Reviewerly/exHarmony huggingface dataset:
Lossless Wideband RF Compression via Lifting-Based IIR Subband Decomposition

This correspondence presents a scheme for the lossless compression of wideband spectrally and/or temporally sparse radio-frequency intercepts. It decomposes intercepts into time/frequency partitions using an adaptive invertible channelizer based on infinite impulse response orthogonal wavelet filter banks. The resulting subband decomposition is compressed via arithmetic coding.
Pairwise Cluster Similarity Domain Adaptation for Multimodal Deep Learning Architecture

In this work, we investigate the drawback of direct application from a dataset to the new domain with a different probability distribution, known as domain shifting. The two different probability distribution of datasets is generally known as source domain and target domain. Regularly, when we use large datasets from the open repository (source domain) for the new problem (target domain), we need to bridge for adapting these two domains. Notably, this research focuses on learning setup for the multimodal task which is commonly known as domain adaptation. To minimize the difference between the source and target domain, we employ cluster analysis with a modification of the loss function of a deep neural network. The modification of the loss function is done by combining the cross-entropy with covariance between source and target domains. We propose a method named Pairwise Cluster Similarity Domain Adaptation (PCSDA) that orders the distance among clusters from the source and target domains. Comprehensive experimental results of image captioning task (object detection on image and text generation) demonstrate the robustness and effectiveness of the proposed method.
Study of EEG characteristics during the observation of an educational material

In this paper we applied analysis of multivariate time series for detection of changes in functional relations in brain during observation of educational material. Applied method is based on definition of mutual interdependence of processes and is known as Recurrent Measure of Dependence. In the paper we analyzed multichannel EEG signals obtained during experiments with observation of educational material by human subjects. We applied the method to EEG signals and showed qualitative changes in brain dynamics during educational process in comparison to dynamics of background activity.
Logistics of Electronic Home-Project-Based Course

Some educational approaches designed to replace traditional lecture-based courses are known: Active Learning, E-Learning, PBL and others.Educators in many famous universities agree that despite significant popularity of the above techniques, real-life laboratory is an obligatory requirement for the education of an electronic engineer.Although, several well-known electronic simulation-software can be successfully used as a valuable and nearly free educational tool, nevertheless students that never experienced real components may get the false impression that all cables are always good, all measurement devices are ideal and that electronic components always behave as described in the datasheet.Hence, traditional electronics laboratory with measurement equipment, electronic components and cables is still a must at least for the basic electronic courses in the first and second year of the curriculum.However, for some advanced electronic courses, an alternative organization course can be proposed: a home project-based course.During the four semesters (2016-2018), groups of 20 students each (80 in total) were involved in two elective courses of that kind: Electronic Instrumentation and Real-Time DSP.In those courses, pairs of students were asked to execute small laboratory experiments at their homes by using electronic kits provided to them by the lecturer.Additionally, they implemented at home, and presented in the class two micro-projects using both simulation software and the above kits.At the end of each semester, 25% of the students (20 in total) were interviewed about their experience in the course.Only three were unsatisfied, while the other 17 students expressed very positive attitudes toward the course.A typical citation raised by some students is: "It's my first time doing something that really works, not just a simulation or an exercise."We believe the presented method increases both the interest and the understanding level of the students.
<i>Designing the Invisible: An Introduction to Service Design</i>, by Lara Penin

Designing the Invisible provides a foundational collection of the main definitions, theories, and concepts necessary for understanding and learning about the Service Design field. The main aim of t...
Multi-Sensor Activity Monitoring: Combination of Models with Class-Specific Voting

This paper presents a multi-sensor model combination system with class-specific voting for physical activity monitoring, which combines multiple classifiers obtained by splicing sensor data from different nodes into new data frames to improve the diversity of model inputs. Data obtained from a wearable multi-sensor wireless integrated measurement system (WIMS) consisting of two accelerometers and one ventilation sensor have been analysed to identify 10 different activity types of varying intensities performed by 110 voluntary participants. It is noted that each classifier shows better performance on some specific activity classes. Through class-specific weighted majority voting, the recognition accuracy of 10 PA types has been improved from 86% to 92% compared with the non-combination approach. Furthermore, the combination method has shown to be effective in reducing the subject-to-subject variability (standard deviation of recognition accuracies across subjects) in activity recognition and has better performance in monitoring physical activities of varying intensities than traditional homogeneous classifiers.
Item analysis in language assessment

This chapter introduces the reader to the important role that item analysis plays when exploring language test data. This analysis is usually the first statistical procedure to take place once the test data have been entered into a program such as IBM SPSS or Winsteps. It provides three types of information: first of all, it indicates the difficulty level of each item (also referred to as the facility value); in other words, it tells the researcher how many test takers have answered the item correctly. Second, the analysis provides insights into how well the items discriminate between the test takers and thus whether the stronger ones are answering the items correctly and the weaker answering them incorrectly. Third, it demonstrates the degree to which each individual item is contributing to the task’s internal consistency reliability, that is, whether the items are measuring the same construct. A sample study, based on a task consisting of 11 multiple-choice items, which was given to 102 test takers, is used to illustrate these three procedures of item analysis.
Automatic Object Segmentation Based on GrabCut

Object segmentation is used in multiple image processing applications. It is generally difficult to perform the object segmentation fully automatically. Most object segmentation schemes are developed based on prior information, training process, existing annotation, special mechanical settings or the human visual system modeling. We proposed a fully automatic segmentation method not relying on any training/learning process, existing annotation, special settings or the human visual system. The automatic object segmentation is accomplished by an objective object weight detection and modified GrabCut segmentation. The segmentation approach we propose is developed only based on the inherent image features. It is independent with various datasets and could be applied to different scenarios. The segmentation result is illustrated by testing a large dataset.
A Graph-Theoretic Framework for Summarizing First-Person Videos

First-person video summarization has emerged as an important problem in the areas of computer vision and multimedia communities. In this paper, we present a graph-theoretic framework for summarizing first-person (egocentric) videos at frame level. We first develop a new way of characterizing egocentric video frames by building a center-surround model based on spectral measures of dissimilarity between two graphs representing the center and the surrounding regions in a frame. The frames in a video are next represented by a weighted graph (video similarity graph) in the feature space constituting center-surround differences in entropy and optic flow values along with PHOG (Pyramidal HOG) features. The frames are finally clustered using a MST based approach with a new measure of inadmissibility for edges based on neighbourhood analysis. Frames closest to the centroid of each cluster are used to build the summary. Experimental comparisons on two standard datasets clearly indicate the advantage of our solution.
Eco-Efficient Resource Management in HPC Clusters through Computer Intelligence Techniques

High Performance Computing Clusters (HPCCs) are common platforms for solving both up-to-date challenges and high-dimensional problems faced by IT service providers. Nonetheless, the use of HPCCs carries a substantial and growing economic and environmental impact, owing to the large amount of energy they need to operate. In this paper, a two-stage holistic optimisation mechanism is proposed to manage HPCCs in an eco-efficiently manner. The first stage logically optimises the resources of the HPCC through reactive and proactive strategies, while the second stage optimises hardware allocation by leveraging a genetic fuzzy system tailored to the underlying equipment. The model finds optimal trade-offs among quality of service, direct/indirect operating costs, and environmental impact, through multiobjective evolutionary algorithms meeting the preferences of the administrator. Experimentation was done using both actual workloads from the Scientific Modelling Cluster of the University of Oviedo and synthetically-generated workloads, showing statistical evidence supporting the adoption of the new mechanism.
Combining Very Deep Convolutional Neural Networks and Recurrent Neural Networks for Video Classification

Convolutional Neural Networks (CNNs) have been demonstrated to be able to produce the best performance in image classification problems. Recurrent Neural Networks (RNNs) have been utilized to make use of temporal information for time series classification. The main goal of this paper is to examine how temporal information be- tween frame sequences can be used to improve the performance of video classification using RNNs. Using transfer learning, this paper presents a comparative study of seven video classification network architectures, which utilize either global or local features extracted by VGG-16, a very deep CNN pre-trained for image classification. Hold-out validation has been used to optimize the ratio of dropout and the number of units in the fully-connected layers in the proposed architectures. Each network architecture for video classification has been executed a number of times using different data splits, with the best architecture identified using the independent T-test. Experimental results show that the network architecture using local features extracted by the pre-trained CNN and ConvLSTM for making use of temporal information can achieve the best accuracy in video classification.
Applying Cluster Refinement to Improve Crowd-Based Data Duplicate Detection Approach

In this paper, we present an extension on a hybrid-based deduplication technique in entity reconciliation (ER), by proposing an algorithm that builds clusters upon receiving a pre-specified K number of clusters, and second developing a crowd-based procedure for refining the results of the clusters produced after the clustering generation phases. With the clusters refined, we aim to minimize the cost metric Λ'(R) of the solitary and compound cluster generation algorithms, to achieve an improved and efficient deduplication method, to have an increase in accuracy in identifying duplicate records, and finally, further reduce the crowdsourcing overheads incurred. In this paper, in the experiments, we made use of three datasets commonly known to hybrid-based deduplication such as paper, product, and restaurant. The performance results and evaluations demonstrate clear superiority to the methods compared with our work offering low-crowdsourcing cost and high accuracy of deduplication, as well as better deduplication efficiency due to the clusters being refined.
Real-time kinematical optimal trajectory planning for haptic feedback manipulators

To obtain real-time haptic interactions in virtual cockpit systems (VCSs), a real-time trajectory planning method based on kinematical optimization for haptic feedback manipulators (HFMs) is presented in this paper. Firstly, the control panel area is extracted in the workspace of the HFM, in which the interacting point is located. Then a feasible interacting configuration is calculated as the objective configuration of the trajectory encoded by a parametric representation. The trajectory planning problem is formulated as a non-linear optimization problem based on kinematics, which is solved in real-time by finding a good initial solution with machine learning methods. Simulations show that trajectories with a compromise between safety and rapidity can be calculated in real-time by this method, which provides a basis for haptic interaction in VCSs.
The Reselection of Adjacent Sets by Consistency-Based Feature Selection Algorithm

In this paper, we introduce the reselection of adjacent sets by the consistency-based feature selection algorithm. Here, an adjacent set is the feature set obtained by applying the algorithm to the data set such that a single feature in the feature set selected by the algorithm is removed and we call such an application the reselection of adjacent sets. Then, we give a simple example that, for the algorithm CWC, the cardinality of an adjacent set is not always larger than that of the feature set selected by CWC. Next, we give experimental results to evaluate the results of the reselection of adjacent sets by CWC, by using real data and nucleotide sequences.
The general probabilistic fuzzy set for modeling and its application in EMG robots

The probabilistic fuzzy set (PFS) is designed for handling uncertainties with both fuzzy and stochastic nature, so the probabilistic fuzzy logic system (PFLS) has the ability to handle more complex uncertainties in process. In this paper, the general probabilistic fuzzy set is proposed, and the con vergence analyses of its secondary probability density function (PDF) are conducted. It discloses the distribution regularity of membership degree in general PFS, which improves the information and interpretability of PFS. Then, according to convergence, a new method to tuning parameters for PFLS is proposed. This method avoids the parameters into local inefficiency, and also reduces the number of learning parameters in PFLS. Last, the new tuning method is applied to the electromyography (EMG) robots modeling problem. The comparison shows that the probabilistic fuzzy logic system based on general PFS (GPFLS) can achieve a simple modeling process, and also, it improves the learning speed compared to PFLS. The work presented will improve the potential application of probabilistic fuzzy logic system.
Sed‐Dedup: An efficient secure deduplication system with data modifications

Summary The amount of outsourced data grows rapidly. In recent years, cloud service providers integrate data deduplication systems with convergent encryption (CE) methods, in which a file encryption key is determined by its own content instead of the secret of a specific user, to save the storage cost and ensure the security of outsourced data. However, present secure deduplication systems failed to deal with data modifications efficiently. We observe that when a client makes small changes on an existing file, the current chunking algorithms cannot effectively detect the similarities and always create chunks with largely overlapped contents. It reduces data deduplication ratios and results in unnecessary overhead. In this paper, we propose Sed‐Dedup, an efficient secure delta encoding deduplication system to address this problem. In Sed‐Dedup, we introduce a novel delta encoding approach to store modified contents in delta files and leave the original files intact. Two schemes with different encoding policies are designed. Both of them can solve the issue and improve the secure deduplication performance. To evaluate the performance, we implement a prototype and conduct extensive experiments based on synthetic and real‐world datasets. Our experimental results show that Sed‐Dedup is superior to the state‐of‐the‐art secure deduplication systems.
The Influence of Human Walking Activities on the Doppler Characteristics of Non-stationary Indoor Channel Models

This paper analyzes the time-variant (TV) Doppler power spectral density of a 3D non-stationary fixed-to-fixed indoor channel simulator after feeding it with realistic trajectories of a walking person. The trajectories of the walking person are obtained by simulating a full body musculoskeletal model in OpenSim. We provide expressions of the TV Doppler frequencies caused by these trajectories. Then, we present the complex channel gain consisting of fixed scatterers and a cluster of moving scatterers. After that, we use the concept of the spectrogram to analyze the TV Doppler power spectral density of the complex channel gain. Finally, we present expressions of the TV mean Doppler shift and Doppler spread. The work of this paper is important for human activity recognition systems using radio-frequency (non-wearable) sensors as the demand for such systems has increased nowadays.
Towards Applying River Formation Dynamics in Continuous Optimization Problems

River Formation Dynamics (RFD) is a metaheuristic that has been successfully used by different research groups to deal with a wide variety of discrete combinatorial optimization problems. However, no attempt has been done to adapt it to continuous optimization domains. In this paper we propose a first approach to obtain such objective, and we evaluate its usefulness by comparing RFD results against those obtained by other more mature metaheuristics for continuous domains. In particular, we compare with the results obtained by Particle Swarm Optimization, Artificial Bee Colony, Firefly Algorithm, and Social Spider Optimization.
A Feature Extraction Method Based on BSO Algorithm for Flight Data

The feature extraction problem for flight data has aroused increasing attention in the practical and the academic aspects. It can reveal the inherent correlation relation among different parameters for the conditional maintenance of the aircraft. However, the high-dimensional and continuous features in the real number field bring challenges to the extraction algorithms for flight data. Brain Storm Optimization (BSO) algorithm can acquire the optimal solutions by continuously converging and diverging the solution set. In this chapter, a feature extraction method based on BSO algorithm is proposed to mine the associate rules from flight data. By using the designed real-number encoding strategy, the intervals and rule template can be handled directly without data discretization and rule template preset processes. Meanwhile, as the frequent item generation process is unnecessary in our proposed algorithm, the time and space complexity will be reduced simultaneously. In addition, we design the fitness function using support, confidence and length of the rules for the purpose of extracting more practical and intelligible rules without predetermining the parameter thresholds. Besides, high-dimensional problems can also be solved using our algorithm. The experiments using substantial flight data are conducted to illustrate the excellent performance of the proposed BSO algorithm comparing to the Apriori algorithm and Genetic algorithm (GA). Furthermore, the classification problems with two datasets from UCI database are also used to verify the practicability and universality of the proposed method based on BSO algorithm.
Calculating the Optimal Step in Shift-Reduce Dependency Parsing: From Cubic to Linear Time

We present a new cubic-time algorithm to calculate the optimal next step in shift-reduce dependency parsing, relative to ground truth, commonly referred to as dynamic oracle. Unlike existing algorithms, it is applicable if the training corpus contains non-projective structures. We then show that for a projective training corpus, the time complexity can be improved from cubic to linear.
Development of an Educational Mobile Game Applications for Grade 5 for Knowledge Channel Inc.

This paper discusses the development of mobile games for grade five students in learning Mathematics. The study specifically aims to have an additional tool in leaning grade five Mathematics. With the study, the students would be able to identify, define, describe, compare, choose, visualize, simulate, demonstrate and solve grade five Mathematics problems and other related problems. The design and development of the study were made possible by the Technological Institute of the Philippines (T.I.P.) in partnership with the Knowledge Channel (KC) Incorporated. Series of presentations were made during the development, testing, and evaluation of these mobile applications. The study includes the assessment of the developed mobile applications using the Knowledge Channel Focus Group Discussion (FGD). The mobile games had gathered an average mean of 3.67 which was interpreted as "excellent." Aside from the FGD, the study got an average score of 4.65 as an evaluation using questionnaires drafted from the ISO 25010. This checks the quality of a system to the degree to which the mobile applications satisfy the stated and implied needs of its various stakeholders, and thus provides value. Furthermore, pre-test and post-test were conducted to identify if the study was able to help students in learning. Upon comparing the results, the study is proven that mobile applications were able to improve student learning when the games were played. An average rate of 48.86% increase was observed.
A Study on the Cultivation of Values of Maritime College Students’ Occupational Pride

In view of the problems that the college students in Maritime major are weak in core values and lack of occupational pride at present,the questionnaire and the form of the symposium have been adopted to investigate the main influencing factors.This paper puts forward the educational countermeasures of constructing the core value system of occupational pride in Maritime College that combining traditional culture with modern occupational knowledge education from the establishment of specialized work institutions, the construction of the campus culture with distinctive maritime characteristics,the strengthening of the construction of the system and culture of Maritime Colleges and Universities, and the guidance of self-development self-education for students.
Functional and Non-Functional Requirements Modeling for the Design of New Online Social Networks

Online social networks (OSNs) such as Facebook and LinkedIn are now widely used. They count users in the hundreds of millions. This chapter surveys popular social networks in order to present a pattern of recurring functional requirements as well as non-functional requirements, and a model of that pattern in the i* requirements modelling language. The pattern can serve as a starting point for requirements engineering of new OSNs. The authors test their model by applying it to a popular OSN, namely Twitter.