Reviewerly/exHarmony huggingface dataset:
Big Data Analytics to Enable Integrated Research of Biodiversity and Climate Datasets in the Amazon Basin

With the mass adoption of data analysis in several scientific fields such as climatology, medicine, astronomy and astrophysics, the availability of an appropriate analytics infrastructure has become a necessity increasingly recognized by the scientific community. However, appropriate tools and applications are required to process the large volume of data collected and generated by researchers. One of the biggest challenges lies in the fact that these tools need to be gathered to be applied in specific domains. The area of bioclimatic data is a scientific field that still has much to improve in this matter. It is a field of study that lacks great efforts in the direction to provide methodologies and tools to facilitate the understanding of the complex phenomena involved in the influence that environmental variables have on biodiversity on the planet. Thus, the purpose of this work is to propose a big data analytics architecture that presents an ecosystem that systematizes and facilitates the task of the scientists to deal with the complexity in the bioclimatic data analysis, providing tools for storage, management, analysis using machine learning algorithms and data mining, and visualization tools. The methodological approach of this work was to make a thorough bibliographical study to verify the most used tools and the suitability of each one to the purpose of the work. In addition, the literature provided indications of software ecosystem implementations methodologies that served as a guide in the architecture design. Within the architecture, we attempted to gather a set of bioclimatic data based on a subset of data obtained from the Atmospheric Radiation Measurement (ARM) data repository for climatic data, and the Brazilian Biodiversity Portal for biodiversity data. As a result, we were able to gather a series of tools to access data such as Cassandra, distribution of processing such as Spark, programming interface represented by Jupyter Notebook, system modules for data format conversion, machine learning algorithms libraries and software for data visualization. This research discuss the importance of a domain purpose design of a data analysis architecture for bioclimatic data. We concluded that this type of ecosystem is imperative to facilitate the research process and increase the quality of the results.
Cross-lingual transfer learning for spoken language understanding

Typically, spoken language understanding (SLU) models are trained on annotated data which are costly to gather. Aiming to reduce data needs for bootstrapping a SLU system for a new language, we present a simple but effective weight transfer approach using data from another language. The approach is evaluated with our promising multi-task SLU framework developed towards different languages. We evaluate our approach on the ATIS and a real-world SLU dataset, showing that i) our monolingual models outperform the state-of-the-art, ii) we can reduce data amounts needed for bootstrapping a SLU system for a new language greatly, and iii) while multitask training improves over separate training, different weight transfer settings may work best for different SLU modules.
Camera Exposure Control for Robust Robot Vision with Noise-Aware Image Quality Assessment

In this paper, we propose a noise-aware exposure control algorithm for robust robot vision. Our method aims to capture the best-exposed image which can boost the performance of various computer vision and robotics tasks. For this purpose, we carefully design an image quality metric which captures complementary quality attributes and ensures light-weight computation. Specifically, our metric consists of a combination of image gradient, entropy, and noise metrics. The synergy of these measures allows preserving sharp edge and rich texture in the image while maintaining a low noise level. Using this novel metric, we propose a real-time and fully automatic exposure and gain control technique based on the Nelder-Mead method. To illustrate the effectiveness of our technique, a large set of experimental results demonstrates higher qualitative and quantitative performances when compared with conventional approaches.
Confirmatory Aspect-based Opinion Mining Processes

A new opinion extraction method is proposed to summarize unstructured, user-generated content (i.e., online customer reviews) in the fixed topic domains. To differentiate the current approach from other opinion extraction approaches, which are often exposed to a sparsity problem and lack of sentiment scores, a confirmatory aspect-based opinion mining framework is introduced along with its practical algorithm called DiSSBUS. In this procedure, 1) each customer review is disintegrated into a set of clauses; 2) each clause is summarized to bi-terms-a topic word and an evaluation word-using a part-of-speech (POS) tagger; and 3) each bi-term is matched to a pre-specified topic relevant to a specific domain. The proposed processes have two primary advantages over existing methods: 1) they can decompose a single review into a set of bi-terms related to pre-specified topics in the domain of interest and, therefore, 2) allow identification of the reviewer's opinions on the topics via evaluation words within the set of bi-terms. The proposed aspect-based opinion mining is applied to customer reviews of restaurants in Hawaii obtained from TripAdvisor, and the empirical findings validate the effectiveness of the method. Keywords: Clause-based sentiment analysis, Customer review, Opinion mining, Topic modeling, User-generate-contents.
BERT-Based Multi-Head Selection for Joint Entity-Relation Extraction

In this paper, we report our method for the Information Extraction task in 2019 Language and Intelligence Challenge. We incorporate BERT into the multi-head selection framework for joint entity-relation extraction. This model extends existing approaches from three perspectives. First, BERT is adopted as a feature extraction layer at the bottom of the multi-head selection framework. We further optimize BERT by introducing a semantic-enhanced task during BERT pre-training. Second, we introduce a large-scale Baidu Baike corpus for entity recognition pre-training, which is of weekly supervised learning since there is no actual named entity label. Third, soft label embedding is proposed to effectively transmit information between entity recognition and relation extraction. Combining these three contributions, we enhance the information extracting ability of the multi-head selection model and achieve F1-score 0.876 on testset-1 with a single model. By ensembling four variants of our model, we finally achieve F1 score 0.892 (1st place) on testset-1 and F1 score 0.8924 (2nd place) on testset-2.
G-IOTA: Fair and confidence aware tangle

This paper proposes strategies to improve the IOTA tangle in terms of resilience to splitting attacks. Our contribution is two fold. First, we define the notion of confidence fairness for tips selection algorithms to guarantee the first approval for all honest tips. Then, we analyze IOTA-tangle from the point of view of confidence fairness and identify its drawbacks. Second, we propose a new selection mechanism, G-IOTA, that targets to protect tips left behind. G-IOTA therefore has a good confidence fairness. G-IOTA lets honest transactions increase their confidence efficiently. Furthermore, G-IOTA includes an incentive mechanism for users who respect the algorithm and punishes conflicting transactions. Additionally, G-IOTA provides a mutual supervision mechanism that reduces the benefits of speculative and lazy behaviours.
SegSort: Segmentation by Discriminative Sorting of Segments

Almost all existing deep learning approaches for semantic segmentation tackle this task as a pixel-wise classification problem. Yet humans understand a scene not in terms of pixels, but by decomposing it into perceptual groups and structures that are the basic building blocks of recognition. This motivates us to propose an end-to-end pixel-wise metric learning approach that mimics this process. In our approach, the optimal visual representation determines the right segmentation within individual images and associates segments with the same semantic classes across images. The core visual learning problem is therefore to maximize the similarity within segments and minimize the similarity between segments. Given a model trained this way, inference is performed consistently by extracting pixel-wise embeddings and clustering, with the semantic label determined by the majority vote of its nearest neighbors from an annotated set. As a result, we present the SegSort, as a first attempt using deep learning for unsupervised semantic segmentation, achieving $76\%$ performance of its supervised counterpart. When supervision is available, SegSort shows consistent improvements over conventional approaches based on pixel-wise softmax training. Additionally, our approach produces more precise boundaries and consistent region predictions. The proposed SegSort further produces an interpretable result, as each choice of label can be easily understood from the retrieved nearest segments.
Analyzing Trade-offs in Reversible Linear and Binary Search Algorithms

Reversible algorithms are algorithms in which each step represents a partial injective function; they are useful for performance optimization in reversible systems. In this study, using Janus, a reversible imperative high-level programming language, we have developed reversible linear and binary search algorithms. We have analyzed the non-trivial space-time trade-offs between them, focusing on the memory usage disregarding original inputs and outputs, the size of the output garbage disregarding the original inputs, and the maximum amount of traversal of the input. The programs in this study can easily be adapted to other reversible programming languages. Our analysis reveals that the change of the output data and/or the data structure affects the design of efficient reversible algorithms. For example, the number of input data traversals depends on whether the search has succeeded or failed, while it expectedly never changes in corresponding irreversible linear and binary searches. Our observations indicate the importance of the selection of data structures and what is regarded as the output with the aim of the reversible algorithm design.
Benchmarking Learning Networks on Eat-Sleep Conditions

Human activity recognition technologies are key to promote healthy life styles, and potential to offer explanations to study the origin of complex diseases. In particular, it is well-known that the quick transition between eating and sleeping is known to trigger unfavorable conditions for healthy life style. In this paper we describe our observations and insights in the benchmarking of the state of the art classification models based on graph representations to classify activities comprising drinking, eating, walking, running and sleeping.
Conjugate Gradients for Kernel Machines

Regularized least-squares (kernel-ridge / Gaussian process) regression is a fundamental algorithm of statistics and machine learning. Because generic algorithms for the exact solution have cubic complexity in the number of datapoints, large datasets require to resort to approximations. In this work, the computation of the least-squares prediction is itself treated as a probabilistic inference problem. We propose a structured Gaussian regression model on the kernel function that uses projections of the kernel matrix to obtain a low-rank approximation of the kernel and the matrix. A central result is an enhanced way to use the method of conjugate gradients for the specific setting of least-squares regression as encountered in machine learning. Our method improves the approximation of the kernel ridge regressor / Gaussian process posterior mean over vanilla conjugate gradients and, allows computation of the posterior variance and the log marginal likelihood (evidence) without further overhead.
SynSig2Vec: Learning Representations from Synthetic Dynamic Signatures for Real-world Verification

An open research problem in automatic signature verification is the skilled forgery attacks. However, the skilled forgeries are very difficult to acquire for representation learning. To tackle this issue, this paper proposes to learn dynamic signature representations through ranking synthesized signatures. First, a neuromotor inspired signature synthesis method is proposed to synthesize signatures with different distortion levels for any template signature. Then, given the templates, we construct a lightweight one-dimensional convolutional network to learn to rank the synthesized samples, and directly optimize the average precision of the ranking to exploit relative and fine-grained signature similarities. Finally, after training, fixed-length representations can be extracted from dynamic signatures of variable lengths for verification. One highlight of our method is that it requires neither skilled nor random forgeries for training, yet it surpasses the state-of-the-art by a large margin on two public benchmarks.
Metal Earth: Role of multidisciplinary geophysical methods to improve knowledge of mineral deposition across Precambrian rocks

SummaryRecently, there was an increase of costs to explore new economical mineral occurrences, while the success of discovery of new deposits has diminished. Therefore, Metal Earth was conceived with the objective to improve understanding of the processes controlling the distribution of mineral endowment, with a focus on Archean greenstone belts of the Canadian Superior Province as a global representative of these belts. Part of Metal Earth is to study thirteen different transects with a total length of >1000 km associated with Archean greenstone belts with various degrees of mineralisation (from less endowed to well-endowed) using high-resolution multidisciplinary geological and geophysical data. The goal is to reliably identify deeper underlying crustal/mantle conditions controlling mineralisation and also to validate existing ore deposit models by better identifying components contributing to mineral endowment.In this paper, we discuss acquiring, processing and preliminary modelling of multidisciplinary geophysical data (i.e. seismic, magnetotelluric, potential field data and petrophysical characterisations) to reliably model subsurface features along the transects. Across the Swayze transect, seismic sections clearly show sub-horizontal and dipping geological boundaries and magnetotelluric model reveals pathways as narrow upper crustal sub-vertical zones of low resistivity. In addition, joint inversion of gravity and magnetic data on initial models constrained by surface geology, seismic and magnetotelluric models and petrophysical characterisations have improved the models by better detecting at depth the limits of lithological units, low-density batholiths, dykes and fault zones.New models reveal there are significant crustal difference between the well-endowed Abitibi and the less prospective Wabigoon subprovinces. The final models will map the lithospheric architecture and constrain large-scale intracratonic controls on magmatism, crustal and tectonic evolution, and subsequently mineralisation.
Design and verification of TCP/IP offload engine in quantum key distribution system

Since the quantum key distribution (QKD) system is designed based on the basic principles of quantum mechanics, the theoretical security of information can be achieved by encrypting the communication content using the key generated by the QKD system. As the key generation rate increases, the QKD system has an increasing demand for information interaction through the network. This paper optimizes the network interaction part of the QKD system, unloading the TCP/IP protocol from the central processing unit to the hardware. Compared with the TCP/IP implemented in the dual-core processor, the interaction speed is increased by 114%, up to 560 Mbps. In addition, this paper designs identity authentication and encryption protection algorithms for all the interactive data, preventing a third-party from tampering and eavesdropping, further improving the security of the system.
Arm Motion Classification Using Curve Matching of Maximum Instantaneous Doppler Frequency Signatures

Hand and arm gesture recognition using the radio frequency (RF) sensing modality proves valuable in manmachine interface and smart environment. In this paper, we use curve matching techniques for measuring the similarity of the maximum instantaneous Doppler frequencies corresponding to different arm gestures. In particular, we apply both Frechet and dynamic time warping (DTW) distances that, unlike the Euclidean (L2) and Manhattan (L1) distances, take into account both the location and the order of the points for rendering two curves similar or dissimilar. It is shown that improved arm gesture classification can be achieved by using the DTW method, in lieu of L2 and L1 distances, under the nearest neighbor (NN) classifier.
Hierarchical Decomposition Mechanism by König-Egérvary Layer-Subgraph with Application on Vertex-Cover.

K\{o}nig-Eg\'{e}rvary (KE) graph and theorem provides useful tools and deep understanding in the graph theory, which is an essential way to model complex systems. KE properties are strongly correlated with the maximum matching problem and minimum vertex cover problem, and have been widely researched and applied in many mathematical, physical and theoretical computer science problems. In this paper, based on the structural features of KE graphs and applications of maximum edge matching, the concept named KE-layer structure of general graphs is proposed to decompose the graphs into several layers. To achieve the hierarchical decomposition, an algorithm to verify the KE graph is given by the solution space expression of Vertex-Cover, and the relation between multi-level KE graphs and maximal matching is illustrated and proved. Furthermore, a framework to calculate the KE-layer number and approximate the minimal vertex-cover is proposed, with different strategies of switching nodes and counting energy. The phase transition phenomenon between different KE-layers are studied with the transition points located, the vertex cover numbers got by this strategy have comparable advantage against several other methods, and its efficiency outperforms the existing ones just before the transition point. Also, the proposed method performs stability and satisfying accuracy at different scales to approximate the exact minimum coverage. The KE-layer analysis provides a new viewpoint to understand the structural organizations of graphs better, and its formation mechanism can help reveal the intrinsic complexity and establish heuristic strategy for large-scale graphs/systems recognition.
An Evaluation of Turkish Economy's Performance Under the Information Economics

Economics is the art of providing unlimited human needs with scarce resources as it comes to teaching. Very few of these resources have been used in such a way that they are ready in nature. Almost all of the goods and services that can meet the needs are obtained by the application of human labor (labor force) and capital to commodity. In recent years, labor capital and commodity factors were first added to entrepreneurs and then technology. Thus, the system we call the production process emerges.&#x0D; This study based on the information economy, the information economics indicator with Turkey's latest data is intended to demonstrate the performance. First of all, after the conceptual framework of the information economics, its distinctive qualities and development process, the effects of these developments on societies and economies will be evaluated. For the intended purpose, indicators such as R&amp;D activities and R&amp;D personnel employment, patent application and registration numbers, number of scientific publications and access to information were examined in Turkish economy. Finally, Turkey made the comparison with developed countries in economic terms and has been observed that the desired level of the Turkish economy in terms of the information economics.
Where from Mass Came in the Universe? Did the Mass Originate from a Zero Rest Mass less Particle in Higgs Field

A very interesting hypothesis based upon the possibility of a “Zero Rest Mass Particle in Higgs Field” in the Universe is described in this study. This is review of some hypothesis on mathematical basis on possibility of zero rest mass particles in the earliest moments of the Big Bang where Higgs Particles fused to give mass in Higg’s Scalar Field. The hypothesis is validated and considered the same truly correct that the zero rest mass  sub quark 2 particles do exists/ or existed in this universe ever in atomic structures  and in universe and their possible implications as future source of energy, time travel. The value of the electron mass, in particular, remains deeply mysterious even in our most advanced speculations about unification and string theory. Keywords: Zero rest mass, Big Bang Theory, Higg’s Field, universe, particles, energy. Cite this Article Pranab Kumar Bhattacharya, Rupak Bhattacharya, Ritwik Bhattacharya, et al. Where from Mass Came in the Universe? Did the Mass Originate from a Zero Rest Mass Less Particle in Higgs Field. Research & Reviews: Journal of Space Science and Technology. 2016; 5(3): 42–63p.
Data-Driven Approach for Quality Evaluation on Knowledge Sharing Platform

In recent years, voice knowledge sharing and question answering (Q&A) platforms have attracted much attention, which greatly facilitate the knowledge acquisition for people. However, little research has evaluated on the quality evaluation on voice knowledge sharing. This paper presents a data-driven approach to automatically evaluate the quality of a specific Q&A platform (Zhihu Live). Extensive experiments demonstrate the effectiveness of the proposed method. Furthermore, we introduce a dataset of Zhihu Live as an open resource for researchers in related areas. This dataset will facilitate the development of new methods on knowledge sharing services quality evaluation.
Course Knowledge Graph Review

In recent years, researches based on knowledge graph have attracted widespread attention from various fields. Taking the application of knowledge graph in pedagogy as an example, this study firstly introduces the significance, value and status quo of the course knowledge graph research, and then expounds and analyzes the application of knowledge graph in different courses in China.
SPM: Modeling Spark Task Execution Time from the Sub-stage Perspective

Tasks are the basic unit of Spark application scheduling, and its execution is affected by various configurations of Spark cluster. Therefore, the prediction of task execution time is a challenging job. In this paper, we analyze the features of task execution procedure on different stages, and propose the method of prediction of each sub-stage execution time. Moreover, the correlative time overheads of GC and shuffle spill are analyzed in detail. As a result, we propose SPM, a task-level execution time prediction model. SPM can be used to predict the task execution time of each stage according to the input data size and configuration of parallelism. We further apply SPM to the Spark network emulation tool SNemu, which can determine the start time of each shuffle procedure for emulation effectively. Experimental results show that the prediction method can achieve high accuracy in a variety of Spark benchmarks on Hibench.