Reviewerly/exHarmony huggingface dataset:
Automated Cell Counting System For Chronic Leukemia

Leukemia is a group of cancers which create a large amount of immature white blood cells. Abnormal numbers of white blood cells may suggest a screening of leukemia, and the blood sample is examined under the microscope to observe if the cells appear abnormal. The manual screening of chronic leukemia is time consuming and tedious while the Automated Hematology Analyzer is too expensive, particularly for the third world countries. This has been made exacerbated by the gold standard of biopsy inspiration which is painful and invasive for the patient. An automated cell counting (ACC) system for chronic leukemia has been developed to support and ease the routine of hematologist and technologist in the screening process and to give a quick and accurate result. The fusion of image processing technique has been proposed, which include four main stages, i.e. image acquisition, image segmentation, noise removal and counting process. Based on the sensitivity test over 100 images of chronic cells, an overall result shows 98.94% sensitivity of the system performance and the processing time recorded is less than 6 second per image. This proved an excellent level of ACC system performance. It is concluded that the system is suitable to be used as an automated counting system for chronic leukemia disease due to its sensitivity and ability to reduce the time taken for screening process.
Text Deduplication with Minimum Loss Ratio

Text deduplication is an important operation for text document analysis applications. Given a set of text documents, we often need to remove the text documents whose similarity values are not less than the specified threshold. However, if the set of similar text documents to be removed is too large, the remaining set of text documents may be not enough for text analysis. In this paper, we consider the problem on how to balance the removed set and the remaining set of text documents. We try to reduce the duplication information as much as possible with the minimum number of text documents to be removed. We propose a greedy algorithm for our problem based on the concept of similarity graph which can represent the similar relationship for a set of text documents. We also consider the incremental algorithm for the dynamic settings. The experimental results based on the real news document datasets show the efficiency of the proposed algorithms.
New statistical methods for the comparison and characterization of particle shape

Abstract This paper presents novel methods for robust statistical testing of particle shape data. Shape (the relative lengths of three orthogonal axes) is a key property of sedimentary particles, providing information on provenance, transport history and depositional environment. However, the usefulness of shape data, including the ability to make robust comparisons between samples, has been constrained by the absence of a satisfactory definition of the mean shape for a sample of particles. Such a definition is proposed and used to develop confidence regions for the population mean shape using both parametric (theoretical) and computational (bootstrap) methods. These techniques are based on a transform that permits multivariate statistical methods for the analysis of compositional data to be extended to shape. These techniques are validated with reference to a dataset of 169 clast samples and found to perform well. A statistical test on the mean – using the multivariate extension of Student's t ‐test, Hotelling's T 2 – is presented. The benefits of the methods presented are demonstrated with reference to a case study. © 2019 John Wiley &amp; Sons, Ltd.
Prompting the use of online application on smartphone (integral calculator) in learning integration techniques

The bondage of smartphone should be overcome wisely. In fact, students could not be separated from smartphone in the class. Therefore, as an educator we can use it to create a learning process of integration using application in the Internet. This study aims to develop classroom activities that support engineering students for learning integral using Integral Calculator in Internet. Design research was chose as an appropriate method to achieve the goals since this study will provide a descriptive learning evidence how the students use the application to understand integration technique. Around thirty-eight engineering students in Adisutjipto College of Technology that divided in 10 groups involved in this study. The teaching experiment result of cycle 1 showed that only a few students knew about Integral Calculator before this study. At the first, they were amazed and thought it was very useful to find the answer. Later on, when they asked to understand the steps given by this application they found some difficulties. However, with small clue they can understand it. In other words, this application really helpful for students to get the calculation of integral problems, but students still need guidance from teacher or friends to process the steps. Therefore, the use of integral calculator fits best for classroom activities while teacher or group discussion could lead students to comprehend integral techniques.
Cardiovascular PPG biometric key generation for IoT in healthcare domain

The wearable IoT monitor device becomes increasingly popular in a market where heart-rate monitors, pulse oximeters sensor are integrated into a device and already play an important role in everyday life. With these considerations in mind, it is important to maintain the security and privacy of users. Biometric authentication offers several benefits such as improved facilitation, enablement, and automation. However, traditional biometric modalities such as fingerprint, face, and iris require specific hardware or sensors to capture the biometric. In this paper, we introduce next-generation biometric called photoplethysmograph (PPG) that are internal to the body, offering a number of advantages. First of all, they are harder to clone, to harvest and to potentially hack, by the nature of the fact they are internal. Other benefits include liveness detection, and interoperability, which traditional modalities don't necessarily have. In this study, we developed the PPG biometric-based key generation that can be extracted by our adaptive quantization approach. The experimental result is shown that 175 key bits with 99.9% average reliability and 0.89 min-entropy can be achieved.
Hi Doppelgänger : Towards Detecting Manipulation in News Comments

Public opinion manipulation is a serious threat to society, potentially influencing elections and the political situation even in established democracies. The prevalence of online media and the opportunity for users to express opinions in comments magnifies the problem. Governments, organizations, and companies can exploit this situation for biasing opinions. Typically, they deploy a large number of pseudonyms to create an impression of a crowd that supports specific opinions. Side channel information (such as IP addresses or identities of browsers) often allows a reliable detection of pseudonyms managed by a single person. However, while spoofing and anonymizing data that links these accounts is simple, a linking without is very challenging.
Classification of military occupational specialty codes for agent learning in human-agent teams

With the exponential growth of technology, future military operations will be comprised of not just ground operations but a multi-domain battlespace. Paramount to mission success will be the reliance on intelligent adaptive computational agents and effective human-agent teaming. An agent teammate can assist the Soldier with tasks that may be seen as physically difficult, cognitively fatiguing, or high risk. However, successful teaming is compromised when an agent lacks the attributes that contribute to effective human-human collaboration, such as knowledge about team-members' work preferences or capabilities. One way to provide agents with a sense of team-member preferences or capabilities is to quantitatively characterize such preferences as a function of the job the human intends to perform. To address this, we analyzed a modified survey from the Army Research Institute that is commonly used to identify work-abilities variables in military personnel based on the service member's Military Occupational Specialty (MOS). Using machine learning techniques, statistical comparisons are made in order to quantitatively assess populationaveraged responses that Soldiers from various MOS codes provided on an Army Abilities questionnaire. Similarities and differences across groupings of MOS codes can provide a set of observations that might be parametrized into a computational agent's framework. The goal of this work is to identify MOS code related parameters that might be incorporated into a computational agent's framework in the future development of flexibly adaptive agents for Soldieragent teams.
Regional scale prospecting for non-sulphide zinc deposits using ASTER data and different spectral processing methods

The Ravar-Kuhbanan-Bahabad belt (RKBB) in Central Iran contains several carbonate-hosted non-sulphide Zn (zinc)-Pb (lead) deposits. The Gujer Zn mine area located in the middle of the RKBB was selected as the case study. Due to its large extent, dolomitic envelope in carbonate host rocks can be considered as a more appropriate exploratory target than small Zn-rich gossans or blind karst filling ore. Based on previous studies, the occurrence of red sandstone as a candidate of supplying metal for mineralization and evaporate as sulphate source for mineralized liquids in the vicinity of carbonate rocks can be important exploratory key in the RKBB. Non-sulphide Zn deposits were formed through oxidation of primary Mississippi Valley-type (MVT) deposits in the study area. Remote sensing studies were undertaken using visible to near-infrared (VNIR) and shortwave infrared (SWIR) bands of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) with the objective of lithological mapping. Five traverse lines containing a total of 81 samples were designed and followed with subsequent chemical analysis, thin section studies, and spectroscopy to verify the results. Two types of carbonates, namely, magnesian dolomite as host rock and surrounding calcitic limestone, were realized through using magnesium oxide (MgO) to calcium oxide (CaO) ratio. Based on spectroscopy studies, calcite and dolomite showed distinct absorption features at 2.35 µm and 2.32 µm, respectively, in ASTER band 8 while a shoulder at 2.25 µm was seen in ASTER band 7 for dolomite. Three image processing methods including spectral angle mapper (SAM), linear spectral unmixing (LSU), and mixture-tuned matched-filtering (MTMF) were applied to separate dolomite and limestone. The accuracy of image classification was numerically estimated using a confusion matrix. Limestone with the accuracy of 95.83% was more precisely enhanced using MTMF method compared to SAM and LSU methods. Highest accuracy of 75% for dolomite was obtained through using LSU method. Red sandstone and evaporate units were classified using MTMF and SAM/LSU methods, respectively. Rock units with the highest accuracy were selected and simply overlain on an image of ASTER in a GIS platform to create the potential map of the study area. Results showed that ASTER data can be successfully used to prepare a potential map for regional scale prospecting for carbonate-hosted non-sulphide Zn-Pb deposits in geological setting and climate condition similar to the RKBB.
Human Tracking for Facility Surveillance

This research provides two main changes based on Detect-And-Track. To improve the Multi-Object Tracking Accuracy (MOTA) while keeping the lightweight of the original approach, this paper proposes a gradient approach to obtain higher MOTA. We use the location of two previous frames of the same identified person to calculate the gradient for the location prediction of the current frame. Then, the predicted and the detected locations are compared. We also compare the current and previous detections. With a weighted combination for matching, we increase the MOTA score and improve the results of Detect-And-Track. Moreover, this research replaces cosine distance, the original feature extractor, with Euclidean distance. By doing so, feature extraction can match Intersection over Union (IoU) better. The weighted combination, which consists of IoU and Euclidean distance, provides a better MOTA than Detect-And-Track. In addition, a greedy approach facilitates a higher MOTA when implement with IoU and Euclidean distance. This weighted combination utility is superior than the combination of IoU and cosine distance, achieving 56.1% MOTA in total on the validation data of PoseTrack ICCV’17 dataset.
Block-chain Privacy Protection Based on Fully Homomorphic Encryption

This paper introduces a block chain privacy protection algorithm based on c encryption. At the same time, the shortcomings of existing protection mechanisms and the prospect of future encryption technology are also presented.
Guaranteed Compression Rate for Activations in CNNs using a Frequency Pruning Approach

Convolutional Neural Networks have become state of the art for many computer vision tasks. However, the size of Neural Networks prevents their application in resource constrained systems. In this work, we present a lossy compression technique for intermediate results of Convolutional Neural Networks. The proposed method offers guaranteed compression rates and additionally adapts to performance requirements. Our experiments with networks for classification and semantic segmentation show, that our method outperforms state-of-the-art compression techniques used in CNN accelerators.
Inference Adaptive Thresholding based Non-Maximum Suppression for Object Detection in Video Image Sequence

This study proposes a novel inference adaptive thresholding based non-maximum suppression (NMS) (IAT-NMS) algorithm for deriving temporal cues between video sequences. The inference of temporal connectivity is first derived according to an overlapping measure of the bounding boxes between adjacent frames. Frames with high-confidence detection object are taken as key frames to leverage the scores of neighbor detections and preserve potential detections of blurred objects with low scores. Then, bounding boxes within each frame are ranked via their confidence scores and the overlapping ratio between the bounding box with the highest score against the remaining surrounding boxes is computed. This measure of overlapping is brought into a Gaussian function to estimate weights for adaptive suppression and to softly suppress the detection scores of possible severely overlapped objects. The proposed method is compared with state-of-the-art video object detection techniques. With the application of IAT-NMS, overlapping objects originally undistinguishable in the compared methods become detectable. Experimental results demonstrate that this simple and unsupervised method outperforms state-of-the-art NMS algorithms, with an increase of 6% in mean average precision (mAP) on the ImageNet VID dataset. Our study on performance limitations and sensitivity to parametric variations also finds that IAT-NMS demonstrates better detection capability than does the three compared algorithms, which fail to detect all targets or distinguish in the presence of multiple overlapping targets.
SRC-Disp: Synthetic-Realistic Collaborative Disparity Learning for Stereo Matching

Stereo matching task has been greatly improved by convolutional neural networks, especially the fully-convolutional network. However, existing deep learning methods always overfit to specific domains. In this paper, focus on domain adaptation problem of disparity estimation, we present a novel training strategy to conduct synthetic-realistic collaborative learning. At first, we design a compact model that consists of shallow feature extractor, correlation feature aggregator and disparity encoder-decoder. Our model enables end-to-end disparity regression with fast speed and high accuracy. To perform collaborative learning, we then propose two distinct training schemes, including guided label distillation and semi-supervised regularization, both of which are used to alleviate the lack of disparity labels in realistic datasets. Finally, we evaluate the trained models on different datasets that belong to various domains. Comparative results demonstrate the capability of our designed model and the effectiveness of collaborative training strategy.
Secret Sharing Scheme with Efficient Cheating Detection

Some recent secret sharing schemes with cheating detection based on the Shamir's scheme use a decomposition technique to improve its efficiency on a check digit function evaluation. As consequence, this allows a more efficient cheating detection. In this paper, we show a brief analysis of the possible values of the check digit function for some recently proposed schemes and notice some considerations that should been taken for new proposals. Moreover, we propose a scheme based on a decomposition technique over the ring Z2m that allows a more efficient implementation of the cheating detection in most of the cases.
Spectrally-shaped correlation with application to image registration

Image correlation has proven useful for image filtering, matching, pattern recognition, and image registration over many decades. The two classical correlation forms, amplitude and phase correlation, display different properties. Amplitude correlation often provides a low, broad peak in the correlation domain. The broadness of the peak provides robustness to matching imagery exhibiting non-translational geometric offsets, such as rotation or scale differences. By contrast, phase correlation tends to provide a high, narrow peak. The high peak signifies high matching confidence while the narrow peak width provides accurate shift localization. However, the phase correlation peak degrades rapidly when matching against images with non-translational geometric offset. To provide tradeoffs between properties of these traditional correlation forms, in this paper we present a general, flexible form of correlation called Spectrally-Shaped Correlation (SSC). SSC provides control over the Fourier domain normalization of the correlation components. We apply SSC to the problem of image registration. We show how SSC contains the classical amplitude, phase, and phase-only correlation forms as special cases. First, we present the general theory of Fourier transforms for multi-channel imagery, modeled as hypercomplex-valued imagery. We present mathematical details of the transform techniques and develop the SSC approach. We then present numerical results demonstrating registration of real image data, acquired from a UAV operating in an urban environment, to reference imagery. We demonstrate a performance improvement of the SSC over the classical forms of correlation.
Machine learning approach to inter-well radio wave survey data imaging

Inter-well measurements are used to reduce drilling costs with no reduce small kimberlite body detection.The radio wave method enables measurement of the apparent absorption coefficient that is proportional to the effective electrical resistance of the rock.Our point is to build a three-dimensional model of distribution of electrical properties of inter-well space throughout the entire exploration region.The measured data is distributed unevenly because data points are grouped along the linear clusters.The distance between neighbor points composing a cluster is much smaller than distance between clusters.In terms of geostatistics, this means a significant spatial anisotropy of data distribution that is difficult to take into account using standard geostatistical approach.We have shown that the problem could be solved by methods developed within the theory of machine learning.To build a three-dimensional model of attenuation coefficient we used a modified method of -nearest neighbors.
RSQLG: The Reverse SQL Question Generation Algorithm

Nowadays, the people who learn and teach SQL commands require to get a hand-on practice with real environment to make the learning effective. The creating SQL exercise is a time-consuming task for instructors. As a result, students might not get enough questions to meet their demands. The reverse SQL question generation algorithm (RSQLG) is developed to solve this problem. The RSQLG has ability to automatically generate SQL exercise for students. The RSQLG can reverse the manual question creation process which starts from creating question to start creating query answer first instead. The RSQLG considers the existing data and database structure by using various constraints. The instructors also can specify the language, format and explanation of the questions. The RSQLG supports DML commands - SELECT, INSERT, UPDATE and DELETE - and support data retrieval from multiple tables which perform by JOIN and subquery operations. The algorithm has ability to generate bulk questions with less effort. The instructor is not required to writing requirements and validating the queries. The RSQLG can be implemented in e-learning to enhance sustainable practices and improve learning outcome for students.
Identifying the source of illicit gold from South America

Abstract Illicit trade in gold, mainly sourced from South America and Africa, is used to fund criminal activities such as the drug trade. Laundering money through the purchase of illicit gold, then having it refined and being paid for in clean funds is a major problem, and drives further growth in illicit mining. In order to restrict the influx of illicit gold into the legal domain, it is necessary to be able to identify the traded item as being of a licit or illicit nature. The only way that this can be done with any certainty is by determining, from its chemical composition, the source of the gold and the means by which it has been processed and upgraded. The example dealt with here shows how the use of compositional, processing and geological knowledge is able to discriminate gold from different origins. Data derived from a physical sample is far better able to constrain the source than a paper trail of certification, which can be manipulated.
Evaluating Design Rationale in Architecture

Although architecture is often seen as the culmination of design decisions, design rationale is a suppositious child in architecture documentation. Many architecture frameworks and standards, like TOGAF and ISO/IEC 42010, recognize the importance, but do not offer any support in the rationale process. Recent initiatives have shown that simple means help in providing more rationale. However, there are very few studies that give evidence whether more rationale indeed leads to better quality. In this paper, we propose a non-invasive method, the Rationale Capture Cycle, that supports architects in capturing rationale during the design process. Through a controlled experiment with 10 experienced architects, we observe the effectiveness of the method in terms of design quality through different measures. The results of our experiments show that: (1) better rationale is strongly correlated with high quality, and (2) the test group with our proposed method outperforms the control group.
Sentiment Classification into Three Classes Applying Multinomial Bayes Algorithm, N-Grams, and Thesaurus

The paper is devoted to development of the method that classifies texts in English and Russian by sentiments into positive, negative, and neutral. The proposed method is based on the Multinomial Naive Bayes classifier with additional n-grams application. The classifier is trained either on three classes, or on two contrasting classes with a threshold to separate neutral texts. Experiments with texts on various topics showed significant improvement of classification quality for reviews from a particular domain. Besides, the analysis of thesaurus relationships application to sentiment classification into three classes was done, however it did not show significant improvement of the classification results.