Reviewerly/exHarmony huggingface dataset:
MDER: Multi-Dimensional Event Recommendation in Social Media Context

Abstract Events represent a tipping point that affects users’ opinions and vary depending upon their popularity from local to international. Indeed, social media offer users platforms to express their opinions and commitments to events that attract them. However, owing to the volume of data, users are encountering a difficulty to accede to the preferred events according to their features that are stored in their social network profiles. To surmount this limitation, multiple event recommendation systems appeared. Nevertheless, these systems use a limited number of event dimensions and user’s features. Besides, they consider users’ features stored in a single user’s profile and disregard the semantic concept. In this research, an approach for multi-dimensional event recommendation is set forward to recommend events to users resting on several event dimensions (engagement, location, topic, time and popularity) and some user’s features (demographic data, position and user’s/friend’s interests) stored in multi-user’s profiles by considering the semantic relationships between user’s features, specifically user’s interests. The performance of our approach was assessed using error rate measurements (mean absolute error, root mean squared error and cross-validation). Experiment that results on real-world event data sets confirmed that our approach recommends events that fit the user more than the previous approaches with the lowest error rate values.
Heuristic Sensing: An Uncertainty Exploration Method in Imperfect Information Games

Imperfect information games have served as benchmarks and milestones in fields of artificial intelligence (AI) and game theory for decades. Sensing and exploiting information to effectively describe the game environment is of critical importance for game solving, besides computing or approximating an optimal strategy. Reconnaissance blind chess (RBC), a new variant of chess, is a quintessential game of imperfect information where the player’s actions are definitely unobserved by the opponent. This characteristic of RBC exponentially expands the scale of the information set and extremely invokes uncertainty of the game environment. In this paper, we introduce a novel sense method, Heuristic Search of Uncertainty Control (HSUC), to significantly reduce the uncertainty of real-time information set. The key idea of HSUC is to consider the whole uncertainty of the environment rather than predicting the opponents’ strategy. Furthermore, we realize a practical framework for RBC game that incorporates our HSUC method with Monte Carlo Tree Search (MCTS). In the experiments, HSUC has shown better effectiveness and robustness than comparison opponents in information sensing. It is worth mentioning that our RBC game agent has won the first place in terms of uncertainty management in NeurIPS 2019 RBC tournament.
Breast cancer risk assessment and early diagnosis using Principal Component Analysis and support vector machine techniques

Breast cancer (BCa) is one of the leading causes of cancer mortality among women globally and the specific causes of the disease remain unknown, but studies have shown several risk factors associated with the morbid condition. Breast cancer risk assessment and diagnosis can be achieved using clinical acumen of physicians, medical imaging and computational techniques. Early diagnosis has been identified as one of the ways to reduce BCa mortality. However, accuracy of the diagnosis is not always guaranteed due to human error; radiologists' divergent results from interpretations given to medical images; and computational errors due to use of data imbued with some errors. Thus, in this study, we adopted the hybrid of Principal Component Analysis (PCA) and Support Vector Machine (SVM) to develop BCa risk assessment and early diagnosis model (i.e. BC-RAED) that is capable of accurately establishing BCa at the early stage. PCA was used to extract features at the first preprocessing and the features were further reduced after the second preprocessing. The multi pre-processed data were assessed for breast cancer's risk and diagnosis using SVM. BC-RAED presents accuracy of 97.62%, sensitivity of 95.24% and specificity of 100% on BCa risk assessment and diagnosis. The new levels of accuracy, sensitivity and specificity were significant at 5% level of significance (p < 0.05) when compared with documented values in literature and this confirmed the viability of BC-RAED. Based on this result, it was concluded that BC-RAED has the potential to multi pre-process breast cancer data and classify patients into likely and unlikely categories, based on risk factors, and classify cancer cases into malignant and benign, based on established technical indicators reported in literature.
Redundancy Reduction in Face Detection of Viola-Jones using the Hill Climbing Algorithm

In this study, we improved Viola-Jones face detection using Hill Climbing algorithm in order to reduce the redundancy rates. Viola-Jones's algorithm has succeeded in determining the best face scale factor, but it produces a non-comparable face window that leads the redundant face detection. Hill Climbing algorithm which is one of the local search family, is proposed to serve the local-maxima which represents a set of faces that has been selected from redundant data. We have evaluated and compared the accurate, accurate and recall tests the performance of the proposed method using LFW dataset. Traditional Viola Jones achieves accuracy up to 77% and the proposed method achieves accuracy up to 85%. The two sets of values concluded that the proposed method reduce the redundancy problem.
Nonlinear dynamics in an optoelectronic feedback delay oscillator with piecewise linear transfer functions from the laser diode and photodiode

We investigate the nonlinear dynamics of a recent architecture of an optoelectronic oscillator, where the emitting laser and the receiving diode are connected in a head-to-tail configuration via an optical fiber delay line. The resulting nonlinear transfer function is a piecewise linear profile, and its interplay with the delay leads to many complex behaviors such as relaxation oscillations and deterministic chaos. This system belongs to a recent class of optoelectronic oscillators where the nonlinearity does not originate from the sinusoidal transfer function of an imbalanced interferometer, and, in particular, it is a simple optoelectronic oscillator configuration that is capable of displaying a chaotic behavior. The results of the analytic study are confirmed by numerical simulations and experimental measurements.
Topology-Change-Aware Volumetric Fusion for Dynamic Scene Reconstruction

Topology change is a challenging problem for 4D reconstruction of dynamic scenes. In the classic volumetric fusion-based framework, a mesh is usually extracted from the TSDF volume as the canonical surface representation to help estimating deformation field. However, the surface and Embedded Deformation Graph (EDG) representations bring conflicts under topology changes since the surface mesh has fixed-connectivity but the deformation field can be discontinuous. In this paper, the classic framework is re-designed to enable 4D reconstruction of dynamic scene under topology changes, by introducing a novel structure of Non-manifold Volumetric Grid to the re-design of both TSDF and EDG, which allows connectivity updates by cell splitting and replication. Experiments show convincing reconstruction results for dynamic scenes of topology changes, as compared to the state-of-the-art methods.
Transformation of the Educational Ecosystem in the Singularity Environment

This study was aimed at creating an effective model of the educational ecosystem in a singularity environment. The study is based on a system of general scientific methods of scientific knowledge, in particular, the method of expert assessment, the method of analogy and comparative analysis. The experts, with the support of facilitators, visualized the possible future of education as a symbiosis of individual and collective learning paths that connect learners with many learning spaces and educational opportunities. To ensure the operability of such a model of the educational ecosystem, it is necessary to create tools and processes that support personal and collective learning and development throughout life, including: processes and tools that help determine the goals of learners; educational processes integrated into the educational trajectory and developing various aspects of personal and collective existence through holistic educational experiences, including play and co-creation; processes and tools that measure learning outcomes; educational spaces and technologies that help to combine personal and collective educational trajectories, coordinating individual educational needs with the evolving needs of communities. These processes can be integrated into "ecosystem" systems of educational process management (in which personal and collective learning paths can be combined, and which connect learners with many learning spaces and educational opportunities).
Tilt-Responsive Techniques for Digital Drawing Boards

Drawing boards offer a self-stable work surface that is continuously adjustable. On digital displays, such as the Microsoft Surface Studio, these properties open up a class of techniques that sense and respond to tilt adjustments. Each display posture-whether angled high, low, or somewhere in-between-affords some activities, but not others. Because what is appropriate also depends on the application and task, we explore a range of app-specific transitions between reading vs. writing (annotation), public vs. personal, shared person-space vs. task-space, and other nuances of input and feedback, contingent on display angle. Continuous responses provide interactive transitions tailored to each use-case. We show how a variety of knowledge work scenarios can use sensed display adjustments to drive context-appropriate transitions, as well as technical software details of how to best realize these concepts. A preliminary remote user study suggests that techniques must balance effort required to adjust tilt, versus the potential benefits of a sensed transition.
Constant Ciphertext-Rate Non-committing Encryption from Standard Assumptions

Non-committing encryption (NCE) is a type of public key encryption which comes with the ability to equivocate ciphertexts to encryptions of arbitrary messages, i.e., it allows one to find coins for key generation and encryption which “explain” a given ciphertext as an encryption of any message. NCE is the cornerstone to construct adaptively secure multiparty computation [Canetti et al. STOC’96] and can be seen as the quintessential notion of security for public key encryption to realize ideal communication channels. A large body of literature investigates what is the best message-to-ciphertext ratio (i.e., the rate) that one can hope to achieve for NCE. In this work we propose a near complete resolution to this question and we show how to construct NCE with constant rate in the plain model from a variety of assumptions, such as the hardness of the learning with errors (LWE), the decisional Diffie-Hellman (DDH), or the quadratic residuosity (QR) problem. Prior to our work, constructing NCE with constant rate required a trusted setup and indistinguishability obfuscation [Canetti et al. ASIACRYPT’17].
Subword Regularization: An Analysis of Scalability and Generalization for End-to-End Automatic Speech Recognition

Subwords are the most widely used output units in end-to-end speech recognition. They combine the best of two worlds by modeling the majority of frequent words directly and at the same time allow open vocabulary speech recognition by backing off to shorter units or characters to construct words unseen during training. However, mapping text to subwords is ambiguous and often multiple segmentation variants are possible. Yet, many systems are trained using only the most likely segmentation. Recent research suggests that sampling subword segmentations during training acts as a regularizer for neural machine translation and speech recognition models, leading to performance improvements. In this work, we conduct a principled investigation on the regularizing effect of the subword segmentation sampling method for a streaming end-to-end speech recognition task. In particular, we evaluate the subword regularization contribution depending on the size of the training dataset. Our results suggest that subword regularization provides a consistent improvement of (2-8%) relative word-error-rate reduction, even in a large-scale setting with datasets up to a size of 20k hours. Further, we analyze the effect of subword regularization on recognition of unseen words and its implications on beam diversity.
On the Lesson Design of Online College English Class during the COVID-19 Pandemic

The COVID-19 pandemic shocked school education, bringing a lot of challenges. The pandemic has changed how millions around the globe are educated and new solutions for education could bring much needed innovation. During the pandemic, online teaching has become a necessary way for teachers and students in primary schools, middle schools, high schools and even universities around the world. College English teaching becomes increasingly difficult as there are many affecting factors. The article first analyzed the advantages and challenges of the pandemic on the college English online teaching, and then put forward the strategies of online teaching design, in order to supply some reference and enlightenment for the college English teaching in China.
A Comparison of Automatic versus Manual World Alteration for Network Game Latency Compensation

Cloud-based game streaming services add network latency to even single-player games, making them harder to play. World alteration has the potential to mitigate the effects of latency by adjusting the game world to keep the difficulty similar to that with no network delay. This paper presents results from a user study evaluating world alteration as a latency compensation technique. Data from 18 users show world alteration does not totally overcome network latency, but improves performance and quality of experience over an uncompensated game.
Teaching Scrum with a Virtual Sprint Simulation: Initial Design and Considerations

Scrum is a well-developed and utilized agile project management framework, which requires extensive training and hands-on experience to master. The latter is not always possible, i.e. during the recent lockdown due to COVID-19. Thus, we propose the creation of a multi-user collaborative virtual simulation of a Scrum sprint that can provide an immersive training experience to remote trainees. Herein, we discuss the design considerations, elements of the virtual learning environment and the development process for the platform.
Graph-based Reinforcement Learning for Active Learning in Real Time: An Application in Modeling River Networks

Effective training of advanced ML models requires large amounts of labeled data, which is often scarce in scientific problems given the substantial human labor and material cost to collect labeled data. This poses a challenge on determining when and where we should deploy measuring instruments (e.g., in-situ sensors) to collect labeled data efficiently. This problem differs from traditional pool-based active learning settings in that the labeling decisions have to be made immediately after we observe the input data that come in a time series. In this paper, we develop a real-time active learning method that uses the spatial and temporal contextual information to select representative query samples in a reinforcement learning framework. To reduce the need for large training data, we further propose to transfer the policy learned from simulation data which is generated by existing physics-based models. We demonstrate the effectiveness of the proposed method by predicting streamflow and water temperature in the Delaware River Basin given a limited budget for collecting labeled data. We further study the spatial and temporal distribution of selected samples to verify the ability of this method in selecting informative samples over space and time.
Network-to-Network Translation with Conditional Invertible Neural Networks

Given the ever-increasing computational costs of modern machine learning models, we need to find new ways to reuse such expert models and thus tap into the resources that have been invested in their creation. Recent work suggests that the power of these massive models is captured by the representations they learn. Therefore, we seek a model that can relate between different existing representations and propose to solve this task with a conditionally invertible network. This network demonstrates its capability by (i) providing generic transfer between diverse domains, (ii) enabling controlled content synthesis by allowing modification in other domains, and (iii) facilitating diagnosis of existing representations by translating them into interpretable domains such as images. Our domain transfer network can translate between fixed representations without having to learn or finetune them. This allows users to utilize various existing domain-specific expert models from the literature that had been trained with extensive computational resources. Experiments on diverse conditional image synthesis tasks, competitive image modification results and experiments on image-to-image and text-to-image generation demonstrate the generic applicability of our approach. For example, we translate between BERT and BigGAN, state-of-the-art text and image models to provide text-to-image generation, which neither of both experts can perform on their own.
Further Analysis of Outlier Detection with Deep Generative Models

The recent, counter-intuitive discovery that deep generative models (DGMs) can frequently assign a higher likelihood to outliers has implications for both outlier detection applications as well as our overall understanding of generative modeling. In this work, we present a possible explanation for this phenomenon, starting from the observation that a model's typical set and high-density region may not conincide. From this vantage point we propose a novel outlier test, the empirical success of which suggests that the failure of existing likelihood-based outlier tests does not necessarily imply that the corresponding generative model is uncalibrated. We also conduct additional experiments to help disentangle the impact of low-level texture versus high-level semantics in differentiating outliers. In aggregate, these results suggest that modifications to the standard evaluation practices and benchmarks commonly applied in the literature are needed.
Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs

Combinatorial optimization problems are notoriously challenging for neural networks, especially in the absence of labeled instances. This work proposes an unsupervised learning framework for CO problems on graphs that can provide integral solutions of certified quality. Inspired by Erdos' probabilistic method, we use a neural network to parametrize a probability distribution over sets. Crucially, we show that when the network is optimized w.r.t. a suitably chosen loss, the learned distribution contains, with controlled probability, a low-cost integral solution that obeys the constraints of the combinatorial problem. The probabilistic proof of existence is then derandomized to decode the desired solutions. We demonstrate the efficacy of this approach to obtain valid solutions to the maximum clique problem and to perform local graph clustering. Our method achieves competitive results on both real datasets and synthetic hard instances.
Online Bayesian Goal Inference for Boundedly Rational Planning Agents

People routinely infer the goals of others by observing their actions over time. Remarkably, we can do so even when those actions lead to failure, enabling us to assist others when we detect that they might not achieve their goals. How might we endow machines with similar capabilities? Here we present an architecture capable of inferring an agent's goals online from both optimal and non-optimal sequences of actions. Our architecture models agents as boundedly-rational planners that interleave search with execution by replanning, thereby accounting for sub-optimal behavior. These models are specified as probabilistic programs, allowing us to represent and perform efficient Bayesian inference over an agent's goals and internal planning processes. To perform such inference, we develop Sequential Inverse Plan Search (SIPS), a sequential Monte Carlo algorithm that exploits the online replanning assumption of these models, limiting computation by incrementally extending inferred plans as new actions are observed. We present experiments showing that this modeling and inference architecture outperforms Bayesian inverse reinforcement learning baselines, accurately inferring goals from both optimal and non-optimal trajectories involving failure and back-tracking, while generalizing across domains with compositional structure and sparse rewards.
Semantic Segmentation Datasets for Resource Constrained Training

Several large scale datasets, coupled with advances in deep neural network architectures have been greatly successful in pushing the boundaries of performance in semantic segmentation in recent years. However, the scale and magnitude of such datasets prohibits ubiquitous use and widespread adoption of such models, especially in settings with serious hardware and software resource constraints. Through this work, we propose two simple variants of the recently proposed IDD dataset, namely IDD-mini and IDD-lite, for scene understanding in unstructured environments. Our main objective is to enable research and benchmarking in training segmentation models. We believe that this will enable quick prototyping useful in applications like optimum parameter and architecture search, and encourage deployment on low resource hardware such as Raspberry Pi. We show qualitatively and quantitatively that with only 1 h of training on 4 GB GPU memory, we can achieve satisfactory semantic segmentation performance on the proposed datasets.
Revisiting Frank-Wolfe for Polytopes: Strict Complementarity and Sparsity

In recent years it was proved that simple modifications of the classical Frank-Wolfe algorithm (aka conditional gradient algorithm) for smooth convex minimization over convex and compact polytopes, converge with linear rate, assuming the objective function has the quadratic growth property. However, the rate of these methods depends explicitly on the dimension of the problem which cannot explain their empirical success for large scale problems. In this paper we first demonstrate that already for very simple problems and even when the optimal solution lies on a low-dimensional face of the polytope, such dependence on the dimension cannot be avoided in worst case. We then revisit the addition of a strict complementarity assumption already considered in Wolfe's classical book \cite{Wolfe1970}, and prove that under this condition, the Frank-Wolfe method with away-steps and line-search converges linearly with rate that depends explicitly only on the dimension of the optimal face. We motivate strict complementarity by proving that it implies sparsity-robustness of optimal solutions to noise.