Reviewerly/exHarmony huggingface dataset:
Performance Evaluation of Threshold-Based and k-means Clustering Algorithms Using Iris Dataset

Background: Clustering is one of the data mining tools which classify the raw data reasonably into disjoint clusters. Researchers have developed many algorithms to cluster large data sets based on specific parameters. Objective: This study is centered around the popular partitioning-based technique, i.e., k-means. It requires the number of clusters to be generated as an input parameter; it does not provide a global solution of the problem; and it is sensitive to outliers and initial seed selection. Methods: In this paper, authors have discussed threshold-based clustering method, single pass method, which overcomes the above limitations but it requires a threshold value as an input parameter. Other researchers’ work related to k-means published in patent form is noteworthy and paving path for the researchers. Results: To assess the quality of clustering, numerous validity measures and indices have been assessed on the Iris dataset for both k-means and threshold-based clustering algorithms. It has been observed from the experiments that threshold-based method generates more separated and compact clusters, in addition, there is significant improvement in the validity indices. Conclusion: Threshold-based clustering generates the clusters automatically which are not sensitive to initial seeds selection and outlier; it is more scalable. It will inevitably be an efficient approach of partitioning based clustering whenever one will select the threshold value carefully or will propose new functions for deciding the value of threshold.
Cooperative Periodic Coverage With Collision Avoidance

In this paper we propose a periodic solution to the problem of persistently covering a finite set of interest points with a group of autonomous mobile agents. These agents visit periodically the points and spend some time carrying out the coverage task, which we call coverage time. Since this periodic persistent coverage problem is NP-hard, we split it into three subproblems to counteract its complexity. In the first place, we plan individual closed paths for the agents to cover all the points. Second, we formulate a quadratically constrained linear program to find the optimal coverage times and actions that satisfy the coverage objective. Finally, we join together the individual plans of the agents in a periodic team plan by obtaining a schedule that guarantees collision avoidance. To this end, we solve a mixed integer linear program that minimizes the time in which two or more agents move at the same time. Eventually, we apply the proposed solution to an induction hob with mobile inductors for a domestic heating application and show its performance with experiments on a real prototype.
Are mid-air dynamic gestures applicable to user identification?

Unlike the existing gesture related research predominantly focusing on gesture recognition (classification), this work explores the feasibility and the potential of mid-air dynamic gesture based user identification through presenting an efficient bidirectional GRU (Gated Recurrent Unit) network. From the perspective of the feature analysis from the Bi-GRU network used for different recognition tasks, we make a detailed investigation on the correlation and the difference between the gesture type features and the gesture user identity characteristics. During this process, two unsupervised feature representation methods – PCA and hash ITQ (Iterative Quantization) are fully used to perform feature reduction and feature binary coding. Experiments and analysis based on our dynamic gesture data set (60 individuals) exemplify the effectiveness of the proposed mid-air dynamic gesture based user identification approach and clearly reveal the relationship between the gesture type features and the gesture user identity characteristics.
A Mean-Field Optimal Control Formulation for Global Optimization

This paper is concerned with variational optimal control constructions whose solution yields a sampling algorithm. The particular form of the sampling algorithm considered here is a particle filter, designed to numerically approximate the solution to the global optimization problem. The theoretical significance of this study comes from its variational aspects. Specifically, the control input represents the solution of a mean-field-type optimal control problem. Its parametric counterpart, obtained when a parametric form of density is known <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> , is shown to be equivalent to the natural gradient algorithm. Explicit formulae for the filter are derived when the objective function is quadratic and the density is Gaussian. The optimal control construction of the particle filter is a significant departure from the classical importance sampling–resampling-based approaches.
Persuasive Technologies in m-Learning for Training Professionals: How to Keep Learners Engaged With Adaptive Triggering

Global corporations are characterized by a large number of employees and geographically dispersed offices. Moreover, the competitiveness in the global market requires them to invest in their human resources to be able to remain a step ahead of competition. Implementing large scale classical education in such environments is challenging and costly. Mobile e-learning (m-learning) allows users to tailor their professional training and education to their needs and time constraints. However, in self-paced education, it is very hard to keep user retention and engagement. To achieve the latter, we have designed and developed an m-learning platform for corporate environments based on the triggering persuasive technology principle that try to incite users in regularly using the platform. We have evaluated the application in-the-wild in corporate environments of differently sized companies with 300 users. Users were subjected to three different conditions: no triggering, simple regular triggering, and adaptive triggering. The results show that the use of adaptive triggering in m-learning increases user engagement as well as course completion rates more than simple regular triggering and no triggering.
Securely Connecting Wearables to Ambient Displays with User Intent

Wearables are often small and have limited user interfaces, hence they often wirelessly interface with a personal smartphone or a personal computer to relay information from the wearable for display. In this paper, we envision a new method LightTouch by which a wearable can establish a secure connection to an ambient display, such as a television or computer monitor, based on the user's intention to connect to the display. Such connections must be secure to prevent impersonation attacks, must work with unmodified display hardware, and must be easy to establish. LightTouch uses standard RF methods for communicating the data to display, securely bootstrapped with a key shared via a brightness channel between the low cost, low power, ambient light sensor of a wearable and the screen of the display. A screen touch gesture is adopted by users to ensure the modulation of screen brightness can be accurately and securely captured by the ambient light sensor. We further propose novel on-screen localization and correlation algorithms to improve security and reliability. Through experiments we demonstrate that LightTouch is compatible with current display and wearable designs, easy-to-use (5-6 seconds), reliable for connecting displays (98 percent success connection ratio), and secure against impersonation attacks.
Grayification: A meaningful grayscale conversion to improve handwritten historical documents analysis

This paper presents an improvement of handwriting binarization techniques on colored historical documents. We introduce a novel preprocessing step into the usual document image analysis (DIA) workflow. Before binarization, we propose a grayification step to enhance the input image with the help of a new grayscale conversion algorithm, namely the grayification algorithm. This new algorithm uses luminance and color information to improve the contrast between the foreground and the background. Especially on documents with non-black ink and moreover with diverse colors, e.g., illuminations in historical manuscripts, we expect an increased performance. The binarization give then better results on this enhanced grayscale image, and in particular color text is binarized as well as black text. In fact, by adding a preprocessing step to enhance the input grayscale image, the results on all the following tasks of the analysis chain should be improved. This modification of the usual workflow of historical document analysis eases the binarization task as well as other following tasks like layout analysis, line segmentation, OCR, etc. We demonstrate the effects of our novel preprocessing technique on a set of challenging historical documents, which we make publicly available for research purpose, and two publicly available datasets. This improvement is illustrated in this paper on the binarization task, where the results of four different binarization methods are successfully improved.
Aerial image matching method based on HSI hash learning

Specific to characteristics of aerial images like large size, high resolution and rich edges and texture, HSI hash learning matching algorithm for aerial image is proposed. Firstly, the original RGB component image is converted to HSI component image, then HSI-based local multi-feature descriptor is constructed. Secondly, in order to decrease the encoding mapping error in the traditional hash learning during model parameter optimization, objective function for each component image is constructed by the hash degree and the similarity information. And then, the projection matrix and bias threshold are determined. Finally, in the Hamming matching stage, the minimized loss distance function is defined and the spatial similarity between the Euclidean space and the Hamming space is preserved. Thus, the quantization loss in hash learning is reduced significantly. The proposed HSI hash learning algorithm evaluated on the standard dataset and actual aerial images demonstrates that it may significantly outperform the traditional algorithms in terms of efficiency and performance. As shown by experimental results, the matching accuracy of the proposed method is at least 20% higher than that of other binary descriptors, with desired computation time consumption.
Light Invariant Video Imaging for Improved Performance of Convolution Neural Networks

Light conditions affect the performance of computer vision algorithms by creating spatial changes in color and intensity across a scene. Convolutional neural networks (CNNs) use color components of the input image and, as a result, are sensitive to ambient light conditions. This work analyzes the influence of ambient light conditions on CNN classifiers. We suggest a method for boosting the performance of CNN-based object detection and classification algorithms by using light invariant video imaging (LIVI). LIVI neutralizes the influence of ambient light conditions and renders the perceived object's appearance independent of the light conditions. Training sets consist mainly, if not only, of objects in natural light conditions. As such, using LIVI boosts CNN performance by matching object appearance to that expected by the CNN model, which was created according to the training set. We further investigate the use of LIVI as a general self-supervised learning framework for CNN. Faster region-based CNN (Faster R-CNN) was used as a case study in order to validate the importance of light conditions on CNN performance and on how it can be improved by using LIVI as an input or feedback mechanism in a self-supervised framework. We show that LIVI enables reduced CNN size, enhanced performance and improved training.
The Seven Traits of a Learning Environment: A Framework for Evaluating Mobile Learning Engagement

Mobile technology is increasingly used both in and out of school classrooms by students to remain actively engaged in learning and extend their learning.Developments in mobile technology have made it possible to access and interact with learning resources, other learners and teachers in ways not possible just a few years ago.Instructional designers and teachers struggle to maintain understandings of how changes in technology impact student interactions and learning engagement.Instead of focusing on particular tools, instructional designers and teachers should focus on the design of the learning environment to gain understanding of what students are doing while engaged in learning.The Seven-Traits of a Learning Environment framework is introduced here to help educators develop more effective and engaging instructional design for mobile learning environments.The SAMR technology integration model is discussed as a potential tool to help measure student engagement through the lens of the 7-Traits framework.
A Secure End-to-End Verifiable E-Voting System Using Zero-Knowledge Proof and Blockchain

Conducting an election is an important and challenging task of a government to establish the democracy in the country. The constraints of conducting a secure election are remarkably strict. In the last two decades, direct-recording electronic (DRE) machines are extensively used for voting at polling stations around the world.
Quantized Dialog – A general approach for conversational systems

We propose Quantized Dialog, a novel approach for the development of conversational systems. The methodology relies on the semantic quantization and clustering of the dialog utterances in order to reduce the dialog interaction space, making prediction of the next utterance more tractable. The effectiveness of this method is showcased using the goal-oriented dataset of the sixth Dialog System Technology Challenge (DSTC6). We compare the performance of Quantized Dialog based on an n-gram language model for next-utterance prediction against other models that employ popular deep-learning architectures, such as multi-layer neural network classifiers, memory networks, long short-term memory recurrent neural networks and convolutional neural networks. The experimental results demonstrate the promising potential of the new quantized approach in goal-oriented dialog prediction.
Early estimation model for 3D-discrete indian sign language recognition using graph matching

Machine translation of sign language is a critical task of computer vision. In this work, we propose to use 3D motion capture technology for sign capture and graph matching for sign recognition. Two problems related to 3D sign matching are addressed in this work: (1) how to identify same signs with different number of motion frames and (2) sign extraction from a clutter of non-sign hand motions. These two problems make the 2D or 3D sign language machine translation a challenging task. We propose graph matching with early estimation model to address these problems in two phases. The first phase consists of intra graph matching for motion frame extraction, which retains motion intensive frames in database and query 3D videos. The second phase applies inter graph matching with early estimation model on motion extracted query and dataset 3D videos. The proposed model increases the speed of the graph matching algorithm in estimating a sign with fewer frames. To test the graph matching model, we recorded 350 words of Indian sign language with 3D motion capture technology. For testing 4 variations per sign are captured for all signs with 5 different signers at same, slower, faster hand speeds and sign mixed cluttered hand motions. The early estimation graph matching model is tested for accuracy and efficiency in classifying 3D signs with the two induced real time constraints. In addition to 3D sign language dataset, the proposed method is validated on five benchmark datasets and against the state-of-the-art graph matching methods.
Towards Encrypted In-Network Storage Services with Secure Near-Duplicate Detection

In-network storage is recognized as a vital component of many emerging network architectures, which facilitates high-quality and efficient content-centric services. In this trend, providing content-based near-duplicate detection (NDD) services among in-network storage becomes naturally necessary for network traffic alleviation and resource optimization. However, due to the increasing attacking surfaces, storing data in the networked environment inevitably raises new concerns about user privacy exposure and unauthorized data access. Therefore, we aim to design a secure NDD service in the context of encrypted in-network storage. For efficiency, we first leverage the fingerprint techniques and locality-sensitive hashing to convert the problem of NDD into the keyword search. We then adopt an efficient multi-key searchable encryption scheme, which requires only one encrypted query from the user even the data are from multiple content providers encrypted with different keys. As simply combining the above methods does not appear to directly locate accurate results, we then devise a secure result refining scheme via Yao's garbled circuits to avoid user-side post-processing. Furthermore, we enhance our design to address the potential malicious behavior of in-network servers. Extensive evaluations of real-world image dataset demonstrate that our design can achieve comparable accuracy to the plaintext with modest security overhead.
Towards Non-Interactive Zero-Knowledge for NP from LWE

Non-interactive zero-knowledge ( $$\mathsf {NIZK}$$ ) is a fundamental primitive that is widely used in the construction of cryptographic schemes and protocols. Despite this, general purpose constructions of $$\mathsf {NIZK}$$ proof systems are only known under a rather limited set of assumptions that are either number-theoretic (and can be broken by a quantum computer) or are not sufficiently well understood, such as obfuscation. Thus, a basic question that has drawn much attention is whether it is possible to construct general-purpose $$\mathsf {NIZK}$$ proof systems based on the learning with errors ( $$\mathsf {LWE}$$ ) assumption. Our main result is a reduction from constructing $$\mathsf {NIZK}$$ proof systems for all of $$\mathbf {NP}$$ based on $$\mathsf {LWE}$$ , to constructing a $$\mathsf {NIZK}$$ proof system for a particular computational problem on lattices, namely a decisional variant of the Bounded Distance Decoding ( $$\mathsf {BDD}$$ ) problem. That is, we show that assuming $$\mathsf {LWE}$$ , every language $$L \in \mathbf {NP}$$ has a $$\mathsf {NIZK}$$ proof system if (and only if) the decisional $$\mathsf {BDD}$$ problem has a $$\mathsf {NIZK}$$ proof system. This (almost) confirms a conjecture of Peikert and Vaikuntanathan (CRYPTO, 2008). To construct our $$\mathsf {NIZK}$$ proof system, we introduce a new notion that we call prover-assisted oblivious ciphertext sampling ( $$\mathsf {POCS}$$ ), which we believe to be of independent interest. This notion extends the idea of oblivious ciphertext sampling, which allows one to sample ciphertexts without knowing the underlying plaintext. Specifically, we augment the oblivious ciphertext sampler with access to an (untrusted) prover to help it accomplish this task. We show that the existence of encryption schemes with a $$\mathsf {POCS}$$ procedure, as well as some additional natural requirements, suffices for obtaining $$\mathsf {NIZK}$$ proofs for $$\mathbf {NP}$$ . We further show that such encryption schemes can be instantiated based on $$\mathsf {LWE}$$ , assuming the existence of a $$\mathsf {NIZK}$$ proof system for the decisional $$\mathsf {BDD}$$ problem.
Synchro-squeezed adaptive wavelet transform with optimum parameters for arbitrary time series

In this study, a synchro-squeezed adaptive wavelet transform (SSAWT) is proposed and defined as an average of overlapped short-time wavelet transforms with optimized time-varying resolution in a synchro-squeezed time-frequency representation. The time-frequency resolution is automatically updated with a simplified procedure to determine optimal wavelet parameters. The instantaneous frequency spectra are accumulated over time to extract time-insensitive frequency characteristics in arbitrary time series. An illustrative signal with four time segments covering various frequency distribution cases indicated a 1.1% error of the proposed method, which is at least 5 times more accurate than the conventional synchro-squeezed wavelet transform. Due to synchro-squeezing process, SSAWT also exhibited more accurate results than the adaptive wavelet transform that has recently been developed by the authors. The proposed SSAWT was then applied to the impact echo responses experimentally recorded from a 60″ × 36″ × 7.25″ concrete slab. The improvement in time-frequency resolution and corresponding frequency spectra led to more successful detections of deep or shallow or no delamination from 40 sets of experimental data within 1.5% estimation error in deep delamination depth and 5% estimation error in slab thickness.
Pricing Data Tampering in Automated Fare Collection with NFC-Equipped Smartphones

Automated Fare Collection (AFC) systems have been globally deployed for decades, particularly in the public transportation network where the transit fee is calculated based on the length of the trip (a.k.a., distance-based pricing AFC systems). Although most messages of AFC systems are insecurely transferred in plaintext, system operators did not pay much attention to this vulnerability, since the AFC network is basically isolated from the public network (e.g., the Internet)-there is no way of exploiting such a vulnerability from the outside of the AFC network. Nevertheless, in recent years, the advent of Near Field Communication (NFC)-equipped smartphones has opened up a channel to invade into the AFC network from the mobile Internet, i.e., by Host-based Card Emulation (HCE) over NFC-equipped smartphones. In this paper, we identify a novel paradigm of attacks, called LessPay, against modern distance-based pricing AFC systems, enabling users to pay much less than what they are supposed to be charged. The identified attack has two important properties: 1) it is invisible to AFC system operators because the attack never causes any inconsistency in the back-end database of the operators; and 2) it can be scalable to affect a large number of users (e.g., 10,000) by only requiring a moderate-sized AFC card pool (e.g., containing 150 cards). To evaluate the efficacy of the attack, we developed an HCE app to launch the LessPay attack; and the real-world experiments demonstrate not only the feasibility of the LessPay attack (with 97.6 percent success rate) but also its low cost in terms of bandwidth and computation. Finally, we propose, implement and evaluate four types of countermeasures, and present security analysis and comparison of these countermeasures on defending against the LessPay attack.
Towards More Reliable Transfer Learning

Multi-source transfer learning has been proven effective when within-target labeled data is scarce. Previous work focuses primarily on exploiting domain similarities and assumes that source domains are richly or at least comparably labeled. While this strong assumption is never true in practice, this paper relaxes it and addresses challenges related to sources with diverse labeling volume and diverse reliability. The first challenge is combining domain similarity and source reliability by proposing a new transfer learning method that utilizes both source-target similarities and inter-source relationships. The second challenge involves pool-based active learning where the oracle is only available in source domains, resulting in an integrated active transfer learning framework that incorporates distribution matching and uncertainty sampling. Extensive experiments on synthetic and two real-world datasets clearly demonstrate the superiority of our proposed methods over several baselines including state-of-the-art transfer learning methods. Code related to this paper is available at: https://github.com/iedwardwangi/ReliableMSTL .