Reviewerly/exHarmony huggingface dataset:
Contribution of Remote Sensing for the Mapping of the Yaoundé Metadiorites

Geologists face a number of problems, mainly related to the difficulty of covering the entire terrain, leading to various pieces of information collected and its extrapolation for drawing maps. To overcome these problems, we have proposed to use remote sensing which is a current tool for rock mapping. Remote sensing is a modern tool to highlight several information that conventional mapping methods do not allow. Thus, the objective of this work is to update the geological contours of the Yaoundé metadiorites by processing satellite images coupled with the classical approach. Sentinel 1-A radar images were used. A textural analysis of these images was carried out using the GLCM (Grey Level Co-occurrence Matrix) method, resulting in eight co-occurrence indices, among which three were chosen to perform colored compositions. The colored compositions obtained are VMH and HMV. And the contrasts obtained were compared with maps from previous work and also with field work. The metasedimentary rocks (kyanite - garnet migmatites and garnet micaschists) and metaigneous rocks (metadiorite) constitute the metamorphic complex distinctly mapped by exploiting remote sensing data, superposition maps from previous work and integrating the new sampling points. Remote sensing in geological mapping thus plays an important role mainly in the urbanized study area as it detects the metadiorites under the metasediments despite the existence of anthropogenic works and low vegetation cover.
Ethereum, IPFS and neural compression to decentralize and protect patient data in computational pathology

The field of digital pathology produces a large number of images associated with patient metadata that are the raw material of computational pathology. The process of making images available with adequate privacy and data protection considerations takes a long time. Given that the Ethereum network associated with InterPlanetary File System (IPFS) promotes the exchange of information in a secure, private and decentralized manner, this association could be an important partner between digital and computational pathology. Therefore, here we propose and discuss a prototype with the aforementioned parts and the addition of neural compression, as an essential information preservation step. This prototype could constitute a link for the exchange of information in a secure way, providing transparency and reliability to the chain and empowering the field of manufacturing artificial vision solutions for the medical field
Multi-threaded approach in generating frequent itemset of Apriori algorithm based on trie data structure

This research is about the application of multi-threaded and trie data structures to the support calculation problem in the Apriori algorithm. The support calculation results can search the association rule for market basket analysis problems. The support calculation process is a bottleneck process and can cause delays in the following process. This work observed five multi-threaded models based on Flynn’s taxonomy, which are single process, multiple data (SPMD), multiple process, single data (MPSD), multiple process, multiple data (MPMD), double SPMD first variant, and double SPMD second variant to shorten the processing time of the support calculation. In addition to the processing time, this works also consider the time difference between each multi-threaded model when the number of item variants increases. The time obtained from the experiment shows that the multi-threaded model that applies a double SPMD variant structure can perform almost three times faster than the multi-threaded model that applies the SPMD structure, MPMD structure, and combination of MPMD and SPMD based on the time difference of 5-itemsets and 10-itemsets experimental result.
A Deep Learning-Based Assisted Teaching System for Oral English

The progress of global economic integration has forced English learners to have an urgent need to improve their oral English. College students’ oral English ability is currently the worst of the four abilities of listening, speaking, reading, and writing. The main reasons are internal and external. The internal reason is that the pronunciation characteristics of Chinese students are different from those of English. The external cause is that the practice environment and tools of oral English are not ideal, which affects the improvement of learners’ oral English. This study proposes using a deep learning algorithm (DLA) English in the evaluation of oral English quality to improve learners’ oral English level. The quality of oral English can be comprehensively evaluated in terms of pitch, speed of sound, and rhythm. The standard of pronunciation is the foundation of oral English and is the most critical factor. In many DLAs, the input unit of DNN at a certain moment and its upper and lower moment input units have no relationship and are independent of each other, and the timing dependencies of adjacent units are not fully considered. The results are generally not very good on speech recognition tasks. This study proposes a time-delay neural network (TDNN) and a long short-term memory (LSTM) network to calculate the posterior probability of the model state to model context-dependent features in order to solve this problem. The fusion model TDNN-LSTM is applied in the English spoken pronunciation recognition task. To compare the accuracy of oral English pronunciation, several classic DLAs are introduced. The experimental results show that the method described in this study has a number of advantages. Although the performance improvement of this method in terms of recognition accuracy is not large, a certain degree of improvement is also very important for the oral English teaching assistant system.
An Optimized Straggler Mitigation Framework for Large-Scale Distributed Computing Systems

Nowadays, Big Data becomes a research focus in industrial, banking, social network, and other fields. In addition, the explosive increase of data and information require efficient processing solutions. Therefore, Spark is considered as a promising candidate of Large-Scale Distributed Computing Systems for big data processing. One primary challenge is the straggler problem that occurred due to the presence of heterogeneity where a machine takes an extra-long time to finish execution of a task, which decreases the system throughput. To mitigate straggler tasks, Spark adopts speculative execution mechanism, in which the scheduler launches additional backup to avoid slow task processing and achieve acceleration. In this paper, a new Optimized Straggler Mitigation Framework is proposed. The proposed framework uses a dynamic criterion to determine the closest straggler tasks. This criterion is based on multiple coefficients to achieve a reliable straggler decision. Also, it integrates the historical data analysis and online adaptation for intelligent straggler judgment. This guarantees the effectiveness of speculative tasks by improving cluster performance. Experimental results on various benchmarks and applications show that the proposed framework achieves 23.5% to 30.7% execution time reductions, and 25.4 to 46.3% increase of the cluster throughputs compared with spark engine.
Reconstruction of Language Learning in Universities in the Post Covid-19

Background:&#x0D; After the Covid-19 pandemic forced educational institutions to change learning methods and techniques, especially at universities in Indonesia, because face-to-face meetings have been carried out in stages. This research generally aims to describe the Reconstruction of Indonesian Language Learning in Post-Covid-19 Universities.&#x0D; Methodology: &#x0D; This study uses the literature review method, searching and researching literature by reading various books, journals, and other publications.&#x0D; Findings: &#x0D; The findings of this study include post-covid-19 brings good benefits in terms of online-based learning, which is undoubtedly one of the demands of digitalization of education today. The strategy that can be used in reconstructing Indonesian learning after COVID-19 is to use the Blended Learning method, namely by a combination of learning models carried out in online and offline contexts; Supporting factors that exist in reconstructing Indonesian language learning in universities after the covid-19 pandemic include: students are accustomed and trained in online learning which is nothing but the demands of the era of digitalization of education at this time; besides that it can also trigger the acceleration of transformation for the world of education in Indonesia; the emergence of online learning applications as a learning reference that supports students; the spread of free online courses and the emergence of unlimited creativity; By studying online at home, students can at least apply it to their home environment.&#x0D; Conclusion: &#x0D; The obstacle in reconstructing Indonesian language learning at universities in the context of digitizing education at this time is the limited facilities and infrastructure in the form of internet quotas, internet signals or networks, wifi, facilities and infrastructure, response, and technology. Moreover, maximizing the blended learning method is the right strategy for overcoming the Various Obstacles in Reconstructing Indonesian Language Learning in Post-Covid-19 Universities.
Shape-based Evaluation of Epidemic Forecasts

Infectious disease forecasting for ongoing epidemics has been traditionally performed, communicated, and evaluated as numerical targets - 1, 2, 3, and 4 week ahead cases, deaths, and hospitalizations. While there is great value in predicting these numerical targets to assess the burden of the disease, we argue that there is also value in communicating the future trend (description of the shape) of the epidemic -- for instance, if the cases will remain flat or a surge is expected. To ensure what is being communicated is useful we need to be able to evaluate how well the predicted shape matches with the ground truth shape. Instead of treating this as a classification problem (one out of $n$ shapes), we define a transformation of the numerical forecasts into a ``shapelet''-space representation. In this representation, each dimension corresponds to the similarity of the shape with one of the shapes of interest (a shapelet). We prove that this representation satisfies the property that two shapes that one would consider similar are mapped close to each other, and vice versa. We demonstrate that our representation is able to reasonably capture the trends in COVID-19 cases and deaths time-series. With this representation, we define an evaluation measure and a measure of agreement among multiple models. We also define the shapelet-space ensemble of multiple models as the mean of their shapelet-space representations. We show that this ensemble is able to accurately predict the shape of the future trend for COVID-19 cases and trends. We also show that the agreement between models can provide a good indicator of the reliability of the forecast.
Learning to Rank Ace Neural Architectures via Normalized Discounted Cumulative Gain

One of the key challenges in Neural Architecture Search (NAS) is to efficiently rank the performances of architectures. The mainstream assessment of performance rankers uses ranking correlations (e.g., Kendall's tau), which pay equal attention to the whole space. However, the optimization goal of NAS is identifying top architectures while paying less attention on other architectures in the search space. In this paper, we show both empirically and theoretically that Normalized Discounted Cumulative Gain (NDCG) is a better metric for rankers. Subsequently, we propose a new algorithm, AceNAS, which directly optimizes NDCG with LambdaRank. It also leverages weak labels produced by weight-sharing NAS to pre-train the ranker, so as to further reduce search cost. Extensive experiments on 12 NAS benchmarks and a large-scale search space demonstrate that our approach consistently outperforms SOTA NAS methods, with up to 3.67% accuracy improvement and 8x reduction on search cost.
A Novel Approach to Image Forgery Detection Techniques in Real World Applications

Image integrity is threatened because of the usage of modern techniques in order to manipulate the images to gain personal or monetary benefits. Image forgery has been adversely affecting the fields concerned with the usage of image as a prime source of data such as medicine and healthcare, social media, journalism and newspapers, criminal investigation, art and paintings, deep fake industry. Passive forgery is generally carried out to a greater extent in order to tamper and circulate the manipulated images. A novel design is presented which uses several Convolutional Neural Network Architectures including EfficientNetB0, VGG-16, and VGG-19 to detect copymove forging. It was trained and verified on MICC F2000 dataset and tested on MICC220 and CoMoFoD. After comparative analysis of these architectures it was found that EfficientNetB0 has the best accuracy of above 98%.
Latent Discriminant Deterministic Uncertainty

Predictive uncertainty estimation is essential for deploying Deep Neural Networks in real-world autonomous systems. However, most successful approaches are computationally intensive. In this work, we attempt to address these challenges in the context of autonomous driving perception tasks. Recently proposed Deterministic Uncertainty Methods (DUM) can only partially meet such requirements as their scalability to complex computer vision tasks is not obvious. In this work we advance a scalable and effective DUM for high-resolution semantic segmentation, that relaxes the Lipschitz constraint typically hindering practicality of such architectures. We learn a discriminant latent space by leveraging a distinction maximization layer over an arbitrarily-sized set of trainable prototypes. Our approach achieves competitive results over Deep Ensembles, the state-of-the-art for uncertainty prediction, on image classification, segmentation and monocular depth estimation tasks. Our code is available at https://github.com/ENSTA-U2IS/LDU
The Rise of the Lottery Heroes: Why Zero-Shot Pruning is Hard

Recent advances in deep learning optimization showed that just a subset of parameters are really necessary to successfully train a model. Potentially, such a discovery has broad impact from the theory to application; however, it is known that finding these trainable sub-network is a typically costly process. This inhibits practical applications: can the learned sub-graph structures in deep learning models be found at training timeƒ In this work we explore such a possibility, observing and motivating why common approaches typically fail in the extreme scenarios of interest, and proposing an approach which potentially enables training with reduced computational effort. The experiments on either challenging architectures and datasets suggest the algorithmic accessibility over such a computational gain, and in particular a trade-off between accuracy achieved and training complexity deployed emerges.
Automatic Grammar Error Correction Model Based on Encoder-decoder Structure for English Texts

The role of information transmission in social life is irreplaceable, and language is a very important information carrier. Among all kinds of languages, English always occupies an important position. In the process of English learning, grammar error has become a difficult problem for most learners. In this paper, we propose an automatic grammar error correction model based on encoder-decoder structure. Different from traditional encoders, we design a dual-encoder structure to capture the information of source sentence and context sentence separately. The decoder is designed with a gated structure, it can effectively integrate output information of encoders. At the same time, the self-attention mechanism is combined to better solve the problem of long-distance information extraction. In addition, we propose a dynamic beam search algorithm to improve the accuracy of the word prediction process, and achieve dynamic extraction of the decoder output by combining kernel sampling techniques. We add a penalty factor to reduce the probability of generating repeated words, while suppressing the model's preference for generating shorter sentences. Finally, the proposed method is validated on the official English grammar error correction dataset. Experiments show that the dual encoder model in this paper has a good performance.
[Spatial-temporal Variations and Their Driving Forces of the Ecological Vulnerability in the Loess Plateau].

The Loess Plateau is one of the most eco-fragile regions in China, and therefore the scientific evaluation of its ecological vulnerability provides a premise for the effective implication of ecological protection and management practices. However, previous studies have mainly focused on the ecological vulnerability in a small region, which cannot reflect the overall picture of the ecological vulnerability in the Loess Plateau. Based on the "exposure-sensitivity-adaptation" framework, this study investigated the spatial-temporal patterns and their driving forces of the ecological vulnerability in the Loess Plateau from 2000 to 2015 through a combined use of the analytic hierarchy process, spatial principal component analysis, and Geodetector analysis. The results showed that the ecological vulnerability was overall at a moderate to high level, and the vulnerability was clearly higher in the northwestern part of the Loess Plateau than that in the southeastern counterparts. Additionally, the ecological vulnerability differed greatly by land use type. The ecological vulnerability decreased after an increase from 2000 to 2015 and in general decreased slightly throughout the study period. Therein, approximately 64% of the total land area experienced an upward or downward trend in the vulnerability. Vegetation coverage and precipitation were the two main factors contributing to the spatial-temporal variability in the ecological vulnerability, and there were significant interactions among all the used indicators. This study suggests that climate change and human activities may help reduce the ecological vulnerability over the Loess Plateau, although their contributions are limited.
The Role of Subjective Perception in Visualization, Controllability Hypothesis and Abstract Visualization

Visual analytics tools are used when interpreting and analyzing weakly formalized or non-formalized data.The paper considers the problem of the influence of subjective factors on efficiency of the visualization tools.Based on the previously proposed and developed semiotic visualization model, a system of initiating factors is formulated that have a significant impact on the effectiveness of visualization tools.A controllability hypothesis is formulated, on the basis of which an assumption is made about limitations of visualization systems It is proposed to take these limitations into consideration when designing new visual analytics tools.Approbation of the assertions presented in the work was carried out on the example of social data.
Strategic Assessment of Cyber Security Contenders to the Brazilian Agribusiness in the Beef Sector

The current international commercial structure places Brazilian Agribusiness in constant conflict to protect its interests before other nations in the global market. Technological innovations are used in all stages from the simplest production tasks, up to the design of negotiation tactics at high-level affairs. This paper has the objective of finding Brazilian contenders in the beef market with cyber capabilities and commercial interest to act in favor of their interests. To construct such a list, a review of the literature on Threat and Cyber Threat Intelligence is presented, followed by a background presentation of how embedded technology is in nowadays agriculture and supply chains in general, and the real necessity for those sectors to be seen as critical infrastructure by governments in general. Also as background information recent cyber attack cases and attacker countries are shown. A Step-by-Step multidisciplinary method is presented that involves the extent of international trade, the interest on specific markets, and the intersection of country cyber capacity index. After applying the method and criteria generated a list of five contender countries. The method may be replicated and/or applied, considering adequate data source assessment and following specifics of each sector.
Small-scale Farmers’ Information Needs and Obstacles: Evidence from the Volta Region, Ghana

Agricultural information is assuming increasing importance in the face of current biotic and abiotic stresses, coupled with changing dynamics of agricultural practice globally. This paper contributes to the discourse by examining information needs and obstacles of small-scale farmers in the Volta Region, Ghana. Structured questionnaires were used to collect primary data from six hundred and thirty (630) small-scale farmers, selected through multistage sampling technique. The study showed that information on marketing, post-harvest techniques, and diseases and pests management were the critical information needs of small-scale farmers in the Region. The study further established that majority of small-scale farmers were aware of information sources in the Region with the major information sources consulted by farmers in order of preference being family members, friends, radio, television and extension agents. A chi-square analysis showed that educational level of farmers was not significant at ρ ≤ 0.05 and therefore did not affect farmers' information needs. Besides, the study showed that the major obstacles facing small-scale farmers were lack of skills to access information, inadequate information resources, insufficient agricultural extension agents, and absence of Agricultural Information Centres. These findings will certainly shape agricultural extension program planning in the Region and inform national extension policy.
Joint learning of variational representations and solvers for inverse problems with partially-observed data

Designing appropriate variational regularization schemes is a crucial part of solving inverse problems, making them better-posed and guaranteeing that the solution of the associated optimization problem satisfies desirable properties. Recently, learning-based strategies have appeared to be very efficient for solving inverse problems, by learning direct inversion schemes or plug-and-play regularizers from available pairs of true states and observations. In this paper, we go a step further and design an end-to-end framework allowing to learn actual variational frameworks for inverse problems in such a supervised setting. The variational cost and the gradient-based solver are both stated as neural networks using automatic differentiation for the latter. We can jointly learn both components to minimize the data reconstruction error on the true states. This leads to a data-driven discovery of variational models. We consider an application to inverse problems with incomplete datasets (image inpainting and multivariate time series interpolation). We experimentally illustrate that this framework can lead to a significant gain in terms of reconstruction performance, including w.r.t. the direct minimization of the variational formulation derived from the known generative model.