Reviewerly/exHarmony huggingface dataset:
Implementation of Data Preprocessing Techniques on Distributed Big Data Platforms

We are now in the era of Big Data, and the need for tools which can process and analyze such data is yet to be fulfilled. Big data mining aims to extract meaningful and valuable information from voluminous data that traditional data mining tools can not handle. One of the most vital steps of any data mining process is the preprocessing of the data. Our aim was to provide distributed implementation of some algorithms for two of the data preprocessing steps: outlier analysis and missing value imputation. The algorithms were implemented on Spark and this paper will focus on the details and performance of these algorithms on different distributed system setups.
A Revisit of Hashing Algorithms for Approximate Nearest Neighbor Search

Approximate Nearest Neighbor Search (ANNS) is a fundamental problem in many areas of machine learning and data mining. During the past decade, numerous hashing algorithms are proposed to solve this problem. Every proposed algorithm claims to outperform Locality Sensitive Hashing (LSH), which is the most popular hashing method. However, the evaluation of these hashing article was not thorough enough, and the claim should be re-examined. If implemented correctly, almost all the hashing methods will have their performance improved as the code length increases. However, many existing hashing article only report the performance with the code length shorter than 128. In this article, we carefully revisit the problem of search-with-a-hash-index and analyze the pros and cons of two popular hash index search procedures. Then we proposed a simple but effective novel hash index search approach and made a thorough comparison of eleven popular hashing algorithms. Surprisingly, the random-projection-based Locality Sensitive Hashing ranked the first, which is in contradiction to the claims in all the other 10 hashing article. Despite the extreme simplicity of random-projection-based LSH, our results show that the capability of this algorithm has been far underestimated. For the sake of reproducibility, all the codes used in the article are released on GitHub, which can be used as a testing platform for a fair comparison between various hashing algorithms.
View-Consistent 4D Light Field Superpixel Segmentation

Many 4D light field processing applications rely on superpixel segmentations, for which occlusion-aware view consistency is important. Yet, existing methods often enforce consistency by propagating clusters from a central view only, which can lead to inconsistent superpixels for non-central views. Our proposed approach combines an occlusion-aware angular segmentation in horizontal and vertical EPI spaces with an occlusion-aware clustering and propagation step across all views. Qualitative video demonstrations show that this helps to remove flickering and inconsistent boundary shapes versus the state-of-the-art approach, and quantitative metrics reflect these findings with improved boundary accuracy and view consistency scores.
Interpretable Online Banking Fraud Detection Based On Hierarchical Attention Mechanism

Online banking activities are constantly growing and are likely to become even more common as digital banking platforms evolve. One side effect of this trend is the rise in attempted fraud. However, there is very little work in the literature on online banking fraud detection. We propose an attention based architecture for classifying online banking transactions as either fraudulent or genuine. The proposed method allows transparency to its decision by identifying the most important transactions in the sequence and the most informative features in each transaction. Experiments conducted on a large dataset of real online banking data demonstrate the effectiveness of the method in terms of both classification accuracy and interpretability of the results.
Using Special Text Points in the Recognition of Documents

The chapter develops the concept of a textual key point, the detector of which is a certain OCR. The descriptor of a textual key point is determined. Examples of algorithms for analyzing documents, using textual key points, are given. The chapter deals with the tasks of recognized document classification, localization of images of recognized documents and comparison of images of documents for finding differences. The results of the algorithms for the data sets of the documents of the Russian Federation are given. The proposed methods allow achieving high accuracy of complexly structured documents analysis with entering document images in modern cyber-physical systems based on big data technologies.
Southern China's manganese resource assessment: An overview of resource status, mineral system, and prediction model

Manganese is, an important industrial mineral used in the manufacture of various products, including in the refinement of iron ore and as an alloy in steel. China has rich resources of manganese (Mn), with the majority of deposits located in southern China. The common types of Mn deposits are sedimentary, hydrothermally modified, and supergene. Exploring a suitable method to quantitatively assess the Mn mineral resources at the craton-scale is, therefore, an open challenge for the geologist. The purpose of this contribution is to present a framework for the region-scale assessment of manganese based on mineral-system analysis and the model-based synthetic volume method. The framework involves: (1) summarising the status of Mn resources in regions of southern China considered to be prospective for the metal; (2) constructing datasets based on mineral exploration; (3) completion of mineral-system analysis of Mn deposits in each prospective area; (4) establishing a prediction model based on mineral-system analysis; (5) construction of geographic information system (GIS) layers to represent the mineralisation processes; (6) using a mathematical model to conduct a spatial analysis of areas considered to be prospective; (7) estimation of mineral resources based on the synthetic volume method; and (8) delineation of "minimum potential area" (MPA) and the calculation of the total predicted resources. The innovations of this research are the translation of the ore-forming processes into mappable features in the geosciences datasets by applying the concept of mineral system, and quantitative assessment of the Mn mineral resources based on the assumption that the larger the volume of a trap, the more resources it probably holds. This framework was used for China's "National Mineral Resources Assessment Initiative" in recent years, and help to assess the Mn mineral resources at the craton-scale, and determine the critical characteristic of the Mn deposits that can be used for the construction of model-based prospectivity mapping, especially in southern China, which can be used for manganese exploration.
Multi-robot Trajectory Generation for an Aerial Payload Transport System

In this work, we consider the problem of planning safe, feasible trajectories for a team of quadrotors with slung-loads operating in an obstacle-free, three-dimensional workspace. We are particularly interested in generating dynamic trajectories—trajectories where robots’ payloads are allowed to swing in accordance with the system’s natural dynamics—for fast, agile, coordinated payload transportation. This capability is applicable to tasks such as construction, where a single crane performing sequential tasks could be replaced by multiple quadrotors performing tasks in parallel for increased efficiency. We model this problem as a labeled multi-robot planning problem, where robots must navigate payloads from given start positions to fixed, non-interchangeable goal positions. Our system presents three novel challenges: (1.) Each vehicle has eight degrees-of-freedom, significantly increasing the size of the team’s joint state space. (2.) Each vehicle is a nonlinear, $$6\mathrm{th}$$-order dynamical system with four degrees of under-actuation. (3.) Each vehicle is a multi-body system. We present a safe and complete Quadratic Programming solution and validate its practicality with experiments containing up to nine quadrotors.
Reverse Firewalls for Actively Secure MPCs

Reverse firewalls were introduced at Eurocrypt 2015 by Miro-nov and Stephens-Davidowitz, as a method for protecting cryptographic protocols against attacks on the devices of the honest parties. In a nutshell: a reverse firewall is placed outside of a device and its goal is to “sanitize” the messages sent by it, in such a way that a malicious device cannot leak its secrets to the outside world. It is typically assumed that the cryptographic devices are attacked in a “functionality-preserving way” (i.e. informally speaking, the functionality of the protocol remains unchanged under this attacks). In their paper, Mironov and Stephens-Davidowitz construct a protocol for passively-secure two-party computations with firewalls, leaving extension of this result to stronger models as an open question. In this paper, we address this problem by constructing a protocol for secure computation with firewalls that has two main advantages over the original protocol from Eurocrypt 2015. Firstly, it is a multiparty computation protocol (i.e. it works for an arbitrary number n of the parties, and not just for 2). Secondly, it is secure in much stronger corruption settings, namely in the active corruption model. More precisely: we consider an adversary that can fully corrupt up to $$n-1$$ parties, while the remaining parties are corrupt in a functionality-preserving way. Our core techniques are: malleable commitments and malleable non-interactive zero-knowledge, which in particular allow us to create a novel protocol for multiparty augmented coin-tossing into the well with reverse firewalls (that is based on a protocol of Lindell from Crypto 2001).
Tightly Secure Inner Product Functional Encryption: Multi-input and Function-Hiding Constructions

Tightly secure cryptographic schemes have been extensively studied in the fields of chosen-ciphertext secure public-key encryption, identity-based encryption, signatures and more. We extend tightly secure cryptography to inner product functional encryption (IPFE) and present the first tightly secure schemes related to IPFE. We first construct a new IPFE scheme that is tightly secure in the multi-user and multi-challenge setting. In other words, the security of our scheme does not degrade even if an adversary obtains many ciphertexts generated by many users. Our scheme is constructible on a pairing-free group and secure under the matrix decisional Diffie-Hellman (MDDH) assumption, which is the generalization of the decisional Diffie-Hellman (DDH) assumption. Applying the known conversions by Lin (CRYPTO 2017) and Abdalla et al. (CRYPTO 2018) to our scheme, we can obtain the first tightly secure function-hiding IPFE scheme and multi-input IPFE (MIPFE) scheme respectively. Our second main contribution is the proposal of a new generic conversion from function-hiding IPFE to function-hiding MIPFE, which was left as an open problem by Abdalla et al. (CRYPTO 2018). We obtain the first tightly secure function-hiding MIPFE scheme by applying our conversion to the tightly secure function-hiding IPFE scheme described above. Finally, the security reductions of all our schemes are fully tight, which means that the security of our schemes is reduced to the MDDH assumption with a constant security loss.
Garbled Circuits in the Cloud using FPGA Enabled Nodes

Data privacy is an increasing concern in our interconnected world. Garbled circuits is an important approach used for Secure Function Evaluation (SFE); however it suffers from long garbling times. In this paper we present garbled circuits in the cloud using Amazon Web Services, and particularly Amazon F1 FPGA enabled nodes. We implement both garbler and evaluator in software, and show how F1 instances can accelerate the garbling process and rapidly adapt to several different applications. Experimental results, measured on AWS, indicate a 15 times speedup for garbling done using an FPGA. This results in total application speedup, including garbling, communications and evaluation, of close to three times over a large range of application sizes.
Implicit Discourse Relation Recognition via a BiLSTM-CNN Architecture With Dynamic Chunk-Based Max Pooling

Implicit discourse relation recognition is a serious challenge in discourse analysis, which aims to understand and annotate the latent relations between two discourse arguments, such as temporal and comparison.Most neural network-based models encode linguistic features (such as syntactic parsing and position information) as embedding vectors, which are prone to error propagation due to unsuitable pre-processing.Other methods apply different attention or memory mechanisms, mainly considering the key points in the discourse, yet ignore some valuable clues.In particular, those using convolution neural networks retain local contexts but lose word order information due to the standard pooling operation.The methods that use bidirectional long short-term memory network consider the word sequence and retain the global information, but cannot capture the context with different range sizes.In this paper, we propose a novel Dynamic Chunk-based Max Pooling BiLSTM-CNN framework (DC-BCNN) to address these issues.First, we exploit BiLSTMs to capture the semantic representations of discourse arguments.Second, we adopt the proposed convolutional layer to automatically extract the ''multi-granularity'' features (just like n-gram) by setting different convolution filter sizes.Then, we design a dynamic chunk-based max pooling strategy to obtain the important scaled features of different parts in one discourse argument.This strategy can dynamically divide each argument into several segments (called chunks) according to the argument length and the number of current pooling layer in the CNN and then select the maximum value of each chunk to indicate crucial information.We further utilize a fully connected layer with a softmax function to recognize discourse relations.The experimental results on two corpora (i.e., PDTB and HIT-CDTB) show that our proposed model is effective in implicit discourse relation recognition.
Efficient subaperture stitching method for measurement of large area microstructured topography

Microstructured surfaces over large areas are typical in diffractive optics and biomimetic surfaces. The topography measurement is challenging due to the multi-scale nature. In this paper, an efficient stitching method is proposed for fusion of multiple subapertures to produce a large area topography. The first step is detecting edge features and then projecting them on the OXY plane. The next step is correction of lateral coordinates by registering the edge curves in overlapping regions. The change of lateral coordinates is linearly related to the lateral shifts and clocking angle which are estimated by simply solving a linear least-squares problem. In the final step, height change is also linearly related to the piston and tip-tilt of subapertures. Hence it is corrected by minimizing the height difference in overlapping regions, which is again modeled as a linear least-squares problem. The stitching method is experimentally verified on a holographic sample. Totally seven by seven subapertures are measured with an interference microscope and seamlessly stitched together.
Towards building arabic paraphrasing benchmark

This research describes a paraphrasing benchmark of Arabic sentences for evaluating algorithms developed to measure Semantic Similarity for sentences and Paraphrasing identification task. The sentences are formed based on a set of rules for Arabic paraphrasing. These sentences are constructed from words of different types of Arabic books; educational, semantic science, lexicons and AWSS dataset. The benchmark consists of 1011 sentence pairs and labeled with human ratings for semantic similarity between sentence pair.
Achieving Data Integrity and Confidentiality Using Image Steganography and Hashing Techniques

Most existing steganography algorithms keen on achieving data confidentiality only by embedding the data into a cover-media. This research paper introduced a new steganography technique that achieves both data confidentiality and integrity. Data confidentiality is achieved by embedding the data bits in a secret manner into stego-image. Integrity is achieved using SHA 256 hashing algorithm to hash the decoding and encoding variables. The proposed model performed a high PSNR values for using a dataset of different image sizes with an average PSNR of 82.933%.
k-Reciprocal nearest neighbors algorithm for one-class collaborative filtering

In this paper, we study an important recommendation problem of exploiting users' one-class positive feedback such as "likes", which is usually termed as one-class collaborative filtering (OCCF). For modeling users' preferences beneath the observed one-class positive feedback, the similarity between two users is a key concept of constructing a neighborhood structure with like-minded users. With a well-constructed neighborhood, we can make a prediction of a user's preference to an un-interacted item by aggregating his or her neighboring users' tastes. However, the neighborhood constructed by a typical traditional method is usually asymmetric, meaning that a certain user may belong to the neighborhood of another user but the inverse is not necessarily true. Such an asymmetric structure may result in a less strong neighborhood, which is our major finding in this paper. As a response, we exploit the reciprocal neighborhood among users in order to construct a better neighborhood structure with more high-value users, which is expected to be less influenced by the active users. We then design a corresponding recommendation algorithm called k-reciprocal nearest neighbors algorithm (k-RNN). Extensive empirical studies on two large and public datasets show that our k-RNN performs significantly better than a closely related algorithm with the traditional asymmetric neighborhood and some competitive model-based recommendation methods.
Lethal Autonomous Weapons: Meaningful Human Control or Meaningful Human Certification? [Opinion]

Discusses the debate over the use of autonomous weapons in the military, whether they should be banned for offensive uses, and even whether such technologies threaten human existence.
Evolving Fuzzy Clustering Approach: An Epoch Clustering That Enables Heuristic Postpruning

Clustering is an unsupervised machine learning method that is used both individually and as a part of the preprocessing stage for the supervised machine learning methods. Due to its unsupervised nature, clustering results have less accuracy compared to the supervised learning. This article aims to introduce a new perspective in clustering by defining an approach for data pruning. The method also enables clustering using multiple sets of prototypes instead of only one set to improve clustering accuracy. Consequently, this approach has the potential to be used independently or as a part of a preprocessing to prepare purified data for the training step of a supervised learning approach. An evolving fuzzy clustering approach (EFCA) utilizes the fuzzy membership concept to breakdown clustering in epochs instead of running the clustering on all data at once. In some cases, for supervised learning, we rather have a smaller subset of highly accurate labeled data instead of a dataset with less accurate labels. The EFCA's “epoch cut” enables postpruning ability to eliminate obscure data points, which results in more clustering accuracy. The EFCA has been applied to a set of eight multivariate and ten time-series datasets, and for example, after deploying epoch cut and eliminating obscure data (20% of data) by automatic postpruning, it achieved 100% accuracy for the rest 80% Iris data.
Static Independent Task Scheduling on Virtualized Servers in Cloud Computing Environment

Cloud Computing is the advanced design of client-server computing, cluster computing and grid computing. The cloud providers provide cloud services mainly as Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Software as a Service (SaaS) to the users who can access publicly, privately or hybrid via the Internet. In Cloud computing, there are many research areas like task scheduling, allocation of resource, security and privacy etc. Task scheduling is a vital area in the cloud computing, and it must be optimized by considering different parameters. Nowadays, there are a lot of different scheduling algorithms to minimize execution time and cost, to improve the quality of service, system performance and to maximize resource utilization and load balancing, etc. This paper proposed a Static Independent Task Scheduling on Virtualized Servers in Cloud Computing Environment in which tasks are allocated to the suitable VM by measuring the availability of each resource with respect to its processing power, cost and the number of available processing elements and by grouping tasks according to their instruction length. This method is simulated on Cloud Simulator (Cloudsim toolkit) and results show the proposed method that maximizes total execution time and minimizes execution cost for all tasks than scheduling algorithms such as Shortest Job First (SJF) and First Come First Serve (FCFS) algorithms.
Comparison of the chemistry and mineralogy of ilmenite concentrates sourced from fluvial (Brahmaputra River) and beach placer (Cox’s Bazar) deposits, Bangladesh

Bangladesh has known concentrations of heavy mineral (HM) sand resources in shoreline placer deposits along the southern and eastern coastlines and also within river sand bar placer deposits associated with the extensive river systems throughout the country. The high-grade Cox's Bazar beach placer deposits are in populated and environmentally sensitive areas making the future of mineral sands exploitation uncertain. In comparison, the Brahmaputra fluvial deposits are less rich in total HMs but constitute a potential major resource. The recovery of Ti-rich ilmenite from beach placer deposits is widespread but rare from fluvial deposits however many river systems in Bangladesh are annually dredged to maintain open waterways offering a new source of heavy minerals. Exploitation will depend on the quality of the ilmenite compared to existing commercially traded material. In this study, ilmenite concentrates derived from fluvial deposits (Brahmaputra River) and beach placer deposits (Cox's Bazar) were compared using XRF, XRD and EPMA techniques. The level of impurities associated with the Brahmaputra River ilmenite was higher than in the equivalent Cox's Bazar fractions suggesting possible issues with liberation or the properties of the associated gangue minerals reacted differently to the separation conditions, or both. Ilmenite from both regions exhibited texturally alike grain types and chemically similar compositions with detrital ilmenite from Cox's Bazar characterised by TiO2 contents ranging from 49–53 wt-% (average 51 wt-%) while the range for the Brahmaputra River was 44–53 wt-% (average 49 wt-%). The slightly higher and much narrower range in TiO2 for the Cox's Bazar material was consistent with this material having been subject to more chemical weathering resulting from a longer transit time and reworking through wave action. Based on the major and minor element compositions of ilmenites from both sources, the most likely route for processing the ilmenite-rich component would be via the sulphate route to pigment.