Reviewerly/exHarmony huggingface dataset:
The Revolution of Mobile Phone-Enabled Services for Agricultural Development (m-Agri Services) in Africa: The Challenges for Sustainability

The provision of information through mobile phone-enabled agricultural information services (m-Agri services) has the potential to revolutionise agriculture and significantly improve smallholder farmers’ livelihoods in Africa. Globally, the benefits of m-Agri services include facilitating farmers’ access to financial services and sourcing agricultural information about input use, practices, and market prices. There are very few published literature sources that focus on the potential benefits of m-Agri services in Africa and none of which explore their sustainability. This study, therefore, explores the evolution, provision, and sustainability of these m-Agri services in Africa. An overview of the current landscape of m-Agri services in Africa is provided and this illustrates how varied these services are in design, content, and quality. Key findings from the exploratory literature review reveal that services are highly likely to fail to achieve their intended purpose or be abandoned when implementers ignore the literacy, skills, culture, and demands of the target users. This study recommends that, to enhance the sustainability of m-Agri services, the implementers need to design the services with the users involved, carefully analyse, and understand the target environment, and design for scale and a long-term purpose. While privacy and security of users need to be ensured, the reuse or improvement of existing initiatives should be explored, and projects need to be data-driven and maintained as open source. Thus, the study concludes that policymakers can support the long-term benefit of m-Agri services by ensuring favourable policies for both users and implementers.
A New Chaotic Image Watermarking Scheme Based on SVD and IWT

Image watermarking schemes based on singular value decomposition (SVD) have become popular due to a good trade-off between robustness and imperceptibility.However, the false positive problem (FPP) is the main drawback of SVD-based watermarking schemes.The singular value is the main cause of FPP issues because it a fixed value that does not hold structural information of an image.In this paper, a new SVD-based image watermarking scheme that uses a chaotic map is proposed to overcome this issue.The secret key is first extracted from both the host and watermark image.This key is used to generate a new chaotic matrix and chaotic multiple scaling factors (CMSF) to increase the sensitivity of the proposed scheme.The watermark image is then transformed based on the chaotic matrix before being directly embedded into the singular value of the host image by using the CMSF.The extracted secret key is unique to the host and the watermark images, which improves security and overcomes FPP issues.Experimental results show that the proposed scheme fulfils all watermarking requirements in terms of robustness, imperceptibility, security, and payload.Furthermore, it achieves high robustness with different scaling factors, and outperforms several existing schemes.
From Arguments to Key Points: Towards Automatic Argument Summarization

Generating a concise summary from a large collection of arguments on a given topic is an intriguing yet understudied problem.We propose to represent such summaries as a small set of talking points, termed key points, each scored according to its salience.We show, by analyzing a large dataset of crowd-contributed arguments, that a small number of key points per topic is typically sufficient for covering the vast majority of the arguments.Furthermore, we found that a domain expert can often predict these key points in advance.We study the task of argument-to-key point mapping, and introduce a novel large-scale dataset for this task.We report empirical results for an extensive set of experiments with this dataset, showing promising performance.
Accelerating Federated Learning over Reliability-Agnostic Clients in Mobile Edge Computing Systems

Mobile Edge Computing (MEC), which incorporates the Cloud, edge nodes and end devices, has shown great potential in bringing data processing closer to the data sources. Meanwhile, Federated learning (FL) has emerged as a promising privacy-preserving approach to facilitating AI applications. However, it remains a big challenge to optimize the efficiency and effectiveness of FL when it is integrated with the MEC architecture. Moreover, the unreliable nature (e.g., stragglers and intermittent drop-out) of end devices significantly slows down the FL process and affects the global model's quality Xin such circumstances. In this paper, a multi-layer federated learning protocol called HybridFL is designed for the MEC architecture. HybridFL adopts two levels (the edge level and the cloud level) of model aggregation enacting different aggregation strategies. Moreover, in order to mitigate stragglers and end device drop-out, we introduce regional slack factors into the stage of client selection performed at the edge nodes using a probabilistic approach without identifying or probing the state of end devices (whose reliability is agnostic). We demonstrate the effectiveness of our method in modulating the proportion of clients selected and present the convergence analysis for our protocol. We have conducted extensive experiments with machine learning tasks in different scales of MEC system. The results show that HybridFL improves the FL training process significantly in terms of shortening the federated round length, speeding up the global model's convergence (by up to 12X) and reducing end device energy consumption (by up to 58%).
Global-to-Local Neural Networks for Document-Level Relation Extraction

Relation extraction (RE) aims to identify the semantic relations between named entities in text.Recent years have witnessed it raised to the document level, which requires complex reasoning with entities and mentions throughout an entire document.In this paper, we propose a novel model to document-level RE, by encoding the document information in terms of entity global and local representations as well as context relation representations.Entity global representations model the semantic information of all entities in the document, entity local representations aggregate the contextual information of multiple mentions of specific entities, and context relation representations encode the topic information of other relations.Experimental results demonstrate that our model achieves superior performance on two public datasets for document-level RE.It is particularly effective in extracting relations between entities of long distance and having multiple mentions.
When BERT Plays the Lottery, All Tickets Are Winning

Large Transformer-based models were shown to be reducible to a smaller number of selfattention heads and layers.We consider this phenomenon from the perspective of the lottery ticket hypothesis, using both structured and magnitude pruning.For fine-tuned BERT, we show that (a) it is possible to find subnetworks achieving performance that is comparable with that of the full model, and (b) similarly-sized subnetworks sampled from the rest of the model perform worse.Strikingly, with structured pruning even the worst possible subnetworks remain highly trainable, indicating that most pre-trained BERT weights are potentially useful.We also study the "good" subnetworks to see if their success can be attributed to superior linguistic knowledge, but find them unstable, and not explained by meaningful self-attention patterns.
Virtual Body Ownership Illusions for Mental Health: A Narrative Review

Over the last 20 years, virtual reality (VR) has been widely used to promote mental health in populations presenting different clinical conditions. Mental health does not refer only to the absence of psychiatric disorders but to the absence of a wide range of clinical conditions that influence people’s general and social well-being such as chronic pain, neurological disorders that lead to motor o perceptual impairments, psychological disorders that alter behaviour and social cognition, or physical conditions like eating disorders or present in amputees. It is known that an accurate perception of oneself and of the surrounding environment are both key elements to enjoy mental health and well-being, and that both can be distorted in patients suffering from the clinical conditions mentioned above. In the past few years, multiple studies have shown the effectiveness of VR to modulate such perceptual distortions of oneself and of the surrounding environment through virtual body ownership illusions. This narrative review aims to review clinical studies that have explored the manipulation of embodied virtual bodies in VR for improving mental health, and to discuss the current state of the art and the challenges for future research in the context of clinical care.
Analysing Students’ Reasons for Keeping Their Webcams on or off during Online Classes

Since, in some higher education institutions, it is not mandatory for students to turn their webcams on during online classes, teachers have complained that their students have adopted this behaviour once the educational activities moved online due to the COVID-19 pandemic. Considering this, the present research aimed to identify the reasons behind students’ choice to hide their faces during online classes and find possible solutions to remedy the situation to enhance the educational process’s sustainability. Thus, this article presents the results obtained by applying an online questionnaire between December 2020 and January 2021 among the students pursuing an academic degree, recording 407 responses. The results highlighted the fact that more than half of the students participating in the study reported that they do not agree to keep their webcams on during online classes, the main reasons being anxiety/fear of being exposed/shame/shyness, desire to ensure privacy of the home/personal space, and chances that other people might walk into the background. The relevance of the research, besides the scarcity of studies on the topic, is also given by the fact that finding and understanding the reasons for this behaviour are, in fact, the first steps in undertaking regulatory interventions on it.
Lessons from countries implementing find, test, trace, isolation and support policies in the rapid response of the COVID-19 pandemic: a systematic review

Objective To systematically learn lessons from the experiences of countries implementing find, test, trace, isolate, support (FTTIS) in the first wave of the COVID-19 pandemic. Design, data sources and eligibility criteria We searched MEDLINE (PubMed), Cochrane Library, SCOPUS and JSTOR, initially between 31 May 2019 and 21 January 2021. Research articles and reviews on the use of contact tracing, testing, self-isolation and quarantine for COVID-19 management were included in the review. Data extraction and synthesis We extracted information including study objective, design, methods, main findings and implications. These were tabulated and a narrative synthesis was undertaken given the diverse research designs, methods and implications. Results We identified and included 118 eligible studies. We identified the core elements of an effective find, test, trace, isolate, support (FTTIS) system needed to interrupt the spread of a novel infectious disease, where treatment or vaccination was not yet available, as pertained in the initial stages of the COVID-19 pandemic. We report methods used to shorten case finding time, improve accuracy and efficiency of tests, coordinate stakeholders and actors involved in an FTTIS system, support individuals isolating and make appropriate use of digital tools. Conclusions We identified in our systematic review the key components of an FTTIS system. These include border controls, restricted entry, inbound traveller quarantine and comprehensive case finding; repeated testing to minimise false diagnoses and pooled testing in resource-limited circumstances; extended quarantine period and the use of digital tools for contact tracing and self-isolation. Support for mental or physical health and livelihoods is needed for individuals undergoing self-isolation/quarantine. An integrated system with rolling-wave planning can best use effective FTTIS tools to respond to the fast-changing COVID-19 pandemic. Results of the review may inform countries considering implementing these measures.
Bi-Classifier Determinacy Maximization for Unsupervised Domain Adaptation

Unsupervised domain adaptation challenges the problem of transferring knowledge from a well-labelled source domain to an unlabelled target domain. Recently, adversarial learning with bi-classifier has been proven effective in pushing cross-domain distributions close. Prior approaches typically leverage the disagreement between bi-classifier to learn transferable representations, however, they often neglect the classifier determinacy in the target domain, which could result in a lack of feature discriminability. In this paper, we present a simple yet effective method, namely Bi-Classifier Determinacy Maximization (BCDM), to tackle this problem. Motivated by the observation that target samples cannot always be separated distinctly by the decision boundary, here in the proposed BCDM, we design a novel classifier determinacy disparity (CDD) metric, which formulates classifier discrepancy as the class relevance of distinct target predictions and implicitly introduces constraint on the target feature discriminability. To this end, the BCDM can generate discriminative representations by encouraging target predictive outputs to be consistent and determined, meanwhile, preserve the diversity of predictions in an adversarial manner. Furthermore, the properties of CDD as well as the theoretical guarantees of BCDM's generalization bound are both elaborated. Extensive experiments show that BCDM compares favorably against the existing state-of-the-art domain adaptation methods.
ACE: Ally Complementary Experts for Solving Long-Tailed Recognition in One-Shot

One-stage long-tailed recognition methods improve the overall performance in a "seesaw" manner, i.e., either sacrifice the head’s accuracy for better tail classification or elevate the head’s accuracy even higher but ignore the tail. Existing algorithms bypass such trade-off by a multi-stage training process: pre-training on imbalanced set and fine-tuning on balanced set. Though achieving promising performance, not only are they sensitive to the generalizability of the pre-trained model, but also not easily integrated into other computer vision tasks like detection and segmentation, where pre-training of classifiers solely is not applicable. In this paper, we propose a one-stage long-tailed recognition scheme, ally complementary experts (ACE), where the expert is the most knowledgeable specialist in a sub-set that dominates its training, and is complementary to other experts in the less-seen categories without being disturbed by what it has never seen. We design a distribution-adaptive optimizer to adjust the learning pace of each expert to avoid over-fitting. Without special bells and whistles, the vanilla ACE outperforms the current one-stage SOTA method by 3 ~ 10% on CIFAR10-LT, CIFAR100-LT, ImageNet-LT and iNaturalist datasets. It is also shown to be the first one to break the "seesaw" trade-off by improving the accuracy of the majority and minority categories simultaneously in only one stage. Code and trained models are at https://github.com/jrcai/ACE.
On Analyzing Hashtags in Twitter

Hashtags, originally introduced in Twitter, are now becoming the most used way to tag short messages in social networks since this facilitates subsequent search, classification and clustering over those messages. However, extracting information from hashtags is difficult because their composition is not constrained by any (linguistic) rule and they usually appear in short and poorly written messages which are difficult to analyze with classic IR techniques. In this paper we address two challenging problems regarding the meaning of hashtags — namely, hashtag relatedness and hashtag classification - and we provide two main contributions. First we build a novel graph upon hashtags and (Wikipedia) entities drawn from the tweets by means of topic annotators (such as TagME); this graph will allow us to model in an efficacious way not only classic co-occurrences but also semantic relatedness among hashtags and entities, or between entities themselves. Based on this graph, we design algorithms that significantly improve state-of-the-art results upon known publicly available datasets. The second contribution is the construction and the public release to the research community of two new datasets: the former is a new dataset for hashtag relatedness, the latter is a dataset for hashtag classification that is up to two orders of magnitude larger than the existing ones. These datasets will be used to show the robustness and efficacy of our approaches, showing improvements in F1 up to two-digits in percentage (absolute).
Scheduling Live Migration of Virtual Machines

Every day, numerous VMs are migrated inside a datacenter to balance the load, save energy or prepare production servers for maintenance. Although VM placement problems are carefully studied, the underlying migration schedulers rely on vague adhoc models. This leads to unnecessarily long and energy-intensive migrations. We present mVM, a new and extensible migration scheduler. To provide schedules with minimal completion times, mVM parallelizes and sequentializes the migrations with regards to the memory workload and the network topology. mVM is implemented as a plugin of BtrPlace and its current library allows administrators to address temporal and energy concerns. Experiments on a real testbed shows mVM outperforms state-of-the-art migration schedulers. Compared to schedulers that cap the migration parallelism, mVM reduces the individual migration duration by 20.4 percent on average and the schedule completion time by 28.1 percent. In a maintenance operation involving 96 VMs migrated between 72 servers, mVM saves 21.5 percent Joules against BtrPlace. Compared to the migration model inside the cloud simulator CloudSim, the prediction error of the migrations duration is about 5 times lower with mVM. By computing schedules involving thousands of migrations performed over various fat-tree network topologies, we observed that the mVM solving time accounts for about 1 percent of the schedule execution time.
Accurate 3D Reconstruction from Small Motion Clip for Rolling Shutter Cameras

Structure from small motion has become an important topic in 3D computer vision as a method for estimating depth, since capturing the input is so user-friendly. However, major limitations exist with respect to the form of depth uncertainty, due to the narrow baseline and the rolling shutter effect. In this paper, we present a dense 3D reconstruction method from small motion clips using commercial hand-held cameras, which typically cause the undesired rolling shutter artifact. To address these problems, we introduce a novel small motion bundle adjustment that effectively compensates for the rolling shutter effect. Moreover, we propose a pipeline for a fine-scale dense 3D reconstruction that models the rolling shutter effect by utilizing both sparse 3D points and the camera trajectory from narrow-baseline images. In this reconstruction, the sparse 3D points are propagated to obtain an initial depth hypothesis using a geometry guidance term. Then, the depth information on each pixel is obtained by sweeping the plane around each depth search space near the hypothesis. The proposed framework shows accurate dense reconstruction results suitable for various sought-after applications. Both qualitative and quantitative evaluations show that our method consistently generates better depth maps compared to state-of-the-art methods.
Quantum multiplexing

Distributing entangled pairs is a fundamental operation required for many quantum information science and technology tasks. In a general entanglement distribution scheme, a photonic pulse is used to entangle a pair of remote quantum memories. Most applications require multiple entangled pairs between remote users, which in turn necessitates several photonic pulses (single photons) being sent through the channel connecting those users. Here we present an entanglement distribution scheme using only a single photonic pulse to entangle an arbitrary number of remote quantum memories. As a consequence, the spatial temporal resources are dramatically reduced. We show how this approach can be simultaneously combined with an entanglement purification protocol to generate even-higher-fidelity entangled pairs. The combined approach is faster to generate those high-quality pairs and requires fewer resources in terms of both matter qubits and photons consumed. To estimate the efficiency of our scheme we derive a normalized rate taking into account the raw rate at which the users can generate purified entangled pairs divided by the total resources used. We compare the efficiency of our system with the Deutsch protocol in which the entangled pairs have been created in a traditional way. Our scheme outperforms this approach in terms of both generation rate and resources required. Finally, we show how our approach can be extended to more general error correction and detection schemes with higher normalized generation rates naturally occurring.
Paleo-environmental constraints on uranium mineralization in the Ordos Basin: Evidence from the color zoning of U-bearing rock series

Color zoning of strata of Mesozoic continental basins in North China is closely correlated with the sedimentary environments of sandstone-type uranium deposits. Systematic research has been conducted on the colors of U-bearing rock series within the Ordos Basin by using data of several ten thousands of drill holes from coal and oil fields. Taking an ore cluster as the unit, well-tie sections of deposit areas were compiled to analyze the vertical and lateral spatial variations in different color zones of rocks. Taking the basin as the unit, two typical well-tie sections in the central-northern basin were compiled in order to summarize the rock color zoning laws of U-bearing rock series within ore clusters and the entire basin. Lithogeochemical characteristics of different color zones in deposit areas and ore barren areas were studied. Especially, variations in trace elements in different color zones and the paleo-sedimentary environments they reflect were discussed. Within the Ordos Basin, from the basin margin to the center and from the U ore clusters to the ore barren areas, colors of the Zhiluo Formation U-bearing rock series generally display vertical zoning (i.e., from up downwards, red–green–gray [grayish black]) and show no lateral zoning characteristics. U ore beds mostly occur in the lower part of the green zone and the upper part of the gray zone. This conclusion differs greatly from the understanding on rock color zoning related to classic interlayer-oxidation-zone metallogenic theory. Hence, a correct understanding on color zoning of U-bearing rock series within the Ordos Basin is of great significance not only to U mineralization exploration but also to research on sandstone-type U mineralization theory.