices without a central point of failure or lack of proper scalability. This paper describes a novel public key infrastructure system that discerns itself from conventional implementations by establishing a distributed network rather than a monolithic one. This enables one to horizontally scale the solution, as there is no reliance on a central authority. Instead, a blockchain is deployed and utilized as the record-keeping of device identities in tandem with smart contracts that interface with the underlying blockchain storage. This solution solves the issues of central point of failure, increases the overall resilience of the public key infrastructure security component and can be arbitrarily enhanced to support a wider array of functionality by introducing new smart contracts within the network itself.
Towards Federated Learning for HVAC Analytics

In recent years, many machine learning (ML) models have been developed for enhancing the performance of heating, ventilation and air conditioning (HVAC) systems. In all these studies, it is commonly assumed that building data is collected and stored at a central location, usually a cloud server, where the ML models are trained. Collecting data in a centralized location introduces privacy concerns since building data can reveal sensitive information such as the arrival and departure patterns of occupants. In this paper, we advocate federated learning (FL), a new distributed learning paradigm, where an overall ML model is trained without the need for exchanging raw data between the data source and the cloud.
Bagging-based saliency distribution learning for visual saliency detection

Saliency detection is still very challenging in computer vision and image processing. In this paper, we propose a novel visual saliency detection framework via bagging-based saliency distribution learning (BSDL). Given an input image, we firstly segment it into superpixels as basic units. Then two prior knowledge containing background prior and center prior are integrated to generate an initial prior map, which is used to select training samples from all superpixels to train the BSDL model. Specifically, the BSDL contains two stages: In the first stage, we use bagging-based sampling method to train K saliency classifiers from all training samples. K saliency classifiers are used to predict each superpixel saliency value. In the second stage, we aim to learn a saliency distribution model, whose goal is to infer the relationship between each classifier and each superpixel. i.e., for each superpixel, the BSDL not only trains K saliency classifiers to predict its saliency value, but also infers the reliability of using each saliency classifier to predict its saliency value. As a result, each superpixel's saliency value is determined by its K prediction saliency values and saliency distribution. After the BSDL, we propose a so called foreground consistency saliency optimization framework (FCSO) to further refine saliency map obtained by BSDL. To improve computation efficiency, a prejudgment rule is proposed to evaluate the quality of saliency map obtained by BSDL, which is used to decide whether the FCSO is needed for input image. Experimental results on four public datasets demonstrate the superiority of the proposed method than other state-of-the-art methods.
A Perspective of Missing Value Imputation Approaches

A massive amount of data is emerging continuously in the era of big data; the missing value is a common yet challenging problem. Missing data or missing value can be described as the data whose values in any given dataset are unknown. It is a recurrent phenomenon and can give biased results from the data to be observed and affect the value of the learning process. Therefore, it is mandatory to use missing value imputation techniques. Missing value imputation provides (MVI) optimal solutions for missing values in a dataset. There are diverse missing value imputation techniques such as statistical method and machine learning methods proposed till date, each having their own significances and flaws. Reasonably, good imputation results may be produced by machine learning MVI approaches but they take greater imputation times than statistical approaches usually. In this paper, we have reviewed some of the missing value imputation approaches proposed. In order to show the efficiency of their proposed approaches, they had compared their proposed methods with some baseline imputation algorithms like mode/median, k-nearest neighbor, Naive Bayes, support vector machine. The outcome of each proposed method is analyzed and their extension scopes from the perspectives of research focus. The main idea of this literature is to give a general review of missing value imputation approaches.
Mapping and accuracy assessment of siltation of recharge dams using remote sensing technique

Abstract This study demonstrates the use of ASTER data to map the siltation of the Al-Khod Dam constructed at the lower reaches of Samail Catchment area, north Muscat, Sultanate of Oman. In this study, the decorrelated image of spectral bands 6, 3, and 1 clearly discriminated the distribution of siltation. The occurrence of siltation is confirmed by the detection of minerals using the VNIR-SWIR bands by the Spectral Angle Mapper (SAM) method. In addition, the siltation of the dam is studied for the years 1987 to 2018 using Landsat satellite images. Interpretation of images showed a gradual increase of the siltation. Mapping accuracy for the occurrence of siltation is assessed by a confusion matrix which produced an overall accuracy of 97.88% and Kappa coefficient of 0.98 in the Maximum Likelihood method. Results of image analyses are verified in the field and confirmed through laboratory analyses. The study of spectral absorption characters of field samples using a Portable Infrared Mineral Analyzer (PIMA) showed the presence of OH molecule bearing minerals (clays, serpentines, etc.) and carbonate minerals (calcite and dolomite) in the silt deposits. The occurrence of these minerals is confirmed further by X-ray diffraction (XRD) and Scanning Electron Microscope (SEM) analyses.
Chosen Ciphertext Security from Injective Trapdoor Functions

We provide a construction of chosen ciphertext secure public-key encryption from (injective) trapdoor functions. Our construction is black box and assumes no special properties (e.g. "lossy", "correlated product secure") of the trapdoor function.
Towards a speech therapy support system based on phonological processes early detection

Phonological disorders are characterized by substitutions, insertion and/or deletions of sounds during the process of language acquisition, which are known as Phonological Processes (PPs). In the speech therapy domain, an early identification of PPs allows the diagnosis and treatment of various pathologies and may improve clinical tasks, however, there are few proposals that focus on the identification of PPs for supporting Speech-Language Pathologists (SLPs). Recent research applied Case-Based Reasoning (CBR) in medical domain to identify specific cases related to patients. Situation-Awareness (SA) is a technique that allows computing systems to adapt itself and respond to users or other systems according to environment information. Moreover, there is no indicative in related literature of CBR and SA being used for detecting PPs that may occur in pronunciation. In this paper, we introduce the union of SA and CBR, tied to machine learning algorithms for proposing a system to predict PPs, supporting specialists in their clinical decisions. To evaluate the system, we implemented it in a software architecture prototype and evaluated the prototypes using a knowledge base containing near one hundred thousand audio files, collected from more than 1,000 pronunciation assessments. The evaluation of the prototypes showed an accuracy over 93% in the prediction of PPs, resulting in a efficient tool for clinical decision support and therapeutic planning. We also presented a direct qualitative comparison between our approach and related work.
TLPCM: Transfer Learning Possibilistic $C$-Means

Traditional machine learning and data mining have made tremendous progress in many knowledge-based areas, such as clustering, classification, and regression. However, the primary assumption in all of these areas is that the training and testing data should be in the same domain and have the same distribution. This assumption is difficult to achieve in real-world applications due to the limited availability of labeled data. Associated data in different domains can be used to expand the availability of prior knowledge about future target data. In recent years, transfer learning has been used to address such cross-domain learning problems by using information from data in a related domain and transferring that data to the target task. In this article, a transfer-learning possibilistic c-means (TLPCM) algorithm is proposed to handle the PCM clustering problem in a domain that has insufficient data. Moreover, TLPCM overcomes the problem of differing numbers of clusters between the source and target domains. The proposed algorithm employs the historical cluster centers of the source data as a reference to guide the clustering of the target data. The experimental studies presented here were thoroughly evaluated, and they demonstrate the advantages of TLPCM in both synthetic and real-world transfer datasets.
Hate speech and political media discourse in Nigeria: The case of the Indigenous People of Biafra

The study adopts approaches in linguistics and critical discourse analysis to interpret media speeches and public statements of the Biafra secessionist movement leader, Nnamdi Kanu, as hate speech. The study shows that hate speech in discourses produced by the separatist Indigenous People of Biafra appears as language aggression, such as insults and verbal attacks, as well as threats. Discourse structures such as the use of interrogation and metaphor also appear in the hate narratives. Compared with the Rwandan case, the study argues that hate speech could result in similar incitement and violence. While hate speech caused genocide in Rwanda, it did not work in Nigeria, largely because of the division among the Biafra campaigners and the Igbo political elite about the Biafra independence campaign.
Blockchain-Based Trusted Achievement Record System Design

The primary purpose of this paper is to provide a design of a blockchain-based system, which produces a verifiable record of achievements. Such a system has a wide range of potential benefits for students, employers and higher education institutions. A verifiable record of achievements enables students to present academic accomplishments to employers, within a trusted framework. Furthermore, the availability of such a record system would enable students to review their learning throughout their career, giving them a platform on which to plan for their future accomplishments, both individually and with support from other parties (for example, academic advisors, supervisors, or potential employers). The proposed system will help students in universities to increase their extra-curricular activities and improve non-academic skills. Moreover, the system will facilitate communication between industry, students, and universities for employment purposes and simplify the search for the most appropriate potential employees for the job.
A Qualitative Study of Various Aspects of the Application of IoT in Disaster Management

Considering the vital issue of disaster management and the requirement of its additional development due to the increase in the rate of disasters over the recent years, using new technologies in all stages of disaster management including preparation stages for pre-disaster periods, reaction to disaster during its occurrence as well as the post-disaster reconstruction is very important. Therefore, the capacity of IoT in creating intelligence in objects to investigate different environmental parameters, the capacity of analyzing information, the intelligence required to predict some events and consequently, responding timely to any event and even proposing programs and systems for human performance are very effective in managing disasters. The existing resources for investigating the usages of Internet of Things (IoT) in disaster management could each address a particular aspect of this application. This paper has attempted to present a comprehensive view regarding the different dimensions of application of IoT in disaster management. The advantages and disadvantages of this application are mentioned, some instances of previous systems and models have also been discussed. The novelty of this work is the extent of examined dimensions and also addressing what can be done in different kinds of disasters.