Reviewerly/exHarmony huggingface dataset:
Handling Missing Observations with an RNN-based Prediction-Update Cycle

In tasks such as tracking, time-series data inevitably carry missing observations. While traditional tracking approaches can handle missing observations, recurrent neural networks (RNNs) are designed to receive input data in every step. Furthermore, current solutions for RNNs, like omitting the missing data or data imputation, are not sufficient to account for the resulting increased uncertainty. Towards this end, this paper introduces an RNN-based approach that provides a full temporal filtering cycle for motion state estimation. The Kalman filter inspired approach enables to deal with missing observations and outliers. For providing a full temporal filtering cycle, a basic RNN is extended to take observations and the associated belief about its accuracy into account for updating the current state. An RNN prediction model, which generates a parametrized distribution to capture the predicted states, is combined with an RNN update model, which relies on the prediction model output and the current observation. By providing the model with masking information, binary-encoded missing events, the model can overcome limitations of standard techniques for dealing with missing input values. The model abilities are demonstrated on synthetic data reflecting prototypical pedestrian tracking scenarios.
Using Sampling to Estimate and Improve Performance of Automated Scoring Systems with Guarantees

Automated Scoring (AS), the natural language processing task of scoring essays and speeches in an educational testing setting, is growing in popularity and being deployed across contexts from government examinations to companies providing language proficiency services. However, existing systems either forgo human raters entirely, thus harming the reliability of the test, or score every response by both human and machine thereby increasing costs. We target the spectrum of possible solutions in between, making use of both humans and machines to provide a higher quality test while keeping costs reasonable to democratize access to AS. In this work, we propose a combination of the existing paradigms, sampling responses to be scored by humans intelligently. We propose reward sampling and observe significant gains in accuracy (19.80% increase on average) and quadratic weighted kappa (QWK) (25.60% on average) with a relatively small human budget (30% samples) using our proposed sampling. The accuracy increase observed using standard random and importance sampling baselines are 8.6% and 12.2% respectively. Furthermore, we demonstrate the system's model agnostic nature by measuring its performance on a variety of models currently deployed in an AS setting as well as pseudo models. Finally, we propose an algorithm to estimate the accuracy/QWK with statistical guarantees (Our code is available at https://git.io/J1IOy).
National Sport Institute Case: Automated Data Migration Using Talend Open Studio with ‘Trickle Approach’

In the current era of globalization, old systems are replaced or improved daily. Before the transition happens, IT experts need to carefully plan the best course of action to move the massive amount of data from old to new systems. If there are flaws unnoticed, the data may be lost during the migration process where it will feed unreliable data to the end-users. Recently, National Sports Institute had launched a new system the National Coaching Academy System where it was outsourced and developed by a vendor. Unfortunately, the system cannot be used immediately because there are no data from the old system in its database. This kind of problem occurs when data migration is not considered during the development phase of a new system. Because of this, the staff need to manually navigate between the two old and new systems to obtain accurate data of their coaches. Thus, in this paper, we will discuss the process that needs to be analyzed and what are the best strategies to solve the problem. The result should be able to increase the efficiency and make it easier for the staff when working with the coach’s data.
Improved CNN-based Path Planning So an Autonomous UAV Can Climb Stairs By using a LiDAR Sensor

Unmanned aerial vehicles (UAVs) have tremendous potential in civil and public areas. These are especially beneficial in applications where human lives are threatened. Autonomous navigation in unknown environments is a challenging issue for UAVs where decision-based navigation is required. In this paper, a deep learning (DL) approach is presented that aids autonomous navigation for UAVs in completely unknown, GPS-denied indoor environments. The UAV is equipped with a monocular camera and a light detection and ranging (LiDAR) sensor to determine each next maneuver and distance calculation, respectively. For deeper feature extraction, a version of You Only Look Once (YOLOv3-tiny) is improved by adding a convolution layer with different filter sizes. The process is observed as an exercise where the DL model classifies the targeted image as stairs or not stairs. We created our dataset considering the indoor scenario for specific implementation. Comprehensive experimental results are compared with YOLOv3-tiny, indicating better performance in terms of accuracy, recall, F1-score, precision, and maneuvering movements.
A Hybrid Trust Model for Cloud Service Provider Selection with NLP Support and Malicious User Feedback Filtering

With the rapid development of Cloud Computing and the increase in Cloud Service Providers(Csp),there is an immense rise in the cloud services provided to the users. With a mass choice of CSPs, trust has become a major concern for both CSPs and the Cloud Service Users(CSU). It plays an important role in evaluating cloud services and helps in the robust selection of CSPs without compromising security, performance, and privacy. Research has been done in this field that talks about one-way trust between CSPs and CSUs but little to no work has been done which includes bi-directional trust in the same model. We propose a Trust model for cloud service selection that considers the bi-directional trust i.e., trust based on user requirements and user feedback, making it more reliable in real-life scenarios. An NLP approach that extracts requirement values from both text and numerical input and a Malicious Feedback Filtering Mechanism are proposed.
Power Terminal Data Security and Efficient Management Mechanism Based on Master-Slave Blockchain

How to realize the safe and efficient use of power terminal equipment data is a key research content to enhance the value of smart grid data. In order to solve this problem, based on the master-slave blockchain theory, this paper designs an intelligent terminal data management system architecture based on the master-slave blockchain. The architecture includes three modules: master blockchain, slave blockchain, and system service interface. In order to improve the security of data in the process of data use, this paper conducts research from the two dimensions of data security storage and data access control, and proposes a data security storage mechanism and a data access control mechanism. In order to improve the efficiency of data query when the amount of data is large, this paper adopts the grouping bloom filtering strategy and the B+ tree index strategy to reduce the number of data queries and improve the efficiency of data query. In the experimental part, from the performance analysis of the improved Bloom filter based on grouping and the performance analysis of the block query mechanism based on the B+ tree index, it is verified that the data usage mechanism proposed in this paper has high block creation and data query efficiency.
Studying word order through iterative shuffling

As neural language models approach human performance on NLP benchmark tasks, their advances are widely seen as evidence of an increasingly complex understanding of syntax. This view rests upon a hypothesis that has not yet been empirically tested: that word order encodes meaning essential to performing these tasks. We refute this hypothesis in many cases: in the GLUE suite and in various genres of English text, the words in a sentence or phrase can rarely be permuted to form a phrase carrying substantially different information. Our surprising result relies on inference by iterative shuffling (IBIS), a novel, efficient procedure that finds the ordering of a bag of words having the highest likelihood under a fixed language model. IBIS can use any black-box model without additional training and is superior to existing word ordering algorithms. Coalescing our findings, we discuss how shuffling inference procedures such as IBIS can benefit language modeling and constrained generation.
SIMILAR: Submodular Information Measures Based Active Learning In Realistic Scenarios

Active learning has proven to be useful for minimizing labeling costs by selecting the most informative samples. However, existing active learning methods do not work well in realistic scenarios such as imbalance or rare classes, out-of-distribution data in the unlabeled set, and redundancy. In this work, we propose SIMILAR (Submodular Information Measures based actIve LeARning), a unified active learning framework using recently proposed submodular information measures (SIM) as acquisition functions. We argue that SIMILAR not only works in standard active learning, but also easily extends to the realistic settings considered above and acts as a one-stop solution for active learning that is scalable to large real-world datasets. Empirically, we show that SIMILAR significantly outperforms existing active learning algorithms by as much as ~5% - 18% in the case of rare classes and ~5% - 10% in the case of out-of-distribution data on several image classification tasks like CIFAR-10, MNIST, and ImageNet.
Diagnosing the First-Order Logical Reasoning Ability Through LogicNLI

Recently, language models (LMs) have achieved significant performance on many NLU tasks, which has spurred widespread interest for their possible applications in the scientific and social area.However, LMs have faced much criticism of whether they are truly capable of reasoning in NLU.In this work, we propose a diagnostic method for first-order logic (FOL) reasoning with a new proposed benchmark, LogicNLI.LogicNLI is an NLI-style dataset that effectively disentangles the target FOL reasoning from commonsense inference and can be used to diagnose LMs from four perspectives: accuracy, robustness, generalization, and traceability.Experiments on BERT, RoBERTa, and XLNet, have uncovered the weaknesses of these LMs on FOL reasoning, which motivates future exploration to enhance the reasoning ability.
The Effect of Intrinsic Motivation on Students’ Improvement of English Speaking Skill

The objectives of the study is as follows to know the students’ intrinsic motivation in speaking English Skill, a case of the second grade of MA Futuhiyyah 2 Demak in the Academic Year of 2020/2021, to know the speaking skill is a case study of the second grade of MA Futuhiyyah 2 demak in the academic year of 2020/2021, to describe the effect of the students’ intrinsic motivation to improve English speaking skill, a case of the second grade of MA Futuhiyyah 2 demak in the academic year of 2020/2021.
Dynamic Modeling and Active Disturbance Rejection Control of Parallel Aerial Manipulator System

In this paper, a 6-DOF Stewart parallel manipulator is installed on a quad-rotor unmanned aerial vehicle in order to solve the problems of low flexibility and difficulty in high precision aerial operations. Based on the reaction force of telescopic legs, the dynamics model of the aerial manipulator system is derived by Newton Euler method. Considering the dynamic influence of the manipulator motion on the quad-rotor, a cascade PID controller is designed for the stabilization control and trajectory tracking control, and the linear quadratic regulator (LQR) method is used for parameter tuning. Based on the cascade structure, the active disturbance rejection controller is designed for the velocity loop and the angular velocity loop, the stability of the system is also analyzed. The results of simulation experiments show that the active disturbance rejection controller can effectively overcome the dynamic influence of the parallel aerial manipulator and obtain good performance.
Knowledge-Based Approach for the Perception Enhancement of a Vehicle

An autonomous vehicle relies on sensors in order to perceive its surroundings. However, there are multiple causes that would hinder a sensor’s proper functioning, such as bad weather or lighting conditions. Studies have shown that rainfall and fog lead to a reduced visibility, which is one of the main causes of accidents. This work proposes the use of a drone in order to enhance the vehicle’s perception, making use of both embedded sensors and its advantageous 3D positioning. The environment perception and vehicle/Unmanned Aerial Vehicle (UAV) interactions are managed by a knowledge base in the form of an ontology, and logical rules are used in order to detect and infer the environmental context and UAV management. The model was tested and validated in a simulation made on Unity.
Enlivening Redundant Heads in Multi-head Self-attention for Machine Translation

Multi-head self-attention recently attracts enormous interest owing to its specialized functions, significant parallelizable computation, and flexible extensibility.However, very recent empirical studies show that some selfattention heads make little contribution and can be pruned as redundant heads.This work takes a novel perspective of identifying and then vitalizing redundant heads.We propose a redundant head enlivening (RHE) method to precisely identify redundant heads, and then vitalize their potential by learning syntactic relations and prior knowledge in text without sacrificing the roles of important heads.Two novel syntax-enhanced attention (SEA) mechanisms: a dependency mask bias and a relative local-phrasal position bias, are introduced to revise self-attention distributions for syntactic enhancement in machine translation.The importance of individual heads is dynamically evaluated during the redundant heads identification, on which we apply SEA to vitalize redundant heads while maintaining the strength of important heads.Experimental results on WMT14 and WMT16 English→German and English→Czech language machine translation validate the RHE effectiveness.
Improved prediction error expansion and mirroring embedded samples for enhancing reversible audio data hiding

<h2>Abstract</h2> Many applications work by processing either small or big data, including sensitive and confidential ones, through computer networks like cloud computing. However, many systems are public and may not provide enough security mechanisms. Meanwhile, once the data are compromised, the security and privacy of the users will suffer from serious problems. Therefore, security protection is much required in various aspects, and one of how it is done is by embedding the data (payload) in another form of data (cover) such as audio. However, the existing methods do not provide enough space to accommodate the payload, so bigger data can not be taken; the quality of the respective generated data is relatively low, making it much different from its corresponding cover. This research works on these problems by improving a prediction error expansion-based algorithm and designing a mirroring embedded sample scheme. Here, a processed audio sample is forced to be as close as possible to the original one. The experimental results show that this proposed method produces a higher quality of stego data considering the size of the payloads. It achieves more than 100 dB, which is higher than that of the compared algorithms. Additionally, this proposed method is reversible, which means that both the original payload and the audio cover can be fully reconstructed.
A Survey-driven Feature Model for Software Traceability Approaches

Abstract Traceability is the capability to represent, understand and analyze the relationships between software artefacts. Traceability is at the core of many software engineering activities. This is a blessing in disguise as traceability research is scattered among various research subfields, which impairs a global view and integration of the different innovations around the recording, identification, evaluation and management of traces. This also limits the adoption of traceability solutions in industry. In this sense, the goal of this paper is to present a characterization of the traceability mechanism as a feature model depicting the shared and variable elements in any traceability proposal. The features in the model are derived from a survey of papers related to traceability published in the literature. We believe this feature model is useful to assess and compare different proposals and provide a common terminology and background. Beyond the feature model, the survey we conducted also help us to identify a number of challenges to be solved in order to move traceability forward, especially in a context where, due to the increasing importance of AI techniques in Software Engineering, traces are more important than ever in order to be able to reproduce and explain AI decisions.
Why Spectral Normalization Stabilizes GANs: Analysis and Improvements

Spectral normalization (SN) is a widely-used technique for improving the stability of Generative Adversarial Networks (GANs) by forcing each layer of the discriminator to have unit spectral norm. This approach controls the Lipschitz constant of the discriminator, and is empirically known to improve sample quality in many GAN architectures. However, there is currently little understanding of why SN is so effective. In this work, we show that SN controls two important failure modes of GAN training: exploding and vanishing gradients. Our proofs illustrate a (perhaps unintentional) connection with the successful LeCun initialization technique, proposed over two decades ago to control gradients in the training of deep neural networks. This connection helps to explain why the most popular implementation of SN for GANs requires no hyperparameter tuning, whereas stricter implementations of SN have poor empirical performance out-of-the-box. Unlike LeCun initialization which only controls gradient vanishing at the beginning of training, we show that SN tends to preserve this property throughout training. Finally, building on this theoretical understanding, we propose Bidirectional Spectral Normalization (BSN), a modification of SN inspired by Xavier initialization, a later improvement to LeCun initialization. Theoretically, we show that BSN gives better gradient control than SN. Empirically, we demonstrate that BSN outperforms SN in sample quality on several benchmark datasets, while also exhibiting better training stability.
An Introduction to Mobile Data Visualization

In this chapter, several characteristics are proposed that help us identify and describe the scope of mobile data visualization, which stretches beyond an intuitive understanding of the term. The focus lies on those characteristics that, particularly in their extremes, differentiate mobile data visualization from other forms of data visualization. These characteristics give rise to dimensions of a design space for mobile data visualization, against which instances may be classified and positioned. The chapter discusses a number of examples to illustrate how the design space makes it possible to describe and compare mobile visualizations.
Image-based Animal Recognition based on Transfer Learning

Automatic image-based recognition systems have been widely used to solve different computer vision tasks. In particular, animals' identification in farms is a research field of interest for the computer vision and the agriculture community. It is then necessary to develop robust and precise algorithms to support detection, recognition, and monitoring tasks to enhance farm management. Traditionally, deep learning approaches have been proposed to solve image-based detection tasks. Nonetheless, databases holding many instances are required to achieve competitive performances, not mentioning the hyperparameters tuning issues. In this paper, we propose a transfer learning approach for image-based animal recognition. We enhance a pre-trained Convolutional Neural Network model for animal classification from noisy and low-quality images. First, a dog vs. cat task is tested from the well-known CIFAR database. Further, a cow vs. no cow database is built to test our transfer learning approach. The achieved results show competitive classification performance using different types of architectures compared to state-of-the-art methodologies.
A Two Stage Task Scheduler for Effective Load Optimization in Cloud – FoG Architectures

In recent times, computing technologies have moved over to a new dimension with the advent of cloud platforms which provide seamless rendering of required services to consumers either in static or dynamic state. In addition, the nature of data being handled in today’s scenario has also become sophisticated as mostly real time data acquisition systems equipped with High-Definition capture (HD) have become common. Lately, cloud systems have also become prone to computing overheads owing to huge volume of data being imparted on them especially in real time applications. To assist and simplify the computational complexity of cloud systems, FoG platforms are being integrated into cloud interfaces to streamline and provide computing at the edge nodes rather at the cloud core processors, thus accounting for reduction of load overhead on cloud core processors. This research paper proposes a Two Stage Load Optimizer (TSLO) implemented as a double stage optimizer with one being deployed at FoG level and the other at the Cloud level. The computational complexity analysis is extensively done and compared with existing benchmark methods and superior performance of the suggested method is observed and reported.