Reviewerly/exHarmony huggingface dataset:
Learning to Generalize for Sequential Decision Making

We consider problems of making sequences of decisions to accomplish tasks, interacting via the medium of language.These problems are often tackled with reinforcement learning approaches.We find that these models do not generalize well when applied to novel task domains.However, the large amount of computation necessary to adequately train and explore the search space of sequential decision making, under a reinforcement learning paradigm, precludes the inclusion of large contextualized language models, which might otherwise enable the desired generalization ability.We introduce a teacher-student imitation learning methodology and a means of converting a reinforcement learning model into a natural language understanding model.Together, these methodologies enable the introduction of contextualized language models into the sequential decision making problem space.We show that models can learn faster and generalize more, leveraging both the imitation learning and the reformulation.Our models exceed teacher performance on various held-out decision problems, by up to 7% on in-domain problems and 24% on out-of-domain problems.
Verifiable Cloud Data Access: Design, Analysis, and Implementation

In this article, we consider the following cloud storage security problem: A user outsources his/her data to a cloud and later frequently updates the data by adding new data items and deleting existing ones. When the user requests an existing data item from the cloud, the cloud may claim that there is no such data item. This could happen due to cloud management issues, hardware/software failures, hackers, or economic incentives, such as saving cloud’s operation costs. The ability to confirm the data existence/nonexistence when accessing a data item on the dynamic outsourced cloud storage is referred to as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dynamic verifiable data access</i> (DVDA). Existing cloud security research works, including encrypted search and proof of storage (i.e., proof of retrievability and proof of data possession), are not sufficient for addressing dynamic verifiable data access. In this article, we present a solution for the dynamic verifiable data access problem. In order to support data dynamics and verifiability, we enhance a hash authentication tree that is build on top of a hash table with a new semantics. With the enhanced hash authentication tree, the proposed protocol supports adding/deleting data in the outsourced storage while satisfying data access verifiability requirement simultaneously. Our open-source experimental evaluation shows that it only takes less than 0.1 ms to verify a data access on a dataset with 400 items using communication cost around 1.7 KB.
PRANK: motion Prediction based on RANKing

Predicting the motion of agents such as pedestrians or human-driven vehicles is one of the most critical problems in the autonomous driving domain. The overall safety of driving and the comfort of a passenger directly depend on its successful solution. The motion prediction problem also remains one of the most challenging problems in autonomous driving engineering, mainly due to high variance of the possible agent's future behavior given a situation. The two phenomena responsible for the said variance are the multimodality caused by the uncertainty of the agent's intent (e.g., turn right or move forward) and uncertainty in the realization of a given intent (e.g., which lane to turn into). To be useful within a real-time autonomous driving pipeline, a motion prediction system must provide efficient ways to describe and quantify this uncertainty, such as computing posterior modes and their probabilities or estimating density at the point corresponding to a given trajectory. It also should not put substantial density on physically impossible trajectories, as they can confuse the system processing the predictions. In this paper, we introduce the PRANK method, which satisfies these requirements. PRANK takes rasterized bird-eye images of agent's surroundings as an input and extracts features of the scene with a convolutional neural network. It then produces the conditional distribution of agent's trajectories plausible in the given scene. The key contribution of PRANK is a way to represent that distribution using nearest-neighbor methods in latent trajectory space, which allows for efficient inference in real time. We evaluate PRANK on the in-house and Argoverse datasets, where it shows competitive results.
QR Code based Library Management System

Library management system from ancient age one of the research area and bar codes are extensively used in most of the modern library management system. This Research project is aimed to develop smart library management system using QR code. QR codes are two dimensional matrix barcodes. QR codes can be widely used in both commercial and institutions with the increased use of smart phones, as smart mobile phones can scan the sensible data. Use of QR code in Library management system is very easy but still in primary stage. Library should try to keep up with digital world with various technologies in this modern age. The importance of library system is because these systems are used as primary resource of information by many peoples. Most of the libraries currently working with large number of persons for daily activities and supervision. QR code based Library management system is very efficient and reliable for Librarian, staff and students. This system will simplify library processes to keep track of all records, and make library more directly accessible to its users. In this system python Qt5 frame work application is being used for College Library database which can be accessed through out the institution. This system works in real time and easy access to information to every member associated with the system. Users must be registered with the system administration after which they can access their accounts and database. It reduces the bourdon on the librarian as it will help to track the books available and issued person for any book and total number of books along with ageing analysis.
Prediction improvement of potential PV production pattern, imagery satellite-based

Abstract The results obtained by using an existing model to estimate global solar radiation (GHI) in three different locations in Tunisia. These data are compared with GHI meteorological measurements and PV_Gis satellite imagery estimation. Some statistical indicators (R, R 2 , MPE, AMPE, MBE, AMBE and RMSE) have been used to measure the performance of the used model. Correlation coefficient for the different stations was close to 1.0. The meteorology and satellite determination coefficient (R 2 ) were also near 1.0 except in the case of Nabeul station in which the meteorology measurements (R) were equals to 0.5848 because of the loss of data in this location due to meteorological conditions. This numerical model provides the best performance according to statistical results in different locations; therefore, this model can be used to estimate global solar radiation in Tunisia. The R square values are used as a statistical indicator to demonstrate that the model’s results are compatible with those of meteorology with a percentage of error less than 10%.
Log-Linear Reformulation of the Noisy Channel Model for Document-Level Neural Machine Translation

We seek to maximally use various data sources, such as parallel and monolingual data, to build an effective and efficient documentlevel translation system.In particular, we start by considering a noisy channel approach (Yu et al., 2020) that combines a target-to-source translation model and a language model.By applying Bayes' rule strategically, we reformulate this approach as a log-linear combination of translation, sentence-level and documentlevel language model probabilities.In addition to using static coefficients for each term, this formulation alternatively allows for the learning of dynamic per-token weights to more finely control the impact of the language models.Using both static or dynamic coefficients leads to improvements over a context-agnostic baseline and a context-aware concatenation model.
Effectiveness of Project-Based Learning in Improving Listening Competency among ESL Learners at a Malaysian TVET College

Listening plays a fundamental role as the first basic skill in English language communication but the skill seems to be neglected in ESL research and classrooms. The limited published studies in the literature reporting on the teaching of listening and the lack of details in the existing studies warrant more attention in finding more effective strategies to teach listening. Thus, the quasiexperimental study sought to ascertain the effectiveness of using project-based learning (PjBL) activities as a teaching strategy in improving the listening competency of ESL learners in a Communicative English course. Data were gathered from 44 students after a 16-week study conducted at a Malaysian TVET college. A PjBL teaching module comprising a number of listening activities was used in the experimental group as a treatment while the control group was taught using the conventional teaching strategy. A listening competency test was administered as pre-test and post-test on both groups. Data were analysed using t-tests, ANOVA and Tukey post hoc test. The study found that the learners in the PjBL group significantly outperformed those in the control group in their listening competency and dialogue listening comprehension with a large effect size. The study is important to increase the understanding of teachers and practitioners in the area of teaching of listening in ESL classrooms.
Active Testing: An Unbiased Evaluation Method for Distantly Supervised Relation Extraction

Distant supervision has been a widely used method for neural relation extraction for its convenience of automatically labeling datasets.However, existing works on distantly supervised relation extraction suffer from the low quality of test set, which leads to considerable biased performance evaluation.These biases not only result in unfair evaluations but also mislead the optimization of neural relation extraction.To mitigate this problem, we propose a novel evaluation method named active testing through utilizing both the noisy test set and a few manual annotations.Experiments on a widely used benchmark show that our proposed approach can yield approximately unbiased evaluations for distantly supervised relation extractors.
A Fragment Fracture Surface Segmentation Method Based on Learning of Local Geometric Features on Margins Used for Automatic Utensil Reassembly

To achieve the automatic reassembly (piecing) of utensil fragments, a fracture surface extraction method based on the learning of local geometric features (core focus) and a utensil reassembly method (secondary focus) are presented in this paper. The steps of the methodological framework are as follows. First, based on obtained 3D models of utensil fragments, a triangle cell descriptor is proposed to describe the geometric features of spatial neighborhoods. Second, a set of feature mapping images (FMIs) is established as a training dataset. Third, after labeling of the ground-truth data, a convolutional neural network (CNN) is trained using the FMIs. Fourth, based on processing to eliminate mislabeled triangle cells, skeletons of the fracture surface margins can be generated. Fifth, a shortcut-based strategy is proposed to eliminate residual triangle cells to extract the fracture surfaces. Sixth, a control-point- and vector-based strategy is proposed to achieve the matching and prealignment of the fracture surfaces. Finally, a cyclic error iteration strategy is designed to assemble the fragments into a holonomic utensil. This learning-based framework is more effective at extracting the key geometric data (fracture surfaces) of utensil fragments than several classical methods. It may also enable a new strategy for 3D graph processing.
A comparison of two popular machine learning frameworks

Using artificial neural networks is an important approach for drawing inferences and making predictions when analyzing large and complex data sets. TensorFlow and PyTorch are two widely-used machin...
Challenging the Adversarial Robustness of DNNs Based on Error-Correcting Output Codes

The existence of adversarial examples and the easiness with which they can be generated raise several security concerns with regard to deep learning systems, pushing researchers to develop suitable defence mechanisms. The use of networks adopting error-correcting output codes (ECOC) has recently been proposed to counter the creation of adversarial examples in a white-box setting. In this paper, we carry out an in-depth investigation of the adversarial robustness achieved by the ECOC approach. We do so by proposing a new adversarial attack specifically designed for multilabel classification architectures, like the ECOC-based one, and by applying two existing attacks. In contrast to previous findings, our analysis reveals that ECOC-based networks can be attacked quite easily by introducing a small adversarial perturbation. Moreover, the adversarial examples can be generated in such a way to achieve high probabilities for the predicted target class, hence making it difficult to use the prediction confidence to detect them. Our findings are proven by means of experimental results obtained on MNIST, CIFAR-10, and GTSRB classification tasks.
CIA_NITT at WNUT-2020 Task 2: Classification of COVID-19 Tweets Using Pre-trained Language Models

This paper presents our models for WNUT 2020 shared task2.The shared task2 involves identification of COVID-19 related informative tweets.We treat this as binary text classification problem and experiment with pretrained language models.Our first model which is based on CT-BERT achieves F1-score of 88.7% and second model which is an ensemble of CT-BERT, RoBERTa and SVM achieves F1-score of 88.52%.
Assessing the Quality of RDF Mappings with EvaMap

Linked Data (LD) is a set of best practices to publish reusable data on the web in RDF format. Despite the benefits of LD, many datasets are not published as RDF. Transforming structured datasets into RDF datasets is possible thanks to RDF Mappings. But, for the same dataset, different mappings can be proposed. We believe that a tool capable of evaluating the quality of an RDF mapping would make the creation of mappings easier. In this paper, we present EvaMap, a framework to assess the quality of RDF mappings. The demonstration shows how EvaMap can be used to evaluate and improve RDF mappings.
Detecting Hands and Recognizing Physical Contact in the Wild

We investigate a new problem of detecting hands and recognizing their physical contact state in unconstrained conditions. This is a challenging inference task given the need to reason beyond the local appearance of hands. The lack of training annotations indicating which object or parts of an object the hand is in contact with further complicates the task. We propose a novel convolutional network based on Mask-RCNN that can jointly learn to localize hands and predict their physical contact to address this problem. The network uses outputs from another object detector to obtain locations of objects present in the scene. It uses these outputs and hand locations to recognize the hand's contact state using two attention mechanisms. The first attention mechanism is based on the hand and a region's affinity, enclosing the hand and the object, and densely pools features from this region to the hand region. The second attention module adaptively selects salient features from this plausible region of contact. To develop and evaluate our method's performance, we introduce a large-scale dataset called ContactHands, containing unconstrained images annotated with hand locations and contact states. The proposed network, including the parameters of attention modules, is end-to-end trainable. This network achieves approximately 7\% relative improvement over a baseline network that was built on the vanilla Mask-RCNN architecture and trained for recognizing hand contact states.
They Are Not All Alike: Answering Different Spatial Questions Requires Different Grounding Strategies

In this paper, we study the grounding skills required to answer spatial questions asked by humans while playing the GuessWhat?! game.We propose a classification for spatial questions dividing them into absolute, relational, and group questions.We build a new answerer model based on the LXMERT multimodal transformer and we compare a baseline with and without visual features of the scene.We are interested in studying how the attention mechanisms of LXMERT are used to answer spatial questions since they require putting attention on more than one region simultaneously and spotting the relation holding among them.We show that our proposed model outperforms the baseline by a large extent (9.70% on spatial questions and 6.27% overall).By analyzing LXMERT errors and its attention mechanisms, we find that our classification helps to gain a better understanding of the skills required to answer different spatial questions.
Probabilistic Symbolic Analysis of Neural Networks

Neural networks are powerful tools for automated decision-making, with applications ranging from image recognition to hiring decisions and safety-critical autonomous driving. However, due to their black-box nature and large scale, reasoning about their behavior is challenging. Statistical analysis is often used to infer probabilistic properties of a network, such as its robustness to noise and inaccurate inputs or the fairness of its decisions. While scalable, statistical methods can only provide probabilistic guarantees on the quality of their results and may underestimate the impact of low probability inputs leading to undesired behavior of the network. In this paper, we investigate the use of symbolic analysis and constraint solution space quantification to precisely quantify probabilistic properties in neural networks. We collect symbolic constraints corresponding to the network's response to concrete inputs, while efficiently rejecting inputs whose responses have been seen before. We further propose a quantification procedure for the collected constraints, producing arbitrarily tight, sound interval bounds on the estimated probabilities. The proposed approach is an anytime algorithm, increasing in precision with more paths explored. We implemented our approach in SpaceScanner and demonstrate its potential in analyzing fairness, robustness, and sensitivity properties of neural networks.
A Semantics-based Approach to Disclosure Classification in User-Generated Online Content

As users engage in public discourse, the rate of voluntarily disclosed personal information has seen a steep increase.So-called self-disclosure can result in a number of privacy concerns.Users are often unaware of the sheer amount of personal information they share across online forums, commentaries, and social networks, as well as the power of modern AI to synthesize and gain insights from this data.This paper presents an approach to detect emotional and informational self-disclosure in natural language.We hypothesize that identifying frame semantics can meaningfully support this task.Specifically, we use Semantic Role Labeling to identify the lexical units and their semantic roles that signal self-disclosure.Experimental results on Reddit data show the performance gain of our method when compared to standard text classification methods based on BiLSTM, and BERT.In addition to improved performance, our approach provides insights into the drivers of disclosure behaviors.
Compressing Images by Encoding Their Latent Representations with Relative Entropy Coding

Variational Autoencoders (VAEs) have seen widespread use in learned image compression. They are used to learn expressive latent representations on which downstream compression methods can operate with high efficiency. Recently proposed 'bits-back' methods can indirectly encode the latent representation of images with codelength close to the relative entropy between the latent posterior and the prior. However, due to the underlying algorithm, these methods can only be used for lossless compression, and they only achieve their nominal efficiency when compressing multiple images simultaneously; they are inefficient for compressing single images. As an alternative, we propose a novel method, Relative Entropy Coding (REC), that can directly encode the latent representation with codelength close to the relative entropy for single images, supported by our empirical results obtained on the Cifar10, ImageNet32 and Kodak datasets. Moreover, unlike previous bits-back methods, REC is immediately applicable to lossy compression, where it is competitive with the state-of-the-art on the Kodak dataset.
Adapting NMT to caption translation in Wikimedia Commons for low-resource languages

The research leading to these results was carried out as part of the TADEEP project (Spanish Ministry of Economy and Competitiveness TIN2015-70214-P, with FEDER funding). This work has been supported by the ADAPT Centre for Digital Content Technology which is funded under the SFI Research Centres Programme (Grant 13/RC/2106) and is co-funded under the European Regional Development Fund.
Face Recognition with Convolutional Neural Network

Biometrics is a framework in which we used to perceive human based on its physical or conduct attributes. Face recognition has been a dynamic research zone in the example recognition and PC vision spaces. Each face in this world has uniqueness. Accordingly, it is a character of human. This personality because of its uniqueness can be utilized for validation and access control in various applications. In this survey paper the face recognition and facial feature is a key territory of examination. Hence this paper incorporates the study of various as of late created face recognition system and techniques which are professed to give a powerful and exact strategy for face recognition. What's more of that another model for perceiving face is likewise presented in this paper. That model is actualized in not so distant future and their execution is contrasted and comparative methodology.
Distributed Genetic Algorithms for Low-Power, Low-Cost and Small-Sized Memory Devices

This work presents a strategy to implement a distributed form of genetic algorithm (GA) on low power, low cost, and small-sized memory aiming for increased performance and reduction of energy consumption when compared to standalone GAs. This strategy focuses on making a distributed version of GA feasible to run as a low cost and a low power consumption embedded system utilizing devices such as 8-bit microcontrollers (µCs) and Serial Peripheral Interface (SPI) for data transmission between those devices. Details about how the distributed GA was designed from a previous standalone implementation made by the authors and how the project is structured are presented. Furthermore, this work investigates the implementation limitations and shows results about its proper operation, most of them collected with the Hardware-In-Loop (HIL) technique, and resource consumption such as memory and processing time. Finally, some scenarios are analyzed to identify where this distributed version can be utilized and how it is compared to the single-node standalone implementation in terms of performance and energy consumption.