Reviewerly/exHarmony huggingface dataset:
Dynamic Cross-Feature Fusion for American Sign Language Translation

While a significant amount of work has been done on the commonly used, tightly -constrained weather-based, German sign language (GSL) dataset, little has been done for continuous sign language translation (SLT) in more realistic settings, including American sign language (ASL) translation. Also, while CNN - based features have been consistently shown to work well on the GSL dataset, it is not clear whether such features will work as well in more realistic settings when there are more heterogeneous signers in non-uniform backgrounds. To this end, in this work, we introduce a new, realistic phrase-level ASL dataset (ASLing), and explore the role of different types of visual features (CNN embeddings, human body keypoints, and optical flow vectors) in translating it to spoken American English. We propose a novel Transformer-based, visual feature learning method for ASL translation. We demonstrate the explainability efficacy of our proposed learning methods by visualizing activation weights under various input conditions and discover that the body keypoints are consistently the most reliable set of input features. Using our model, we successfully transfer-learn from the larger GSL dataset to ASLing, resulting in significant BLEU score improvements. In summary, this work goes a long way in bringing together the AI resources required for automated ASL translation in unconstrained environments.
Cuckoo search with dual-subpopulation and information-sharing strategy

Cuckoo search (CS) algorithm is simple and powerful in dealing with the global optimisation problem. However, how to strike a good balance between exploration and exploitation in CS is still an open question. The paper proposes a modified CS with dual-subpopulation and information-sharing strategy (DSIS_CS). In DSIS_CS, the population is divided into two subpopulations which are assigned different update tasks. Then, random solutions are selected from the dissimilar subpopulations in order to avoid the results from easily falling into the local optima. In addition, the DSIS strategy can be incorporated into other state-of-the-art CS variants to improve their optimisation performance. Extensive experiments on 28 functions chosen from CEC 2013 have been carried out. The results suggest that the DSIS strategy helps both the CS and its variants to achieve a better trade-off between exploration and exploitation.
Governance and foreign direct investment: is the low and middle income Africa region different?

This paper examines the effect of political governance, economic governance and institutional governance on FDI inflows on a sample of 32 African countries: 15 low income countries and 17 middle income countries, over the period of 1996–2019. Our empirical study used both aggregated analysis and the system generalised method of moments (System-GMM). The aggregate approach consists to use three composite indicators [Political Governance encompassing political stability (PS) and voice and accountability (VA)], economic governance [representing by government effectiveness (GE) and regulatory quality (RQ)] and institutional governance [including the control of corruption (CC), the rule of law (RL)]. Our findings show that overall governance indicators attract FDI inflows in African and its sub-regions. Therefore, improvements in political governance, economic governance and institutional governance are deterministic conditions in the attractiveness of FDI in African economies.
Comparison and Analysis of Two Cartoon Face Recognition

Face recognition is regularly used in daily life, such as used for authentication on phones, hotels, shops. Sometimes when people find some gorgeous cartoon characters images on the Internet and they may be very curious about who are these characters and which cartoon or anime these characters are from. However, just using image searching on the browser is not good enough to identify different characters. Hence, many programmers try to set up neural networks about cartoon characters recognition. This paper is going to compare two different types of deep learning neural networks about cartoon face recognition and find out how can people improve this kind of neural network. One of the case is a simpler neural network from college students and another one from iQIYI is much more complicated. For example, the first one only uses normal cross-entropy as the loss function and the second one uses a loss function composed of three parts: classification loss, unknown identity rejection loss, and domain transfer loss. [1] They are also different in sizes of the datasets, types of data, number of training, and so on. This study confirmed that these differences do influence the final accuracy of the program. Hence, by knowing and changing these differences, people can create a better cartoon face identification neural network.
Expression-Latent-Space-guided GAN for Facial Expression Animation based on Discrete Labels

Facial expression animation aims to synthesize face images that correspond to the target expression in a continuum. This is a challenging task because the animation not only cares about the smooth transition in the generated sequence but also needs to take the facial expression and identity details into consideration. Most existing expression animation methods resort to continuous expression labels, e.g. Action Units (AUs) or landmark sequences. Compared with discrete expression labels, the annotations of a considerable part of them are ambiguous and prone to errors. However, how to animate facial expression conditioned on discrete expression labels is less investigated and existing methods cannot generate satisfactory facial details. To tackle these problems, we propose an end-to-end Expression-Latent-Space-guided Generative Adversarial Network (ELS-GAN) model, which utilizes discrete expression labels as input to generate images with expected expressions, and employs expression latent space learning to control the expression changing process. An expression ranking loss is also proposed to strengthen expression intensity learning during generation. Moreover, we put forward a Self-Attention Generator to synthesize face images with fine details by considering both local areas and the long-range dependency of different areas. Extensive experiments show that our method can generate continuous intermediate expression between source and target expressions only conditioned on discrete labels and superior results are achieved compared with state-of-the-art methods.
Bangla Optical Character Recognition (OCR) Using Deep Learning Based Image Classification Algorithms

Optical Character Recognition (OCR) refers to the process of converting images of printed, typed, or handwritten text into machine-readable text. OCR is one of the most widely researched topics in the field of computer vision. Furthermore, highly accurate, and sophisticated Optical Character Recognition systems have been built for most of the major languages of the world such as English, French, German, Mandarin, etc. However, despite having 300 million native speakers (4.00% of the world population) and being the 5th most spoken language of the world, the Bengali language still does not have a state-of-the-art OCR system. Moreover, most of the existing systems are not able to recognize compound letters. This study strives to resolve this issue by proposing three neural network based image classification models for Bangla OCR. These models are Inception V3, VGG16, and Vision Transformer. These models have been trained on the BanglaLekha-Isolated dataset that contains 98,950 images of Bengali characters (vowels, consonants, digits, compound letters). The accuracy provided by the VGG-16, Inception V3, and Vision Transformer on the test set are 98.65%, 97.82%, and 96.88% respectively. Each of these models is much more accurate than the existing systems. Real-time implementation of these three models will be instrumental in building a state-of-the-art Bangla OCR system.
A new approach for writer verification based on segments of handwritten graphemes

Abstract Traditional literature considers complex biometric sources such as words, letters and signatures for writer verification/identification. In this work the use of small segments of the handwritten stroke for writer verification is proposed. A grapheme is defined as the concatenation of smaller segments or fragments. Two models of grapheme are developed based on the idea that the segments are parts of a circle with or without direction. The average of Gray Level of the Perpendicular Line to the Skeleton and Local Binary Pattern are adopted as descriptors. A database of 3,000 images of 50 writers, with 6 types of segments and with 10 samples per segment has been developed. A binary output support vector machine was applied as classifier. Thus, 50 classifiers were trained using 100 balanced data sets generated using subsampling of the majority class. Experiments are carried out with the proposed models, with an identity verification hit rate of 97 % on average. The positive results of experiments show that it is possible to achieve high precision in writer verification using segment of graphemes.
Toward blockchain adoption for the automotive industry

Abstract Blockchain proves to be a powerful technology that can drive forward the automotive industry. This paper describes how blockchain has been applied, which automotive areas are most interested in the technology, as well as the benefits and challenges of blockchain for the automotive industry by a systematic literature review. Blockchain offers many advantages, such as: transparency, security, high speed of operations. There are numerous areas where this technology could be applied in the automotive domain: better protection against counterfeit spare parts and materials, effective preventive maintenance, by informing the services based on the data received from the car sensors, monitoring of the cars by the rental companies, the safe storage of the data used by intelligent navigation systems, in order not to affect the safety of the passengers, autonomous transactions, triggered by the vehicle for making payments in charging stations, all with the purpose of facilitating a good and safer medium for drivers. The future vehicle will be connected both with other vehicles, but also with the existing infrastructure, so a database containing cryptographically secure information is needed, and blockchain is such a solution that can be applied in the dynamic nature of traffic.
Towards Addressing the Spatial Sparsity of MDT Reports to Enable Zero Touch Network Automation

Minimization of Drive Test (MDT) reports are a key enabler for Machine Learning (ML)-based zero-touch automation envisioned for emerging cellular networks. However, due to numerous factors, the MDT reports are spatially sparse in nature. This sparsity undermines the performance of ML models that are built on the MDT data to estimate and optimize network KPIs. In this paper, we present and evaluate a framework to address this challenge. We leverage generative models, specifically, Gener-ative Adversarial Networks (GAN) and Variational Autoencoders (VAE) to augment the sparse multi-dimensional MDT data. Unlike image data where the quality of synthetic images produced by the generative models can be evaluated visually, establishing the authenticity of tabular synthetic data is a more complex problem. We address this problem by leveraging a tripartite approach: 1) We use several statistical measures to quantify the resemblance of synthetic data with original data. 2) We compare the performance of an ensemble learning model trained on augmented data, with that of trained on original data only 3) We benchmark the performance of the generative models with several classical ML models. This analysis is carried out for varying levels of sparsity and reveals insights about robustness of generative models against training data sparsity as well as on suitability of various methods for evaluating the quality of the generated synthetic tabular data. Results show GAN performs considerably better compared to other approaches. The presented solution thus can be used to overcome the sparsity problem in MDT reports thereby enabling ML-based automation use cases.
Feature Engineering

Feature Engineering (FE) is a set of techniques that allows human knowledge and intuitions to be added to an ML solution by controlling the input of raw data during the ML process. There are a number of well-understood methods and transformations that can be applied to the features. This process is better done iteratively, starting from EDA and performing EA after each iteration. This chapter showcases basic FE operations, allowing a data scientist to decide whether or not to engage in a FE process. The FE operations will be exemplified in a case study about Airbnb price predictions.
Multilevel Feature Exploration Network for Image Superresolution

Image superresolution (SR) is a classical issue in computer vision area. Recently, there are elaborated convolutional neural networks (CNNs) demonstrating remarkable effectiveness on image SR. However, most of the previous works lack effective exploration on the structural information, which plays a critical role for image quality. In this paper, we find that the hierarchical design can effectively restore the structural information and devise a multilevel feature exploration network for image SR (MFSR). Specially, we design an encoder-decoder architecture to concentrate on structural information from different levels and devise a spatial attention mechanism to address the inherent correlation among features for effective restoration. Experimental results show the proposed MFSR can restore more correct edges and lines and achieves both better objective and subjective performances than the state-of-the-art methods with higher PSNR/SSIM results, indicating the effectiveness on structural information restoration.
Equilibrium-non-equilibrium of the brine-kimberlite system in the Udachnaya pipe, Russia, based on physicochemical modeling

The paper presents the results of a study of groundwater saturation degree in relation to the minerals of kimberlite breccia based on the physicochemical modeling of equilibrium in the brine-kimberlite system. Highly concentrated Ca Cl brines with salinity of up to 400 g/L were discovered through boreholes drilled in the deep horizons of the diamond-bearing deposit of the Udachnaya pipe (Western Yakutia, Russia). The sampling depth of kimberlite was 1034–1145 m and the depth of the water sample from the ore body was 869 m. The pipe rocks are kimberlite breccias that consist of minerals such as serpentine, calcite, chlorite, mica, and quartz. The simulation results, using HydroGeo software, showed that the brines in the deep horizons of the ore body of the Udachnaya pipe remain unsaturated in relation to almost all groups of kimberlite-forming minerals (carbonate, sulfate, chloride, clay, and aluminosilicate) at any temperature and pressure in the reservoir. The nature and degree of equilibrium in the brine-kimberlite system, formed in the course of their long geochemical evolution, were influenced by the processes of both dissolution-precipitation of the chemical components of the system and the dilution of ground brines with low-salinity or fresh waters.
GazeGrid: A Novel Interaction Method Based on Gaze Estimation

Appearance-based gaze estimation aims to directly learn a mapping from face images to gaze directions. Via the front camera of mobile phones or tablets, face and eye images are obtained and used to extract Convolutional Neural Net-work(CNN) features. As a natural human computer interaction method, gaze is very efficient and has potential to be widely used on mobile phones and tablets. Grid layout is popular and commonly used in mobile applications user interface(UI) design and interaction. In this study, we propose a novel interaction method based on gaze estimation-GazeGrid. We conduct experiments on a large-scale dataset. The proposed method achieves more than 86% and 93% accuracy rates on mobile phones and tablets. Further more, we provide optimal GazeGrid sizes for 11 types of mobile devices based on experimental results. We believe that GazeGrid can help to accelerate gaze driven applications and interactions.
Federated Learning over Time-Varying Channels

We study distributed machine learning (ML) sys-tems where independent workers compute local gradients based on their local datasets and send them to a parameter server (PS) through a time-varying multipath fading multiple access channel (MAC) via orthogonal frequency-division multiplexing (OFDM). We assume that the workers do not have channel state information (CSI), and hence the PS employs multiple antennas to remove the fading effects. Time variations in the wireless channel result in inter-carrier interference (ICI), which has a detrimental effect on the performance of OFDM systems, especially when the channel variations are rapid. To examine the effects of channel variations on federated learning systems, we perform an analysis of the interference in the aggregate gradient term at the PS due to Doppler, and show that the undesired effects caused by them are limited. Specifically, the ICI term becomes insignificant for slow to moderate time variations. We also validate our theoretical expectations via simulations and demonstrate that the destructive effect of ICI can be alleviated for moderate level of channel variations.
Granitoids and trachytes of Um Shager-Hamadat area, Central Eastern Desert, Egypt: Remote sensing characterization and petrology

The late Neoproterozoic granitoids of Gebel Um Shager and Gebel Hamadat, Central Eastern Desert of Egypt, in addition to Paleozoic trachytes of both areas were discriminated using Landsat-8, and Sentinel-2 dataset. Band rationing and Principal Component Analysis techniques were applied. The studied granitoids represent the extreme end-members of the subduction-related I-type calc-alkaline granitic magmas (primitive to more evolved varieties) that developed over a long period of time. These granites were extruded by Paleozoic intra-plate trachytes. Petrography and petrogenesis of these rocks were dealt with and discussed. The Um Shager Older Granitoids (calcic tonalite to quartz monzodiorite) represent poorly differentiated metaluminous magma characterized by very low Rb/Sr ratio ∼0.07. Evolutionary, they follow the calc-alkaline subtype trend and most probably were originated from parent gabbroic magma by fractional crystallization in a volcanic arc setting. On contrary, the Hamadat Younger Granites represent alkali calcic magma which was developed in post-collision setting. They comprise evolved subsolvus metaluminous syenogranite and alkali feldspar granite. Evolutionary, they follow the subalkaline subtype trend. Crystallization courses of both syenogranite and alkali feldspar granite were started by alkali feldspar, followed by cotectic crystallization of alkali feldspar and quartz and ended by eutectic crystallization of all quartzofeldspathic minerals. The trachytes represent intra-plate continental alkaline magmatism. The Um Shager trachytes were developed at local extension zones, while the Hamadat trachytes were originated during global continental rifting system. Syenogranite, alkali feldspar granite and trachytes are important sources of alkali feldspar, which has wide spectrum applications in industry and agriculture.
Short-Utterance Embedding Enhancement Method Based on Time Series Forecasting Technique for Text-Independent Speaker Verification

Short-utterance embedding, which is a speaker embedding extracted from a short utterance, shows poor speaker verification performance due to insufficient speaker information. To address the problem, we propose a method to map the set of short-utterance embeddings to a set of long-utterance embeddings based on a neural network. Specifically, a speech utterance is cropped into multiple segments whose durations are gradually increasing, and the speaker embeddings are extracted from the sequence of cropped segments using a pre-trained speaker embedding extractor. Subsequently, the sequence of embeddings is divided into a group of short-utterances embeddings and that of long-utterance embeddings. In our method, a sequence-to-sequence model based forecasting technique is employed, where an encoder transforms the group of short-utterance embeddings to a fixed-dimensional vector, and then a decoder converts the vector into a group of long-utterance embeddings. Experimental results on the VoxCeleb and Speakers in the Wild datasets show that our method improves the text-independent speaker verification performance under short utterance condition.
The problem with open geospatial data for on‐farm research

Abstract Scientists are under increased pressure to provide research data freely and openly to all interested parties as a means of furthering science. More than sharing data, there is an additional expectation that data comply to Findable, Accessible, Interoperable, Reusable (FAIR) principles. The requirement to share data presents challenges for farm geospatial data. The primary contention of this commentary is that data sharing requirements will make on‐farm research increasingly difficult while also not achieving the stated purposes of opening data. De‐identification of farm geospatial data is not sufficient to protect privacy, reducing likelihood of participation in research. Moreover, de‐identified farm geospatial data will have greatly reduced reusability. Limited guidance is available on appropriate mechanisms for sharing of farm geospatial data. This commentary briefly summarizes benefits and realities of data sharing, expands discussion to support the primary contention, and concludes with high‐level suggestions for moving forward.
Source‐anchored, trace‐anchored, and general match score‐based likelihood ratios for camera device identification

Forensic camera device identification addresses the scenario, where an investigator has two pieces of evidence: a digital image from an unknown camera involved in a crime, such as child pornography, and a person of interest's (POI's) camera. The investigator wants to determine whether the image was taken by the POI's camera. Small manufacturing imperfections in the photodiode cause slight variations among pixels in the camera sensor array. These spatial variations, called photo-response non-uniformity (PRNU), provide an identifying characteristic, or fingerprint, of the camera. Most work in camera device identification leverages the PRNU of the questioned image and the POI's camera to make a yes-or-no decision. As in other areas of forensics, there is a need to introduce statistical and probabilistic methods that quantify the strength of evidence in favor of the decision. Score-based likelihood ratios (SLRs) have been proposed in the forensics community to do just that. Several types of SLRs have been studied individually for camera device identification. We introduce a framework for calculating and comparing the performance of three types of SLRs - source-anchored, trace-anchored, and general match. We employ PRNU estimates as camera fingerprints and use correlation distance as a similarity score. Three types of SLRs are calculated for 48 camera devices from four image databases: ALASKA; BOSSbase; Dresden; and StegoAppDB. Experiments show that the trace-anchored SLRs perform the best of these three SLR types on the dataset and the general match SLRs perform the worst.