Reviewerly/exHarmony huggingface dataset:
A Survey of Recommendation Systems

Today's internet is able to discover almost any product or piece of information. The large amounts of unfiltered information returned by an internet query calls for filters able to validate and rank the available options. Recommender systems (RSs) are a software tool designed to qualify the options available and make suggestions that align with the user's requirements and expectations. This paper reviews some significant applications of RSS in various areas like videos, music, eCommerce sites, news, and many more. It also reviews various filtering techniques like collaborative, content based, and hybrid.
Multipose Face Recognition-Based Combined Adaptive Deep Learning Vector Quantization

Multipose face recognition system is one of the recent challenges faced by the researchers interested in security applications. Different researches have been introduced discussing the accuracy improvement of multipose face recognition through enhancing the face detector as Viola-Jones, Real Adaboost, and Cascade Object Detector while others concentrated on the recognition systems as support vector machine and deep convolution neural networks. In this paper, a combined adaptive deep learning vector quantization (CADLVQ) classifier is proposed. The proposed classifier has boosted the weakness of the adaptive deep learning vector quantization classifiers through using the majority voting algorithm with the speeded up robust feature extractor. Experimental results indicate that, the proposed classifier provided promising results in terms of sensitivity, specificity, precision, and accuracy compared to recent approaches in deep learning, statistical, and classical neural networks. Finally, the comparison is empirically performed using confusion matrix to ensure the reliability and robustness of the proposed system compared to the state-of art.
Unsupervised Few-Shot Feature Learning via Self-Supervised Training

Learning from limited exemplars (few-shot learning) is a fundamental, unsolved problem that has been laboriously explored in the machine learning community. However, current few-shot learners are mostly supervised and rely heavily on a large amount of labeled examples. Unsupervised learning is a more natural procedure for cognitive mammals and has produced promising results in many machine learning tasks. In this paper, we propose an unsupervised feature learning method for few-shot learning. The proposed model consists of two alternate processes, progressive clustering and episodic training. The former generates pseudo-labeled training examples for constructing episodic tasks; and the later trains the few-shot learner using the generated episodic tasks which further optimizes the feature representations of data. The two processes facilitate each other, and eventually produce a high quality few-shot learner. In our experiments, our model achieves good generalization performance in a variety of downstream few-shot learning tasks on Omniglot and MiniImageNet. We also construct a new few-shot person re-identification dataset FS-Market1501 to demonstrate the feasibility of our model to a real-world application.
Smartphone Motion Sensor-Based Complex Human Activity Identification Using Deep Stacked Autoencoder Algorithm for Enhanced Smart Healthcare System

Human motion analysis using a smartphone-embedded accelerometer sensor provided important context for the identification of static, dynamic, and complex sequence of activities. Research in smartphone-based motion analysis are implemented for tasks, such as health status monitoring, fall detection and prevention, energy expenditure estimation, and emotion detection. However, current methods, in this regard, assume that the device is tightly attached to a pre-determined position and orientation, which might cause performance degradation in accelerometer data due to changing orientation. Therefore, it is challenging to accurately and automatically identify activity details as a result of the complexity and orientation inconsistencies of the smartphone. Furthermore, the current activity identification methods utilize conventional machine learning algorithms that are application dependent. Moreover, it is difficult to model the hierarchical and temporal dynamic nature of the current, complex, activity identification process. This paper aims to propose a deep stacked autoencoder algorithm, and orientation invariant features, for complex human activity identification. The proposed approach is made up of various stages. First, we computed the magnitude norm vector and rotation feature (pitch and roll angles) to augment the three-axis dimensions (3-D) of the accelerometer sensor. Second, we propose a deep stacked autoencoder based deep learning algorithm to automatically extract compact feature representation from the motion sensor data. The results show that the proposed integration of the deep learning algorithm, and orientation invariant features, can accurately recognize complex activity details using only smartphone accelerometer data. The proposed deep stacked autoencoder method achieved 97.13% identification accuracy compared to the conventional machine learning methods and the deep belief network algorithm. The results suggest the impact of the proposed method to improve a smartphone-based complex human activity identification framework.
Improving Driver Gaze Prediction With Reinforced Attention

We consider the task of driver gaze prediction: estimating where the location of the focus of a driver should be, based on a raw video of the outside environment. In practice, we output a probability map that gives the normalized probability of each point in a given scene being the object of the driver attention. Most existing methods ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Coarse-to-Fine</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Multi-branch</i> ) take an image or a video as input and directly output the fixation map. While successful, these methods can often produce highly scattered predictions, rendering them unreliable for real-world usage. Motivated by this observation, we propose the reinforced attention (RA) model as a regulatory mechanism to increase prediction density. Our method is built directly on top of existing methods, making it complementary to current approaches. Specifically, we first use <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Multi-branch</i> to obtain an initial fixation map. Then, RA is trained using deep reinforcement learning to learn a location prediction policy, producing a reinforced attention. Finally, in order to obtain the final gaze prediction result, we combine the fixation map and the reinforced attention by a mask-guided multiplication. Experimental results show that our framework improves the accuracy of gaze prediction, and provides state-of-the-art performance on the DR(eye)VE dataset.
Stepwise Extractive Summarization and Planning with Structured Transformers

We propose encoder-centric stepwise models for extractive summarization using structured transformers -HiBERT (Zhang et al., 2019) and Extended Transformers (Ainslie et al., 2020).We enable stepwise summarization by injecting the previously generated summary into the structured transformer as an auxiliary sub-structure.Our models are not only efficient in modeling the structure of long inputs, but they also do not rely on task-specific redundancy-aware modeling, making them a general purpose extractive content planner for different tasks.When evaluated on CNN/DailyMail extractive summarization, stepwise models achieve state-of-the-art performance in terms of Rouge without any redundancy aware modeling or sentence filtering.This also holds true for Rotowire tableto-text generation, where our models surpass previously reported metrics for content selection, planning and ordering, highlighting the strength of stepwise modeling.Amongst the two structured transformers we test, stepwise Extended Transformers provides the best performance across both datasets and sets a new standard for these challenges.
TextHide: Tackling Data Privacy in Language Understanding Tasks

An unsolved challenge in distributed or federated learning is to effectively mitigate privacy risks without slowing down training or reducing accuracy.In this paper, we propose Tex-tHide aiming at addressing this challenge for natural language understanding tasks.It requires all participants to add a simple encryption step to prevent an eavesdropping attacker from recovering private text data.Such an encryption step is efficient and only affects the task performance slightly.In addition, Tex-tHide fits well with the popular framework of fine-tuning pre-trained language models (e.g., BERT) for any sentence or sentence-pair task.We evaluate TextHide on the GLUE benchmark, and our experiments show that TextHide can effectively defend attacks on shared gradients or representations and the averaged accuracy reduction is only 1.9%.We also present an analysis of the security of TextHide using a conjecture about the computational intractability of a mathematical problem. 1
ALICE: Active Learning with Contrastive Natural Language Explanations

Training a supervised neural network classifier typically requires many annotated training samples.Collecting and annotating a large number of data points are costly and sometimes even infeasible.Traditional annotation process uses a low-bandwidth human-machine communication interface: classification labels, each of which only provides a few bits of information.We propose Active Learning with Contrastive Explanations (ALICE), an expert-in-the-loop training framework that utilizes contrastive natural language explanations to improve data efficiency in learning.AL-ICE learns to first use active learning to select the most informative pairs of label classes to elicit contrastive natural language explanations from experts.Then it extracts knowledge from these explanations using a semantic parser.Finally, it incorporates the extracted knowledge through dynamically changing the learning model's structure.We applied ALICE in two visual recognition tasks, bird species classification and social relationship classification.We found by incorporating contrastive explanations, our models outperform baseline models that are trained with 40-100% more training data.We found that adding 1 explanation leads to similar performance gain as adding 13-30 labeled training data points.
PHINC: A Parallel Hinglish Social Media Code-Mixed Corpus for Machine Translation

Code-mixing is the phenomenon of using more than one language in a sentence.In the multilingual communities, it is a very frequently observed pattern of communication on social media platforms.Flexibility to use multiple languages in one text message might help to communicate efficiently with the target audience.But, the noisy user-generated codemixed text adds to the challenge of processing and understanding natural language to a much larger extent.Machine translation from monolingual source to the target language is a wellstudied research problem.Here, we demonstrate that widely popular and sophisticated translation systems such as Google Translate fail at times to translate code-mixed text effectively.To address this challenge, we present a parallel corpus of the 13,738 code-mixed Hindi-English sentences and their corresponding human translation in English.In addition, we also propose a translation pipeline build on top of Google Translate.The evaluation of the proposed pipeline on P HIN C demonstrates an increase in the performance of the underlying system.With minimal effort, we can extend the dataset and the proposed approach to other code-mixing language pairs.
On obstacle avoidance path planning in unknown 3D environments: A fluid-based framework

Compared with preprocessed obstacle environments, unknown environments are more challenging for path planning. In unknown environments, an agent can make decisions only by relying on the obstacle information detected by its onboard sensors. However, when facing non-convex obstacles, this limited detection information can easily trap the agent in a local optimum. In this paper, a nature-inspired methodology called Interfered Fluid Dynamic System (IFDS) is extended to anti-local-optimum obstacle avoidance in unknown 3D environments for the first time and a novel fluid-based path planning framework is proposed. First, the detection region of the agent is discretized. Then, spherical virtual obstacles (SVOs) located at detected discrete points are generated and memorized. Thus, obstacle avoidance in unknown environments is transformed into the avoidance of known SVOs. Next, the currently generated and memorized SVOs are input to the core of the framework, the IFDS algorithm, to produce repulsive effects, and the corresponding 3D avoidance path is resolved. On this basis, to address local optimum in cases with non-convex obstacles, and considering compatibility with the IFDS, the direction coefficient and sink-heading angular rate adjustment strategies, which belong to the same system as the IFDS, are introduced to modify the IFDS in this framework. Finally, receding horizon control is introduced to improve the obstacle avoidance performance. Simulations show that the proposed framework can enable the agent to autonomously jump out of the 3D non-convex obstacle environments with typical features of the local optimum, including wall-like and cave-like obstacles, and safely reach the destination.
Oblivious Transfer Is in MiniQCrypt

MiniQCrypt is a world where quantum-secure one-way functions exist, and quantum communication is possible. We construct an oblivious transfer (OT) protocol in MiniQCrypt that achieves simulation-security in the plain model against malicious quantum polynomial-time adversaries, building on the foundational work of Crépeau and Killian (FOCS 1988) and Bennett, Brassard, Crépeau and Skubiszewska (CRYPTO 1991). Combining the OT protocol with prior works, we obtain secure two-party and multi-party computation protocols also in MiniQCrypt. This is in contrast to the classical world, where it is widely believed that one-way functions alone do not give us OT. In the common random string model, we achieve a constant-round universally composable (UC) OT protocol.
Studying the Role of Haptic Feedback on Virtual Embodiment in a Drawing Task

The role of haptic feedback on virtual embodiment is investigated in this paper in a context of active and fine manipulation. In particular, we explore which haptic cue, with varying ecological validity, has more influence on virtual embodiment. We conducted a within-subject experiment with 24 participants and compared self-reported embodiment over a humanoid avatar during a coloring task under three conditions: force feedback, vibrotactile feedback, and no haptic feedback. In the experiment, force feedback was more ecological as it matched reality more closely, while vibrotactile feedback was more symbolic. Taken together, our results show significant superiority of force feedback over no haptic feedback regarding embodiment, and significant superiority of force feedback over the other two conditions regarding subjective performance. Those results suggest that a more ecological feedback is better suited to elicit embodiment during fine manipulation tasks.
Optical and SAR Image Matching Using Pixelwise Deep Dense Features

Image matching is a primary technology to fuse the complementary information from optical and SAR images. Due to the high nonlinear radiometric and geometric relationship, the optical and SAR image matching task remains a widely unsolved challenge. In this study, we propose to use a Siamese convolutional neural network (CNN) architecture to learn pixelwise deep dense features. The proposed network is able to balance the learning of high-level semantic information and low-level fine-grained information, which is nonnegligible for feature matching task. Under the local searching framework, the loss function is defined based on the score map produced by the sum of squared differences (SSDs) between the learned pixelwise dense features of local optical and the SAR image patches, with a fast implementation in the frequency domain. The hardest negative mining strategy is adopted to increase the discrimination of the network. Extensive experiments are conducted on optical and SAR image pairs of different spatial resolution and different landcover types, verifying the superiority and robustness of the proposed method in terms of matching accuracy and matching precision.
Analysis of Meteorological Factor Multivariate Models for Medium- and Long-Term Photovoltaic Solar Power Forecasting Using Long Short-Term Memory

Solar power generation is an increasingly popular renewable energy topic. Photovoltaic (PV) systems are installed on buildings to efficiently manage energy production and consumption. Because of its physical properties, electrical energy is produced and consumed simultaneously; therefore solar energy must be predicted accurately to maintain a stable power supply. To develop an efficient energy management system (EMS), 22 multivariate numerical models were constructed by combining solar radiation, sunlight, humidity, temperature, cloud cover, and wind speed. The performance of the models was compared by applying a modified version of the traditional long short-term memory (LSTM) approach. The experimental results showed that the six meteorological factors influence the solar power forecast regardless of the season. These are, from most to least important: solar radiation, sunlight, wind speed, temperature, cloud cover, and humidity. The models are rated for suitability to provide medium- and long-term solar power forecasts, and the modified LSTM demonstrates better performance than the traditional LSTM.
Galileo at SemEval-2020 Task 12: Multi-lingual Learning for Offensive Language Identification Using Pre-trained Language Models

This paper describes Galileo's performance in SemEval-2020 Task 12 on detecting and categorizing offensive language in social media.For Offensive Language Identification, we proposed a multi-lingual method using Pre-trained Language Models, ERNIE and XLM-R.For offensive language categorization, we proposed a knowledge distillation method trained on soft labels generated by several supervised models.Our team participated in all three sub-tasks.In Sub-task A -Offensive Language Identification, we ranked first in terms of average F1 scores in all languages.We are also the only team which ranked among the top three across all languages.We also took the first place in Sub-task B -Automatic Categorization of Offense Types and Sub-task C -Offence Target Identification.
Quasiprobability decompositions with reduced sampling overhead

Quantum error mitigation techniques can reduce noise on current quantum hardware without the need for fault-tolerant quantum error correction. For instance, the quasiprobability method simulates a noise-free quantum computer using a noisy one, with the caveat of only producing the correct expected values of observables. The cost of this error mitigation technique manifests as a sampling overhead which scales exponentially in the number of corrected gates. In this work, we present a new algorithm based on mathematical optimization that aims to choose the quasiprobability decomposition in a noise-aware manner. This directly leads to a significantly lower basis of the sampling overhead compared to existing approaches. A key element of the novel algorithm is a robust quasiprobability method that allows for a tradeoff between an approximation error and the sampling overhead via semidefinite programming.
Analysis of FDM and DLP 3D-Printing Technologies to Prototype Electromagnetic Devices for RFID Applications

In this work, the application in Radiofrequency Identification (RFID) of different additive manufacturing (AM) 3D-printing technologies is discussed. In particular, the well-known Fused Deposition Modeling (FDM) technology is compared with the promising Digital Light Processing (DLP), which is based on the photopolymerization of liquid resins. Based on the research activity of the authors on this topic, a brief introduction to the fundamentals of 3D-printing in electromagnetics as well as to the different applications of both FDM and DLP in realizing Radio Frequency (RF) devices, is firstly given. Then, a comparison of the two technologies is deeply faced. Finally, after evaluated the rugosity of substrates produced with both techniques to verify the potential impact on the design of electromagnetic structures, the two techniques are both exploited for the realization of the dielectric parts of a tunable RFID tag with unconventional shape. It consists of two elements interlinked one each other. The movement between them enables tuning of the resonance frequency as well as the impedance of the antenna. Despite the differences in terms of losses, rugosity, resolution, and dielectric constant, both techniques guaranteed satisfactory values of tag sensitivity, maximum reading range, and tunability. Nevertheless, the careful analysis of the results proposed at the end of the paper suggests how the selection of one technique over the other must be taken considering the specific application constraints.
Distribution Adaptive INT8 Quantization for Training CNNs

Researches have demonstrated that low bit-width (e.g., INT8) quantization can be employed to accelerate the inference process. It makes the gradient quantization very promising since the backward propagation requires approximately twice more computation than forward one. Due to the variability and uncertainty of gradient distribution, a lot of methods have been proposed to attain training stability. However, most of them ignore the channel-wise gradient distributions and the impact of gradients with different magnitudes, resulting in the degradation of final accuracy. In this paper, we propose a novel INT8 quantization training framework for convolutional neural network to address the above issues. Specifically, we adopt Gradient Vectorized Quantization to quantize the gradient, based on the observation that layer-wise gradients contain multiple distributions along the channel dimension. Then, Magnitude-aware Clipping Strategy is introduced by taking the magnitudes of gradients into consideration when minimizing the quantization error, and we present a theoretical derivation to solve the quantization parameters of different distributions. Experimental results on broad range of computer vision tasks, such as image classification, object detection and video classification, demonstrate that the proposed Distribution Adaptive INT8 Quantization training method has achieved almost lossless training accuracy for different backbones, including ResNet, MobileNetV2, InceptionV3, VGG and AlexNet, which is superior to the state-of-the-art techniques. Moreover, we further implement the INT8 kernel that can accelerate the training iteration more than 200% under the latest Turing architecture, i.e., our method excels on both training accuracy and speed.