Reviewerly/exHarmony huggingface dataset:
An E-learning outreach program for public schools: Findings and lessons learned based on a pilot program in Makati City and Cabuyao City, Laguna, Philippines

The E-Learning Outreach Program (ELOP) is a multisectoral, collaborative, and commitment-driven action research that focuses on providing relevant and useful computer and Internet literacy short courses for public elementary students. This study presents the critical action plans of setting up a pilot e-Learning station in Hen. Pio del Pilar Elementary School in Makati City and in Pulo Elementary School in Cabuyao City, Laguna, Philippines. The school's computer laboratory was converted to an E-Learning station with additional devices and technical support from De La Salle University (DLSU). The study aims to account for a process-based heuristic approach in laying down the different phases of the program, specifically to: (1) coordinate and orient parents, pupils, teachers and school administrators, (2) set-up an e-Learning environment (3) design and write course modules (4) assess learning skills and experience of pupils, and (5) evaluate the program based on research team's observation and results of the focus group discussion (FGD) with teachers and student-participants. In Pulo Elementary School, a pretest and posttests were conducted with results showing that the program has improved participants' e-Learning knowledge and skills except for advance skills in hardware utilization. The participants also showed a strong positive attitude towards the ELOP.
Near-Online Multi-Pedestrian Tracking via Combining Multiple Consistent Appearance Cues

An important cue for multi-pedestrian tracking in video is the consistent appearance of an individual for quite a while. In this paper, we address multi-pedestrian tracking by learning a robust appearance model from the paradigm of tracking by detection. To separate detections of different pedestrians while assembling detections of the same pedestrian, we take advantage of the cue of consistent appearance and exploit three types of evidence from the recent, past and near-future. Existing online approaches only exploit the detection-to-detection and sequence-to-detection metrics, which focus on the recent and past appearance patterns respectively, while the future pedestrian appearance is simply ignored. This drawback is remedied in this paper by further considering the sequence-to-sequence metric, which resorts to near-future appearance presentation. Adaptive combination weights are learned to fuse these three different metrics. Moreover, we propose a novel Focal Triplet Loss to make the model focus more on hard examples than the easy ones. We demonstrate that this can significantly enhance the discriminating power of the model compared with treating every sample equally. Effectiveness and efficiency of the proposed method is verified by conducting comprehensive ablation studies and comparing with many competitive (offline/online/near-online) counterparts on the MOT16 and MOT17 Challenges.
Towards Security of Cyber-Physical Systems using Quantum Computing Algorithms

For cyber-physical systems (CPS), ensuring process and data security is critically important since the corresponding infrastructure needs to have high operational efficiency with no downtime. There are many techniques available that make communications in CPS environments secure – such as enabling traffic encryption between sensors and the computers processing the sensor’s data, incorporating message authentication codes to achieve integrity, etc. However, most of these techniques are dependent on some form of symmetric or asymmetric cryptographic algorithms like AES and RSA. These algorithms are under threat because of the emerging quantum computing paradigm: with quantum computing, these encryption algorithms can be potentially broken. It is therefore desirable to explore the use of quantum cryptography – which cannot be broken by quantum computing – for securing the classical communications infrastructure deployed in CPS. In this paper, we discuss possible consequences of this option. We also explain how quantum computers can help even more: namely, they can be used to maximize the system’s security where scalability is never a constraint, and to ensure we are not wasting time cycles on communicating and processing irrelevant information.
Predicting Learners Need for Recommendation Using Dynamic Graph-Based Knowledge Tracing

Personalized recommendation as a practical approach to overcoming information overloading has been widely used in e-learning. Based on learners individual knowledge level, we propose a new model that can predict learners needs for recommendation using dynamic graph-based knowledge tracing. By applying the Gated Recurrent Unit (GRU) and the Attention model, this approach designs a dynamic graph over different time steps. Through learning feature information and topology representation of nodes/learners, this model can predict with high accuracy of 80,63% learners with low knowledge acquisition and prepare them for further recommendation.
Collaborative Recommendation System in Users of Anime Films

Abstract The recommendation system is one method to know the preference consumer by showing the potential object. This recommendation also helps the consumer gets the preference object. One of the popular objects in the recommendation is anime film. In this case, we conduct research to recommend anime films based on ratings of previously watched films. Collaborative filtering is a technique that consists of calculating similarities, predictions, and recommendations. This study is taken from dataset Kaggle which consists of 73,516 users and 12,294 anime. A user’s history will be matched with the whole user’s history with alternating least squares (ALS) method. The anime will be recommended based on that results. This method is expected to help millions of users find the desired anime.
Adversarial sliced Wasserstein domain adaptation networks

Domain adaptation has become a resounding success in learning a domain agnostic model that performs well on target dataset by leveraging source dataset which has related data distribution. Most of existing works aim at learning domain-invariant features across different domains, but they ignore the discriminability of learned features although it is import to improve the model's performance. This paper proposes a novel adversarial sliced Wasserstein domain adaptation network (AWDAN) that uses a shared encoder and classifier along with a domain classifier to enhance the discriminability of the domain-invariant features. AWDAN utilizes adversarial learning to learn domain-invariant features in feature space and simultaneously minimizes sliced Wasserstein distance in label space to enforce the generated features to be discriminative that guarantees the transfer performance. Meanwhile, we propose to fix the weights of the pre-trained CNN backbone to guarantee its adaptability. We provide theoretical analysis to demonstrate the efficacy of AWDAN. Experimental results show that the proposed AWDAN significantly outperforms existing domain adaptation methods on three visual domain adaptation tasks. Feature visualizations verify that AWDAN learns both domain-invariant and discriminative features, and can achieve domain agnostic feature learning.
A Refined Non-Driving Activity Classification Using a Two-Stream Convolutional Neural Network

It is of great importance to monitor the driver's status to achieve an intelligent and safe take-over transition in the level 3 automated driving vehicle. We present a camera-based system to recognise the non-driving activities (NDAs) which may lead to different cognitive capabilities for take-over based on a fusion of spatial and temporal information. The region of interest (ROI) is automatically selected based on the extracted masks of the driver and the object/device interacting with. Then, the RGB image of the ROI (the spatial stream) and its associated current and historical optical flow frames (the temporal stream) are fed into a two-stream convolutional neural network (CNN) for the classification of NDAs. Such an approach is able to identify not only the object/device but also the interaction mode between the object and the driver, which enables a refined NDA classification. In this paper, we evaluated the performance of classifying 10 NDAs with two types of devices (tablet and phone) and 5 types of tasks (emailing, reading, watching videos, web-browsing and gaming) for 10 participants. Results show that the proposed system improves the averaged classification accuracy from 61.0% when using a single spatial stream to 90.5%.
Perfect state transfer on oriented graphs

Quantum walks on undirected graphs have been studied using symmetric matrices, such as the adjacency or Laplacian matrix, and many results about perfect state transfer are known. We extend some of those results to oriented graphs. We also study the phenomena, unique to oriented graphs, of multiple state transfer, where there is a set of vertices such that perfect state transfer occurs between every pair in that set. We give a characterization of multiple state transfer, and a new example of a graph where it occurs.
Centralized vs Decentralized Targeted Brute-Force Attacks: Guessing With Side-Information

According to recent empirical studies, a majority of users have the same, or very similar, passwords across multiple password-secured online services. This practice can have disastrous consequences, as one password being compromised puts all the other accounts at much higher risk. Generally, an adversary may use any side-information he/she possesses about the user, be it demographic information, password reuse on a previously compromised account, or any other relevant information to devise a better brute-force strategy (so called targeted attack). In this work, we consider a distributed brute-force attack scenario in which m adversaries, each observing some side information, attempt breaching a password secured system. We compare two strategies: an uncoordinated attack in which the adversaries query the system based on their own side-information until they find the correct password, and a fully coordinated attack in which the adversaries pool their side-information and query the system together. For passwords X of length n, generated independently and identically from a distribution PX, we establish an asymptotic closed-form expression for the uncoordinated and coordinated strategies when the side-information Y(m) are generated independently from passing X through a memoryless channel PY|X, as the length of the password n goes to infinity. We illustrate our results for binary symmetric channels and binary erasure channels, two families of side-information channels which model password reuse. We demonstrate that two coordinated agents perform asymptotically better than any finite number of uncoordinated agents for these channels, meaning that sharing side-information is very valuable in distributed attacks.
Foregrounding data in co-design – An exploration of how data may become an object of design

The rise of Big Data and data science has prompted a focus on data as an essential component of making and innovating data-based services. Traditionally, however, digital data has not been object to co-design as have other physical or functional dimensions of IT application design. This is problematic, because it hinders domain experts who are not IT professionals from taking part in the discussions and design of the data-based services they use and provide. We argue that to address this challenge, it is necessary to empower such domain experts to be able to consider data as an object of design, so they may contribute their expertise to the design of data-based services and their underlying data structures. This paper describes how data may be foregrounded as an explicit element of design that support domain experts' understanding of data as something that can be designed. We present a detailed interaction analysis of video recordings of three collaborative design workshops, in which we propose a form of data notation and two data representations. We find that data may become an object of design for domain experts when tangible and flexible representations of data are used. Based on our findings, we discuss five lessons learned for foregrounding data in co-design. Together, these provide practical insights for future work.
Evolving dynamic self-adaptation policies of mHealth systems for long-term monitoring

• A Long-term monitoring system should adapt its functioning to the patient condition. • Manual customization of long-term monitoring systems is expensive and error-prone. • A Dynamic Software Product Line approach to automate customization in mHealth Systems.. • Expert knowledge encoded using fuzzy logic to drive system self-adaptation. • The system is tested in a real device, the Smart Cane platform, with good results. Tele-rehabilitation can complement traditional rehabilitation therapies by providing valuable information that can help in the evaluation, monitoring, and treatment of patients. Many patient tele-monitoring systems that integrate wearable technology are emerging as an effective tool for the long-term surveillance of rehabilitation progression, enabling continuous sampling of patient real-time movement in a non-invasive way, without affecting the normal daily activity of the outpatient, who, therefore, will not need to make frequent clinic visits. One of the main challenges of tele-rehabilitation systems is to pay special attention to the diversity of dysfunctions in patients by offering devices with customized behaviours adaptable to the physical conditions of each patient at the different stages of the rehabilitation therapy. Long-term monitoring systems need an adaptation policy to autonomously reconfigure their behaviour according to vital signs read during the physical activity of the patient, the remaining battery level, or the required accuracy of collected data. However, it would alsobe desirable to adjust such adaptation policies over time, according to the patient’s evolution. This work presents a wearable patient-monitoring system for tele-rehabilitation that is able to dynamically self-configure its internal behaviour to the current context of the outpatient according to a set of adaptation policies that optimize battery consumption, taking into account other QoS parameters at the same time. Our system is also able to self-adapt its internal adaptation policies as a patient’s condition improves, while maintaining the system’s efficiency. We illustrate our proposal with a real mHealth case study. The results of the experiments show that the system updates the adaptation policies, taking into account specific indicators of the disease. The validation results show that the evolution of the self-adaptation policies correlates with the progression of different patients.
Optimizing the Estimation of a Histogram-Bin Width—Application to the Multivariate Mixture-Model Estimation

A maximum-likelihood estimation of a multivariate mixture model’s parameters is a difficult problem. One approach is to combine the REBMIX and EM algorithms. However, the REBMIX algorithm requires the use of histogram estimation, which is the most rudimentary approach to an empirical density estimation and has many drawbacks. Nevertheless, because of its simplicity, it is still one of the most commonly used techniques. The main problem is to estimate the optimum histogram-bin width, which is usually set by the number of non-overlapping, regularly spaced bins. For univariate problems it is usually denoted by an integer value; i.e., the number of bins. However, for multivariate problems, in order to obtain a histogram estimation, a regular grid must be formed. Thus, to obtain the optimum histogram estimation, an integer-optimization problem must be solved. The aim is therefore the estimation of optimum histogram binning, alone and in application to the mixture model parameter estimation with the REBMIX&amp;EM strategy. As an estimator, the Knuth rule was used. For the optimization algorithm, a derivative based on the coordinate-descent optimization was composed. These proposals yielded promising results. The optimization algorithm was efficient and the results were accurate. When applied to the multivariate, Gaussian-mixture-model parameter estimation, the results were competitive. All the improvements were implemented in the rebmix R package.
Designing Drone Chi

Drone Chi is a Tai Chi inspired human-drone interaction experience. As a design research project, situated within somaesthetic interaction design, where a central topic is cultivating bodily and sensory appreciation to improve one's quality of life. Drone Chi investigates the potential of autonomous micro-quadcopters as a design material for somaesthetic HCI. Through a quasi-chronological account of the design process, this pictorial articulates how the sensory experiences of Tai Chi were integrated into Drone Chi. Taking a slow and open-ended design research approach, we iteratively developed the project through somaesthetic, product design and engineering perspectives and drew heavily on design analogies and imagery for inspiration. This elevated the influence of the soma amongst narrow engineering parameters and usability requirements. This pictorial serves as a reflective resource for designers who are experimenting with merging their native discipline with someasthetic interaction design.
Probabilistic reasoning about epistemic action narratives

We propose the action language EPEC – Epistemic Probabilistic Event Calculus – that supports probabilistic, epistemic reasoning about narratives of action occurrences and environmentally triggered events, and in particular facilitates reasoning about future belief-conditioned actions and their consequences in domains that include both perfect and imperfect sensing actions. To provide a declarative semantics for sensing and belief conditioned actions in a probabilistic, narrative setting we introduce the novel concept of an epistemic reduct. We then formally compare our language with two established frameworks for probabilistic reasoning about action – the action language PAL by Baral et al., and the extension of the situation calculus to reason about noisy sensors and effectors by Bacchus et al. In both cases we prove a correspondence with EPEC for a class of domains representable in both frameworks.
Set-based collision avoidance applications to robotic systems

A robotic system can consist of a single or multiple agents with a fixed or mobile base, with full or under-actuation, and possibly redundancy. Collision avoidance is a crucial task for any robotic system and is necessary to ensure safe operation. In this paper, we use a set-based approach to ensure collision avoidance as a high-priority task of a robotic system while simultaneously defining one or more tasks for the system to achieve. The set-based approach is highly generic and flexible, and we present theoretical results, practical implementation and experimental results of the approach applied to a redundant, fully actuated robot manipulator, a full-scale underactuated surface vessel and a multi-agent system of unicycles.
Development of VR/AR Applications for Experimental Tests of Beams, Columns, and Frames

This paper depicts a set of virtual reality (VR) and augmented reality (AR) applications conceived for the enrichment of laboratory experiences within the field of structural engineering. The experimental program corresponds to the study of beams, columns, and frames of austenitic stainless steel subjected to different types of static loading. The development of these applications encompasses the use of measured data from sensors, the use of 3D modeling tools, the use of game engines, and the corresponding mathematical treatment and postprocess of the structural tests in a real-time fashion. The developed applications provide new possibilities for structural engineering laboratory experiences. In both cases (VR and AR), the developed applications are meant to enhance the experimental program experience to a variety of target users (researchers, technicians, students) by adding customized information related to the structural behavior of all elements during the tests as well as to basic concepts of health and safety in structural engineering laboratories.
Application of algebra and chaos theory in security of color images

In the current era of digital communication, the security of data transmitted relies heavily on cryptographic algorithms. Transmission of digital images through an insecure channel is liable for various types of cryptanalytic attacks. Therefore, there is a strong need for efficient encryption algorithms to protect data from adversaries. In this paper, we have proposed a new technique of color image encryption and decryption using chaos theory and algebra. The algorithm is designed by using two stages of affine Hill cipher (AHC) over SLm(Fp) and Mm(Fp) domains, before and next to two-dimensional discrete chaotic Hénon map. The non-linear nature, high sensitiveness to its parameters, and initial conditions, the Hénon map employs complex permutations. The diffusion is achieved by the affine Hill cipher technique to dissipate the statistical structure. The proposed system is not only key-dependent but their arrangement is fundamental, therefore, the cryptanalyst cannot hack the system by knowing only the key parameters. Simulation results and performance analysis are presented to verify the feasibility and efficiency of the proposed technique. Further, the security analysis describes that the system is robust against diverse cryptanalytic attacks and the comparison reveals that the proposed cryptosystem outperforms some existing and traditional encryption techniques.
Nondestructive photon counting in waveguide QED

Number-resolving single-photon detectors represent a key technology for a host of quantum optics protocols, but despite significant efforts, state-of-the-art devices are limited to few photons. In contrast, state-dependent atom counting in arrays can be done with extremely high fidelity up to hundreds of atoms. We show that in waveguide QED, the problem of photon counting can be reduced to atom counting, by entangling the photonic state with an atomic array in the collective number basis. This is possible as the incoming photons couple to collective atomic states and can be achieved by engineering a second decay channel of an excited atom to a metastable state. Our scheme is robust to disorder and finite Purcell factors, and its fidelity increases with atom number. Analyzing the state of the re-emitted photons, we further show that if the initial atomic state is a symmetric Dicke state, dissipation engineering can be used to implement a nondestructive photon-number measurement, in which the incident state is scattered into the waveguide unchanged. Our results generalize to related platforms, including superconducting qubits.