Reviewerly/exHarmony huggingface dataset:
Importance of Fracturing in Uranium Mineralization in Gulcheru Quartzite Host: A Case from Ambakapalle Area, Cuddapah Basin, Andhra Pradesh, India

Fracture and fault zones can tap the uranium-bearing fluid and help to migrate into the structural trap where the hydrothermal fluids cool and precipitate the uranium mineral. Such a structural setting gives sufficient time to react the fluid with receptive country rocks. Fracture zones have played a major role in the mineralizing event in Ambakapalle area, where uranium anomalies are located along fracture zone strike for about 1.4 km intermittently. The localization of uranium is influenced by E-W fault zone which have provided the pathway for uprising hydrothermal fluids with decreasing heat and pressure. In this context, the structural attributes are studied by mapping the fault zone and by projecting the subsurface geometry. Intersections of fractures are observed as better locations of uranium anomalies and hence given attention for prospecting the uranium mineralization. The block movement along fault is characterized as diagonal slip normal fault with a net slip direction parallel to the trace of bedding on the fault plane, which is also called trace slip fault. Therefore, from plan view, there is no apparent displacement of lithocontact across the fault. This trace slip fault with respect to the strata is interpreted as the most important structure from uranium mineralization point of view. The alteration index values for the host rocks are determined for different types of chemical as well as hydrothermal alteration, and their bearing with uranium mineralization is interpreted. High values of chlorite-carbonate-pyrite index [CCPI = (MgO + FeO) × 100/(MgO + FeO + Na2O + K2O)] indicate intense alteration of Fe- or Mg-rich minerals, such as chlorite. Low to medium ranges of Chemical Index of Alteration [CIA = Al2O3 × 100/(Al2O3 + CaO + K2O + Na2O)] values indicate variable intensity of chemical weathering due to removal of labile cations (Ca2+, Na+, K+) relative to stable residual constituent (Al3+, Ti4+). The correlation among different alteration indices and uranium is noteworthy. It is interpreted that the initial hydrothermal alteration along fault zone is masked by later low-temperature alterations. Therefore, the surficial manifestation of uranium anomalies is disturbed, and intermittent patches of uraniferous anomalies are seen along the trend of fault.
Mining and Clustering Phrases for English for Special Purpose: Travel Writing

We introduce a method for learning to find paraphrases in a given corpus. In our approach, phrases are transformed into word embeddings aimed at finding the most close phrases to retrieve phrases based on machine learning. The method involves using language models to generate the word vector representation and find paraphrases. At run-time, phrases are transformed into a set of word embeddings, and re-ranking is performed on the retrieved paraphrases. We present a prototype model that applies the method to find common phrases and their paraphrases in a travel guide. Experimental evaluation on Lonely Planet Taiwan (Country Guide)[7] shows promising results.
Applications of the Metadata Standards

Abstract This chapter addresses issues related to the practical use of the metadata standards, including syntactic interoperability and concrete scenarios from molecular modelling and simulation. It discusses challenges that arise from semantic heterogeneity, wherever multiple interoperability standards are concurrently employed for identical or overlapping domains of knowledge, or where domain ontologies need to be matched to top-level ontologies such as the European Materials and Modelling Ontology (EMMO).
Prospects for photonic implementations of neuromorphic devices and systems

Analog signal processing is one of the promising paths to enhance performance and power efficiency of neural network inference and training. Recently, analog optical neuromorphic computing concepts also gained increasing interest. We discuss properties of photonic systems in view of neuromorphic computing, applications thereof and present corresponding devices and subsystems.
Learning for Free: Object Detectors Trained on Synthetic Data

A picture is worth a thousand words, or if you want it labeled, it's worth about ten cents per bounding box. Data is the fuel that powers modern technologies run by AI engines. High quality data is important to produce accurate machine learning models. Acquiring high quality labeled data however, can be expensive and time consuming. For small companies, academic researchers, or hobbyists, gathering large datasets that are not already publicly available is challenging. This research paper will describe the ability to generate labeled image data synthetically which can be used in supervised learning for object detection. This paper describes a system using 3D modeling software in conjunction with Generative Adversarial Networks and image augmentation that can create a diverse dataset of images containing objects with bounding boxes and labels. The result of this effort is an accurate object detector in an environment of aerial surveillance with no cost to the end user.
Survey Paper on Visibility Restoration of Underwater Optical Images and Enhancement Techniques

Abstract We have investigated the problem of underwater hazy image enhancement and restoration in this paper studied. Underwater image processing has several applications in the field of oceanic research work and scientific applications such as archaeology, geology, underwater environmental assessment, laying of long distance gas pipelines and communication links across the continents which demand geo-referential surveying of the oceanic bed and prospection of ancient shipwreck. There are many difficulties for undersea optical imaging. To submerging a camera in underwater enough space is required. The maneuvering of the camera with the help from remote place or in person at the site is likewise a complex task. However, the major challenge is imposed by underwater medium properties. Underwater haze image enhancement has gained widespread importance with the rapid development of modern imaging equipment. Though, the contrast enhancement of single underwater hazy image is a cumbersome task for scientific exploration and computational application. At extreme depth, because of attenuation in light propagation, the underwater images are susceptible to inferior visibility.
Alert and Response of Earthquake and Tsunami for Community Based Disaster Risk Reduction

Abstract Indonesia had been suffered by earthquake and tsunami for many centuries. Since many lives have taken by tsunami strikes, alert and response system are very important as important part of Disaster Risk Reduction, especially in area with high population density. This research conducted in Kangkung fishing village in Bandar Lampung coastal area which is an earthquake and tsunami prone area. The aim of the research is to study alert and response of Community Based Disaster Risk Reduction. The research conducted by qualitative method and purposive-descriptive approach. There were 53 respondents which participated in the survey and interview. It is reported that “Smart Resilient City” model suitable for community needs of alert and response of earthquake and tsunami disaster. This research meets conclusion that “Smart Resilient City” model may become a great solution in Community Based Disaster Risk Reduction to cope with the hazard of earthquake and tsunami comprehensively and sustainably.
Medical Record Storage Model Based on Credibility Assessment H-Algorand Consensus Mechanism

Abstract In order to solve the problems of poor security and low throughput of the current electronic medical records, this paper proposes an electronic medical record storage model based on H-Algorand consensus mechanism of credibility assessment combined with blockchain technology. To ensure the normal operation of blockchain system effectively, the model used a credibility assessment algorithm to obtain the trust value and replace the account balance in the H-Algorand consensus mechanism with the trust value as the node weight. The results show that the storage model adopted here has higher security and throughput efficiency.
General Domain Adaptation Through Proportional Progressive Pseudo Labeling

Domain adaptation helps transfer the knowledge gained from a labeled source domain to an unlabeled target domain. During the past few years, different domain adaptation techniques have been published. One common flaw of these approaches is that while they might work well on one input type, such as images, their performance drops when applied to others, such as text or time-series. In this paper, we introduce Proportional Progressive Pseudo Labeling (PPPL), a simple, yet effective technique that can be implemented in a few lines of code to build a more general domain adaptation technique that can be applied on several different input types. At the beginning of the training phase, PPPL progressively reduces target domain classification error, by training the model directly with pseudo-labeled target domain samples, while excluding samples with more likely wrong pseudo-labels from the training set and also postponing training on such samples. Experiments on 6 different datasets that include tasks such as anomaly detection, text sentiment analysis and image classification demonstrate that PPPL can beat other baselines and generalize better.
Non-Ghosting Stitching for Street-View Image

Parallax, relative camera motion, and illumination changes can cause artifacts in stitched images. For example, we might see ghosting effect on the pedestrian who is located at the overlapping region in a stitched image. Existing techniques struggle at dealing with the artifacts and perspective distortion. We propose an innovative image stitching algorithm which combines portrait segmentation and image inpainting. Images from different cameras are first segmented into the pedestrian plane and the scene plane by portrait segmentation. Then, images in different planes are stitched. Finally, the pedestrian plane and the scene plane are fused by aligning and inpainting. Experiments show that our method can remove ghosting effect in stitched street-view images more effectively than existing meth-ods.
Semi-supervised semantic segmentation using an improved generative adversarial network

This paper proposes a better semi-supervised semantic segmentation network using an improved generative adversarial network. It is important for the discriminator on the pixel level to know whether it correctly distinguishes the predicted probability map. However, currently there is no correlation between the actual credibility and the confidence map generated by the pixel-level discriminator. We study this problem and a new network is proposed, which includes one generator and two discriminators. One of the discriminators can output more reliable confidence maps on the pixel level and the other is trained to generate the probability on the image level, which is used as the dynamic threshold in the semi-supervised module instead of being set manually. In addition, the trusted region shared by the two discriminators is used to provide the semi-supervised reference. Through experiments on the PASCAL VOC 2012 and Cityscapes datasets, the proposed network brings better gains, proving the effectiveness of the network.
Students' Roles in Learning English through Mobile Assisted Language Learning (MALL): A Teachers' Beliefs View

This study aims to explore teacher’s beliefs about students’ roles in learning English through MALL and the factors contributing to their beliefs. MALL which stands for Mobile Assisted Language Learning were the main instructional media where there was no such direct physical meeting in school. This media became popular nowadays, despite its emergence in the early 2000s, especially during Covid-19 pandemic which prevents both students and teachers to have direct physical meeting in the classroom. It was important to understand what role should be played by the students which in this research was viewed from teachers’ beliefs perspective. Therefore, the research questions formulated by the researcher were; (1) What are teacher’s beliefs regarding students’ roles in learning English through MALL? (2) What are the factors shaping their beliefs? In terms of methodology employed by the researcher, this research was qualitative with descriptive case study design. The subjects of this research were two teachers of Senior High School in Surakarta, Indonesia who were teaching English through MALL since 2017. The subjects were a Javanese male and Bataknese female who were 37 years old. Both of teachers had master degree from English Education Department. The instrument employed to acquire the data was interviews. The data then were categorized into the students’ roles based on the theories. The result indicated that four students’ roles became something that the teacher held as the truth. Those roles were participant, initiator, performer, and passive receptor. Meanwhile, the factors determining teacher’s beliefs were discovered five factors.
Roulette: A Pruning Framework to Train a Sparse Neural Network From Scratch

Due to space and inference time restrictions, finding an efficient and sparse sub-network from a dense and over-parameterized network is critical for deploying neural networks on edge devices. Recent efforts explore obtaining a sparse sub-network by performing network pruning during training procedures to reduce training costs, such as memory and floating-point operations (FLOPs). However, these works take more than 1.4× the total number of iterations and try all possible pruning parameters manually to obtain sparse sub-networks. In this paper, we present a pruning framework Roulette to train a sparse network from scratch. First, we propose a novel method to train a sparse network by Pruning through the lens of Loss Landscape iteratively and automatically (PLL). We do a theoretical analysis that the curvature of the loss function is higher in the initial phase and can conduct us to start network pruning. According to our results on CIFAR-10/100 and ImageNet dataset, PLL saves up to 4× training FLOPs than prior works while maintaining comparable or even better accuracy. Then we design push and pull operations to synchronize the pruned weights on different GPUs during training, scaling PLL to multiple GPUs linearly. To our knowledge, Roulette is the first network pruning framework supporting multiple GPUs linearly.
Enhancing the efficiency of continuous integration environment in DevOps

Abstract Faster release of features to the production environment is the need of the hour of every IT organizations. This faster and continuous software delivery is made possible through DevOps. DevOps is a set of practices that bridges the gap between development team and operations team. The different aspects of DevOps came from agile methodology. Continuous integration is the paramount of DevOps life cycle. In a continuous integration development practice, the development code and the applications code are continuously integrated several times a day. Developers follow the practice of committing changes frequently into the version control system such as git. A change in the version control system triggers the continuous integration system such as Jenkins. This exploratory study presents the ways that support the developers during the build break in continuous integration environment and thereby improve the efficiency of continuous integration process. Our findings contribute that the selected list of plugins in the Jenkins support the developers during the build break, thus developers can come up with faster recovery of build failures which in turn increase the efficiency of continuous integration environment.
Dynamic Performance Improvement of DFIM based on Hybrid Computational Technique

This paper presents a hybrid intelligent nonlinear control, based on particle swarm optimization (PSO) technique and artificial intelligence controller (AI) to improve the dynamic performance of the system. These controllers are destined for the speed control of Doubly Fed Induction Motor (DFIM). The proportional-integral controller for speed regulation of the induction motor is the most extensively used controller. However, given the various operating conditions and the nature of parameter variability, the PI controller has some drawbacks. So, one of the frequently discussed applications of artificial intelligence (AI) in control is the replacement of a proportional integral speed controller with Artificial Neural Network (ANN) speed controller but the choice of the gain's parameters controller is one of the main problems. So, Particle Swarm Optimization (PSO) technique on optimization performance is added to the PI and ANN controllers to find the best gain values. The simulation results for different scenarios illustrate the high performance of the proposed artificial intelligence controller for DFIM running at variable speeds in terms of consistency and stability.
Efficient Handling of Distributed Data Vending Through Blockchains

Data is an essential requirement for a number of approaches that have a requirement for large amounts of data such as PHR or public health records for medical institutions, relevant data for the machine learning prediction algorithms, etc. Therefore, there are data aggregators or vendors that are concerned with the collection management and delivery of data on request. This is a novel scheme with one major drawback that there is a low level of trust between the data vendors and the data providers which hampers the data vending mechanism and impacts the various services that are reliant on this data. Hence, to improve the security of the data and increase the reliability of the system, an efficient access control mechanism is implemented along with the introduction of the blockchain paradigm to provide a secure and reliable data vending system. The proposed methodology utilizes the RSA encryption technology to provide effective security to the data along with the use of NLP protocols and Pearson correlation to achieve efficient access control on the data vending platform.
VQ-oriented data hiding based on adjustable error compensation strategy

Nowadays, we greatly depend on Internet mass transmission, of all kinds of data, including critical information among them. Therefore, secure communication is an important topic. Data hiding can embed critical information into carriers, such as images, videos, and so on. Efficiently embedding information into images is the goal of this research. Image steganography techniques utilise a cover image to hide secret data and produce a stego-image by modifying pixel values. After modification, the stego-image is distorted with respect to the cover image. Vector quantisation (VQ) is a lossy image compression technique. The image recovered from the VQ compressed code has distortion compared to the original image. Based on the VQ image, we can hide secret data by modifying the pixel value in a way that the distortion is compensated. The embedding rate of the proposed scheme is adjustable. Experimental results show that our scheme can achieve a high embedding rate in comparison with related works. For low-quality VQ images, embedding can improve the visual quality of the stego-image at the same time.
Towards Self-Tuning Parameter Servers

Recent years, many applications have been driven advances by the use of Machine Learning (ML). Nowadays, it is common to see industrial-strength machine learning jobs that involve millions of model parameters, terabytes of training data, and weeks of training. Good efficiency, i.e., fast completion time of running a specific ML training job, therefore, is a key feature of a successful ML system. While the completion time of a long-running ML job is determined by the time required to reach model convergence, that is also largely influenced by the values of various system settings. In this paper, we contribute techniques towards building self-tuning parameter servers. Parameter Server (PS) is a popular system architecture for large-scale machine learning systems; and by self-tuning we mean while a long-running ML job is iteratively training the expert-suggested model, the system is also iteratively learning which system setting is more efficient for that job and applies it online. Our techniques are general enough to various PS-style ML systems. Experiments on TensorFlow show that our techniques can reduce the completion times of a variety of long-running TensorFlow jobs from 1.4× to 18×.
Knowledge-Based Named Entity Recognition of Archaeological Concepts in Dutch

The advancement of Natural Language Processing (NLP) allows the process of deriving information from large volumes of text to be automated, making text-based resources more discoverable and useful. The attention is turned to one of the most important, but traditionally difficult to access resources in archaeology; the largely unpublished reports generated by commercial or “rescue” archaeology, commonly known as “grey literature”. The paper presents the development and evaluation of a Named Entity Recognition system of Dutch archaeological grey literature targeted at extracting mentions of artefacts, archaeological features, materials, places and time entities. The role of domain vocabulary is discussed for the development of a KOS-driven NLP pipeline which is evaluated against a Gold Standard, human-annotated corpus.
Balwin-Teaching-Learning-Based Artificial Raindrop Algorithm for UAV Route Planning

The prominent shortcoming of the basic artificial raindrop algorithm in UAV route planning is easily trapped into local optimal solution. In the present work, the original artificial raindrop algorithm is improved. A Balwin-teaching-learning-based artificial raindrop algorithm (BTLARA) is proposed, whereby each raindrop updates itself by using the combination of its own unique mode and Balwin-teaching-learning-based optimization pattern operator. In order to demonstrate the effectiveness of this algorithm, the UAV route planning is utilized for simulation. According to the results, the algorithm proposed in this paper significantly enhances the convergence and can obtain higher-quality navigation trace and convergence, which enables it to better avoid threat paths.
P2T: Pay to Transport

We present Pay To Transport (P2T), a protocol that lets customers buy an item remotely in an atomic, privacy preserving and trustless manner. P2T needs only basic features of a blockchain scripting language and does not need any tracking systems, arbitrator or deposit to preserve its security properties. For this reason the protocol can be implemented on any permissionless blockchain, regardless of its scripting language, without additional trust. Merchants’ and transporters’ addresses are public, but in P2T the parties never pay those addresses directly. Therefore P2T maintains the privacy of customers, merchant and transporters.
Automating Domain Squatting Detection Using Representation Learning

Registering altered domain names with the purpose of confusing users and conducting malicious activities is one of the most widespread types of attacks on the Web, conforming a family of techniques known as domain squatting. Detecting these domains is a difficult task, given the large a mount of combinations and the massive and heterogeneous nature of the Web. In this work, we propose a set of models to firstly learn the distributional regularities from detected squatted domains, and from that, automatically generate realistic modified domains. Our goal is to proactively guide the generation of squatted domains towards malicious domains that exists but have not been detected yet. We conducted an empirical study for both typo-squatting and combo-squatting generation approaches against strong baselines on real world data, showing their feasibility and providing insights to support for proactive defense in the context of cloud security.