Reviewerly/exHarmony huggingface dataset:
Inappropriate Technology: Evidence from Global Agriculture

An influential explanation for global productivity differences is that frontier technologies are adapted to the high-income countries that develop them and “inappropriate” elsewhere. We study this inappropriate technology hypothesis in agriculture by using mismatch in the presence of crop pests and pathogens (CPPs) as a shifter of technology's inappropriateness and investigating its effect on technology diffusion and productivity. CPP mismatch reduces plant-variety transfer at the crop-by-country-pair level, particularly from innovation-intensive origins. CPP mismatch with these innovation-intensive countries reduces crop production. Our estimates, combined with a model, imply that inappropriateness reduces global productivity by 58% and increases cross-country disparities by 15%.
Interleaved $Z_{4}$-Linear Sequences with Improved Correlation for Satellite Navigation

Global Navigation Satellite Systems (GNSSs) typically use low-correlation sequences that have length related to the common clock frequency of 10.23 MHz. In particular, operations in the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$L1$</tex> frequency band, of two major GNSS systems, the Global Positioning System (GPS) and BeiDou Navigation Satellite System (BDS), employ spreading sequences having length 10230. In these two systems, the length 10230 is achieved by padding and truncating respectively, a family of Weil sequences having period that is a prime number, either 10223 or 10243. As is well known, either truncation or padding leads in general, to a degradation in correlation performance. In the present paper, we adopt a different approach, and present the design of a family of Interleaved <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$Z_{4}$</tex> -linear (IZ4) sequences having period exactly equal to 10230. Closed-form expressions for the correlation properties of the sequence family are included. The balance and even-correlation performance of the new family equals or improves upon the corresponding performance of the GPS and BDS signal sets. In particular, the new IZ4 family has maximum even cross-correlation value that is better by 4.4 dB, than that of the truncated or padded Weil sequences employed in these two systems. The sequence family also turns in comparable odd-correlation performance. The sequences can be generated using a simple, shift-register-based implementation presented here.
Maritime filtering for images and videos

Maritime signal processing technologies have emerged as an important area of study because of the increasing popularity of autonomous ships and automatic maritime surveillance systems. However, the various techniques developed for detecting or tracking objects remain unable to address various maritime noise challenges that cause several types of false positives in maritime visual surveillance. Maritime signal processing is challenging because of the prevalence of noise sources such as severe dynamic backgrounds, wakes, and reflections, owing to the complex, unconstrained, and diverse nature of such scenes caused by the surface properties of water. Moreover, few studies have investigated specific maritime noise filtering as a general integrated processing approach with image and video technologies in the context of maritime visual surveillance. In this study, we propose a novel maritime noise prior (MNP) based on a dark channel prior and observations of the characteristics of the sea. A general maritime filtering technique is developed to suppress noise originating from the properties of water in maritime images and videos. The proposed method employs a noniterative, nonlinear, and simple maritime filtering approach based on MNP that does not require specialized knowledge of application scene conditions or structure. We conducted image and video experiments by applying our approach to three publicly available databases. In experiments with color images, our method successfully filtered related background noise and water, i.e., severe boat wakes and reflections, while preserving objects other than water in color images. In the experiments with video sequences, the results demonstrated that the proposed filter improved the overall performance of state-of-the-art background subtraction (BS) algorithms from 36.60%–50.63%. By combining BS algorithms and filtering to enhance foreground detection in video sequences, the proposed method ensures the universal applicability and flexibility required to eliminate noise from images and videos obtained in challenging maritime environments. The results indicate that the proposed method is appropriate for maritime surveillance applications implementing image segmentation and foreground detection, and it can potentially increase the accuracy of maritime visual surveillance.
ChinaCALL in retrospect and prospect: opening a welcome window to the World’s CALLs

Abstract This paper, while sending a welcome message to the world’s CALL communities, presents a historical and developmental review of Chinese practices of educational technologies and ChinaCALL in particular. The review covers topics including the birth and concept of ChinaCALL, ChinaCALL’s pre-CALL heritage, pre-Web ChinaCALL, ChinaCALL in the new millennium, IBOE as a case study of ChinaCALL, and ChinaCALL in prospect. Also discussed are Chinese perceptions of educational technologies occurring at the macro, the meso and ground levels. On the ground level, technology consumers are characterized in terms of pen-pencil conservatives, AV natives, AV-CALL immigrants, CALL natives and CALL standby observers.
Limitations of Mean-Based Algorithms for Trace Reconstruction at Small Distance

Trace reconstruction considers the task of recovering an unknown string <tex>$x$</tex> &#x220A; {0, l]<sup>n</sup>given a number of independent &#x201C;traces&#x201D;, i.e., subsequences of <tex>$x$</tex> obtained by randomly and independently deleting every symbol of <tex>$x$</tex> with some probability p. The information-theoretic limit of the number of traces needed to recover a string of length <tex>$n$</tex> are still unknown. This limit is essentially the same as the number of traces needed to determine, given strings <tex>$x$</tex> and <tex>$y$</tex> and traces of one of them, which string is the source. The most studied class of algorithms for the worst-case version of the problem are &#x201C;mean-based&#x201D; algorithms. These are a restricted class of distinguishers that only use the mean value of each coordinate on the given samples. In this work we study limitations of mean-based algorithms on strings at small Hamming or edit distance. We show on the one hand that distinguishing strings that are nearby in Hamming distance is &#x201C;easy&#x201D; for such distinguishers. On the other hand, we show that distinguishing strings that are nearby in edit distance is &#x201C;hard&#x201D; for mean-based algorithms. Along the way we also describe a connection to the famous Prouhet-Tarry-Escott (PTE) problem, which shows a barrier to finding explicit hard-to-distinguish strings: namely such strings would imply explicit short solutions to the PTE problem, a well-known difficult problem in number theory. Our techniques rely on complex analysis arguments that involve careful trigonometric estimates, and algebraic techniques that include applications of Descartes&#x0027; rule of signs for polynomials over the reals. A full version of this paper is accessible at: https://arxiv.org/abs/2011.13737
Moroccan Dialect “Darija” Automatic Speech Recognition: A Survey

Nowadays, human-machine interaction is growing swiftly, and Automatic Speech Recognition is gaining immense interest to make the daily routines much easier. This could be illustrated by the various applications of Speech Recognition in our daily lives, such as voice dictation, interactive voice response systems, device control, telephone applications, and others. Besides Automatic Speech Recognition, Natural language processing has gained significant improvements in terms of technologies and used approaches. Till today great results have been achieved in those Fields, especially for international languages such as English, Spanish, French, and Arabic. Whereas few results have been reached for dialects of languages such as the case of Moroccan dialect “Darija”. The growing use of Moroccan Darija on social media, videos, chatting and others, opens new research directions for Moroccan Darija speech recognition. The leading goal of this paper is to give a literature review on Moroccan Darija Automatic Speech Recognition. Through presenting the dialect specific constraints, the different works conducted in the field of Moroccan Darija speech recognition, and the progress made in recent years.
Performance Study on Indexing and Accessing of Small File in Hadoop Distributed File System

Hadoop Distributed File System (HDFS) is developed to efficiently store and handle the vast quantity of files in a distributed environment over a cluster of computers. Various commodity hardware forms the Hadoop cluster, which is inexpensive and easily available. The large number of small files stored in HDFS consumed more memory which lags the performance because small files consumed heavy load on NameNode. Thus, the efficiency of indexing and accessing the small files on HDFS is improved by several techniques, such as archive files, New Hadoop Archive (New HAR), CombineFileInputFormat (CFIF), and Sequence file generation. The archive file combines the small files into single blocks. The new HAR file combines the smaller files into a single large file. The CFIF module merges the multiple files into a single split using NameNode, and the sequence file combines all the small files into a single sequence. The indexing and accessing of a small file in HDFS are evaluated using performance metrics, such as processing time and memory usage. The experiment shows that the sequence file generation approach is efficient when compared to other approaches concerning file access time is 1.5[Formula: see text]s, memory usage is 20 KB in multi-node, and the processing time is 0.1[Formula: see text]s.
G-CAM: Graph Convolution Network Based Class Activation Mapping for Multi-label Image Recognition

In most multi-label image recognition tasks, human visual perception keeps consistent for different spatial transforms of the same image. Existing approaches either learn the perceptual consistency with only image-level supervision or preserve the middle-level feature consistency of attention regions but neglect the (global) label dependencies between different objects over the dataset. To address this issue, we integrate graph convolution network (GCN) and propose G-CAM, which learns visual attention consistency via GCN based class attention mapping (CAM) for multi-label image recognition. G-CAM consists of an image feature extraction module to generate the feature maps of the original image and its transformed one and a GCN module to learn weighted classifiers that capture the label dependencies between different objects. Different from previous works which use fully-connected classification layer, G-CAM first fuses weighted classifiers with the feature vector to generate the predicted labels for each input image, then combines weighted classifiers with the feature maps to respectively obtain the transformed attention heatmaps of the original image and the attention heatmaps of its transformed one. We can compute the attention consistency loss according to the distance between these two attention heatmaps. Finally, this loss is combined with the multi-label classification loss to update the whole network in an end-to-end manner. We conduct extensive experiments on three multi-label image datasets including FLICKR25K, MS-COCO and NUS-WIDE. Experimental results demonstrate G-CAM can achieve better performance compared with the state-of-the-art multi-label image recognition methods.
Authentication of travel and breeder documents

Authentication of travel documents (e.g., passports) and breeder documents (e.g., birth certificates) is important to facilitate legal movement of passengers and to prevent cross-border crime, such as terrorism, smuggling, illegal migration and human trafficking. However, it is time consuming and difficult to verify all security features, the border guards differ in experience and expertise, and it is hard to stay alert every minute of a working day. New (artificial-intelligence based) technologies can assist in the automated fraud detection in travel and breeder documents, which may lead to faster and more consistent checks. This paper presents five categories of new technologies in automated document authentication to overcome the limitations of current document analysis systems in automated and non-automated border control scenarios. The first category consists of techniques related to the verification of visual security features on the holder page of travel documents. This category includes the verification of KINEGRAMs and other Optically Variable features under different light sources and lighting angles, and the analysis of printing techniques. The second category consists of techniques related to the analysis of breeder documents. This analysis can be at detail level (e.g., investigation of stamps) and at tactical level (e.g., verification of a check digit in a document number). The third category concerns the analysis of travel patterns, using information from the visa pages in passports. The stamps on these pages can be used to extract a travel pattern to support risk assessments and to detect anomalies. The fourth category is an analysis of the border-guard inspection history based upon a distributed ledger and blockchain technology that enables secure storage and prevents undesired manipulations. The last category analyzes the electronic chip of a passport. The software analyses document signer and country signer certificates on the chip to detect vulnerable cryptographic keys and tactical anomalies.
Hateful Memes, Offensive or Non-offensive!

Memes are a form of friendly exchange between one another and can guide one smoothly over a conflict or come to a meaningful conclusion. Detection of offensive and hate speech-related content in social media memes has been investigated in a single modality only, i.e. considering only text or only image. However, memes are content plus images. Consideration of both modalities to identify offensive memes is vital. Thus, we made a model considering this multimodal nature. The training dataset available from the Facebook challenge had pre-extracted text from a meme. However, testing an original meme, the text had to be extracted with the help of Optical Character Recognition (OCR) technology. So if the text on the meme will be not distorted rather straight, the OCR technology will give an accurate extraction of text. The text had separately been passed through a text module where different techniques like Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and stacked LSTM have been used for detection. The image has also been passed to an image module where a CNN model, i.e. VGG16 has been used for detection. This is followed by the fusion of results from both modules via the basic concatenation technique. All of this procedure had been performed using 2 different word embeddings, i.e. GloVe and Bidirectional Encoder Representations from Transformers (BERT). The best accuracy obtained was 0.485 using BiLSTM + VGG16. Comparing the results between BERT and GloVe, GloVe beat BERT with a very minor margin, though with an even larger dataset BERT could overtake GloVe.
Code Authorship Attribution using content-based and non-content-based features

To attribute authorship (author identification) means to identify the true author of a sample of work among many candidates. Author identification is an important research field in natural language. Machine learning approaches are widely used in natural language analysis, and previous research has shown that similar techniques can be applied in the analysis of computer programming (artificial) languages. This paper focuses on the use of machine learning techniques in the identification of authors of computer programs. We focus on identifying which features capture the writing style of authors in the classification of a computer program according to the author's identity. We then propose a novel approach for computer program author identification. In this method, features from source code of the programs are combined with authors' sociological features (gender and region) to develop the classification model. Several experiments were conducted on two datasets composed of computer programs written in C++. Our models are able to predict an author's identity with a 75% accuracy rate.
Compelling AR Earthquake Simulation with AR Screen Shaking

Our goal is to simulate earthquakes in a familiar environment, for example in one&#x2019;s own office, helping users to take the simulation more seriously. We propose an AR earthquake simulation using a video see-through VR headset, use real earthquake data, and implement a novel AR screen shake technique, realistically shaking the entire view of the user. We run a user study (n=25), where participants experienced an earthquake both in a VR scene and two AR scenes with and without the AR screen shake technique. Our results suggest that both AR scenes offer a more compelling experience compared to the VR scene, and the AR screen shake improved immediacy and was preferred by most participants. This showed us how virtual safety training can greatly benefit from an AR implementation, motivating us to further explore this approach for the case of earthquakes.
CSS Codes that are Oblivious to Coherent Noise

Physical platforms such as trapped ions suffer from coherent noise that does not follow a simple stochastic model. We view coherent errors as rotations about a particular axis, and observe that since they can accumulate coherently over time, they can be more damaging. It is natural to consider coherent noise acting transversally giving rise to an effective error, which is a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$Z$</tex> -rotation on each qubit by some angle <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\theta$</tex> . Rather than addressing coherent noise through active error correction, we instead investigate passive mitigation through decoherence free subspaces. In the language of stabilizer codes, we require the noise to preserve the code space, and to act trivially (as the logical identity operator) on the protected information. Thus, we develop necessary and sufficient conditions for all transversal <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$Z$</tex> -rotations to preserve the code space of a stabilizer code. These conditions require the existence of a large number of weight 2 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$Z$</tex> -stabilizers, and together, these weight 2 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$Z$</tex> -stabilizers generate a direct product of single-parity-check codes. By adjusting the size of these components, we are able to construct a large family of CSS codes, oblivious to coherent noise, that includes the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$[[4L^{2}, 1,2L]]$</tex> Shor codes. Given <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$m$</tex> even and given any <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$[[n, k, d]]$</tex> CSS code, we can construct an <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$[[mn, k, d^{\prime}\geq d]]$</tex> CSS code that is oblivious to coherent noise. This result is generalized to stabilizer codes in [Hu, Liang, Rengaswamy, and Calderbank 2020]. The MacWilliams Identities play a central role in the technical analysis, and classical coding theorists may be interested in connections to classical codes with all weights divisible by some integer <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$d$</tex> .
Particle size distribution inversion in dynamic light scattering by adaptive step-size non-negative least squares

The non-negative least squares algorithm is widely used in dynamic light scattering, however, it suffers the shortcomings of sparse solutions and poor anti-noise performance. It is found that the construction of the kernel matrix of the algorithm is of great significance to the accuracy of the final results. In this paper, we propose an adaptive strategy to find the optimal step-size to construct the kernel matrix. By calculating the L2-norm between the measured and reconstructed light intensity autocorrelation function, the optimal step-size is determined corresponding to the minimum difference error. Then the kernel matrix constructed by this optimal step-size is used in the inversion. The final result is obtained by fitting the solution to a predefined model, such as a Gaussian model. This strategy improves the sparsity of NNLS solution, and thus the accuracy and stability of NNLS inversion results. The simulation and experiments demonstrate that the adaptive step-size strategy improves the stability and accuracy of the non-negative least squares inversion algorithm for dynamic light scattering.
On the Use of Attention in Deep Learning Based Denoising Method for Ancient Cham Inscription Images

Image denoising is one of the most important steps in the document image analysis pipeline thanks to its good effect into the rest of the workflow. However, the noise in historical documents is totally different from the common noise present in other classical problems of image processing. It is particularly the case of the image of Cham inscriptions obtained by the stamping of ancient stele. In this paper, we leverage the advantage of deep learning to adapt with these noisy conditions. The proposed network follows an encoder-decoder structure by combining convolution/deconvolution operators with symmetrical skip connections and residual blocks for improving reconstructed image. Furthermore, global attention fusion is proposed to learn the relevant regions in the image. Our experiments demonstrate the proposed method can't only remove unwanted parts in the image, but also enhance the visual quality for the Cham inscriptions.
Multitask Learning for Visual Question Answering

Visual question answering (VQA) is a task that machines should provide an accurate natural language answer given an image and a question about the image. Many studies have found that the current VQA methods are heavily driven by the surface correlation or statistical bias in the training data, and lack sufficient image grounding. To address this issue, we devise a novel end-to-end architecture that uses multitask learning to promote more sufficient image grounding and learn effective multimodality representations. The tasks consist of VQA and our proposed image cloze (IC) task requires machines to fill in the blanks accurately given an image and a textual description of the image. To ensure our model performs sufficient image grounding as much as possible, we propose a novel word-masking algorithm to develop the multimodal IC task based on the part-of-speech of words. Our model predicts the VQA answer and fills in the blanks after the multimodality representation learning that is shared by the two tasks. Experimental results show that our model achieves almost the equivalent, state-of-the-art, second-best performance on the VQA v2.0, VQA-changing priors (CP) v2, and grounded question answering (GQA) datasets, respectively, with fewer parameters and without additional data compared with baselines.