Reviewerly/exHarmony huggingface dataset:
MLA-SEM Characterization of Sulphide Weathering, Erosion, and Transport at the Voisey’s Bay Orthomagmatic Ni-Cu-Co Sulphide Mineralization, Labrador, Canada

The Voisey’s Bay nickel-copper-cobalt (Ni-Cu-Co) sulphide deposits constitute a significant resource of orthomagmatic mineralization. The deposits are not exposed at the surface except for in a small ferruginous gossan (Discovery Hill). The subsequent geophysical surveys and diamond drilling led to the discovery of the Ovoid ore body, buried beneath 20 m of till, and other deeper deposits in the bedrock. This study was initiated to characterize the sulphide mineralogy of these deposits through various stages of weathering, erosion, and transport. Because the samples ranged from bedrock through to a variety of surficial sediment types, the automated SEM-based identification provided by the MLA-SEM system was the ideal technique to quantitatively evaluate mineral distributions in the different media. The derived MLA-SEM data indicate that, aside from the Discovery Hill gossan, the surface sulphide mineralization at Voisey’s Bay was weathered in a pre-glaciation regolith at the Mini-Ovoid deposit and, on the surface of the Ovoid deposit, the massive sulphide was unoxidized due to a thin calcite-cemented clay cover. Pentlandite is very preferentially oxidized compared to other sulphides in the Voisey’s Bay ore, to depths of up to 10 m in bedrock. Conversely, within the coarse reject samples of crushed drill cores stored in sealed plastic bags, pyrrhotite was altered, whereas pentlandite and chalcopyrite are stable, presumably due to anaerobic reactions. The MLA-SEM detected trace amounts of minute sulphide grains in surficial sediments, but their contents abruptly decreased with distance from the sulphide mineralization. Microtextures such as troilite and pentlandite exsolution or twinning in pyrrhotite, however, could be observed in the fine sulphide grains from till, suggesting a derivation from orthomagmatic sulphide material, such as the Voisey’s Bay mineralization.
Blockchain Based Framework to Maintain Chain of Custody (CoC) in a Forensic Investigation

Cybercrimes are exponentially rising in number and the forensic investigations are now being actively conducted to get to the root of problem. There are many challenges to conduct a smooth forensics investigation. It suffers from the problem of maintaining integrity, ownership, auditability and authenticity of digital evidence. In this work, we elaborately cover as to how blockchain can be used to tackle the challenges faced by forensic investigations. In particular, we propose a framework based on blockchain which can assist in maintaining Chain of Custody while preserving integrity, accountability and authenticity of the acquired digital evidence. We devise our own smart contracts for the execution of scripts under different circumstances as governed by the phases of forensic investigation. We conclude our work by discussing future research directions and open challenges for the same.
The Power of Parallelism in Stochastic Search for Global Optimum: Keynote Paper

We explore the power of parallelism in a stochastic search for global optimum in high-dimensional optimization problems. This search, that often navigates a large solution space through a Markov-Chain Monte Carlo (MCMC) process, needs to make stochastic decisions at every step and needs to escape from local optima. Through parallelism, we are able to survey an entire neighbourhood (all states with Hamming distance of 1) to make efficient moves, use multiple replicas at different temperatures, such as in parallel tempering, or deploy a population of replicas at the same temperature. Once combined, these methods of parallelism can yield 100x to 10,000x speedups.
Association Loss and Self-Discovery Cross-Camera Anchors Detection for Unsupervised Video-Based Person Re-Identification

With the continuous improvement and development of cameras network, surveillance video has become the data source of the column stream, which greatly promotes the development of cross-camera person re-identification (Re-ID). However, supervised learning requires a lot of effort to manually label cross-cameras pairwise training data, which is lack of scalability and practical in actual video surveillance because there is a lack of well-labeled pairs of positive and negative samples under each camera. For addressing these negative effects, we set judgment conditions by using the association ranking method to self-discover positive and negative track-lets pairs of anchors with none of the pairwise ID labels, thereby defining a triplet loss. In order to optimize association loss for learning effective discriminative feature, the triplet loss adds adaptive weights according to the degree of easy-hard samples to generate an Adaptive Weighted Conditional Triplet Loss. Besides, for increasing the accuracy of self-discovering cross-camera anchors independently, which means successfully mine mutually best-matched track-lets and merge them under cross-camera, we use the top-rank from the intra-camera ranking list as a self-matched query sample which can double verify the matched-degree between top-rank. And eventually, we establish a new Association Loss and Self-Discovery Learning (ALSL) model with a complete end-to-end manner. We use three standard datasets, PRID2011, iLIDS-VID and MARS, to train the model and the experimental results prove that ALSL rank-1 is better than some superior video-based unsupervised person Re-ID methods.
Secure Boolean Spatial Keyword Query With Lightweight Access Control in Cloud Environments

Spatial keyword query has attracted wide-spread academic and industrial concerns due to the popularity of location-based services and Internet of Things. To efficiently support the online query processing, the data owners need to outsource their data to cloud platforms. However, the outsourcing services may raise privacy leaking issues. Moreover, access control, another important security concern, is largely ignored. Therefore, we first propose and formalize the problem of secure boolean spatial keyword query under lightweight access control while guaranteeing the widely accepted <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">adaptive indistinguishability against chosen keyword attack</i> model. Then, we devise a novel hybrid Bloom filter encoding strategy, including a linear embedding and exponent-based transformation schemes and a secure index structure, called <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SAGTree</monospace> . They can maintain both geo-text and access policy information together in a secure way while answering the encrypted queries under access control without decryption. Finally, we present the in-depth security analysis and demonstrate the performance of our proposed algorithms.
CausCF

To improve user experience and profits of corporations, modern industrial recommender systems usually aim to select the items that are most likely to be interacted with (e.g., clicks and purchases). However, they overlook the fact that users may purchase the items even without recommendations. The real effective items are the ones that can contribute to purchase probability uplift. To select these effective items, it is essential to estimate the causal effect of recommendations. Nevertheless, it is difficult to obtain the real causal effect since we can only recommend or not recommend an item to a user at one time. Furthermore, previous works usually rely on the randomized controlled trial (RCT) experiment to evaluate their performance. However, it is usually not practicable in the recommendation scenario due to its expensive experimental cost. To tackle these problems, in this paper, we propose a causal collaborative filtering (CausCF) method inspired by the widely adopted collaborative filtering (CF) technique. It is based on the idea that similar users not only have a similar taste on items but also have similar treatment effects under recommendations. CausCF extends the classical matrix factorization to the tensor factorization with three dimensions---user, item, and treatment. Furthermore, we also employ regression discontinuity design (RDD) to evaluate the precision of the estimated causal effects from different models. With the testable assumptions, RDD analysis can provide an unbiased causal conclusion without RCT experiments. Through dedicated experiments on both offline and online experiments, we demonstrate the effectiveness of our proposed CausCF on the causal effect estimation and ranking performance improvement.
The Smart City, Smart Contract, Smart Health Care, Internet of Things (IoT), Opportunities, and Challenges

The smart city idea is discussed in the literature in multitude of ways. Researcher Komninos specifies the smart cities and surrounding larger regions as environments with a high potential for learning and innovation, utilizing the ingenuity of population and societies with digital infrastructures to function in the physical, uniform, and numerical spaces of cities. The smart city model is defined in the literature in numerous types of ways. As cities develop and expand their services, governance and management are becoming more and more complex. Consequently, cities must adapt to address the economic, social, engineering, and environmental challenges of these transformations. Cities must become smart to face the challenges properly and increase livability and quality of life. This secondary research utilizes detailed literature review of multifaceted sources of information, such as peer-reviewed and quality academic journal articles from renowned databases. With the introduction of blockchain, numerous fields like banking, finance, health care, and supply chain shall experience positive effects. The sustainability of the smart cities can be further enhanced and ensured with the application of the blockchain technology. One important area in which the blockchain represents the important future is real estate and smart cities. Real estate has been going through global transformation. The challenges surfacing in record keeping in scenarios where the same household and/or property is put up for sale to several parties by the means of fraudulently forging the documents or records. The opportunities and challenges must be properly researched, addressed, and weighted out against each other in order to ensure a sustainable future to benefit the most if not all.
Improved handling of motion blur for grape detection after deblurring

Breakthroughs in the convolution neural network(CNN) have resolved and improved many challenges of pattern recognition in natural images. With the increased use of proximal sensing and low-cost cameras, monitoring and automation systems have gained popularity in the agriculture fields. Detection, segmentation, clustering, and counting are some fundamental problems associated with it. Here we are working on the detection of wine grapes, a crop with a variety of shapes, colors, sizes, and structures. Object detection is a challenging task especially when we are working on natural images. It is an even more difficult task when we are working on blurred images. Blur arises when images are taken via handheld camera, moving object in the automation system, or low rate video frame.Here we are trying to solve the motion blur problem in grape detection using three existing image deblurring algorithms. Performance of the deblurring algorithm is generally measured by peak-signal to noise ratio(PSNR) and structure similarity index(SSIM), but in addition to it, we have also considered blind/referenceless image spatial quality evaluator(BRISQUE). In this paper, we have comparatively analyzed: Scale recurrent network(SRN) for deep image deblurring, Multiscale convolution neural network for dynamic scale deblurring(Deep deblur), and DeblurGANv2: Deblurring(orders of magnitude) faster and better. Grape detection has experimented with yolov5x. Raw images from the standard dataset(GoPro and WGSID) were corrupted with various kinds of motion blurs. From the obtained result we can conclude that image deblurring significantly improves the performance of grape detection on the corrupted motion blur dataset.
Technical Debt Impacting Lead-Times: An Exploratory Study

Background: Technical Debt is a consolidated notion in software engineering research and practice. However, the estimation of its impact (interest of the debt) is still imprecise and requires heavy empirical and experimental inquiry. Objective: We aim at developing a data-driven approach to calculate the interest of Technical Debt in terms of delays in resolving affected tasks.Method: We conducted a case study to estimate the Technical Debt interest by analyzing its association with the lead time variation of resolving related Jira issues.Results: Data-driven approaches could significantly change the Technical Debt estimation and improve the removing Technical Debt prioritization. Our case study shows that the presence of Code Technical Debt did not affect the lead time for resolving the issues.Conclusion: Future works include the further refinement of this approach and its application to a larger data-set and on different type of issues.
Multi-hop Reading on Memory Neural Network with Selective Coverage for Medication Recommendation

Medication recommendation aiming at accurate prescription is a significant clinical application that assists caregivers in professional practice of medicine, and obtaining informative patient representations plays an important role in building effective recommendation models. Meanwhile, conducting attentive multi-hop reading on Memory Neural Network (MemNN) that stores knowledge from previous admissions is widely applied to derive contextual patterns for accurate patient representations. However, regular attentive reading may repeatedly attend to the same slots of MemNN. Although the coverage mechanism is proposed to tackle the problem, it is based on the assumption that there is one-to-one alignment between source information and target outputs, which medical records do not follow. In pursuit of a valuable model for medication recommendation, we propose the Multi-hop Reading with Selective Coverage (MRSC). MRSC firstly conducts information selection on MemNN based on the coverage of each slot. Then the method involves coverage into the attention calculation during the multi-hop reading on MemNN, making sure that all important historical records is fully utilized by balancing attention within selected information. Experiments on real-world clinical dataset demonstrate that MRSC successfully derives informative patient representations for the recommendation by conducting selection on MemNN and limiting attention adjustment within selected information.
Automating MySQL Database Complexity Estimation Based on Quantitative Metrics

The article examines the problems of automating assessment of the level of database complexity using quantitative metrics based on the developed application. The main approaches and methods of researching the quality of databases are analyzed. An algorithm for assessing the complexity of a physical database scheme based on metric characteristics: the number of tables, keys, attributes, is presented. As a result of the best solutions review, several directions in the development of software for analyzing database metrics were identified. The necessity of creating a program with the ability to customize the weight coefficients using a matrix of paired comparisons is substantiated, which allows one to get rid of subjectivity and adjust the calculation of the assessment for specific tasks. The main functionality of the developed application, settings and management of the database complexity level estimation are described. A distinctive feature of the developed software package is automatic connection to MySQL, analysis of additional metric characteristics that are absent in similar solutions, calculation of the resulting estimate using matrices of paired comparisons. Integration with other database management systems, the creation of a database manager, which enables to load a dump through the program and delete unnecessary databases, is considered as prospects for the development of the presented approach to automating the assessment of the databases complexity; as well as the introduction of statistics calculation, on the basis of which to create a statistical setting of the coefficients.
Towards Enriching Responses with Crowd-sourced Knowledge for Task-oriented Dialogue

Task-oriented dialogue agents are built to assist users in completing various tasks. Generating appropriate responses for satisfactory task completion is the ultimate goal. Hence, as a convenient and straightforward way, metrics such as success rate, inform rate etc., have been widely leveraged to evaluate the generated responses. However, beyond task completion, there are several other factors that largely affect user satisfaction, which remain under-explored. In this work, we focus on analyzing different agent behavior patterns that lead to higher user satisfaction scores. Based on the findings, we design a neural response generation model EnRG. It naturally combines the power of pre-trained GPT-2 in response semantic modeling and the merit of dual attention in making use of the external crowd-sourced knowledge. Equipped with two gates via explicit dialogue act modeling, it effectively controls the usage of external knowledge sources in the form of both text and image. We conduct extensive experiments. Both automatic and human evaluation results demonstrate that, beyond comparable task completion, our proposed method manages to generate responses gaining higher user satisfaction.
CANCN-BERT

Pre-Trained Models (PTMs) can learn general knowledge representations and perform well in Natural Language Processing (NLP) tasks. For the Chinese language, several PTMs are developed, however, most existing methods concentrate on modern Chinese and are not ideal for processing classical Chinese due to the differences in grammars and semantics between these two forms. In this paper, in order to process two forms of Chinese uniformly, we propose a novel Classical and Modern Chinese pre-trained language model (CANCN-BERT), with the advantage of effectively processing both classical and modern Chinese, which is an extension of BERT. Form-aware pre-training tasks are elaborately designed to train our model, so as to better adapt it to classical and modern Chinese corpus. Moreover, we define a joint model, proposing dedicated optimization methods through different paths with the control of the switch mechanism. Our model merges characteristics of both classical and modern Chinese, which can adequately and efficiently enhance the representation ability for both forms. Extensive experiments show that our model outperforms baseline models on processing classical and modern Chinese and achieves significant and consistent improvements. Also, the results of ablation experiments demonstrate the effectiveness of each module.
Ricardian Contracts for Industry 4.0 via the Arrowhead Contract Proxy

Industry 4.0 will require unprecedented degrees of integration across organizational boundaries, which will put new demands on infrastructure for managing agreements between industrial stakeholders. In this paper, we present a system-of-systems architecture for cross-organizational negotiation of Ricardian contracts. We also describe our implementation of it, based on Eclipse Arrowhead, and how it can produce non-repudiable contracts between local clouds, potentially owned by distinct parties. We discuss how our architecture could impact current business paradigms, as well as arguing that our design, in contrast to most solutions based on smart contracts, avoids to deviate significantly from contemporary legal praxis, which should create better opportunity for industry adoption.
Buy Me That Look: An Approach for Recommending Similar Fashion Products

Have you ever looked at an Instagram model, or a model in a fashion e-commerce web-page, and thought "Wish I could get a list of fashion items similar to the ones worn by the model!". This is what we address in this paper, where we propose a novel computer vision based technique called ShopLook to address the challenging problem of recommending similar fashion products. The proposed method has been evaluated at Myntra (www.myntra.com), a leading online fashion e-commerce platform. In particular, given a user query and the corresponding Product Display Page (PDP) against the query, the goal of our method is to recommend similar fashion products corresponding to the entire set of fashion articles worn by a model in the PDP full-shot image (the one showing the entire model from head to toe). The novelty and strength of our method lies in its capability to recommend similar articles for all the fashion items worn by the model, in addition to the primary article corresponding to the query. This is not only important to promote cross-sells for boosting revenue, but also for improving customer experience and engagement. In addition, our approach is also capable of recommending similar products for User Generated Content (UGC), eg., fashion article images uploaded by users. Formally, our proposed method consists of the following components (in the same order): i) Human keypoint detection, ii) Pose classification, iii) Article localisation and object detection, along with active learning feedback, and iv) Triplet network based image embedding model.
ALADDIN

To speed up the training of massive deep neural network (DNN) models, distributed training has been widely studied. In general, a centralized training, a type of distributed training, suffers from the communication bottleneck between a parameter server (PS) and workers. On the other hand, a decentralized training suffers from increased parameter variance among workers that causes slower model convergence. Addressing this dilemma, in this work, we propose a novel centralized training algorithm, ALADDIN, employing "asymmetric" communication between PS and workers for the PS bottleneck problem and novel updating strategies for both local and global parameters to mitigate the increased variance problem. Through a convergence analysis, we show that the convergence rate of ALADDIN is O(1 ønk ) on the non-convex problem, where n is the number of workers and k is the number of training iterations. The empirical evaluation using ResNet-50 and VGG-16 models demonstrates that (1) ALADDIN shows significantly better training throughput with up to 191% and 34% improvement compared to a synchronous algorithm and the state-of-the-art decentralized algorithm, respectively, (2) models trained by ALADDIN converge to the accuracies, comparable to those of the synchronous algorithm, within the shortest time, and (3) the convergence of ALADDIN is robust under various heterogeneous environments.