Reviewerly/exHarmony huggingface dataset:
Wrist-worn Wearable Sensors to Understand Insides of the Human Body

Wearable sensors have become more commonly used in everyday basis and powerful in terms of computational capacity and sensing resources, including capability to collect data from different bio-signals. The data collected from everyday wearables offers huge opportunities to monitor people's everyday life without expensive laboratory measurements, including also behaviours and conditions only rarely seen in controlled laboratory environments. So far wearable sensors have mostly been used to monitor motion, but bio-sensor powered wearables can do a lot more: they can be used to monitor physiological reactions inside the human body as well as some psycho-physical reactions such as affection and stress. This development enables multiple interesting and important applications, such as early detection of diseases, seizures, and attacks. With stock wearables worn in everyday basis, one of the biggest challenges for such applications is the sensing data itself. In order to train reliable recognition and prediction models, high quality training data with labels needs to be collected. This paper focuses on lessons learned of challenges in data quality and quantity when such data sets are gathered. We discuss our own experiences when collecting data using wearable sensors for early detection of migraine attacks, but the same lessons learned can be generalized to other studies utilizing wearables for recognition medical symptoms and users' everyday behaviour.
An Inquiry into AI University Curriculum and Market Demand

This study aims at exploring the fit/misfit between artificial intelligence (AI) curriculum objectives and AI-relevant job qualifications. In our preliminary analysis, we applied the data collected from a major employment website and multiple university programs to extricate dimensions and properties for academic curriculum design and job qualifications, respectively. After investigating AI career market demand and supply, we recommend aligning AI curriculum with current and future market demands. Envisioning our future research, further theoretical development with supportive evidence should be leveraged to interpret the fit/misfit between AI curriculum design and market needs.
Infrared Face Recognition Using Neural Networks and HOG-SVM

With the rapid development of computer vision applications, infrared face recognition is receiving much attention because of miniscule sensitivity to the face variations due to illumination changes. The infrared image is merely a gray level image but the recognition is interestingly good enough to look at. In this article we present an infrared face recognition system where we used two methods and two databases to train and test our system. We used the Histogram of Gradient (HOG) algorithm along with the Support Vector Machine (SVM) classifier as a first method. In the second method, we implemented the Backpropagation algorithm. Although the results of the HOG-SVM were very promising and we were able to get a better results compared with a previous work, neural network gave us a perfect results by using the same number of training examples.
Client-server cooperative and fair DASH video streaming

Adaptive video streaming over HTTP, such as the MPEG-DASH standard, is now widely used by video service provides to stream their videos to users. But DASH and similar methods are known to suffer from two practical challenges: on the one hand, clients use fixed heuristics that limit their ability to generalize across network conditions, making the clients unable to efficiently predict variations in new networking environments, in turn leading to more buffering. On the other hand, the absence of collaboration among DASH clients leads to unfair bandwidth allocation, and typically pushes the system to an unbalanced equilibrium point. In this paper, we propose a server-side rate adaptation method that significantly improves the fairness of network bandwidth allocation among concurrent DASH users. We formulate the problem as a Decentralized Partially Observable Markov Decision Process (Dec-POMDP) model, and use Reinforcement Learning (RL) to train two neural networks to find an optimal solution to the fairness problem. Since our solution is implemented at the server side, it requires no modifications to the widely-installed DASH clients, making our solution very practical. We show that our proposed method outperforms the state-of-the-art schemes in terms of QoE-efficiency, QoE-fairness, and social welfare by as much as 16%, 21%, and 24% respectively.
Real-Time Age Detection Using a Convolutional Neural Network

The problem of determining people’s age is a recurring theme in areas such as law enforcement, education and sports because age is often used to determine eligibility. The aim of current work is to make use of a lightweight machine learning model for automating the task of detecting people’s age. This paper presents a solution that makes use of a lightweight Convolutional Neural Network model, built according to a modification of the LeNet-5 architecture to perform age detection, for both males and females, in real-time. The UTK-Face Large Scale Face Dataset was used to train and test the performance of the model in terms of predicting age. To evaluate the model’s performance in real-time, Haar Cascades were used to detect faces from video feeds. The detected faces were fed to the model for it to make age predictions. Experimental results showed that age-detection can be performed in real-time. Although, the prediction accuracy of the model requires improvement.
A New Approach for Developing Segmentation Algorithms for Strongly Imbalanced Data

During the past two decades, the problem of how to develop efficient segmentation algorithms for dealing with strongly imbalanced data has been drawing much attention of researchers and practitioners in the field of data mining. A typical approach for this difficult problem is represented by a random under-sampling approach, where the cardinality of the majority set is reduced to that of the minority set through random sampling, thereby enabling one to utilize standard classifiers such as Logistic Regression, Support Vector Machine (SVM) and Random Forest. When the resulting segmentation algorithm is applied to a set of testing data with the original imbalanced-ness, however, its performance could be rather limited. So as to improve the performance, a bagged under-sampling (BUS) approach has been introduced where a random under-sampling is repeated M times, though the effect of BUS turns out to be still not quite satisfactory. The first purpose of this paper is to enhance the performance of BUS by developing a novel way where BUS is employed in a repetitive manner. While the performance improvement of this approach (R-BUS) over BUS is recognizable, it is still not sufficient enough from a practical point of view, especially when the dimension of underlying binary profile vectors is quite large. The second purpose of this paper is to establish a rank reduction (RR) approach for reducing this large dimension. The combined use of R-BUS with RR provides an excellent performance, as we will see through a real-world application of large magnitude.
Development of a Model “Digital Tutor” System for the Project Education in the University

This paper presents the modeling of an information system for collecting and processing data for the implementation and support of modular educational programs in the University. Features using modern educational technologies, including a personalized approach are described. The proposed system is called the “Digital Tutor” System. Use cases, functionality and service integration scheme are shown. The paper shows that this will allow informing students and receiving feedback from students about the learning conditions in the educational program.
An investigation of transfer learning for deep architectures in group activity recognition

Pervasive technologies permeating our immediate surroundings provide a wide variety of means for sensing and actuating in our environment, having a great potential to impact the way we live, but also how we work. In this paper, we address the problem of activity recognition in office environments, as a means for inferring contextual information in order to automatically and proactively assists people in their daily activities. To this end we employ state-of-the-art image processing techniques and evaluate their capabilities in a real-world setup. Traditional machine learning is characterized by instances where both the training and test data share the same distribution. When this is not the case, the performance of the learned model is deteriorated. However, often times, the data is expensive or difficult to collect and label. It is therefore important to develop techniques that are able to make the best possible use of existing data sets from related domains, relative to the target domain. To this end, we further investigate in this work transfer learning techniques in deep learning architectures for the task of activity recognition in office settings. We provide herein a solution model that attains a 94% accuracy under the right conditions.
Block-Diagonal Subspace Clustering with Laplacian Rank Constraint

How to construct the block-diagonal affinity matrix is a focus in subspace clustering based on spectral clustering. Most of existing methods pursue the block-diagonal affinity matrix by indirect methods. In this paper, we propose a directly pursuing block-diagonal affinity matrix method, called Block-Diagonal Subspace Clustering with Laplacian Rank Constrain-t(BDLRC), for subspace clustering. Specifically, a block-diagonal structure of an ideal graph is recovered from its affinity matrix by imposing a rank constraint on the Laplacian matrix. Meanwhile, an adaptive affinity matrix learning approach is employed to construct exactly block-diagonal affinity matrix. BDLRC method is superior to previous subspace clustering methods in that: 1) BDLRC is able to generate an exactly block-diagonal affinity matrix by pursuing block diagonal priors; 2) a simple and efficient solver is proposed for solving the problem of complex non-convex rank constraint. Experimental results on both synthetic and real-world data sets demonstrate the effectiveness of the proposed algorithm.
Domain-Independent Automated Processing of Free-Form Text Data in Telecom

Free-form, unstructured and semi-structured textual data has become increasingly more prevalent in the telecommunications industry, with service and equipment providers alike. Some typical examples include textual data from customer care tickets, machine logs, alarm and alerting systems, and diagnostics. There is a growing business need to rapidly and automatically understand the underlying key topics and categories of this bulk collection of text. With the present mode of operation of relying on domain experts to analyze textual data, there is a clear need to apply text analytics to automate the process. Difficulties arise due to the jargon-filled and fragmented, incomplete nature of textual data in this field. In this paper, we propose a domain-agnostic, unsupervised approach that deploys a multi-stage text processing pipeline for automatically discovering the key topics and categories from free-form text documents. Using anonymized datasets retrieved from actual customer care tickets and system logs, we show that our approach outperforms traditional text mining approaches, and performs comparably to manual categorization tasks that were undertaken by domain experts with full system knowledge.
Optimizing Virtual Machine Live Migration in Distributed Edge Servers Based on Hybrid Memory

In order to meet the users' request for high time-efficiency, edge computing migrates services to edge nodes closer to users through the virtual machine (VM) migration technologies. However, migrating VMs from an edge server to another edge server is done under the WAN with weak network conditions, so the migration will take more time than within the data center. In addition, Hybrid memory employing both Dynamic Random Access Memory (DRAM) and Persistent Memory (PM) is increasingly being adopted by edge servers, but lack support for efficient management in existing virtualization technologies. VM migration generates many random write operations on the destination node's memory which causes a decline in memory performance and brings huge wear to the PM. To address these challenges, this paper presents a virtual machine live migration method in distributed edge clouds based on hybrid memory. In this method, we modified the pre-copy algorithm to reduce the migration time, which transferring the cold pages before hot pages through the memory access feature. The virtual persistent memory (VPM) is used to mapping the physical hybrid memory to VM, and then the algorithm will move cold and hot pages to the PM area and DRAM area of the destination VM. Experimental results with several main workloads in edge computing show that our method can improve the performance of the live migration and memory management in destination VM with graceful degradation of CPU performance.
Confrustion in Learning from Erroneous Examples: Does Type of Prompted Self-explanation Make a Difference?

Confrustion, a mix of confusion and frustration sometimes experienced while grappling with instructional materials, is not necessarily detrimental to learning. Prior research has shown that studying erroneous examples can increase students’ experiences of confrustion, while at the same time helping them learn and overcome their misconceptions. In the study reported in this paper, we examined students’ knowledge and misconceptions about decimal numbers before and after they interacted with an intelligent tutoring system presenting either erroneous examples targeting misconceptions (erroneous example condition) or practice problems targeting the same misconceptions (problem-solving condition). While students in both conditions significantly improved their performance from pretest to posttest, students in the problem-solving condition improved significantly more and experienced significantly less confrustion. When controlling for confrustion levels, there were no differences in performance. This study is interesting in that, unlike prior studies, the higher confrustion that resulted from studying erroneous examples was not associated with better learning outcomes; instead, it was associated with poorer learning. We propose several possible explanations for this different outcome and hypothesize that revisions to the explanation prompts to make them more expert-like may have also made them – and the erroneous examples that they targeted – less understandable and less effective. Whether prompted self-explanation options should be modeled after the shorter, less precise language students tend to use or the longer, more precise language of experts is an open question, and an important one both for understanding the mechanisms of self-explanation and for designing self-explanation options deployed in instructional materials.
Expressing the Tacit Knowledge of a Digital Library System as Linked Data

Library organizations have enthusiastically undertaken semantic web initiatives and in particular the data publishing as linked data. Nevertheless, different surveys report the experimental nature of initiatives and the consumer difficulty in re-using data. These barriers are a hindrance for using linked datasets, as an infrastructure that enhances the library and related information services. This paper presents an approach for encoding, as a Linked Vocabulary, the “tacit” knowledge of the information system that manages the data source. The objective is the improvement of the interpretation process of the linked data meaning of published datasets. We analyzed a digital library system, as a case study, for prototyping the “semantic data management” method, where data and its knowledge are natively managed, taking into account the linked data pillars. The ultimate objective of the semantic data management is to curate the correct consumers’ interpretation of data, and to facilitate the proper re-use. The prototype defines the ontological entities representing the knowledge, of the digital library system, that is not stored in the data source, nor in the existing ontologies related to the system’s semantics. Thus we present the local ontology and its matching with existing ontologies, Preservation Metadata Implementation Strategies (PREMIS) and Metadata Objects Description Schema (MODS), and we discuss linked data triples prototyped from the legacy relational database, by using the local ontology. We show how the semantic data management, can deal with the inconsistency of system data, and we conclude that a specific change in the system developer mindset, it is necessary for extracting and “codifying” the tacit knowledge, which is necessary to improve the data interpretation process.
Entanglement of a new type of two-mode photon-added entangled coherent state

We introduce a new entangled state, which is composed of two photon-added coherent states. We discuss the entanglement of this state by using several sufficient entanglement criteria, such as higher-order entanglement criterion, EPR criterion, SU(1,1) algebra and Cauchy–Schwarz inequality. These criteria reflect some entanglement effects of this new state, but fail to find the degree of entanglement directly. Thus, we use the covariance to measure the entanglement in this state. Our findings show that the degree of entanglement decreases with the increasing of photon-number and the amplitude of the coherent states. One interesting result is that the new entangled state becomes a maximally entangled state when the photon-added number is 1 and the amplitude of the coherent states is zero, which is just one of the four Bell states.
The Effect of Smiling Pictures on Perceptions of Personas

We analyze the effect of a smile in personas pictures on persona perceptions, including credibility, likability, similarity, and willingness to use. We conduct an online experiment with 2,400 participants using a 16-item survey and multiple persona profile treatments of which half have a smiling photo and half do not. We find that persona profiles with a smiling photo result in an increase in perceived similarity with, likability of, and willingness to use the personas. In contrast, a smile does not increase the credibility of the personas. Our research has implications for the design of persona profiles and adds to previous findings of persona research that the picture choice influences individuals' persona perceptions in profound ways.
A Partitional Clustering Approach to Persian Spell Checking

Spell checkers find, suggest and correct incorrect words within documents. Complexities of languages increases the challenge in-hand. Due to the challenges of the Persian language, preprocessing and processing spell checkers requires further analysis and natural language processing. For the past decade, the majority of methods that identify errors in documents have been lexicon-based or probability-based. This paper describes a new approach for Persian spell-checking using a clustered dictionary to save time and storage. Clustering is optimally generated through k-medoids and during processing misspelt words are compared to the centers of the generated clusters rather than the entire dictionary. The proposed spell checker is evaluated using the Faspell spelling error corpus that contains Persian misspellings and is shown to provide suggestions with significant accuracy rates.
Taxonomy of Memory Usage in Swarm Intelligence-Based Metaheuristics

Metaheuristics under the swarm intelligence (SI) class have proven to be efficient and have become popular methods for solving different optimization problems. Based on the usage of memory, metaheuristics can be classified into algorithms with memory and without memory (memory-less). The absence of memory in some metaheuristics will lead to the loss of the information gained in previous iterations. The metaheuristics tend to divert from promising areas of solutions search spaces which will lead to non-optimal solutions. This paper aims to review memory usage and its effect on the performance of the main SI-based metaheuristics. Investigation has been performed on SI metaheuristics, memory usage and memory-less metaheuristics, memory characteristics and memory in SI-based metaheuristics. The latest information and references have been further analyzed to extract key information and mapped into respective subsections. A total of 50 references related to memory usage studies from 2003 to 2018 have been investigated and show that the usage of memory is extremely necessary to increase effectiveness of metaheuristics by taking the advantages from their previous successful experiences. Therefore, in advanced metaheuristics, memory is considered as one of the fundamental elements of an efficient metaheuristic. Issues in memory usage have also been highlighted. The results of this review are beneficial to the researchers in developing efficient metaheuristics, by taking into consideration the usage of memory.
A Feasible Community Cloud Architecture for Provisioning Infrastructure as a Service in the Government Sector

Building a community cloud by federating private clouds is a lower cost alternative for hosting applications that require distributed deployment to meet high availability, and service level compliance. Despite its potential benefits, there are many issues about lack of standardization and interoperability across multiple cloud service providers (CSPs), resulting in low adherence to the model in organizations that are still struggling to adapt its legacy applications to a cloud architecture in complex environments. Currently, there is no seamless approach to migrate from the traditional infrastructure model to a cloud computing model on the most part of government sector because many other approaches require a rapid disruption, taunting a high-coupling way to manage the infrastructure. We propose an architecture for building a community cloud even in scenarios with strong presence of non-cloud native applications by developing a low-coupled infrastructure middleware that supports different corporate hypervisors, a API, a GUI and a CLI. To show the feasibility of our approach, we evaluate the architecture on a set of infrastructures at Superior Courts of Brazilian Judicial Branch, that may compose a cost-effective solution to start the transition to the cloud model in other organizations also.
Challenging SQL-on-Hadoop Performance with Apache Druid

In Big Data, SQL-on-Hadoop tools usually provide satisfactory performance for processing vast amounts of data, although new emerging tools may be an alternative. This paper evaluates if Apache Druid, an innovative column-oriented data store suited for online analytical processing workloads, is an alternative to some of the well-known SQL-on-Hadoop technologies and its potential in this role. In this evaluation, Druid, Hive and Presto are benchmarked with increasing data volumes. The results point Druid as a strong alternative, achieving better performance than Hive and Presto, and show the potential of integrating Hive and Druid, enhancing the potentialities of both tools.
Measurement of mid-spatial frequency errors on freeform optics using deflectometry

Freeform optics have emerged as a new tool for optical designers and integrators. Manufacturing innovations are gradually increasing availability of precision freeform optics. As optics manufacturers strive to improve quality and decrease cost, some focus is placed on improvements in the challenging metrology requirements for freeforms. One relevant technique to be discussed here is the use of deflectometry to measure mid-spatial frequency error in-process or in-situ during the manufacturing of freeform parts. Deflectometry can measure the mid-spatial frequency error on freeform parts orders of magnitude faster than traditional tactile metrology tools at similar or better accuracy.