Reviewerly/exHarmony huggingface dataset:
Lipschitz Lifelong Reinforcement Learning

We consider the problem of knowledge transfer when an agent is facing a series of Reinforcement Learning (RL) tasks. We introduce a novel metric between Markov Decision Processes and establish that close MDPs have close optimal value functions. Formally, the optimal value functions are Lipschitz continuous with respect to the tasks space. These theoretical results lead us to a value-transfer method for Lifelong RL, which we use to build a PAC-MDP algorithm with improved convergence rate. Further, we show the method to experience no negative transfer with high probability. We illustrate the benefits of the method in Lifelong RL experiments.
Guiding Robot Exploration in Reinforcement Learning via Automated Planning

Reinforcement learning (RL) enables an agent to learn from trial-and-error experiences toward achieving long-term goals; automated planning aims to compute plans for accomplishing tasks using action knowledge. Despite their shared goal of completing complex tasks, the development of RL and automated planning has been largely isolated due to their different computational modalities. Focusing on improving RL agents' learning efficiency, we develop Guided Dyna-Q (GDQ) to enable RL agents to reason with action knowledge to avoid exploring less-relevant states. The action knowledge is used for generating artificial experiences from an optimistic simulation. GDQ has been evaluated in simulation and using a mobile robot conducting navigation tasks in a multi-room office environment. Compared with competitive baselines, GDQ significantly reduces the effort in exploration while improving the quality of learned policies.
Conflict and Victimization in Online Drug Markets

In the criminal underworld, transactions generate risk for the parties involved, but in contrast to legal markets, parties are unable to turn to legal recourse when cheated in a transaction. Past research has found that many strategies can be used to manage conflicts, including self-help strategies (vengeance, discipline and rebellion, avoidance, negotiation, settlement, and tolerance) and third-party interventions. In the context of illicit drug markets, ostracism and threats or actual violence are also strategies that have been observed. In this paper, we surveyed 49 online illicit drug market vendors to explore the conflict experiences of drug dealers who participate in online and offline illicit drug markets. The paper aims to describe the conflict and victimization experiences of online drug dealers and to understand the mitigating effect of technologies on these conflicts. The results indicate that conflict and victimization experiences are rare for online drug dealers, but there are still many situations that are not mitigated by the use of anonymizing technologies like those used on online illicit markets. We demonstrate how these conflicts differ between online and physical drug markets.
Memory-Efficient Differentiable Transformer Architecture Search

Differentiable architecture search (DARTS) is successfully applied in many vision tasks.However, directly using DARTS for Transformers is memory-intensive, which renders the search process infeasible.To this end, we propose a multi-split reversible network and combine it with DARTS.Specifically, we devise a backpropagation-with-reconstruction algorithm so that we only need to store the last layer's outputs.By relieving the memory burden for DARTS, it allows us to search with larger hidden size and more candidate operations.We evaluate the searched architecture on three sequence-to-sequence datasets, i.e., WMT'14 English-German, WMT'14 English-French, and WMT'14 English-Czech.Experimental results show that our network consistently outperforms standard Transformers across the tasks.Moreover, our method compares favorably with big-size Evolved Transformers, reducing search computation by an order of magnitude.
Dynamic Graph Learning for Session-Based Recommendation

Session-based recommendation (SBRS) aims to make recommendations for users merely based on the ongoing session. Existing GNN-based methods achieve satisfactory performance by exploiting the pair-wise item transition pattern; however, they ignore the temporal evolution of the session graphs over different time-steps. Moreover, the widely applied cross-entropy loss with softmax in SBRS faces the serious overfitting problem. To deal with the above issues, we propose dynamic graph learning for session-based recommendation (DGL-SR). Specifically, we design a dynamic graph neural network (DGNN) to simultaneously take the graph structural information and the temporal dynamics into consideration for learning the dynamic item representations. Moreover, we propose a corrective margin softmax (CMS) to prevent overfitting in the model optimization by correcting the gradient of the negative samples. Comprehensive experiments are conducted on two benchmark datasets, that is, Diginetica and Gowalla, and the experimental results show the superiority of DGL-SR over the state-of-the-art baselines in terms of Recall@20 and MRR@20, especially on hitting the target item in the recommendation list.
Developing an educational framework for using mobile learning during the era of COVID-19

This paper focuses on the impact of fear emotion upon technology adoption by educators and students during Covid-19 pandemic. Mobile learning (m-learning) has been applied as the educational social platform within higher education institutes, public as well as private. The research hypotheses were associated with the Covid-19 influence on m-learning adoption with the rise of the coronavirus increasing types of fear. Such fears include fear caused by the education failure, family lockdown, and loss of social relationships. Teachers and students are mostly fearful of these aspects of the situation. An integrated model was established within the research, using theoretical models; the Planned Behavior theory, the Technology Acceptance Model, and the Expectation-Confirmation Model. The proposed integrated model (using PLS-SEM software) was analyzed using an online survey data, with 420 respondents from Zayed University, UAE. The findings indicated that attitude was the best predictor for using the m-learning system, followed by continuous intention, expectation confirmation, perceived usefulness, ease-of-use, perceived fear, behavioral control, and satisfaction. According to the research, during the coronavirus pandemic, if the m-learning system is adopted for educational reasons, the learning and teaching outcome proves quite promising. Yet there is a fear of the family being stressed, or of loss of friends, and also a fear of the results of future schooling. It is therefore necessary to assess the students efficiently during this pandemic so that the situation can be managed emotionally.
Novel On-Road Vehicle Detection System Using Multi-Stage Convolutional Neural Network

General object-detection methods based on deep learning have received considerable attention in the field of computer vision.However, when they are applied to vehicle detection (VD) in a straightforward manner to realize an intelligent vehicle (IV), a graphics processing unit (GPU) is required for their real-time implementation.The use of GPUs is unacceptable in commercial VD systems.A novel on-road VD method comprising the use of a multi-stage convolutional neural network (MSCNN) is proposed to solve this problem.In the MSCNN, the properties of the vehicles are exploited, and an efficient region proposal specialized for vehicles is developed.The proposed MSCNN comprises four stages: vehicle lower-boundary detection, vehicle upper-boundary detection, region proposal network (RPN), and vehicle classification.Effective anchor boxes are generated in the lower-and upper-boundary-detection stages with appropriate sizes for vehicles of all scales.The bounding box of the vehicle within the anchor box is determined in the RPN stage.In the last stage, the predicted bounding boxes are classified as vehicles or non-vehicles.Finally, the proposed method is applied to the KITTI, CrowdAI, and AUTTI datasets, and its advantages are demonstrated by comparing its performance with those presented in previous studies.The proposed MSCNN realizes an average precision (72.1%) on the KITTI dataset while running on a central processing unit (CPU).
ALBERTC-CNN Based Aspect Level Sentiment Analysis

In order to solve the problem that most aspect level sentiment analysis networks cannot extract the global and local information of the context at the same time.This study proposes an aspect level sentiment analysis model named Combining with A Lite Bidirection Encoder Represention from TransConvs and ConvNets(ALBERTC-CNN).First, the global sentence information and local emotion information in a text are extracted by the improved ALBERTC network, and the input aspect level text is represented by a word vector.Then, the feature vector is mapped to the emotion classification number by a linear function and a softmax function.Finally, the aspect level sentiment analysis results are obtained.The proposed model is tested on two datasets of the SemEval-2014 open task, the laptop and restaurant datasets, and compared with the traditional networks.The results show that compared with the traditional network, the classification accuracy of the proposed model is improved by approximately 4% and 5% on the two sets, whereas the F1 value is improved by approximately 4% and 8%.Additionally, compared with the original ALBERT network, the accuracy is improved by approximately 2%, and the F1 value is improved by approximately 1%.
Comparing Machine Learning Classifiers for Continuous Authentication on Mobile Devices by Keystroke Dynamics

Continuous authentication (CA) is the process to verify the user’s identity regularly without their active participation. CA is becoming increasingly important in the mobile environment in which traditional one-time authentication methods are susceptible to attacks, and devices can be subject to loss or theft. The existing literature reports CA approaches using various input data from typing events, sensors, gestures, or other user interactions. However, there is significant diversity in the methodology and systems used, to the point that studies differ significantly in the features used, data acquisition, extraction, training, and evaluation. It is, therefore, difficult to establish a reliable basis to compare CA methods. In this study, keystroke mechanics of the public HMOG dataset were used to train seven different machine learning classifiers, including ensemble methods (RFC, ETC, and GBC), instance-based (k-NN), hyperplane optimization (SVM), decision trees (CART), and probabilistic methods (naïve Bayes). The results show that a small number of key events and measurements can be used to return predictions of user identity. Ensemble algorithms outperform others regarding the CA mobile keystroke classification problem, with GBC returning the best statistical results.
Multi-Sensor Network Information for Linear-Gaussian Multi-Target Tracking Systems

Methods for information-theoretic control for networks of sensors are of interest for enabling the development of autonomous sensor systems. In this paper we revisit the fundamentals of information theoretic-based control for multi-target systems and present a systematic approach for determining information-theoretic situational awareness based on mutual information for point processes. The extension to multi-sensor systems is developed using the concept of a broadcast channel from information theory. Analytic results are presented for linear-Gaussian systems which enable low complexity solutions for determining information from multiple sensors and we consider a large number of potential sensor configurations. We consider extensions of single-target methods to multi-target scenarios and present results in simulations.
On Universal D-Semifaithful Coding for Memoryless Sources With Infinite Alphabets

The problem of variable length and fixed-distortion universal source coding (or D-semifaithful source coding) for stationary and memoryless sources on countably infinite alphabets (1-alphabets) is addressed in this paper.The main results of this work offer a set of sufficient conditions (from weaker to stronger) to obtain weak minimax universality, strong minimax universality, and corresponding achievable rates of convergences for the worst-case redundancy for the family of stationary memoryless sources whose densities are dominated by an envelope function (or the envelope family) on 1-alphabets.An important implication of these results is that universal D-semifaithful source coding is not feasible for the complete family of stationary and memoryless sources on 1-alphabets.To demonstrate this infeasibility, a sufficient condition for the impossibility is presented for the envelope family.Interestingly, it matches the well-known impossibility condition in the context of lossless (variable-length) universal source coding.More generally, this work offers a simple description of what is needed to achieve universal D-semifaithful coding for a family of distributions ⇤.This reduces to finding a collection of quantizations of the product space at different block-lengths -reflecting the fixed distortion restriction -that satisfy two asymptotic requirements: the first is a universal quantization condition with respect to ⇤, and the second is a vanishing information radius (I-radius) condition for ⇤ reminiscent of the condition known for lossless universal source coding.
Fast and robust online three-dimensional measurement based on feature correspondence

Online three-dimensional (3D) measurement plays an important role in industry. When phase-shifting profilometry is employed in online 3D measurement, pixel matching is an important step to keep objects at the same coordinate value. However, the correlation operation and marker feature matching algorithms may take a long time, increasing the complexity. So a fast and robust online 3D measurement based on feature correspondence is proposed. In this method, only one frame of the sinusoidal fringe pattern is projected onto the measured object, and image correction technique is employed to rectify pixel size. Then five frames of deformed patterns with equivalent displacement are captured by the camera, and the corresponding modulation patterns are extracted. The oriented fast and rotated brief feature algorithm is used to extract the matching pair of feature points, and the improved grid-based motion statistical feature algorithm can better eliminate the false match to achieve pixel matching. In this way, five frames of deformed patterns with an equivalent shifted-phase can be extracted. Finally, the 3D shape of the measured object is reconstructed by the five-step equivalent phase-shifting algorithm. Experimental results verify the effectiveness and feasibility of the proposed method.
AutoRC: Improving BERT Based Relation Classification Models via Architecture Search

Although BERT based relation classification (RC) models have achieved significant improvements over the traditional deep learning models, it seems that no consensus can be reached on what is the optimal architecture, since there are many design choices available.In this work, we design a comprehensive search space for BERT based RC models and employ a modified version of efficient neural architecture search (ENAS) method to automatically discover the design choices mentioned above.Experiments on eight benchmark RC tasks show that our method is efficient and effective in finding better architectures than the baseline BERT based RC models.Ablation study demonstrates the necessity of our search space design and the effectiveness of our search method.We also show that our framework can also apply to other entity related tasks like coreference resolution and span based named entity recognition (NER).
Co-Design of Free-Space Metasurface Optical Neuromorphic Classifiers for High Performance

Classification of features in a scene typically requires conversion of the incoming photonic field into the electronic domain. Recently, an alternative approach has emerged whereby passive structured materials can perform classification tasks by directly using free-space propagation and diffraction of light. In this manuscript, we present a theoretical and computational study of such systems and establish the basic features that govern their performance. We show that system architecture, material structure, and input light field are intertwined and need to be co-designed to maximize classification accuracy. Our simulations show that a single layer metasurface can achieve classification accuracy better than conventional linear classifiers, with an order of magnitude fewer diffractive features than previously reported. For a wavelength λ, single layer metasurfaces of size 100λ × 100λ with an aperture density λ–2 achieve ∼96% testing accuracy on the MNIST data set, for an optimized distance ∼100λ to the output plane. This is enabled by an intrinsic nonlinearity in photodetection, despite the use of linear optical metamaterials. Furthermore, we find that once the system is optimized, the number of diffractive features is the main determinant of classification performance. The slow asymptotic scaling with the number of apertures suggests a reason why such systems may benefit from multiple layer designs. Finally, we show a trade-off between the number of apertures and fabrication noise.
Hierarchical neural reconstruction for path guiding using hybrid path and photon samples

Path guiding is a promising technique to reduce the variance of path tracing. Although existing online path guiding algorithms can eventually learn good sampling distributions given a large amount of time and samples, the speed of learning becomes a major bottleneck. In this paper, we accelerate the learning of sampling distributions by training a light-weight neural network offline to reconstruct from sparse samples. Uniquely, we design our neural network to directly operate convolutions on a sparse quadtree, which regresses a high-quality hierarchical sampling distribution. Our approach can reconstruct reasonably accurate sampling distributions faster, allowing for efficient path guiding and rendering. In contrast to the recent offline neural path guiding techniques that reconstruct low-resolution 2D images for sampling, our novel hierarchical framework enables more fine-grained directional sampling with less memory usage, effectively advancing the practicality and efficiency of neural path guiding. In addition, we take advantage of hybrid bidirectional samples including both path samples and photons, as we have found this more robust to different light transport scenarios compared to using only one type of sample as in previous work. Experiments on diverse testing scenes demonstrate that our approach often improves rendering results with better visual quality and lower errors. Our framework can also provide the proper balance of speed, memory cost, and robustness.
MorphyNet: a Large Multilingual Database of Derivational and Inflectional Morphology

Large-scale morphological databases provide essential input to a wide range of NLP applications.Inflectional data is of particular importance for morphologically rich (agglutinative and highly inflecting) languages, and derivations can be used, e.g. to infer the semantics of out-of-vocabulary words.Extending the scope of state-of-the-art multilingual morphological databases, we announce the release of Mor-phyNet, a high-quality resource with 15 languages, 519k derivational and 10.1M inflectional entries, and a rich set of morphological features.MorphyNet was extracted from Wiktionary using both hand-crafted and automated methods, and was manually evaluated to be of a precision higher than 98%.Both the resource generation logic and the resulting database are made freely available 12 and are reusable as stand-alone tools or in combination with existing resources.
Uncertainty Evaluation in Vision-Based Techniques for the Surface Analysis of Composite Material Components

In this paper, a methodology is discussed concerning the measurement of yarn’s angle of two different glass-reinforced polypropylene matrix materials, widely used in the production of automotive components. The measurement method is based on a vision system and image processing techniques for edge detection. Measurements of angles enable, if accurate, both useful suggestions for process optimization to be made, and the reliable validation of the simulation results of the thermoplastic process. Therefore, uncertainty evaluation of angle measurement is a mandatory pre-requisite. If the image acquisition and processing is considered, many aspects influence the whole accuracy of the method; the most important have been identified and their effects evaluated with reference to two different materials, which present different optical-type characteristics. The influence of piece geometry has also been taken into account, carrying out measurements on flat sheets and on a semi-spherical object, which is a reference standard shape, to verify the effect of thermoforming and to tune the process parameters. Complete uncertainty in the order of a few degrees has been obtained, which is satisfactory for purposes of simulation validation and consequent process optimization. The uncertainty budget also allowed individuation of the most relevant causes of uncertainty for measurement process improvement.
Quantum algorithm for MMNG-based DBSCAN

Abstract DBSCAN is a famous density-based clustering algorithm that can discover clusters with arbitrary shapes without the minimal requirements of domain knowledge to determine the input parameters. However, DBSCAN is not suitable for databases with different local-density clusters and is also a very time-consuming clustering algorithm. In this paper, we present a quantum mutual MinPts -nearest neighbor graph (MMNG)-based DBSCAN algorithm. The proposed algorithm performs better on databases with different local-density clusters. Furthermore, the proposed algorithm has a dramatic increase in speed compared to its classic counterpart.
Shades of BLEU, Flavours of Success: The Case of MultiWOZ

The MultiWOZ dataset (Budzianowski et al., 2018) is frequently used for benchmarking context-to-response abilities of task-oriented dialogue systems.In this work, we identify inconsistencies in data preprocessing and reporting of three corpus-based metrics used on this dataset, i.e., BLEU score and Inform & Success rates.We point out a few problems of the MultiWOZ benchmark such as unsatisfactory preprocessing, insufficient or underspecified evaluation metrics, or rigid database.We re-evaluate 7 end-to-end and 6 policy optimization models in as-fair-as-possible setups, and we show that their reported scores cannot be directly compared.To facilitate comparison of future systems, we release our standalone standardized evaluation scripts.We also give basic recommendations for corpus-based benchmarking in future works.
The Use of WhatsApp for Flexible Learning: Its Effectiveness in Supporting Teaching and Learning in Sierra Leone's Higher Education Institutions

&lt;p class="Body"&gt;&lt;span&gt;Despite its inherent limitations, Whatsapp messenger has proved beneficial in bridging the gap in technology provision for enhancing students' learning experiences in Sierra Leone. The need to make sure students dedicate time in using Whatsapp for the benefit of improving their learning experiences was emphasised; this as suggested can be done through participation in group collaboration, which involve peer-to-peer and peer-to-instructor interaction. Equally, to advance effective use of flexible learning opportunities, institutions must consider alternatives like Moodle / Blackboard, which already comes with the added capabilities of MIS synchronisation features of integration with students' record. In all of the aforementioned points, there is a need for government support in ensuring the right investment is made in bringing the country's HEIs integrated ICT learning provision to international standard. &lt;/span&gt;&lt;/p&gt;