Reviewerly/exHarmony huggingface dataset:
Methodologies for Mobile and Encrypted Traffic Classification via Machine Learning Approaches

The widespread use of handheld devices (e.g., smartphones) has led to a significant evolution in the way the users connect to the Internet and access contents or services.This entails a substantial change in the nature of network traffic. Traffic classification---the set of techniques suited to infer the applications generating network traffic---is currently the enabler for gathering valuable information for different stakeholders in the Internet traffic delivery supply chain. This includes its application for network management (e.g., service differentiation/blocking and quality-of-service enforcement), network security, and user profiling. On top of that, traffic classification highlights compelling privacy issues related to (the share of) this information in thorny scenarios (e.g., healthcare apps and enterprise environments). Nonetheless, the proliferation of encryption (e.g., anonymity tools) hinders the suitability of solutions based on cleartext traffic inspection and thus challenges current classifiers. Also, the moving-target nature of mobile traffic, due to the daily-expanding set of apps sharing common third-party services, accelerates the performance degradation of design solutions based on standard machine learning approaches.As such, this Thesis presents a set of novel methodologies for mobile traffic classification that can operate under the encrypted-traffic assumption and advances the state-of-the-art from multiple viewpoints. In detail, the present dissertation devises innovative machine learning approaches based on multi- and hierarchical-classification. Furthermore, it pioneers the adoption of the deep learning paradigm to design practical and effective mobile traffic classifiers through the automatic extraction of features reflecting complex data patterns. Then, to overcome the complexity of these solutions, a distributed deployment based on the big-data framework is investigated. Such analysis highlights the non-transparent nature of the big-data accelerator when applied to the training phase of deep learning classifiers, shedding light on intrinsic trade-offs. Extensive experimental evaluations are conducted to assess the performance of proposed approaches and compare them with most related state-of-the-art solutions. This goal is achieved by the definition of a common benchmark encompassing public datasets. In this regard, a novel architecture is designed and implemented to capture and label our publicly-released dataset.
Bag of Tricks and A Strong baseline for Image Copy Detection

Image copy detection is of great importance in real-life social media. In this paper, a bag of tricks and a strong baseline are proposed for image copy detection. Unsupervised pre-training substitutes the commonly-used supervised one. Beyond that, we design a descriptor stretching strategy to stabilize the scores of different queries. Experiments demonstrate that the proposed method is effective. The proposed baseline ranks third out of 526 participants on the Facebook AI Image Similarity Challenge: Descriptor Track. The code and trained models are available at https://github.com/WangWenhao0716/ISC-Track2-Submission.
S3RP: Self-Supervised Super-Resolution and Prediction for Advection-Diffusion Process

We present a super-resolution model for an advection-diffusion process with limited information. While most of the super-resolution models assume high-resolution (HR) ground-truth data in the training, in many cases such HR dataset is not readily accessible. Here, we show that a Recurrent Convolutional Network trained with physics-based regularizations is able to reconstruct the HR information without having the HR ground-truth data. Moreover, considering the ill-posed nature of a super-resolution problem, we employ the Recurrent Wasserstein Autoencoder to model the uncertainty.
Hidden Markov models as recurrent neural networks: An application to Alzheimer’s disease

Hidden Markov models (HMMs) are commonly used for disease progression modeling when the true patient health state is not fully known. Since HMMs may have multiple local optima, performance can be improved by incorporating additional patient covariates to inform estimation. To allow for this, we develop hidden Markov recurrent neural networks (HMRNNs), a special case of recurrent neural networks with the same likelihood function as a corresponding discrete-observation HMM. The HMRNN can be combined with any other predictive neural networks that take patient information as input, with all parameters estimated simultaneously via gradient descent. Using a dataset of Alzheimer's disease patients, we demonstrate how combining the HMRNN with other predictive neural networks improves disease forecasting performance and offers a novel clinical interpretation compared with a standard HMM trained via expectation-maximization.
Effectiveness of Modified Hybrid Brainstorming Learning Program in Increasing Innovative Work Behavior and Innovative Idea Performance among Nurses

The ability to learn and innovate has been known as a key-factor for nurses to deliver quality of care and patient safety. To be successful in nursing innovation, nurses should perform sound innovative behaviors; thus, their innovative work behavior (IWB) should be promoted. A comprehensive learning program aimed at improving IWB with effective cognitive stimulation could enhance innovation abilities and quality of innovative idea performance. This study aimed to evaluate the effectiveness of a learning program that uses Modified Hybrid Brainstorming (MHB) techniques in increasing IWB and improving the quality of ideas generated (fluency, flexibility and originality) among nurses. A sample of 60 nurses were randomly assigned to a within group pre-test and post-test study design. The learning program with the MHB method was developed and implemented for 3 sessions workshop. IWB was measured pre- and 12 weeks post-program. Pre- and post-training session fluency, flexibility, and originality of generated ideas were also measured. The percentage of absolute agreement and the index of item- objective congruence of research instruments were met. Data were analyzed using descriptive statistics, and paired t-test. The results revealed that the average IWB scores of nurses after the implementation of the MHB learning program increased significantly (p &lt; 0.05). There was also a significant difference in post-test scores (p &lt; 0.05) of fluency, flexibility, and originality of ideas. This study showed that offering the MHB learning program to nurses is a feasible method to improve IWB and innovative idea.
THE RELATIONSHIP BETWEEN PRE-SERVICE TEACHERS’ BURNOUT, SATISFACTION WITH ONLINE LEARNING, AND SELF-PERCEIVED PROFESSIONAL PREPAREDNESS

THE RELATIONSHIP BETWEEN PRE-SERVICE TEACHERS’ BURNOUT, SATISFACTION WITH ONLINE LEARNING, AND SELF-PERCEIVED PROFESSIONAL PREPAREDNESS
PENGUKURAN USABILITY PADA APLIKASI SKRIPSI DAN KERJA PRAKTEK (SISKP) MENGGUNAKAN SYSTEM USABILITY SCALE

Information system plays an essential role in higher education institution, particularly the use of Thesis Information System and Job- Training (SISKP) application in Department of Informatics Engineering State University of Gorontalo. One of the most significant components in building a system is having usability that is easy to learn, use, and encourage the users to complete their tasks. Nevertheless, there is no current investigation measuring the SISKP application. Therefore, this research intends to determine the level of usability in the SISKP application based on lecturer’s and informatics engineering students’ perceptions. The measurement method in this condition uses the System Usability Scale (SUS) because the users can give subjective assessments. The result of this research obtain 72 scores of the SUS for SISKP application which includes in Grade C and classifies as a good category, while the NPS gets a Passive label that is the users who do not recommend SISKP or those who gave negative rating toward SISKP. Hence, for further investigation, it is suggested to explore the factors affecting the lower score of usability on the SISKP application.
Deeply Shared Filter Bases for Parameter-Efficient Convolutional Neural Networks

Modern convolutional neural networks (CNNs) have massive identical convolution blocks, and, hence, recursive sharing of parameters across these blocks has been proposed to reduce the amount of parameters. However, naive sharing of parameters poses many challenges such as limited representational power and the vanishing/exploding gradients problem of recursively shared parameters. In this paper, we present a recursive convolution block design and training method, in which a recursively shareable part, or a filter basis, is separated and learned while effectively avoiding the vanishing/exploding gradients problem during training. We show that the unwieldy vanishing/exploding gradients problem can be controlled by enforcing the elements of the filter basis orthonormal, and empirically demonstrate that the proposed orthogonality regularization improves the flow of gradients during training. Experimental results on image classification and object detection show that our approach, unlike previous parameter-sharing approaches, does not trade performance to save parameters and consistently outperforms overparameterized counterpart networks. This superior performance demonstrates that the proposed recursive convolution block design and the orthogonality regularization not only prevent performance degradation, but also consistently improve the representation capability while a significant amount of parameters are recursively shared.
딥러닝을 이용한 언어별 단어 분류 기법

One of the elements of technology that has become extremely critical within the field of education today is Deep learning. It has been especially used in the area of natural language processing, with some word-representation vectors playing a critical role. However, some of the low-resource languages, such as Swahili, which is spoken in East and Central Africa, do not fall into this category. Natural Language Processing is a field of artificial intelligence where systems and computational algorithms are built that can automatically understand, analyze, manipulate, and potentially generate human language. After coming to discover that some African languages fail to have a proper representation within language processing, even going so far as to describe them as lower resource languages because of inadequate data for NLP, we decided to study the Swahili language. As it stands currently, language modeling using neural networks requires adequate data to guarantee quality word representation, which is important for natural language processing (NLP) tasks. Most African languages have no data for such processing. The main aim of this project is to recognize and focus on the classification of words in English, Swahili, and Korean with a particular emphasis on the low-resource Swahili language. Finally, we are going to create our own dataset and reprocess the data using Python Script, formulate the syllabic alphabet, and finally develop an English, Swahili, and Korean word analogy dataset.
Reward-Free Model-Based Reinforcement Learning with Linear Function Approximation

We study the model-based reward-free reinforcement learning with linear function approximation for episodic Markov decision processes (MDPs). In this setting, the agent works in two phases. In the exploration phase, the agent interacts with the environment and collects samples without the reward. In the planning phase, the agent is given a specific reward function and uses samples collected from the exploration phase to learn a good policy. We propose a new provably efficient algorithm, called UCRL-RFE under the Linear Mixture MDP assumption, where the transition probability kernel of the MDP can be parameterized by a linear function over certain feature mappings defined on the triplet of state, action, and next state. We show that to obtain an $\epsilon$-optimal policy for arbitrary reward function, UCRL-RFE needs to sample at most $\tilde O(H^5d^2\epsilon^{-2})$ episodes during the exploration phase. Here, $H$ is the length of the episode, $d$ is the dimension of the feature mapping. We also propose a variant of UCRL-RFE using Bernstein-type bonus and show that it needs to sample at most $\tilde O(H^4d(H + d)\epsilon^{-2})$ to achieve an $\epsilon$-optimal policy. By constructing a special class of linear Mixture MDPs, we also prove that for any reward-free algorithm, it needs to sample at least $\tilde \Omega(H^2d\epsilon^{-2})$ episodes to obtain an $\epsilon$-optimal policy. Our upper bound matches the lower bound in terms of the dependence on $\epsilon$ and the dependence on $d$ if $H \ge d$.
Practice of Training Students’ Comprehensive Capacity in the Process of Reading, Commenting, and Appraising Experimental Report

Experimental report (including preview report) is chief component of the laboratory class.It should reflect phenomenon and result of the experiment, benefit students on promoting knowledge in the process of writing the experimental report.Through reading, commenting and appraising timely the experimental report (including preview report), we can help students master fundamental principle, think deeply experimental questions in the process of improving the experimental report, increase the lab skill and observing ability, and ultimately vastly promote scientific literacy and comprehensive capacity.
Semantic-Aware Contrastive Learning for Multi-object Medical Image Segmentation

Medical image segmentation, or computing voxelwise semantic masks, is a fundamental yet challenging task to compute a voxel-level semantic mask. To increase the ability of encoder-decoder neural networks to perform this task across large clinical cohorts, contrastive learning provides an opportunity to stabilize model initialization and enhance encoders without labels. However, multiple target objects (with different semantic meanings) may exist in a single image, which poses a problem for adapting traditional contrastive learning methods from prevalent 'image-level classification' to 'pixel-level segmentation'. In this paper, we propose a simple semantic-aware contrastive learning approach leveraging attention masks to advance multi-object semantic segmentation. Briefly, we embed different semantic objects to different clusters rather than the traditional image-level embeddings. We evaluate our proposed method on a multi-organ medical image segmentation task with both in-house data and MICCAI Challenge 2015 BTCV datasets. Compared with current state-of-the-art training strategies, our proposed pipeline yields a substantial improvement of 5.53% and 6.09% on Dice score for both medical image segmentation cohorts respectively (p-value<0.01). The performance of the proposed method is further assessed on natural images via the PASCAL VOC 2012 dataset, and achieves a substantial improvement of 2.75% on mIoU (p-value<0.01).
An optimised approach to detect the identity of hidden information in grey scale and colour images

Feature-based steganalysis is an emerging trend in the domain of Information Forensics, aims to discover the identity of secret information present in the covert communication by analysing the statistical features of cover/stego image. Due to massive volumes of auditing data as well as complex and dynamic behaviours of steganogram features, optimising those features is an important open problem. This paper focused on optimising the number of features using the proposed quick artificial bee colony (qABC) algorithm. Here we tested for three steganalysers, namely subtractive pixel adjacency matrix (SPAM), phase aware projection model (PHARM) and colour filter array (CFA) for the break our steganographic system (BOSS) 1.01 datasets. The significant improvement in the convergence nature of qABC quickly improves the solution and fine tune the search than their real counterparts. The results reveal that qABC method with support vector machine (SVM) classifier outperforms the non-optimised version concerning classification accuracy and reduced number of feature sets.
Chemistry beyond the Hartree-Fock limit via quantum computed moments.

Quantum computers hold promise to circumvent the limitations of conventional computing for difficult molecular problems. However, the accumulation of quantum logic errors on real devices represents a major challenge, particularly in the pursuit of chemical accuracy requiring the inclusion of dynamical effects. In this work we implement the quantum computed moments (QCM) approach for hydrogen chain molecular systems up to H$_6$. On a superconducting quantum processor, Hamiltonian moments, $\langle \mathcal{H}^p\rangle$ are computed with respect to the Hartree-Fock state, which are then employed in Lanczos expansion theory to determine an estimate for the ground-state energy which incorporates electronic correlations and manifestly improves on the variational result. Post-processing purification of the raw QCM data takes the estimate through the Hartree-Fock variational limit to within 99.9% of the exact electronic ground-state energy for the largest system studied, H$_6$. Calculated dissociation curves indicate precision at about 10mH for this system and as low as 0.1mH for molecular hydrogen, H$_2$, over a range of bond lengths. In the context of stringent precision requirements for chemical problems, these results provide strong evidence for the error suppression capability of the QCM method, particularly when coupled with post-processing error mitigation. Greater emphasis on more efficient representations of the Hamiltonian and classical preprocessing steps may enable the solution of larger systems on near-term quantum processors.
Determining Impact of Navigation Errors on Mission Capabilities

The mission of an unmanned aerial system (UAS) is highly dependent on the accuracy of the utilized navigation system. UAS navigation systems typically rely on GPS or image navigation. However, GPS-based systems are vulnerable to being jammed or, in some areas, blocked. Image navigation systems require communications and/or landmarks to navigate. Again, these are not always available. Some UAS systems have multiple navigation systems that include inertial system or homing systems. These systems may not be the most accurate, but can provide enough capability to allow the UAS to navigate from a last known position to a point where the primary navigation system can be re-established.The ability for the UAS to understand its navigation accuracy is important for its mission planning capability. Success can depend on whether the navigation solution is within a given accuracy or not. Very accurate navigation solutions are important for the safety of not just the UAS but also whatever is around and below the UAS. If the primary navigation system is not operational or degraded, the secondary navigation systems may take over. Some of these systems, such as an INS, degrade over time. The UAS should use its inherent knowledge of its navigation capabilities to infer if and how it can complete its mission.Navigation failures and degradations impact the mission capabilities of an UAS. An impact assessment technique is developed to identify the UAS navigation capabilities. The concept is to implement a neural network system that estimates the accuracy of the navigation solution with regards to the mission parameters based on the estimated accuracy of the available navigation capabilities. The neural network uses the monitored status of the navigation systems and estimates navigation accuracy. The accuracy of the overall navigation solution is derived from the inertial system, the image capabilities, the GPS effectiveness, and the availability of homing beacons. Each system reports a measure of quality generated by the neural network. The neural network will be trained navigation system accuracy parameters that map to a quality score.The neural network solution will feed a fuzzy evidence accrual system. The evidence accrual system will create estimated measures that let the mission planning determine the safety and effectiveness for the unmanned aerial system to continue its mission. The evidence accrual result will use the navigation accuracy and the mission safety parameters to make a determination if the planned UAS mission can continue.
A Biased Graph Neural Network Sampler with Near-Optimal Regret

Graph neural networks (GNN) have recently emerged as a vehicle for applying deep network architectures to graph and relational data. However, given the increasing size of industrial datasets, in many practical situations the message passing computations required for sharing information across GNN layers are no longer scalable. Although various sampling methods have been introduced to approximate full-graph training within a tractable budget, there remain unresolved complications such as high variances and limited theoretical guarantees. To address these issues, we build upon existing work and treat GNN neighbor sampling as a multi-armed bandit problem but with a newly-designed reward function that introduces some degree of bias designed to reduce variance and avoid unstable, possibly-unbounded pay outs. And unlike prior bandit-GNN use cases, the resulting policy leads to near-optimal regret while accounting for the GNN training dynamics introduced by SGD. From a practical standpoint, this translates into lower variance estimates and competitive or superior test accuracy across several benchmarks.
A New Method for Modeling Learners in a Collaborative Human Learning Environment

Computer Environments for Human Learning (CEHL) offers several pedagogical activities to the learners such as: learning, self-assessment, communication with other learners and the problem solving, etc. this latters can benefit from these services at any time and from any place. But, the learner’s isolation and the lack of a support service have a negative impact on his learning, his achievements, and especially on his motivation during his educational activities. So, to cop this, tutoring is required in CEHL. It can be defined as a social relationship between an expert person called tutor and a person in difficulty, the learner. Since, tutoring is often the ideal solution for learners; this is why several research works were introduced in recent years. However, Most of them have found several difficulties concerning the tutor’s work. This paper presents a novel arch to model learners in a collaborative human learning environment. The aim is to propose a method for the Modeling of learner-tutor. Moreover, we based on the learner’s response to a questionnaire in order to create his or her own profile. This space offers all the tools and mechanisms to support and promote collaboration between learner-tutors in this way the problems of learners is solved.