Reviewerly/exHarmony huggingface dataset:
Learning to Recommend Frame for Interactive Video Object Segmentation in the Wild

This paper proposes a framework for the interactive video object segmentation (VOS) in the wild where users can choose some frames for annotations iteratively. Then, based on the user annotations, a segmentation algorithm refines the masks. The previous interactive VOS paradigm selects the frame with some worst evaluation metric, and the ground truth is required for calculating the evaluation metric, which is impractical in the testing phase. In contrast, in this paper, we advocate that the frame with the worst evaluation metric may not be exactly the most valuable frame that leads to the most performance improvement across the video. Thus, we formulate the frame selection problem in the interactive VOS as a Markov Decision Process, where an agent is learned to recommend the frame under a deep reinforcement learning framework. The learned agent can automatically determine the most valuable frame, making the interactive setting more practical in the wild. Experimental results on the public datasets show the effectiveness of our learned agent without any changes to the underlying VOS algorithms. Our data, code, and models are available at https://github.com/svip-lab/IVOS-W.
Foreign direct investment in sub-Saharan Africa: Beyond its growth effect

This study relates foreign direct investment (FDI) to economic growth, institutional quality and manufacturing value added, using panel data techniques that allow for parameter heterogeneity and non-stationarity. The results confirm that economic growth, institutional quality, and natural resources, each play a positive role in attracting FDI. Besides, institutional quality is itself affected by these variables: economic growth enhances institutional quality, FDI is detrimental to certain aspects. The evidence also reveals the existence of 'institutional' resource curse. Furthermore, FDI has contributed to the 'premature' deindustrialization of the region, except in a few cases where it is non-resource seeking. While confirming established results, the study also identifies detrimental institutional and deindustrializing effects of FDI which have hitherto been overlooked. A policy implication is that countries should be selective on the type of FDI they try to attract by weighing its positive growth effect against its deindustrializing and adverse institutional effects.
Estimation of daily global solar radiation based on MODIS satellite measurements: The case study of Adrar region (Algeria)

In solar energy studies, solar irradiance data at a given site are a fundamental input for reliably predicting performance behavior and the potential of solar applications. Unfortunately, this parameter is not measured in many parts of the world, especially in developing nations, because of the lack of measuring facilities. In this paper, an alternative method for resource assessment is proposed and evaluated. In the proposed method, a model satellite-derived solar irradiance technique was implemented to estimate daily global solar irradiance using satellite earth observation data, particularly products derived from NASA's MODerate‐resolution Imaging Spectroradiometer (MODIS). The estimation results were validated with the observed data collected in the Renewable Energy Research Unit in the Saharan Region of Adrar (URERMS) over a three-year period. The effects of cloud cover and solar zenith angle on the daily mean global solar irradiance are studied. The accuracy of the model is evaluated with various statistical parameters (RMSE, R, MBE, MAE). The results show that the correlation between the estimated and measured values of global solar irradiance is significant, and the effect of cloud and solar zenith angle on the estimation is confirmed.
Collaborative Image and Object Level Features for Image Colourisation

Image colourisation is an ill-posed problem, with multiple correct solutions which depend on the context and object instances present in the input datum. Previous approaches attacked the problem either by requiring intense user-interactions or by exploiting the ability of convolutional neural networks (CNNs) in learning image-level (context) features. However, obtaining human hints is not always feasible and CNNs alone are not able to learn entity-level semantics, unless multiple models pre-trained with supervision are considered. In this work, we propose a single network, named UCapsNet, that takes into consideration the image-level features obtained through convolutions and entity-level features captured by means of capsules. Then, by skip connections over different layers, we enforce collaboration between such the convolutional and entity factors to produce a high-quality and plausible image colourisation. We pose the problem as a classification task that can be addressed by a fully unsupervised approach, thus requires no human effort. Experimental results on three benchmark datasets show that our approach outperforms existing methods on standard quality metrics and achieves state-of-the-art performances on image colourisation. A large scale user study shows that our method is preferred over existing solutions. Code available at https://github.com/Riretta/Image_Colourisation_WiCV_2021.
Tensor Programs I: Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processes

Wide neural networks with random weights and biases are Gaussian processes, as originally observed by Neal (1995) and more recently by Lee et al. (2018) and Matthews et al. (2018) for deep fully-connected networks, as well as by Novak et al. (2019) and Garriga-Alonso et al. (2019) for deep convolutional networks. We show that this Neural Network-Gaussian Process correspondence surprisingly extends to all modern feedforward or recurrent neural networks composed of multilayer perceptron, RNNs (e.g. LSTMs, GRUs), (nD or graph) convolution, pooling, skip connection, attention, batch normalization, and/or layer normalization. More generally, we introduce a language for expressing neural network computations, and our result encompasses all such expressible neural networks. This work serves as a tutorial on the *tensor programs* technique formulated in Yang (2019) and elucidates the Gaussian Process results obtained there. We provide open-source implementations of the Gaussian Process kernels of simple RNN, GRU, transformer, and batchnorm+ReLU network at this http URL.
Sales Monitoring Information System at Mojokopi Coffee Shop Using Website-Based Moving Average Method

Mojokopi is a small coffee shop engaged in the sale of various kinds of coffee products such as milk coffee, Toraja coffee, Flores coffee and others. In the coffee sale transaction at Mojokopi, there is a flow where the buyer must first see the list of menus available at the coffee shop, then the seller will record the order into the book according to the buyer's order, if the order is complete, the buyer will pay according to what the buyer ordered. Of course, it really needs a system to be able to simplify the process of transactions so that the data will be faster and safer than the bookkeeping process. The mojokopi coffee shop sales monitoring information system based on the website can process transaction data so as to produce information in monitoring sales reports without having to open documents, as well as using the application of sales forecasting to get sales prediction results. This website-based monitoring information system conducts interviews and direct observations as a method of data collection. It is designed using the PHP and MYSQL programming languages. With the existence of a monitoring information system, existing problems from monitoring sales can be resolved, therefore a website-based information system is designed.
How important is data quality? Best classifiers vs best features

The task of choosing the appropriate classifier for a given scenario is not an easy-to-solve question. First, there is an increasingly high number of algorithms available belonging to different families. And also there is a lack of methodologies that can help on recommending in advance a given family of algorithms for a certain type of datasets. Besides, most of these classification algorithms exhibit a degradation in the performance when faced with datasets containing irrelevant and/or redundant features. In this work we analyze the impact of feature selection in classification over several synthetic and real datasets. The experimental results obtained show that the significance of selecting a classifier decreases after applying an appropriate preprocessing step and, not only this alleviates the choice, but it also improves the results in almost all the datasets tested.
Rationally Inattentive Path-Planning via RRT

We consider a path-planning scenario for a mobile robot traveling in a configuration space with obstacles under the presence of stochastic disturbances. A novel path length metric is proposed on the uncertain configuration space and then integrated with the existing RRT* algorithm. The metric is a weighted sum of two terms which capture both the Euclidean distance traveled by the robot and the perception cost, i.e., the amount of information the robot must perceive about the environment to follow the path safely. The continuity of the path length function with respect to the topology of the total variation metric is shown and the optimality of the Rationally Inattentive RRT* algorithm is discussed. Three numerical studies are presented which display the utility of the new algorithm.
Trade off-free entanglement stabilization in a superconducting qutrit-qubit system

Quantum reservoir engineering is a powerful framework for autonomous quantum state preparation and error correction. However, traditional approaches to reservoir engineering are hindered by unavoidable coherent leakage out of the target state, which imposes an inherent trade off between achievable steady-state state fidelity and stabilization rate. In this work we demonstrate a protocol that achieves trade off-free Bell state stabilization in a qutrit-qubit system realized on a circuit-QED platform. We accomplish this by creating a purely dissipative channel for population transfer into the target state, mediated by strong parametric interactions coupling the second-excited state of a superconducting transmon and the engineered bath resonator. Our scheme achieves a state preparation fidelity of 84% with a stabilization time constant of 339 ns, leading to a 54 ns error-time product in a solid-state quantum information platform.
Permutation polynomials of the form x + L(x) over <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:msub><mml:mrow><mml:mi mathvariant="double-struck">F</mml:mi></mml:mrow><mml:mrow><mml:msup><mml:mrow><mml:mi>q</mml:mi></mml:mrow><mml:mrow><mml:mn>3</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:msub></mml:math>

In this paper, permutation polynomials of the form xd+L(xs) over finite fields Fq3 are investigated, where q=2m and m is a positive integer. By means of the iterative method, the multivariate method and the resultant elimination, six classes of permutation trinomials are obtained from certain integers d, s and linearized polynomials L(x). It is also showed that the permutations proposed in this paper are quasi-multiplicative inequivalent to all known permutation trinomials over Fq3.
From Agile Ground to Aerial Navigation: Learning from Learned Hallucination

This paper presents a self-supervised Learning from Learned Hallucination (LfLH) method to learn fast and reactive motion planners for ground and aerial robots to navigate through highly constrained environments. The recent Learning from Hallucination (LfH) paradigm for autonomous navigation executes motion plans by random exploration in completely safe obstacle-free spaces, uses hand-crafted hallucination techniques to add imaginary obstacles to the robot’s perception, and then learns motion planners to navigate in realistic, highly-constrained, dangerous spaces. However, current hand-crafted hallucination techniques need to be tailored for specific robot types (e.g., a differential drive ground vehicle), and use approximations heavily dependent on certain assumptions (e.g., a short planning horizon). In this work, instead of manually designing hallucination functions, LfLH learns to hallucinate obstacle configurations, where the motion plans from random exploration in open space are optimal, in a self-supervised manner. LfLH is robust to different robot types and does not make assumptions about the planning horizon. Evaluated in both simulated and physical environments with a ground and an aerial robot, LfLH outperforms or performs comparably to previous hallucination approaches, along with sampling- and optimization-based classical methods.
Multiple Style Transfer Via Variational Autoencoder

Modern works on style transfer focus on transferring style from a single image. Recently, some approaches study multiple style transfer; these, however, are either too slow or fail to mix multiple styles. We propose ST-VAE, a Variational AutoEncoder for latent space-based style transfer. It performs multiple style transfer by projecting nonlinear styles to a linear latent space, enabling to merge styles via linear interpolation before transferring the new style to the content image. To evaluate ST-VAE, we experiment on COCO for single and multiple style transfer. We also present a case study revealing that ST-VAE outperforms other methods while being faster, flexible, and setting a new path for multiple style transfer.
A Model of Software Prototyping based on a Systematic Map

Background: Prototyping is an established practice for user interface design and for requirements engineering within agile software development, even so there is a lack of theory on prototyping. Aims: The main research objective is to provide a means to categorise prototyping instances, in order to enable comparison and reflection of prototyping practices. Method: We have performed a systematic mapping study of methodological aspects of prototyping consisting of thirty-three primary studies upon which we designed a model of prototyping that was validated through a focus group at a case company. Results: Our model consists of four aspects of prototyping, namely purpose, prototype scope, prototype use, and exploration strategy. This model supported the focus group participants in discussing prototyping practices by considering concrete prototyping instances in terms of the concepts provided by our model. Conclusions: The model can be used to categorise prototyping instances and can support practitioners in reflecting on their prototyping practices. Our study provides a starting point for further research on prototyping and into how the practice can be applied more cost-effectively to elicit, validate, and communicate requirements.
The State of the Art in Machine Learning-Based Digital Forensics

Digital forensics of visual-based evidence from video surveillance systems and forensic photographs holds object detection as a key aspect of the process. Recognizing an instance of object classes over a wide range of image data using computational techniques is one of the areas that has gained continuous attention over the years due to their numerous practical applications. Several algorithms and techniques have been specified for object detection and recognition with Machine Learning gaining more prominence and ensuring the remarkable performance of object detection and recognition systems. This study presents a comprehensive review of the frameworks and applications of Machine Learning in object detection and classification with particular applications to Digital Forensics. The analysis covers a wide range of publications between 2007 and 2019 available in different indexed and non-indexed databases and the candidate papers were selected using certain exclusion criteria proposed in the Kitchenham’s methodology. The study in a bid to streamline future researches categorized digital forensic researches into six knowledge areas and identified the convolutional neural network as a state-of-the-art algorithm for machine learning-based digital forensics.
ViTT: Vision Transformer Tracker

This paper presents a new model for multi-object tracking (MOT) with a transformer. MOT is a spatiotemporal correlation task among interest objects and one of the crucial technologies of multi-unmanned aerial vehicles (Multi-UAV). The transformer is a self-attentional codec architecture that has been successfully used in natural language processing and is emerging in computer vision. This study proposes the Vision Transformer Tracker (ViTT), which uses a transformer encoder as the backbone and takes images directly as input. Compared with convolution networks, it can model global context at every encoder layer from the beginning, which addresses the challenges of occlusion and complex scenarios. The model simultaneously outputs object locations and corresponding appearance embeddings in a shared network through multi-task learning. Our work demonstrates the superiority and effectiveness of transformer-based networks in complex computer vision tasks and paves the way for applying the pure transformer in MOT. We evaluated the proposed model on the MOT16 dataset, achieving 65.7% MOTA, and obtained a competitive result compared with other typical multi-object trackers.
Complex Interaction as Emergent Behaviour: Simulating Mid-Air Virtual Keyboard Typing using Reinforcement Learning

Accurately modelling user behaviour has the potential to significantly improve the quality of human-computer interaction. Traditionally, these models are carefully hand-crafted to approximate specific aspects of well-documented user behaviour. This limits their availability in virtual and augmented reality where user behaviour is often not yet well understood. Recent efforts have demonstrated that reinforcement learning can approximate human behaviour during simple goal-oriented reaching tasks. We build on these efforts and demonstrate that reinforcement learning can also approximate user behaviour in a complex mid-air interaction task: typing on a virtual keyboard. We present the first reinforcement learning-based user model for mid-air and surface-aligned typing on a virtual keyboard. Our model is shown to replicate high-level human typing behaviour. We demonstrate that this approach may be used to augment or replace human testing during the validation and development of virtual keyboards.
Online Pricing and Trading of Private Data in Correlated Queries

With the commoditization of private data, data trading in consideration of user privacy protection has become a fascinating research topic. The trading for private web browsing histories brings huge economic value to data consumers when leveraged by targeted advertising. And the online pricing of these private data further helps achieve more realistic data trading. In this paper, we study the trading and pricing of multiple correlated queries on private web browsing history data at the same time. We propose CTRADE, which is a novel online data CommodiTization fRamework for trAding multiple correlateD queriEs over private data. CTRADE first devises a modified matrix mechanism to perturb query answers. It especially quantifies privacy loss under the relaxation of classical differential privacy and a newly devised mechanism with relaxed matrix sensitivity, and further compensates data owners for their diverse privacy losses in a satisfying manner. CTRADE then proposes an ellipsoid-based query pricing mechanism according to a given linear market value model, which exploits the features of the ellipsoid to explore and exploit the close-optimal dynamic price at each round. In particular, the proposed mechanism produces a low cumulative regret, which is quadratic in the dimension of the feature vector and logarithmic in the number of total rounds. Through real-data based experiments, our analysis and evaluation results demonstrate that CTRADE balances total error and privacy preferences well within acceptable running time, indeed produces a convergent cumulative regret with more rounds, and also achieves all desired economic properties of budget balance, individual rationality, and truthfulness.
Limited Data Emotional Voice Conversion Leveraging Text-to-Speech: Two-Stage Sequence-to-Sequence Training

Emotional voice conversion (EVC) aims to change the emotional state of an utterance while preserving the linguistic content and speaker identity. In this paper, we propose a novel 2-stage training strategy for sequence-to-sequence emotional voice conversion with a limited amount of emotional speech data. We note that the proposed EVC framework leverages text-to-speech (TTS) as they share a common goal that is to generate high-quality expressive voice. In stage 1, we perform style initialization with a multi-speaker TTS corpus, to disentangle speaking style and linguistic content. In stage 2, we perform emotion training with a limited amount of emotional speech data, to learn how to disentangle emotional style and linguistic information from the speech. The proposed framework can perform both spectrum and prosody conversion and achieves significant improvement over the state-of-the-art baselines in both objective and subjective evaluation.
STYLER: Style Factor Modeling with Rapidity and Robustness via Speech Decomposition for Expressive and Controllable Neural Text to Speech

Previous works on neural text-to-speech (TTS) have been addressed on limited speed in training and inference time, robustness for difficult synthesis conditions, expressiveness, and controllability. Although several approaches resolve some limitations, there has been no attempt to solve all weaknesses at once. In this paper, we propose STYLER, an expressive and controllable TTS framework with high-speed and robust synthesis. Our novel audio-text aligning method called Mel Calibrator and excluding autoregressive decoding enable rapid training and inference and robust synthesis on unseen data. Also, disentangled style factor modeling under supervision enlarges the controllability in synthesizing process leading to expressive TTS. On top of it, a novel noise modeling pipeline using domain adversarial training and Residual Decoding empowers noise-robust style transfer, decomposing the noise without any additional label. Various experiments demonstrate that STYLER is more effective in speed and robustness than expressive TTS with autoregressive decoding and more expressive and controllable than reading style non-autoregressive TTS. Synthesis samples and experiment results are provided via our demo page, and code is available publicly.
Towards Privacy-Preserving Cloud Storage: A Blockchain Approach

With the rapid development of cloud computing technology, cloud services have now become a new business model for information services. The cloud server provides the IT resources required by customers in a self-service manner... | Find, read and cite all the research you need on Tech Science Press