Reviewerly/exHarmony huggingface dataset:
Feature Re-Balancing for Long-Tailed Visual Recognition

Despite the recent success of visual recognition on artificially balanced datasets, the performance degrades heavily in face of the long-tailed distribution. Existing methods typically tackle this problem by re-balancing the distribution in the data space. However, we observe that more balanced data distribution can not effectively alleviate the problem of uneven feature distribution, still leading to a heavily biased classifier. In this paper, we propose a novel re-balancing framework, Feature Re-Balancing (FeatRB), which directly re-balances the distribution in the feature space by combining the long-tailed initial features and the generated virtual features. The key ideas of FeatRB include: 1) Generating the virtual features. First, we calculate the class-wise feature mean and variance based on the past learned representations and store them in a memory bank. Then we generate virtual features based on the memory bank. And to increase the diversity of generated features, we transfer the variance from similar classes to tail classes. 2) Utilizing the generated features. We introduce a simple but effective sampling strategy, Effective Number Reversed Sampling (ENRS), to assign larger sampling probability for tail classes. 3) Updating the memory bank. We propose an updating method, Adaptive Updating (AU), which adaptively updates the memory bank in the training process to further improve the diversity. By increasing the intra-class diversity, FeatRB enlarges the spatial span for the features of tail classes. Therefore, the discriminative power of tail classes can be enhanced, and then the biased classifier can be calibrated. Extensive experiments on three widely used large-scale long-tailed datasets show that our FeatRB surpasses the current state-of-the-art methods.
Consistent Causal Inference for High Dimensional Time Series

A methodology for high dimensional causal inference in a time series context is introduced. It is assumed that there is a monotonic transformation of the data such that the dynamics of the transformed variables are described by a Gaussian vector autoregressive process. This is tantamount to assume that the dynamics are captured by a Gaussian copula. No knowledge or estimation of the marginal distribution of the data is required. The procedure consistently identifies the parameters that describe the dynamics of the process and the conditional causal relations among the possibly high dimensional variables under sparsity conditions. The methodology allows us to identify such causal relations in the form of a directed acyclic graph. As an application we estimate the directed acyclic graph for the order book on one-minute aggregated data on four stock constituents of the S&P500.
C-NMT: A Collaborative Inference Framework for Neural Machine Translation

Collaborative Inference (CI) optimizes the latency and energy consumption of deep learning inference through the inter-operation of edge and cloud devices. Albeit beneficial for other tasks, CI has never been applied to the sequence-to-sequence mapping problem at the heart of Neural Machine Translation (NMT). In this work, we address the specific issues of collaborative NMT, such as estimating the latency required to generate the (unknown) output sequence, and show how existing CI methods can be adapted to these applications. Our experiments show that CI can reduce the latency of NMT by up to 44% compared to a non-collaborative approach.
The Development of Neural-Network Based on Representation Learning

The goal of representation learning is to extract high-level semantic information from raw data such as texts, images, audio, and store and process the information in vectors. In recent years, the research field of representation learning achieved remarkable results. A series of masked language models led by BERT performed self-supervised learning on large-scale unlabeled data, obtained common feature representations of tokens and sentences, and improved the actual performance of many downstream tasks. Recently, contrastive learning brought representation learning to a new level. Compared with masked language models, the methods of contrastive learning are more efficient in that it is easier to obtain well-generalized feature representations. In addition, multimodal representation learning combines language features with image features, enabling feature representations to be better at handling multi-scenario tasks. In this paper, we focused on representation learning methods in natural language processing, reviewed the development process of representation learning in recent years, and introduced some of its classical methods. Finally, we discussed the key issues and challenges.
Borrowing from yourself: Faster future video segmentation with partial channel update

Semantic segmentation is a well-addressed topic in the computer vision literature, but the design of fast and accurate video processing networks remains challenging. In addition, to run on embedded hardware, computer vision models often have to make compromises on accuracy to run at the required speed, so that a latency/accuracy trade-off is usually at the heart of these real-time systems’ design. For the specific case of videos, models have the additional possibility to make use of computations made for previous frames to mitigate the accuracy loss while being real-time.In this work, we propose to tackle the task of fast future video segmentation prediction through the use of convolutional layers with time-dependent channel masking. This technique only updates a chosen subset of the feature maps at each timestep, bringing simultaneously less computation and latency, and allowing the network to leverage previously computed features. We apply this technique to several fast architectures and experimentally confirm its benefits for the future prediction subtask.
Semi-Simulated Training Data for Multi-Image Super-Resolution

Multi-image super-resolution is a branch of super-resolution reconstruction techniques aiming to resolve a set of lowresolution images into a high-resolution one. Unlike singleimage super-resolution, its goal is to fuse information embedded in different images depicting the same scene, usually captured at different times. Such data combination contains more high-resolution information than a single image, thus allowing for more accurate reconstruction results. One of the most critical challenges is to prepare training data for multi-image super-resolution since only a few datasets are available here, especially for satellite imaging applications. For this reason, many studies are conducted using simulated low-resolution images, but the results obtained for real-life data are often unsatisfactory. To overcome this problem, we propose a new semi-simulated approach of creating lowresolution images for training that resemble real-life ones much more accurately. We also investigate the performance of selected deep learning models trained with simulated and semi-simulated datasets and we show that the latter achieve better results when applied to real-world images.
Collective Decision-Making for Conflict Resolution in Multi-Agent Pathfinding

Multi-Agent Path Planning is important in many applications involving multiple mobile robots. In this paper, we present a novel algorithm to solve the Multi-Agent Pathfinding problem by using Collective Decision-making and indirect communication (stigmergy). We call this planning method Collective Conflict Resolution (CCR). The algorithm has two components: A mechanism to create a prioritization graph through collective decisions and a planning mechanism that is able to plan paths consistent with the priorities given by the graph. The CCR algorithm can be used both in global planning with full knowledge of all paths, and in decentralized settings with limited knowledge and communication. In our experiments, we compare our new planner with two state-of-the-art algorithms: Conflict-based Search (CBS) and Prioritized Planning. Furthermore, we analyze how a limited planning horizon can affect the planning cost. The results show that the proposed method offers a good trade-off between planning cost and solution quality, which results in a better success rate compared to Prioritized Planning, which sometimes does not find a solution. In addition, the algorithm is able to achieve a good solution quality with lower cost compared to the more complex CBS algorithm.
Determinants of Satisfaction of Tecnologico de Monterrey Students with the Implementation of the Flexible-Digital Model During the COVID-19 Crisis

This research aims to identify the main factors that contributed to the satisfaction of Tecnologico de Monterrey students with implementing the Flexible-Digital Model (FDM) during the health crisis and confinement caused by COVID-19. We used information from a virtual survey applied to 315 Tecnologico de Monterrey students and a binary dependent-variable econometric model. The main factors in student satisfaction with their academic level of learning under the FDM were identified and compared with the traditional face-to-face model. Our results suggest that FDM was influential in ensuring the continuity of the teaching–learning process during the health contingency of COVID-19. Also, the findings indicated that the students’ self-motivation, learning styles, course designs, and mastery of the topics by the professors were the main determinants of their satisfaction with educational quality during the implementation of the FDM.
Dual Attention Based Multi-scale Feature Fusion Network for Indoor RGBD Semantic Segmentation

RGBD semantic segmentation combined with color image information and depth information can effectively alleviate the problems of low classification accuracy and difficulty in accurately dividing edges between different semantic regions in indoor scenes caused by complex backgrounds, uneven lighting, similar object textures, spatial overlap, and occlusion. To fully fuse the color features with the spatial position and hierarchical information of objects, this paper proposes a multi-scale network model based on the dual attention mechanism (channel attention and spatial attention)(DAMFNet), which effectively integrates color texture features and spatial structure features, and further improves the semantic segmentation performance of indoor objects. We evaluate the proposed network model on the common indoor dataset SUNRGBD and achieve state-of-the-art results. In addition, this paper also demonstrates the excellent segmentation accuracy and effect of the proposed network model on self-built indoor datasets and in real-world application scenarios.
DeepSet: Deep Learning-based Recommendation with Setwise Preference

Recommendation methods based on deep learning frameworks have drastically increased over recent years, covering virtually all the sub-topics in recommender systems. Among these topics, one-class collaborative filtering (OCCF) as a fundamental problem has been studied most extensively. However, most of existing deep learning-based OCCF methods are essentially focused on either defining new prediction rules by replacing conventional shallow and linear inner products with a variety of neural architectures, or learning more expressive user and item factors with neural networks, which may still suffer from the inferior recommendation performance due to the underlying preference assumptions typically defined on single items. In this paper, we propose to address the limitation and justify the capacity of deep learning-based recommendation methods by adapting the setwise preference to the underlying assumption during the model learning process. Specifically, we propose a new setwise preference assumption under the neural recommendation frameworks and devise a general solution named DeepSet, which aims to enhance the learning abilities of neural collaborative filtering methods by activating the setwise preference at different neural layers, namely 1) the feature input layer, 2) the feature output layer, and 3) the prediction layer. Extensive experiments on four commonly used datasets show that our solution can effectively boost the performance of existing deep learning based methods without introducing any new model parameters.
ETop3PPE: EPOCh’s Top-Three Prediction Probability Ensemble Method for Deep Learning Classification Models

The rapid growth in the field of deep learning (DL) has increased research interests in this area, including computer vision (CV), for which high-quality products have been produced. Many fields, including medicine, the automobile industry, finance, education, and the military have used state-of-art CV results. Any change in CV affects human life through the abovementioned sector. Considering the importance of the development in CV, we proposed Epoch's Top-Three Prediction Probability Ensemble (Etop3PPE) method for DL classification problems. Our method focuses on the use of lost knowledge during training and an optimal way to increase the accuracy of the model. Each epoch during training represents a different prediction space, which is the key to the development of the proposed method. We used the top-three prediction probabilities of each image for the classification task from different epochs of training and ensembled them into the best model prediction probabilities. Applying our method, we partially solved the ensemble error problem in the models and increased the accuracy of our model from 89.32% to 90.91%. We added 32.8% lost knowledge from the prediction spaces of the training epochs that were used during the ensembling. We used the Cifar10dataset to evaluate our method. In addition, we compared the results of our method with those of the classic ensemble (ensembling all prediction probabilities into the best model). The result was surprising, our method overcome by 16% in case of adding lost knowledge of different epochs.
On the Current State and Challenges of Artificial Intelligence

In the article, the strategic goals of Artificial intelligence and the main problems of scientific and technological activities in this field are described. The problems relevant for the development of the main directions and forms of its activity are defined. Approaches to their solution, based on a new technological wave, are suggested. Issues important for the development of this scientific and technological discipline are discussed.
How to Fool Your Supervisor

Reinforcement learning is being broadly studied as a solution for many real-world problems. Reinforcement learning algorithms outperform humans in games such as Go and chess. On the other hand, reinforcement learning still can not achieve human-level in most real-world tasks. When reinforcement learning agents perform similar to humans, they will replace humans in many areas. Many real-world tasks need cooperation, and multi-agent reinforcement learning appears as a solution. However, multi-agent reinforcement learning algorithms are not yet being applied because of the challenge of high-dimensional data, computational complexity, and credit assignment. Meanwhile, people can manage to adapt to a new group without understanding the global goal of the group he or she belongs to. This is because people moderately imitate senior workers. Our work aims to test if the imitation strategy can be applied to a reinforcement learning agent. To validate the proposed methodology, we design a problem named Convoy problem and evaluate our new algorithm on the convoy problem.
Hybrid Political Algorithm Approach for Engineering Optimization Problems

Political Optimizer is a metaheuristic algorithm proposed by scholars recently. It reflects good convergence speed as well as exploitation and exploration capabilities in problems relevant to finding optimal solutions. However, regarding the global optimum, it still has spaces to be improved. This study proposed a Hybrid Political Algorithm, which makes balanced modifications to the exploration process and algorithm functions to effectively improve searching for the optimal solutions in the mathematic problems of Congress on Evolutionary Computation and engineering problems by enabling the particles to move smarter in the computation process. Based on the experimental results, improving the performance of algorithm functions using the methods proposed in this study can also help avoid falling into the local optimum. In addition, compared to other algorithms, this improved algorithm can generate more accurate results.
Supervision Software using Artificial Intelligence

With the dependency on the internet growing immensely everyday, the newer generation is exposed to threats and risks. The aim of this paper is to supervise the activities of a supervisee using Keylogger and Screenshot analyser. Different machine learning algorithms are applied on the logged data and the screenshots which helps in classifying the data in different categories. The supervisor is alerted each time the model raises a red flag. It shows promising results in keeping the track of the user’s activities. This helps in evaluating the user’s web practices, from an innovative, lawful and moral perspective.
Text-Based Cyberbullying Prevention using Toxicity Filtering Mobile Chat Application and API

The internet becoming a ubiquitous thing for most people has both positive and negative consequences. One negative consequence is that everyone’s profile or contact info on any social media is made available to anyone on the internet. With the ever-growing userbase of social media platforms, the risk of being cyberbullied is very high. Since most communication on any social media platform is done through chats, an attempt has been made to curb the cyberbullying on these social media platforms in textual form. This will be done by providing an API (Application Programming Interface) that can receive an input text and respond with an annotation if the text is predicted to be offensive or not and a framework for supporting the same algorithm and running the artificially intelligent model that can understand natural language on mobile devices locally to offer a complex service to the end-users directly without having to depend on the internet and compromising on privacy. Finally, making this available in form of a mobile application would give a lot of user’s access to an extremely useful and helpful system.
Pro-Attention: Efficient Probability Distribution Matching-Based Attention Through Feature Space Conversion

The self-attention mechanism requires a huge amount of computational cost despite its successful use in the transformer. The computational cost linearly increases in proportion to the embedding dimension size, owing to the dot product operation that calculates token similarities in vector spaces. To tackle this problem, we propose a novel efficient self-attention mechanism (Pro-attention) that computes the attention scores through distribution matching in probability space. To this end, we assume that each token has its unique probability distribution and regard each component of a token vector as a sample from the probability distribution. Then, we estimate the statistics of each token-specific probability distribution from the samples, and the token similarities are obtained by using the Kullback-Leibler Divergence. According to the time complexity analysis, the computational cost is markedly saved because the time complexity is independent of the feature dimension size. Our method produces competitive performances in machine translation and language modeling benchmarks such as IWSLT’14 De-En, WMT’14 En-De, WMT’14 En-Fr, and WikiText-103 datasets. Moreover, our model maintains the performances with considerably reduced FLOPs of the self-attention mechanism, which is up to 87% less compared to the baseline transformer. Especially, our model improves the efficiency in a large volume of the training dataset.
Lunar Land: Investigating the Effects of Simulation and Play in Daily Context on Family Functioning

The increasingly overuse of mobile or screen-based products has raised concerns in society. Family, originally a psychosocial support for addiction prevention or treatment, can be weakened in its functioning due to individual members’ over-concentration on the personal addictive devices. In the core of the digital addiction lie simulation and play, which can be repurposed to intervene for more positive family interactions and perceptions. We argue that with always-on sensing and data-driven visualization technologies, interactive tangible artifacts can be designed to detect family members’ use of mobile phones, present simulations that prompt their physical engagements, and hopefully enhance family functioning. This paper presents Lunar Land, which is a smart lamp with its “face” simulating from the crescent to the full moon, when family members put down their phones and take daily-life playful challenges together. Field trials of Lunar Land involved families having the working prototype installed at home for weeks. Usage was automatically logged. Pre- and post-trial surveys were conducted. Families using Lunar Land both for charging phones and taking playful challenges reported higher increase in family time and relationship satisfaction. They also showed higher increase in general family functioning measured by instrument, and more obviously linking the concept of togetherness and “light up.” Results suggest that experiencing simulated outcomes of pausing phone use may assist positive perception of the family. Having the members playing together further enhances the positive perception, because the processes render the positive action-outcome link, from pausing phone use and playing together to joy, cognitively accessible.
An Automatic Detection of Military Weapons Using multi-Level CNN Architecture

This study elucidates an automatic detection of weapons used in military based on deep learning. It utilizes multi-level CNN architecture with three layers of CNN to classify three kinds of visual images for detection of weapons in an image. This proposed system achieved an accuracy of 97.7 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> for the image classification. This experiment was performed on datasets of 2700 images using Tensorflow and Keras.
Evaluation of a New Representation for Noise Reduction in Distant Supervision

Distant Supervision is a relation extraction approach that allows automatic labeling of a dataset. However, this labeling introduces noise in the labels (e.g., when two entities in a sentence are automatically labeled with an invalid relation). Noise in labels makes difficult the relation extraction task. This noise is precisely one of the main challenges of this task. Until now, the methods that incorporate a previous noise reduction step do not evaluate the performance of this step. This paper evaluates the noise reduction using a new representation obtained with autoencoders. In addition, it was incoporated more information to the input of the autoencoder proposed in the state-of-the-art to improve the representation over which the noise is reduced. Also, three methods were proposed to select the instances considered as real. As a result, it was obtained the highest values of the area under the ROC curves using the improved input combined with state-of-the-art anomaly detection methods. Moreover, the three proposed selection methods significantly improve the existing method in the literature.