Reviewerly/exHarmony huggingface dataset:
Between instrumental reason and applied social engineering: the Latin American educational response to Covid-19

The sudden arrival of the pandemic caused by the new coronavirus showed the most visible face of Latin American societies, so marked by their ancestral rejection of the economic and commercial development models imposed by neoliberalism, and so historically aggrieved by local governments that have failed to respond to their legitimate aspirations for equality, inclusion and human development. In this contextual framework, education is subject to new contradictions, categorically exposed when it is demanded to maintain its status quo, without considering the precarious conditions of infrastructure and technological equipment with which the academic dynamics were developed at all educational levels. On this argumentative basis, an analysis is presented on the response of governments to the health contingency, particularly in their educational strategies, centered on the massive use of information and communication technologies, and three categories of analysis are considered: the institutional educational response to the pandemic, the role of teachers in the state's response to the crisis and the social perception of the implementation of educational strategies designed by governments; all of the above framed in the Latin American context.
How Does the Number of Objective Function Evaluations Impact Our Understanding of Metaheuristics Behavior?

Comparing various metaheuristics based on an equal number of objective function evaluations has become standard practice.Many contemporary publications use a specific number of objective function evaluations by the benchmarking sets definitions.Furthermore, many publications deal with the recurrent theme of late stagnation, which may lead to the impression that continuing the optimization process could be a waste of computational capabilities.But is it?Recently, many challenges, issues, and questions have been raised regarding fair comparisons and recommendations towards good practices for benchmarking metaheuristic algorithms.The aim of this work is not to compare the performance of several well-known algorithms but to investigate the issues that can appear in benchmarking and comparisons of metaheuristics performance (no matter what the problem is).This article studies the impact of a higher evaluation number on a selection of metaheuristic algorithms.We examine the effect of a raised evaluation budget on overall performance, mean convergence, and population diversity of selected swarm algorithms and IEEE CEC competition winners.Even though the final impact varies based on current algorithm selection, it may significantly affect the final verdict of metaheuristics comparison.This work has picked an important benchmarking issue and made extensive analysis, resulting in conclusions and possible recommendations for users working with real engineering optimization problems or researching the metaheuristics algorithms.Especially nowadays, when metaheuristic algorithms are used for increasingly complex optimization problems, and meet machine learning in AutoML frameworks, we conclude that the objective function evaluation budget should be considered another vital optimization input variable.
Manipulation Planning for Object Re-Orientation Based on Semantic Segmentation Keypoint Detection

In this paper, a manipulation planning method for object re-orientation based on semantic segmentation keypoint detection is proposed for robot manipulator which is able to detect and re-orientate the randomly placed objects to a specified position and pose. There are two main parts: (1) 3D keypoint detection system; and (2) manipulation planning system for object re-orientation. In the 3D keypoint detection system, an RGB-D camera is used to obtain the information of the environment and can generate 3D keypoints of the target object as inputs to represent its corresponding position and pose. This process simplifies the 3D model representation so that the manipulation planning for object re-orientation can be executed in a category-level manner by adding various training data of the object in the training phase. In addition, 3D suction points in both the object’s current and expected poses are also generated as the inputs of the next operation stage. During the next stage, Mask Region-Convolutional Neural Network (Mask R-CNN) algorithm is used for preliminary object detection and object image. The highest confidence index image is selected as the input of the semantic segmentation system in order to classify each pixel in the picture for the corresponding pack unit of the object. In addition, after using a convolutional neural network for semantic segmentation, the Conditional Random Fields (CRFs) method is used to perform several iterations to obtain a more accurate result of object recognition. When the target object is segmented into the pack units of image process, the center position of each pack unit can be obtained. Then, a normal vector of each pack unit’s center points is generated by the depth image information and pose of the object, which can be obtained by connecting the center points of each pack unit. In the manipulation planning system for object re-orientation, the pose of the object and the normal vector of each pack unit are first converted into the working coordinate system of the robot manipulator. Then, according to the current and expected pose of the object, the spherical linear interpolation (Slerp) algorithm is used to generate a series of movements in the workspace for object re-orientation on the robot manipulator. In addition, the pose of the object is adjusted on the z-axis of the object’s geodetic coordinate system based on the image features on the surface of the object, so that the pose of the placed object can approach the desired pose. Finally, a robot manipulator and a vacuum suction cup made by the laboratory are used to verify that the proposed system can indeed complete the planned task of object re-orientation.
RandRunner: Distributed Randomness from Trapdoor VDFs with Strong Uniqueness

Generating randomness collectively has been a long standing problem in distributed computing.It plays a critical role not only in the design of state-of-the-art Byzantine faulttolerant (BFT) and blockchain protocols, but also for a range of applications far beyond this field.We present RandRunner, a random beacon protocol with a unique set of guarantees that targets a realistic system model.Our design avoids the necessity of a (BFT) consensus protocol and its accompanying high complexity and communication overhead.We achieve this by introducing a novel extension to verifiable delay functions (VDFs) in the RSA setting that does not require a trusted dealer or distributed key generation (DKG) and only relies on well studied cryptographic assumptions.This design allows RandRunner to tolerate adversarial or failed leaders while guaranteeing safety and liveness of the protocol despite possible periods of asynchrony.
An Open-Source Platform for Human Pose Estimation and Tracking Using a Heterogeneous Multi-Sensor System

Human pose estimation and tracking in real-time from multi-sensor systems is essential for many applications. Combining multiple heterogeneous sensors increases opportunities to improve human motion tracking. Using only a single sensor type, e.g., inertial sensors, human pose estimation accuracy is affected by sensor drift over longer periods. This paper proposes a human motion tracking system using lidar and inertial sensors to estimate 3D human pose in real-time. Human motion tracking includes human detection and estimation of height, skeletal parameters, position, and orientation by fusing lidar and inertial sensor data. Finally, the estimated data are reconstructed on a virtual 3D avatar. The proposed human pose tracking system was developed using open-source platform APIs. Experimental results verified the proposed human position tracking accuracy in real-time and were in good agreement with current multi-sensor systems.
Artificial Intelligence and Cognitive Computing: Methods, Technologies, Systems, Applications and Policy Making

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A White Box Analysis of ColBERT

Transformer-based models are nowadays state-of-the-art in adhoc Information Retrieval, but their behavior are far from being understood. Recent work has claimed that BERT does not satisfy the classical IR axioms. However, we propose to dissect the matching process of ColBERT, through the analysis of term importance and exact/soft matching patterns. Even if the traditional axioms are not formally verified, our analysis reveals that ColBERT (i) is able to capture a notion of term importance; (ii) relies on exact matches for important terms.
Weather prediction using random forest machine learning model

The complex numerical climate models pose a big challenge for scientists in weather predictions, especially for tropical system. This paper is focused on presenting the importance of weather prediction using machine learning (ML) technique. Recently many researchers recommended that the machine learning models can produce sensible weather predictions in spite of having no precise knowledge of atmospheric physics. In this work, global solar radiation (GSR) in MJ/m2/day and wind speed in m/s is predicted for Tamil Nadu, India using a random forest ML model. The random forest ML model is validated with measured wind and solar radiation data collected from IMD, Pune. The prediction results based on the random forest ML model are compared with statistical regression models and SVM ML model. Overall, random forest machine learning model has minimum error values of 0.750 MSE and R2 score of 0.97. Compared to regression models and SVM ML model, the prediction results of random forest ML model are more accurate. Thus, this study neglects the need for an expensive measuring instrument in all potential locations to acquire the solar radiation and wind speed data.
CTC-based Compression for Direct Speech Translation

Previous studies demonstrated that a dynamic phone-informed compression of the input audio is beneficial for speech translation (ST).However, they required a dedicated model for phone recognition and did not test this solution for direct ST, in which a single model translates the input audio into the target language without intermediate representations.In this work, we propose the first method able to perform a dynamic compression of the input in direct ST models.In particular, we exploit the Connectionist Temporal Classification (CTC) to compress the input sequence according to its phonetic characteristics.Our experiments demonstrate that our solution brings a 1.3-1.5 BLEU improvement over a strong baseline on two language pairs (English-Italian and English-German), contextually reducing the memory footprint by more than 10%.
WMNet: A Lossless Watermarking Technique Using Deep Learning for Medical Image Authentication

Traditional watermarking techniques extract the watermark from a suspected image, allowing the copyright information regarding the image owner to be identified by the naked eye or by similarity estimation methods such as bit error rate and normalized correlation. However, this process should be more objective. In this paper, we implemented a model based on deep learning technology that can accurately identify the watermark copyright, known as WMNet. In the past, when establishing deep learning models, a large amount of training data needed to be collected. While constructing WMNet, we implemented a simulated process to generate a large number of distorted watermarks, and then collected them to form a training dataset. However, not all watermarks in the training dataset could properly provide copyright information. Therefore, according to the set restrictions, we divided the watermarks in the training dataset into two categories; consequently, WMNet could learn and identify the copyright information that the watermarks contained, so as to assist in the copyright verification process. Even if the retrieved watermark information was incomplete, the copyright information it contained could still be interpreted objectively and accurately. The results show that the method proposed by this study is relatively effective.
Augmenting Few-Shot Learning With Supervised Contrastive Learning

Few-shot learning deals with a small amount of data which incurs insufficient performance with conventional cross-entropy loss. We propose a pretraining approach for few-shot learning scenarios. That is, considering that the feature extractor quality is a critical factor in few-shot learning, we augment the feature extractor using a contrastive learning technique. It is reported that supervised contrastive learning applied to base class training in transductive few-shot training pipeline leads to improved results, outperforming the state-of-the-art methods on Mini-ImageNet and CUB. Furthermore, our experiment shows that a much larger dataset is needed to retain few-shot classification accuracy when domain-shift degradation exists, and if our method is applied, the need for a large dataset is eliminated. The accuracy gain can be translated to a runtime reduction of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.87\times $ </tex-math></inline-formula> in a resource-constrained environment.
Stereotype and Skew: Quantifying Gender Bias in Pre-trained and Fine-tuned Language Models

This paper proposes two intuitive metrics, skew and stereotype, that quantify and analyse the gender bias present in contextual language models when tackling the WinoBias pronoun resolution task.We find evidence that gender stereotype correlates approximately negatively with gender skew in out-of-the-box models, suggesting that there is a trade-off between these two forms of bias.We investigate two methods to mitigate bias.The first approach is an online method which is effective at removing skew at the expense of stereotype.The second, inspired by previous work on ELMo, involves the fine-tuning of BERT using an augmented gender-balanced dataset.We show that this reduces both skew and stereotype relative to its unaugmented fine-tuned counterpart.However, we find that existing gender bias benchmarks do not fully probe professional bias as pronoun resolution may be obfuscated by cross-correlations from other manifestations of gender prejudice.Our code is available online.
UAVs Trajectory Optimization for Data Pick Up and Delivery with Time Window

Unmanned Aerial Vehicles (UAVs), also known as drones, are a class of aircraft without the presence of pilots on board. UAVs have the ability to reduce the time and cost of deliveries and to respond to emergency situations. Currently, UAVs are extensively used for data delivery and/or collection to/from dangerous or inaccessible sites. However, trajectory planning is one of the major UAV issues that needs to be solved. To address this question, we focus in this paper on determining the optimized routes to be followed by the drones for data pickup and delivery with a time window with an intermittent connectivity network, while also having the possibility to recharge the drones’ batteries on the way to their destinations. To do so, we formulated the problem as a multi-objective optimization problem, and we showed how to use the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to solve this problem. Several experiments were conducted to validate the proposed algorithm by considering different scenarios.
Quantifying Membership Privacy via Information Leakage

Machine learning models are known to memorize the unique properties of individual data points in a training set. This memorization capability can be exploited by several types of attacks to infer information about the training data, most notably, membership inference attacks. In this paper, we propose an approach based on information leakage for guaranteeing membership privacy. Specifically, we propose to use a conditional form of the notion of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">maximal leakage</i> to quantify the information leaking about <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">individual</i> data entries in a dataset, i.e., the entrywise information leakage. We apply our privacy analysis to the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Private Aggregation of Teacher Ensembles</i> (PATE) framework for privacy-preserving classification of sensitive data and prove that the entrywise information leakage of its aggregation mechanism is Schur-concave when the injected noise has a log-concave probability density. The Schur-concavity of this leakage implies that increased consensus among teachers in labeling a query reduces its associated privacy cost. Finally, we derive upper bounds on the entrywise information leakage when the aggregation mechanism uses Laplace distributed noise.
A Token-level Reference-free Hallucination Detection Benchmark for Free-form Text Generation

Large pretrained generative models like GPT-3 often suffer from hallucinating non-existent or incorrect content, which undermines their potential merits in real applications.Existing work usually attempts to detect these hallucinations based on a corresponding oracle reference at a sentence or document level.However ground-truth references may not be readily available for many free-form text generation applications, and sentence-or documentlevel detection may fail to provide the finegrained signals that would prevent fallacious content in real time.As a first step to addressing these issues, we propose a novel token-level, reference-free hallucination detection task and an associated annotated dataset named HADES (HAllucination DEtection dataSet) 1 .To create this dataset, we first perturb a large number of text segments extracted from English language Wikipedia, and then verify these with crowd-sourced annotations.To mitigate label imbalance during annotation, we utilize an iterative model-in-loop strategy.We conduct comprehensive data analyses and create multiple baseline models.
Credit Card Fraud Detection using Deep Learning Techniques

The objective of this paper is to identify credit card fraud and this topic can be solved with the help of advanced machine learning and deep learning techniques.Due to the fact that credit card fraud is a serious worldwide problem, we have chosen to create a model for detecting imposter scams by using deep neural networks.The purpose is to understand, determine and learn the normal behavior of the user and more precisely the detection of identity fraud.Each person has a trading pattern, uses certain operating systems, has a specific time to complete the transaction and spends large amounts of money usually within a certain time range.Transactions made by a certain user have a certain pattern, which can be identified with the help of neural networks.Machine learning involves teaching computers to recognize patterns in data in the same way as our brains do.Deep learning is just a subfield of machine learning that deals with algorithms inspired by the structure and function of the brain.Deep learning at the core is the ability to form higher and higher level of abstractions of representations in data and raw patterns.The data used to train the model is real, and it will be processed using the onehot encoding method, so that categorical data/variables can be used by the machine learning algorithm.
Consistency of empirical Bayes and kernel flow for hierarchical parameter estimation

Gaussian process regression has proven very powerful in statistics, machine learning and inverse problems. A crucial aspect of the success of this methodology, in a wide range of applications to complex and real-world problems, is hierarchical modeling and learning of hyperparameters. The purpose of this paper is to study two paradigms of learning hierarchical parameters: one is from the probabilistic Bayesian perspective, in particular, the empirical Bayes approach that has been largely used in Bayesian statistics; the other is from the deterministic and approximation theoretic view, and in particular the kernel flow algorithm that was proposed recently in the machine learning literature. Analysis of their consistency in the large data limit, as well as explicit identification of their implicit bias in parameter learning, are established in this paper for a Matérn-like model on the torus. A particular technical challenge we overcome is the learning of the regularity parameter in the Matérn-like field, for which consistency results have been very scarce in the spatial statistics literature. Moreover, we conduct extensive numerical experiments beyond the Matérn-like model, comparing the two algorithms further. These experiments demonstrate learning of other hierarchical parameters, such as amplitude and lengthscale; they also illustrate the setting of model misspecification in which the kernel flow approach could show superior performance to the more traditional empirical Bayes approach.
Estimation of BMI from facial images using semantic segmentation based region-aware pooling

Body-Mass-Index (BMI) conveys important information about one's life such as health and socio-economic conditions. Large-scale automatic estimation of BMIs can help predict several societal behaviors such as health, job opportunities, friendships, and popularity. The recent works have either employed hand-crafted geometrical face features or face-level deep convolutional neural network features for face to BMI prediction. The hand-crafted geometrical face feature lack generalizability and face-level deep features don't have detailed local information. Although useful, these methods missed the detailed local information which is essential for exact BMI prediction. In this paper, we propose to use deep features that are pooled from different face regions (eye, nose, eyebrow, lips, etc.) and demonstrate that this explicit pooling from face regions can significantly boost the performance of BMI prediction. To address the problem of accurate and pixel-level face regions localization, we propose to use face semantic segmentation in our framework. Extensive experiments are performed using different Convolutional Neural Network (CNN) backbones including FaceNet and VGG-face on three publicly available datasets: VisualBMI, Bollywood and VIP attributes. Experimental results demonstrate that, as compared to the recent works, the proposed Reg-GAP gives a percentage improvement of 22.4% on VIP-attribute, 3.3% on VisualBMI, and 63.09% on the Bollywood dataset.
Investigation of Different Machine Learning Algorithms to Determine Human Sentiment Using Twitter Data

In recent years, with the advancement of the internet, social media is a promising platform to explore what going on around the world, sharing opinions and personal development. Now, Sentiment analysis, also known as text mining is widely used in the data science sector. It is an analysis of textual data that describes subjective information available in the source and allows an organization to identify the thoughts and feelings of their brand or goods or services while monitoring conversations and reviews online. Sentiment analysis of Twitter data is a very popular research work nowadays. Twitter is that kind of social media where many users express their opinion and feelings through small tweets and different machine learning classifier algorithms can be used to analyze those tweets. In this paper, some selected machine learning classifier algorithms were applied on crawled Twitter data after applying different types of preprocessors and encoding techniques, which ended up with satisfying accuracy. Later a comparison between the achieved accuracies was showed. Experimental evaluations show that the Neural Network Classifier’ algorithm provides a remarkable accuracy of 81.33% compared with other classifiers.