Reviewerly/exHarmony huggingface dataset:
Neural Formatting for Spreadsheet Tables

Spreadsheets are popular and widely used for data presentation and management, where users create tables in various structures to organize and present data. Table formatting is an important yet tedious task for better exhibiting table structures and data relationships. However, without the aid of intelligent tools, manual formatting remains a tedious and time-consuming task. In this paper, we propose CellGAN, a neural formatting model for learning and recommending formats of spreadsheet tables. Based on a novel conditional generative adversarial network (cGAN) architecture, CellGAN learns table formatting from real-world spreadsheet tables in a self-supervised fashion without requiring human labeling. In CellGAN we devise two mechanisms, row/column-wise pooling and local refinement network, to address challenges from the spreadsheet domain. We evaluate the effectiveness of CellGAN against real-world datasets using both quantitative metrics and human perception studies. The results indicate remarkable performance gains over rule-based methods, graphical models or direct application of the state-of-the-art cGANs used in visual synthesis tasks. Neural Formatting is the first step towards auto-formatting for spreadsheet tables with promising results.
Decision-based model selection

A key step in data-driven decision making is the choice of a suitable mathematical model. Complex models that give an accurate description of reality may depend on many parameters that are difficult to estimate; in addition, the optimization problem corresponding to such models may be computationally intractable and only approximately solvable. Simple models with only a few unknown parameters may be misspecified, but also easier to estimate and optimize. With such different models and some initial data at hand, a decision maker would want to know which model produces the best decisions. In this paper we propose a decision-based model-selection method that addresses this question.
Dialogue Games for Explaining Medication Choices

SMT solvers can be used efficiently to search for optimal paths across multiple graphs when optimising for certain resources. In the medical context, these graphs can represent treatment plans for chronic conditions where the optimal paths across all plans under consideration are the ones which minimize adverse drug interactions. The SMT solvers, however, work as a black-box model and there is a need to justify the optimal plans in a human-friendly way. We aim to fulfill this need by proposing explanatory dialogue protocols based on computational argumentation to increase the understanding and trust of humans interacting with the system. The protocols provide supporting reasons for nodes in a path and also allow counter reasons for the nodes not in the graph, highlighting any potential adverse interactions during the dialogue.
Antagonizing Ambiguity – Towards a Taxonomy for Agile Development

Agile development has become a standard procedure in the software industry in the course of the past 20 years and is advancing into physical product development nowadays as well. When talking with peers and fellow experts from both academia and industry, ambiguities on the usage of the terms used in agile development becomes apparent. Therefore, this contribution aims at proposing a taxonomy for the field of agile development by comparing its key elements to the traditional field of product development. The terms are well-grounded in scientific literature and by applying a systematic as well as a hierarchical approach, the terms are classified into the three layers of the St. Gallen management model proposed by Bleicher. In order to validate the taxonomy, comparisons to the field of traditional product development are given. In line with this, the alignment of specific terms used in ‘Scrum’ into the taxonomy for agile development underpins its validity.
Looking ahead: Joint small group detection and tracking in crowd scenes

Small group detection and tracking in crowd scenes are basis for high level crowd analysis tasks. However, it suffers from the ambiguities in generating proper groups and in handling dynamic changes of group configurations. In this paper, we propose a novel delay decision-making based method for addressing the above problems, motivated by the idea that these ambiguities can be solved using rich temporal context. Specifically, given individual detections, small group hypotheses are generated. Then candidate group hypotheses across consecutive frames and their potential associations are built in a tree. By seeking for the best non-conflicting subset from the hypothesis tree, small groups are determined and simultaneously their trajectories are got. So this framework is called joint detection and tracking. This joint framework reduces the ambiguities in small group decision and tracking by looking ahead for several frames. However, it results in the unmanageable solution space because the number of track hypotheses grows exponentially over time. To solve this problem, effective pruning strategies are developed, which can keep the solution space manageable and also improve the credibility of small groups. Experiments on public datasets demonstrate the effectiveness of our method. The method achieves the state-of-the-art performance even in noisy crowd scenes.
Automatic First Arrival Picking on P-Wave Seismic Signal Using Support Vector Machine Method

Automatic First Arrival Picking is a system that can get a P-Wave or the first wave that comes in an earthquake wave. Because of the P-Wave is the first wave to come, it needs research that can get the arrival of P-Wave automatically. The aim of this study is to create an Automatic First Arrival Picking system and to test the performance of methods that will later get P-Wave Picking results and also to get the accuracy of the Support Vector Machine (SVM) as its classification method. First, earthquake sample data must go through the Feature Extraction stage so that the feature results can be used as input to the SVM classification method. In this study sample data S-Wave and Noise are considered as No P-Wave, so there are only two classifications in SVM, namely P-Wave and No P-Wave. The results of this research were got an Automatic First Arrival Picking system with an accuracy performance of 88.00%, precision of 90.00%, recall of 73.50%,f1-score of 78.00% with certain time windowing, data partition, and regularization (C) parameter.
Tracking earthquake sequences in real time: application of Seismicity-Scanning based on Navigated Automatic Phase-picking (S-SNAP) to the 2019 Ridgecrest, California sequence

SUMMARY Recent improvements in seismic data processing techniques have enhanced our ability to detail the evolution of major earthquake sequences in space and time. One such advance is new scanning algorithms that allow large volumes of waveform data to be analysed automatically, removing human biases and inefficiencies that inhibit standardized monitoring. The Seismicity-Scanning based on Navigated Automatic Phase-picking (S-SNAP) workflow has previously been shown to be capable of producing high-quality earthquake catalogues for injection-induced seismicity monitoring. In this study, we modify the original S-SNAP workflow to enable it to delineate the spatiotemporal distribution of major earthquake sequences in real time. We apply it to the 2019 Ridgecrest, southern California earthquake sequence, which culminated in an Mw 6.4 foreshock on July 4 and an Mw 7.1 main shock on July 6 and generated tens of thousands of smaller earthquakes. Our catalogue—which spans the period 2019 June 1 to July 16—details the spatiotemporal evolution of the sequence, including early foreshocks on July 1 and accelerating foreshocks on July 4, a seismicity gap before the main shock around its epicentre, seismicity on discrete structures within a broad fault zone and triggered earthquakes outside the main fault zone. We estimate the accuracy and false detection rate of the S-SNAP catalogue based on the reviewed catalogue reported by Southern California Seismic Network (SCSN) and our own visual inspection. We demonstrate the advantages of S-SNAP over a generalized automatic earthquake monitoring software, Seiscomp3, and a customized real-time earthquake information system for southern California, TriNet. In comparison, the S-SNAP catalogue contains five times more events than the Seiscomp3 catalogue and 1.4–2.2 times as many events per hour as the TriNet catalogue at most times. In addition, S-SNAP is more likely to solve phase association ambiguities correctly and provide a catalogue with consistent quality through time. S-SNAP would be beneficial to both routine network operations and the earthquake review process.
A Correlated Equilibrium based Transaction Pricing Mechanism in Blockchain

Although transaction fees are not obligatory in most of the current blockchain systems, extensive studies confirm their importance in maintaining the security and sustainability of blockchain. To enhance blockchain in the long term, it is crucial to design effective transaction pricing mechanisms. Different from the existing schemes based on auctions with more consideration about the profit of miners, we resort to game theory and propose a correlated equilibrium based transaction pricing mechanism through solving a pricing game among users with transactions, which can achieve both the individual and global optimum. To avoid the computational complexity exponentially increasing with the number of transactions, we further improve the game-theoretic solution with an approximate algorithm, which can derive almost the same results as the original one but costs significantly reduced time. Experimental results demonstrate the effectiveness and efficiency of our proposed mechanism.
Implicit Aspect Extraction in Product Reviews Using FIN Algorithm

Online transactions are growing very rapidly right now. Every online transaction is often accompanied by a review. Product reviews from buyers can be used by sellers as feedback. Product reviews provide information as a consideration for decision making for potential buyers to find out the strengths and weaknesses of the product. Identifying specific product features from reviews written by buyers becomes a solution to make it easier to find information. Aspect-based extraction in sentiment analysis is divided into two, explicit aspects and implicit aspects. The explicit aspect is the explicit aspect in the sentence while the implicit aspect is the aspect that is implied in the sentence. The extraction carried out in this study is based on implicit aspects to determine its features because the majority of existing studies extract explicit aspects. Implicit extraction aspects of product reviews using the FIN algorithm in association rule mining. The dataset is in English text where to extract features using TF-IDF and select features using Particle Swarm optimization. Selected features are grouped using k-Means. After features are grouped based on their value, an associative rule is made using the FIN algorithm. The minimum support value applied and the number of sentence variations cause the accuracy value obtained by 0.678.
Pedestrian Detection in Metro Station Based on Improved SSD

In crowded scenes such as subway stations, due to the high pedestrian density and high occlusion, most existing pedestrian detection methods are difficult to obtain a good result, and have poor robustness to small targets. In response to this problem, our work proposes a modified human detection network which based the SSD(Single Shot MultiBox Detector) model. First, combined with the pedestrian data analysis of the scene, the head and shoulder model is introduced. Then, the SF-SSD network is proposed. Based on the SSD network, the low-level feature map is merged and its location information is used to accurately detect small targets. Finally, experiments are carried out on the subway pedestrian dataset. The experimental results show that the proposed method of SF-SSD network effectively reduces the false positive rate and missed detection rate of SSD network on subway pedestrian dataset, and meets the real-time performance requirement of pedestrian detection. At the same time, the robustness is improved, and pedestrian detection in dense crowd scenarios is effectively realized.
Adaptive Attention Generation for Indonesian Image Captioning

Image captioning is one of the most widely discussed topic nowadays. However, most research in this area generate English caption while there are thousands of language exist around the world. With their language uniqueness, there's a need of specific research to generate captions in those languages. Indonesia, as the largest Southeast Asian country, has its own language, which is Bahasa Indonesia. Bahasa Indonesia has been taught in various countries such as Vietnam, Australia, and Japan. In this research, we propose the attention-based image captioning model using ResNet101 as the encoder and LSTM with adaptive attention as the decoder for the Indonesian image captioning task. Adaptive attention used to decide when and at which region of the image should be attended to produce the next word. The model we used was trained with the MSCOCO and Flick30k datasets besides. Both datasets are translated manually into Bahasa by human and by using Google Translate. Our research resulted in 0.678, 0.512, 0.375, 0.274, and 0.990 for BLEU-1, BLEU-2, BLEU-3, BLEU-4, and CIDEr scores respectively. Our model also produces a similar score for the English image captioning model, which means our model capable of being equivalent to English image captioning. We also propose a new metric score by conducting a survey. The results state that 76.8% of our model's caption results are better than validation data that has been translated using Google Translate.
Design and Application of a Digital Factory Model for Factory Restructuring

Factory planning has the task of designing products, plants, processes and the building of a factory. The requirements to the factory planning and the building concerned have changed in the last years. Regular restructuring gains more importance in order to maintain the competitiveness of a factory. Restrictions regarding new areas, shorter life cycles of product and production technology as well as a VUCA (Volatility, Uncertainty, Complexity & Ambiguity) world cause more frequently occurring restructuring measures within a factory. The interfaces between factory planning and building planning are crucial in conversion processes to optimally meet the new requirements of a factory building and to react to short-term adjustments. Because of the high adaptation rate of factories described above, a methodology for restructuring factory buildings based on an up-to-date digital factory model is conceived and designed for practical application in factory restructuring projects. The emphasis is on decision making in building conversion processes of factory buildings. The aim is to shorten the decision-making process on the construction site. This requires the application of a methodology that reduces the deficits of the existing approaches. Finally, it will be shown how to best use the digital factory model in practice. It is about designing a mobile application to meet the needs of factory planners on site. An Augmented Reality (AR) application is designed and created with focus to the UI to provide decision support for planning variants. The time and cost expenditure are represented in the AR tablet solution based on a Building Information Model (BIM).
Fermat’s spiral smooth planar path planning under origin-departing and corner-cutting transitions for autonomous marine vehicles

This research investigates the planar path planning method, particularly where straight survey line in ocean exploration is required. The smooth transition path between multiple straight survey segments in origin-departing and corner-cutting transition modes is studied. First, different transition modes of autonomous marine vehicles (AMVs) in practical ocean survey navigation are realized and distinguished. Based on the definition of different transition modes, the waypoints can be pre-assigned, generated online, or compatible with online and offline assignments. Second, to avoid wiggles of vehicles, zigzags of the tracking trajectory, and to satisfy the requirements on second-order parametric and geometric continuities, the Fermat's Spiral (FS) having advantages of low computational load and engineering practicability is selected to design the transition path between multiple straight segments. The scaling of FS segments is optimized by considering the maneuverability of AMVs. Besides, the FS is parameterized in both transition modes to simplify the subsequent guidance and control scenarios. Thirdly, the closed-form analysis on geometric and parametric continuities of spliced transition path are derived. Finally, based on the parameterized smooth path and a slender underactuated AUV, numerical simulation demonstrates the effectiveness of the FS-based path planning method under integrated origin-departing and corner-cutting transition modes.
A Generalized Stress Inversion Approach With Application to Residual Stress Estimation

Abstract We develop a generalized stress inversion technique (or the generalized inversion method) capable of recovering stresses in linear elastic bodies subjected to arbitrary cuts. Specifically, given a set of displacement measurements found experimentally from digital image correlation (DIC), we formulate a stress estimation inverse problem as a partial differential equation-constrained optimization problem. We use gradient-based optimization methods, and we accordingly derive the necessary gradient and Hessian information in a matrix-free form to allow for parallel, large-scale operations. By using a combination of finite elements, DIC, and a matrix-free optimization framework, the generalized inversion method can be used on any arbitrary geometry, provided that the DIC camera can view a sufficient part of the surface. We present numerical simulations and experiments, and we demonstrate that the generalized inversion method can be applied to estimate residual stress.
A Challenge-Response Assisted Authorisation Scheme for Data Access in Permissioned Blockchains

Permissioned blockchains can be applied for sharing data among permitted users to authorise the data access requests in a permissioned blockchain. A consensus network constructed using pre-selected nodes should verify a data requester's credentials to determine if he or she have the correct permissions to access the queried data. However, current studies do not consider how to protect users' privacy for data authorisation if the pre-selected nodes become untrusted, e.g., the pre-selected nodes are manipulated by attackers. When a user's credentials are exposed to pre-selected nodes in the consensus network during authorisation, the untrusted (or even malicious) pre-selected nodes may collect a user's credentials and other private information without the user's right to know. Therefore, the private data exposed to the consensus network should be tightly restricted. In this paper, we propose a challenge-response based authorisation scheme for permissioned blockchain networks named Challenge-Response Assisted Access Authorisation (CRA3) to protect users' credentials during authorisation. In CRA3, the pre-selected nodes in the consensus network do not require users' credentials to authorise data access requests to prevent privacy leakage when these nodes are compromised or manipulated by attackers. Furthermore, the computational burden on the consensus network for authorisation is reduced because the major computing work of the authorisation is executed by the data requester and provider in CRA3.
SPBC: A self-paced learning model for bug classification from historical repositories of open-source software

One of the areas most in need of improvement in the field of automated bug fixing, localization and triaging systems is that of an effective categorization, as this would bugs to reduce the time, cost and effort required to locate, assign and fix the bug. The existing approaches depend upon the textual similarity of the bug description and category in a given reported bug; accordingly, the challenges of unstructured bugs, technical terms, versatile ways of reporting the same bug, the diverse nature and sizes of datasets etc. are often overlooked. Consequently, this limits the classifier performance to a specific type of dataset, resulting in classification inefficiency. To this end, we propose a novel Self-Paced Bug Classifier (SPBC) that is capable of locating the target categories from the bug description of the historical data, maintained by multiple open-source software packages (Bugzilla, Mentis, Redmine). The proposed model introduces a self-paced back-traceable algorithm, controlled by a self-paced regularizer, which classifies textually independent bug descriptions with weighted data-independent tokens (the easy samples). Later on, the regularizer sets comparatively hard samples for textually dependent classification by capturing intra-class and inter-class discrimination features from bug descriptions, based on the weighted similarities of words; this is done with the help of a Key Feature Identification Matrix (KFIM), a Non-Independent and Identically Distributed (NIID) matrix. Easy-to-hard self-pace learning, integrated with textually dependent and independent classification, makes SPBC capable of simultaneously enhancing the effectiveness and robustness of intelligent systems through a substantial increase in precision (5–15% on average). The main advantage of SPBC is that it targets the spatial relationship between the data and the system, which makes it an apt learner of data and allows it to maintains sample insertion into the classifier at a controlled pace. Additionally, it maintains stability, which is not affected by the dataset’s dimensionality and traits. As is evidenced by the experimental results on four different datasets from open-source projects, our model outperforms the baseline and state-of-the-art methods through a single-stroke solution with improved accuracy and stable performance (average 95% precision and 4% decrease in kappa); hence, it is significant for improving intelligent bug fixing and triaging systems.
Object Tracking with Raspberry Pi using Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM)

Current technological advances have made possible for object tracking activity to become more intelligent. In order to track objects, the camera must be equipped with a computing device that can process video images. A Raspberry Pi embedded computer is chosen because of its smaller size, making it suitable to embed into devices such as camera surveillance. It is used to process the image recorded by the camera so that the camera angle can follow the movement of objects. The image processing is performed using the Histogram Oriented Gradients and Support Vector Machine method which is implemented in the Raspberry Pi. Based on the test results, the best accuracy is achieved using the threshold at 175 with the best distance of 6 meters.