Reviewerly/exHarmony huggingface dataset:
Multi-disciplinary ore deposit exploration in Sonqor, northwest Iran

Multi-disciplinary exploration methods are used to explore for possible ore deposits in the Sonqor area, Iran, which lies within the Sanandaj-Sirjan Zone, and contains significant iron, copper and gold mineralisation. Hydrothermal alteration was mapped using field, remotely sensed, geophysical and geochemical data as well as Landsat-8 Operational Land Imager, Landsat Enhanced Thematic Mapper plus, and two Advanced Space Borne Thermal Emission and Reflection Radiometer images. Image processing techniques, viz band ratio, principal component analysis (PCA), and various spectral analysis methods were applied to map the distribution of hydrothermally altered rocks (geochemical halos) associated with porphyry (e.g. Cu–Au) mineralisation. The geochemical halos enabled vectoring to mineralised zones with mapping of advance argillic, argillic, carbonates, Fe-oxides, phyllic, propylitic and silicification using minerals such as alunite (K/Na), muscovite, kaolinite, illite, chlorite, goethite, hematite, jarosite, calcite and silica/quartz. The band ratio combination of sensors for mapping altered areas show promising results, similar to other more advanced methods. PCA exposed variations in the spatial distribution of more hydroxyl-based minerals like alunite/jarosite, whereas the geophysical magnetic survey identified the main lineaments, possible faults and magmatic intrusion boundaries. While geochemical methods show the potential occurrence of elements like Fe, Au and Fe-bearing Ti in the southeast and southern parts, and skarn-type anomalies in the northern part of Sonqor, leaking of specific geochemical elements (such as Fe, Au and Fe-bearing Ti) produce false anomalies. Copper, Pb and Zn anomalies in the central part show the possibility of sulfide-based mineralisation.KEY POINTSSpace-based satellite technologies were used to identify mineral prospects in the Sonqor area, within the iron, copper and gold-rich Sanandaj-Sirjan Zone, Iran.The Sonqor area, part of the Zagros Orogen, is within the extensive Alpine–Himalayan orogenic belt formed by collision of the Arabian continent with the Iranian microcontinent in the Late Cretaceous to Cenozoic.The western part of the Sonqor area has first rank Hg, Au, Ag, Ta and Sb anomalies with abundant detachment faults. Dacite domes with surrounding alteration have potential for Au epithermal mineralisation (Dashkasan type); the central part may yield Cu, Zn and Pb sulfide mineralisation.The Sonqor geological map suggests good potential for Au, Fe and Fe–Ti mineralisation at the contact between limestones in the eastern and southeastern parts, but greenschist and amphibolite facies metamorphism necessitates caution in interpretation of geochemical anomalies.
Small Data, Big Decisions: Model Selection in the Small-Data Regime

Highly overparametrized neural networks can display curiously strong generalization performance - a phenomenon that has recently garnered a wealth of theoretical and empirical research in order to better understand it. In contrast to most previous work, which typically considers the performance as a function of the model size, in this paper we empirically study the generalization performance as the size of the training set varies over multiple orders of magnitude. These systematic experiments lead to some interesting and potentially very useful observations; perhaps most notably that training on smaller subsets of the data can lead to more reliable model selection decisions whilst simultaneously enjoying smaller computational costs. Our experiments furthermore allow us to estimate Minimum Description Lengths for common datasets given modern neural network architectures, thereby paving the way for principled model selection taking into account Occams-razor.
Breaking the Curse of Many Agents: Provable Mean Embedding $Q$-Iteration for Mean-Field Reinforcement Learning

Multi-agent reinforcement learning (MARL) achieves significant empirical successes. However, MARL suffers from the curse of many agents. In this paper, we exploit the symmetry of in MARL. In the most generic form, we study a mean-field MARL problem. Such a mean-field MARL is defined on mean-field states, which are distributions that are supported on continuous space. Based on the mean embedding of the distributions, we propose MF-FQI algorithm that solves the mean-field MARL and establishes a non-asymptotic analysis for MF-FQI algorithm. We highlight that MF-FQI algorithm enjoys a blessing of many agents property in the sense that a larger number of observed improves the performance of MF-FQI algorithm.
Image-to-image translation based face de-occlusion

Existing deep learning-based object removal methods produce plausible results. However, they generate unsatisfactory results when the object is large, especially in facial images due to the lack of information about the affected region. Most of these methods rely on the object information in terms of a binary segmentation map which is insufficient to provide information about the face boundary and semantics symmetric relation. To address the problem, we propose a two-stage GAN-based image-to-image translation method that exploits the face semantic segmentation instead of the binary segmentation map of the object. Specifically, our model learns a complete facial segmentation map from an input image (face image with unwanted object) in the first stage and translates that generated semantic segmentation map combined with input image into a plausible face image without the object. We also exploit the joint loss function that consists of low-level loss, adversarial loss, and perceptual loss to produce semantically realistic facial images. Experimental results show that our method outperforms previous state-of-the-art methods both quantitatively and qualitatively.
Predictive Coding for Locally-Linear Control

High-dimensional observations and unknown dynamics are major challenges when applying optimal control to many real-world decision making tasks. The Learning Controllable Embedding (LCE) framework addresses these challenges by embedding the observations into a lower dimensional latent space, estimating the latent dynamics, and then performing control directly in the latent space. To ensure the learned latent dynamics are predictive of next-observations, all existing LCE approaches decode back into the observation space and explicitly perform next-observation prediction---a challenging high-dimensional task that furthermore introduces a large number of nuisance parameters (i.e., the decoder) which are discarded during control. In this paper, we propose a novel information-theoretic LCE approach and show theoretically that explicit next-observation prediction can be replaced with predictive coding. We then use predictive coding to develop a decoder-free LCE model whose latent dynamics are amenable to locally-linear control. Extensive experiments on benchmark tasks show that our model reliably learns a controllable latent space that leads to superior performance when compared with state-of-the-art LCE baselines.
InjectionGAN: Unified Generative Adversarial Networks for Arbitrary Image Attribute Editing

Existing image-to-image translation methods usually incorporate encoder-decoder and generative adversarial networks to generate images.The encoder compresses an entire image into a static representation using a sequence of convolution layers until a bottleneck, and then, the intermediate features are decoded to the target image.However, the existence of bottleneck layer in those approaches still has limitations in the sharpness of details, distinct image translation and identity preservation, since different domain translations may be related to the global or local region in the input image.To address these issues, we propose a new model, InjectionGAN, based on a novel generative adversarial network (GAN) for arbitrary attribute transfer.Specifically, conditional on the target domain label, an auto-encoder-like network with multiple linear transformation and refinement connections are trained to translate the input image into the target domain.The connections block better shuttle the low-level information in the encoder to the decoder, which helps to preserve the structural information while modify the appearance slightly at the pixel level through adversarial training.The results on two popular datasets suggest that InjectionGAN achieves a better performance.
Information Teaching Ability of EFL Teachers in Application-oriented College

Abstract This research carries on the investigation to the college teachers’ technology teaching ability clarified the actual situation. The research analyzes the restrictive factors that influence the development of the information teaching ability of the EFL teachers in colleges. The research using random sampling questionnaire surveys and interviews, from three dimensions: information technology teaching awareness, attitude, knowledge, skills, design and implementation. Aimed at investigating information technology teaching ability of foreign language teachers who come from 4 application-oriented college in Wuhan. The research based on descriptive, differential and correlation analysis of questionnaire data, from the teacher self-efficacy, construction and development of foreign language information-based teaching resource system, matching new teaching evaluation system, targeted, diversified and continuous training. The research expounds the concept, strategies to improve the information technology teaching ability of applied EFL college teachers.
COMPARATIVE PERFORMANCE ANALYSIS OF CHAOS BASED IMAGE ENCRYPTION TECHNIQUES

In the current scenario, the exchange of information over open network and internet have grown rapidly. Digital images constitute a major role in the multimedia communication. Hence their security becomes a major concern. The well-known solution for securing the data (in any form) is encryption. The traditional encryption techniques such as AES, DES, RSA, etc., exhibits a low level of security and significantly low level of resistance to the attacks. This problem can be overcome by chaotic image encryption techniques as they have a high level of randomness in key generation. This paper attempts to study various chaos-based image encryption algorithms and techniques and to compare their performance in various environments.
Application of Big Data in the Management System of “Second Classroom Education” Platform in Universities

Abstract Second classroom education, as an important part of expanding education, is an important part of the talent training program and an important way to reform the talent training model. With the advent of the era of big data, the second classroom education in colleges and universities uses the corresponding artificial intelligence model and information technology system, thereby greatly improving the management function of the second classroom education. This essay explains the importance of a “second classroom education” platform management system in the era of big data, and analyzes the current situation and problems of system management. It has been proved that the use of big data is helpful to guide and help college students to participate in the second classroom activities efficiently, so that the ability of college students is fully developed.
Developing and Piloting TB Medication and Refilling Reminder System in Ethiopia.

This project followed waterfall model to develop a context tailored and user-friendly reminder system to support home based TB treatment. Piloting was conducted on expert and patient levels. We developed a flexible and user-friendly web-based system that allows medication and refiling reminder. Participants agreed on its usefulness despite infrastructure and personal related challenges.
RN-VID: A Feature Fusion Architecture for Video Object Detection

Consecutive frames in a video are highly redundant. Therefore, to perform the task of video object detection, executing single frame detectors on every frame without reusing any information is quite wasteful. It is with this idea in mind that we propose RN-VID (standing for RetinaNet-VIDeo), a novel approach to video object detection. Our contributions are twofold. First, we propose a new architecture that allows the usage of information from nearby frames to enhance feature maps. Second, we propose a novel module to merge feature maps of same dimensions using re-ordering of channels and $$1 \times 1$$ convolutions. We then demonstrate that RN-VID achieves better mean average precision (mAP) than corresponding single frame detectors with little additional cost during inference.
Applied Research on Agricultural Big Data

Abstract With the continuous advancement of digital informationization, the agricultural field has naturally entered the era of big data. At present, big data technology has achieved remarkable results in the Internet industry, but it is still in its infancy in the agricultural field. Due to the complexity of the agricultural field itself and the obvious diversity and heterogeneity of data, the agricultural Internet of Things in the big data environment is facing many difficulties and challenges, especially the collection and storage of massive data. The purpose of this article is to study the application of agricultural big data. This article briefly introduces the background of the development of agricultural big data and the significance of developing agricultural big data, and summarizes the meaning of big data. At the same time, this article summarizes and analyzes the major applications of agricultural big data in production process management, agricultural resource management, and agricultural ecology environmental management, agricultural products and food safety management. In the experimental part, this paper designed and implemented an agricultural big data system platform based on Hadoop. This article simulated the query of 10, 100 and 1000 records respectively for 10,000, 100,000, 1 million and 10 million records, and the average response time was 56ms. The experimental results show that the performance of the agricultural big data platform designed in this paper can meet the needs.
Design and Implementation of University Intelligent Library Based on Cloud Computing

Abstract With the development and wide application of the internet, the cloud computing technology with powerful functions has been initially applied in the fields of industry, agriculture, scientific research, medical treatment and so on. Its technical advantages undoubtedly bring the development power to the library. Intelligent library is the advanced form of intelligent library. Cloud computing service makes the user’s data storage and application software in the operation of the internet server, which makes the library network data high integration, intelligent and efficient. On the basis of discussing the basic meaning of cloud computing, we build a cloud service platform of Intelligent library and put forward the architecture design of modern and information-based intelligent library in the cloud computing environment. This paper discusses the function realization of university intelligent library based on cloud computing to provide some references for the innovative paths of university reader service in the new era.
Network Functions Virtualization Access Control as a Service

NFV is an important innovation in networking. It has many advantages such as saving investment cost, optimizing resource consumption, improving operational efficiency and simplifying network service lifecycle management. NFV environments introduce new security challenges and issues since new types of threats and vulnerabilities are inevitably introduced (e.g. security policy and regular compliance failure, vulnerabilities in VNF softwares, malicious insiders, etc.). The impact of these threats can be mitigated by enforcing security policies over deployed network services. In this paper, we introduce an access control as a service model for NFV services. The proposed approach can deploy several kinds of access control model policies (e.g. RBAC, ORBAC, ABAC, etc.) for NFV services and can be easily scaled.
Time Expressions Identification Without Human-Labeled Corpus for Clinical Text Mining in Russian

To obtain accurate predictive models in medicine, it is necessary to use complete relevant information about the patient. We propose an approach for extracting temporary expressions from unlabeled natural language texts. This approach can be used for the first analysis of the corpus, for data labeling as the first stage, or for obtaining linguistic constructions that can be used for a rule-based approach to retrieve information. Our method includes the sequential use of several machine learning and natural language processing methods: classification of sentences, the transformation of word bag frequencies, clustering of sentences with time expressions, classification of new data into clusters and construction of sentence profiles using feature importances. With this method, we derive the list of the most frequent time expressions and extract events and/or time events for 9801 sentences of anamnesis in Russian. The proposed approach is independent of the corpus language and can be used for other tasks, for example, extracting an experiencer of a disease.
A Practical Study on College English Translation Teaching Based on CAT Technology

Abstract With the highly developed computer technology, the behavior of using computer to complete work quickly and efficiently has been regularized. The author highlights that the due attention has to be paid to the importance of CAT technology in the field of English translation. people should realize the indispensable nature of CAT technology in the teaching process. Furthermore, the author makes a multi-directional analysis of the difficulties, such as shortage of skilled CAT teachers, lack of consciousness of applying CAT technology and man-machine cooperation, lack of relevant teaching materials and lack of study on basic translation theory, faced by CAT technology in the teaching of efficient English translation. At the same time, some obvious advantages of CAT application in teaching practice are summarized. Finally, the author calls on teaching researchers to popularize CAT technology in English translation and cultivate the ability of using CAT technology to help students get interest in learning of CAT technology and translation skills to meet the need of global economic development.
Gesture recognition smart home

Abstract Different from the traditional smart home control method, this article uses the PAJ7620U2 chip gesture recognition to add to the smart home system.PAJ7620U2 gesture recognition can recognize 9 different gestures. Based on this performance, a simulated smart home system based on gesture recognition is designed. It contains home intelligent electronic photo frames, audio and video entertainment modules, intelligent security monitoring, ZigBee wireless communication environment detection, and light control. The experimental results show that the system is simple to operate and powerful, and is integrated into the traditional smart home control method without gaps.
Information Capturing in Pre-Hospital Emergency Medical Settings (EMS).

Emergency medical situations are characterized by high physical, cognitive and mental demands on the paramedics on the ground. Studies suggest that crucial information such as treatments administered to patients is often documented retrospectively, during patient transport or once a patient is handed over to an emergency department. Information access may also be surprisingly difficult (e.g. patient medical history). In this paper, we focus on supporting in situ information capturing and report on a realistic laboratory-based study involving experienced paramedics that we used to explore the specific requirements and constraints of supporting in situ information capturing. Specifically, we focused on ways to use audio and visual data capture methods and how they need to be designed to better support paramedics without interfering with their work. We then use the resulting information centric perspective to argue for a roadmap towards smart emergency medical services.
Robust Out-of-distribution Detection via Informative Outlier Mining.

Detecting out-of-distribution (OOD) inputs is critical for safely deploying deep learning models in an open-world setting. However, existing OOD detection solutions can be brittle under small adversarial perturbations. In this paper, we propose a simple and effective method, Adversarial Training with informative Outlier Mining (ATOM), to robustify OOD detection. Our key observation is that while unlabeled data can be used as auxiliary OOD training data, the majority of these data points are not informative to improve the decision boundary of the OOD detector. We show that, by carefully choosing which outliers to train on, one can significantly improve the robustness of the OOD detector, and somewhat surprisingly, generalize to some adversarial attacks not seen during training. We provide additionally a unified evaluation framework that allows future research examining the robustness of OOD detection algorithms. ATOM achieves state-of-the-art performance under a broad family of natural and perturbed OOD evaluation tasks, surpassing previous methods by a large margin. Finally, we provide theoretical insights for the benefit of auxiliary unlabeled data and outlier mining.
RCD: Repetitive causal discovery of linear non-Gaussian acyclic models with latent confounders

Causal discovery from data affected by latent confounders is an important and difficult challenge. Causal functional model-based approaches have not been used to present variables whose relationships are affected by latent confounders, while some constraint-based methods can present them. This paper proposes a causal functional model-based method called repetitive causal discovery (RCD) to discover the causal structure of observed variables affected by latent confounders. RCD repeats inferring the causal directions between a small number of observed variables and determines whether the relationships are affected by latent confounders. RCD finally produces a causal graph where a bi-directed arrow indicates the pair of variables that have the same latent confounders, and a directed arrow indicates the causal direction of a pair of variables that are not affected by the same latent confounder. The results of experimental validation using simulated data and real-world data confirmed that RCD is effective in identifying latent confounders and causal directions between observed variables.
Can Badges Foster a More Welcoming Culture on Q&A Boards?

Thriving online communities rely on a steady stream of newcomers to contribute new content. However, retaining newcomers has proven challenging. In this paper, we measure the success of an intervention used by Stack Exchange question-answering communities to create a more welcoming environment for newcomers. That intervention consisted in highlighting contributions by new users with a special indicator. We hypothesize that Stack Exchange's new policy would reduce negative reactions to new users and, ultimately, increase new user retention. We leverage causal modeling to assess the introduction of the so-called “new contributor indicator”, and we find it did not counter user retention decline in the short- and long-terms. However, our results indicate it did reduce unwelcoming reactions towards newcomers in the short-term. Our work has practical implications for online community managers aiming to improve their onboarding processes.