Reviewerly/exHarmony huggingface dataset:
Perceptually Lossless Compression for Mastcam Multispectral Images: A Comparative Study

The two mast cameras, Mastcams, onboard Mars rover Curiosity are multispectral imagers with nine bands in each.Currently, the images are compressed losslessly using JPEG, which can achieve only two to three times of compression.We present a comparative study of four approaches to compressing multispectral Mastcam images.The first approach is to divide the nine bands into three groups with each group having three bands.Since the multispectral bands have strong correlation, we treat the three groups of images as video frames.We call this approach the Video approach.The second approach is to compress each group separately and we call it the split band (SB) approach.The third one is to apply a two-step approach in which the first step uses principal component analysis (PCA) to compress a nine-band image cube to six bands and a second step compresses the six PCA bands using conventional codecs.The fourth one is to apply PCA only.In addition, we also present subjective and objective assessment results for compressing RGB images because RGB images have been used for stereo and disparity map generation.Five well-known compression codecs, including JPEG, JPEG-2000 (J2K), X264, X265, and Daala in the literature, have been applied and compared in each approach.The performance of different algorithms was assessed using four well-known performance metrics.Two are conventional and another two are known to have good correlation with human perception.Extensive experiments using actual Mastcam images have been performed to demonstrate the various approaches.We observed that perceptually lossless compression can be achieved at 10:1 compression ratio.In particular, the performance gain of the SB approach with Daala is at least 5 dBs in terms peak signal-to-noise ratio (PSNR) at 10:1 compression ratio over that of JPEG.Subjective comparisons also corroborated with the objective metrics in that perceptually lossless compression can be achieved even at 20 to 1 compression.
Tencent Minority-Mandarin Translation System

This paper describes the submissions of the Tencent minority-mandarin translation system for CCMT19. We participate in 3 translation directions including Uighur$$\rightarrow $$Chinese, Tibetan$$\rightarrow $$Chinese and Mongolian$$\rightarrow $$Chinese. Our systems are neural machine translation systems trained with our improved Marian, and are called TenTrans, which are based on Google’s Transformer model architecture. We also adopt most techniques that have been proven effective recently in academia, such as back-translation based sampling, data selection, sequence-level knowledge distillation, ensemble distillation, model ensembling and reranking. By using the above technologies, our submitted systems achieve a stable performance improvement.
NStackSenti: Evaluation of a Multi-level Approach for Detecting the Sentiment of Users

Sentiment Detection plays a vital role worldwide to measure the acceptance level of any products, movies or facts in the market. Text vectorization (converting text from human readable to machine readable format) and machine learning algorithms are widely used to detect the sentiment of users. This paper presents and evaluates a multi-level architecture based approach using stacked generalization technique named NStackSenti. The presented approach enables the combination of machine learning algorithms to improve the accuracy of detection. Here, Extremely Randomized Tree (ET), Random Forest (RF), Gradient Boost (GB), ADA Boost (ADA), Decision Tree (DT) are used as base classifiers and XGBoost classifier is used as meta estimator. The NStackSenti is applied on two separate datasets to demonstrate the effectiveness in terms of accuracy. NStackSenti provides better accuracy with trigram than unigram and bigram. It provides 83.7% and 86.24% accuracy on 2000 and 50000 data respectively.
Channel Max Pooling for Image Classification

A problem of deep convolutional neural networks is that the channel numbers of the feature maps often increases with the depth of the network. This problem can result in a dramatic increase in the number of parameters and serious over-fitting. The $$1\times 1$$ convolutional layer whose kernel size is $$1\times 1$$ is popular for decreasing the channel numbers of the feature maps by offer a channel-wise parametric pooling, often called a feature map pooling or a projection layer. However, the $$1\times 1$$ convolutional layer has numerous parameters that need to be learned. Inspired by the $$1\times 1$$ convolutional layer, we proposed a channel max pooling, which reduces the feature space by compressing multiple feature maps into one feature map via selecting the maximum values of the same locations from different feature maps. The advantages of the proposed method are twofold as follows: the first is that it decreases the channel numbers of the feature maps whilst retaining their salient features, and the second is that it non-parametric which has no increase in parameters. The experimental results on three image classification datasets show that the proposed method achieves good performance and significantly reduced the parameters of the neural network.
Research on Improved Character Encoding Methods Based on QR Code*

With the popularity of mobile terminals, Quick response (QR) code, which acts as a convenient and quick way to transfer graphic information, has been widely used in various fields. QR code is a 2D matrix code which contains information in both vertical and horizontal directions. In order to promote the characters encoding efficiency, we propose the improved character encoding methods based on QR code. Specifically, we divide the types of encoding data into Chinese characters and non-Chinese characters, and thus optimizing their encoding methods separately: we apply the branch-bound algorithm to optimize the encoding pattern selection of QR code; as for Chinese characters, based on the Chinese characters use frequency generated by Url crawler system, we design a new encoding method for Chinese characters. Our approach has been shown to afford better experimental test results compared with traditional encoding methods of QR code.
Fast Action Elimination for Efficient Decision Making and Belief Space Planning Using Bounded Approximations

In this paper we develop a novel paradigm to efficiently solve decision making and planning problems, and demonstrate it for the challenging case of planning under uncertainty. While conventional methods tend to optimize properties of specific problems, and sacrifice performance in order to reduce their complexity, our approach has no coupling to a specific problem, and relies solely on the structure of the general decision problem, in order to directly reduce its computational cost, with no influence over the quality of solution, nor the maintained state. Using bounded approximations of the state, we can easily eliminate unfit actions, while sparing the need to exactly evaluate the objective function for all the candidate actions. The original problem can then be solved considering a minimal subset of candidates. Since the approach is especially relevant when the action domain is large, and the objective function is expensive to evaluate, we later extend the discussion specifically for decision making under uncertainty and belief space planning, and present dedicated and practical tools, in order to apply the method to a sensor deployment problem. This paper continues our previous work towards efficient decision making.
An edge detection mechanism using L*A*B color-based contrast enhancement for underwater images

In Ocean investigations, particularly those deployed by the Autonomous Underwater Vehicles, underwater object detection and recognition is an essential task. Edge detection places a key role and considered one of the pre-processing techniques for several deep learning applications. In an underwater environment, the illumination of light, turbulence in the water, suspended particles present in the seafloor are challenging issues to acquire the quality image. The two major problems in underwater imaging are light scattering and color change. In the former case, the vision sensors connected to the underwater vehicles or dive lights used by the divers themselves cause light dispersion and shadows in the seafloor. In the latter case, the occurrence of color distortion is mainly due to the attenuation of the light, hence the images are having dominant colors in the latter case. The conventional techniques are failed to detect the quality edges in the case of underwater images. Our mechanism focused, instead of applying the edge detection algorithm on the input image directly, it is better to apply edge detection algorithm after color correction and contrast enhancement using L*A*B model. Qualitative and quantitative test results demonstrate that the proposed mechanism is giving better results compared with state-of-the-art methods.
Negotiation Game Model for Big Data Transactions

Given the surge around the research and development of models and tools for big data, businesses continue to improve their implementations to achieve their promised value. This paper proposes and evaluates a price negotiation game model for purchasing big data resources; the model utilizes a network for its structure. The key idea of this model is to consider big data transactions as a multi-player (data owners and consumers) game in which the players have access to imperfect information. The model embodies a deduction problem and proposes a game strategy for price negotiations. The model provides a new approach to evaluate the influences of various factors. We evaluate the influence of reservation prices, information completeness, and platform commission on the profit. The performance of the model is explored using thousands of simulated transactions. The success rate of the transactions, in addition to the rewards of the owners, consumers, and the platform are observed to assess the activity of network.
Utilization and Analysis of Deep Learning for &lt;i&gt;Kuzushiji&lt;/i&gt; Translation

In this study, to promote the translation and digitization of historical documents, we attempted to recognize Japanese classical kuzushiji characters by using the dataset released by the Center for Open Data in the Humanities (CODH). Using deep learning, which has undergone remarkable development in the field of image classification, we analyzed how successfully deep learning could classify more than 1,500-class kuzushiji characters through experiments, and what made it difficult to classify kuzushiji characters. In addition, we introduced a method to automatically eliminate characters that were difficult to classify or characters that were not used during training. In an actual translation work, those ambiguous characters can be left as unknown characters as “ 〓 (geta)” and pass on to the expert to make a decision. Finally, our experiments showed that the classification rate was improved from 72.10% to over 90% by performing the data augmentation and classifying only characters with high confidence and confirmed the effectiveness of our proposed method.
The Use of Bring Your Own Device-based Learning to Measure Student Algebraic Thinking Ability

Many students have algebraic difficulties in learning. This is proven by the fact that students having dif-ficulty facing algebraic elective courses in the 7th semester, where they lack the ability to solve algebraic problems called algebraic thinking skills. This study focuses on finding out the judgment of experts about the use of BYOD-based mathematics knowledge to measure students' algebraic completion skills. This research applied 3 tests in the control class and treatment class with the reliability of Cronbach to see an increase in the use of BYOD-based learning. Thus, there is an increase in the use of BYOD-based learning to measure students' algebraic thinking abilities.
Patch-Wise Blind Image Deblurring Via Michelson Channel Prior

Motion blur exists in many computer vision tasks, including faces, texts, and low-illumination images etc.It has been proved that Dark Channel Prior (DCP) and Bright Channel Prior (BCP) can both help the image deblurring by enhancing the dark or bright channel pixels.However, the pixels between the dark channel pixels and the bright channel pixels are not taken into consideration, which limits the deblurring performance.A novel image channel is proposed in combination with dark channel and bright channel in this paper to consider the effects of the all types of pixels, namely, Michelson channel pixels.Secondly, as the image channels are built based on the series of image patches with different blur kernels, a new method is developed to estimate the blur kernel and can measure the similarity between neighbored kernels.Meanwhile, to perform accurate kernel estimation, the L 0 regularization is applied into the algorithm framework.In the process of image deblurring, by enhancing the Michelson channels and retaining the other channels of the image, we can capture sharper image detail and eliminate the ringing artifacts of the recovered images.Massive experimental results demonstrate that the proposed method is more robust and outperforms the existing art-of-the-state of unsupervised image deblurring methods on both synthesized and natural images.
An Exploratory Study Of Factors Driving Decision Maker Intentions To Adopt Cloud Computing

United States based companies have invested more heavily in cloud services than companies in other nations. Despite numerous benefits claimed by cloud service providers, many organisations are still uncertain about the implementation of cloud computing. The aim of this study is to gain a better understanding of the factors that are important to the usage adoption decision of this information technology delivery model and the implications for organisations in the United States. Decision makers were surveyed to discover which attributes were important in their determination of cloud computing utilisation. Using a sample of executives from manufacturing and service sectors in the United States, multiple factors contributing to the recent growth in cloud computing have been identified using a factor analysis. Four factors emerged from the data analysis include extrinsic motivation, intrinsic motivation, perceived risks, and resource constraint.
Fake Email and Spam Detection: User Feedback with Naives Bayesian Approach

Transmitting of undesirable, unsolicited and fake messages to various email user is part of Email spam. It is a significant nuisance faced by various clients. Email spam can take the form of any commercial of a company/product or some kind of virus that associates to the clients’ email repository without any disclosures. For overcoming and solving this concern, various techniques for detecting and filtering unsolicited mails are being practiced. Amongst these Bayes categorization and anomaly detector for fake and unsolicited (spam) email, classification is deployed on large volumes of emails. Naive Bayesian classifier is quite a probabilistic, efficient and a simple procedure employed for performing spam distribution. Here, implementation for segregation of emails into ham or spam real-time data is being used. Result is discussed by taking into account the frequency statistics of mail (ham/spam) along with user feedback to generate more accurate system.
Improved Image Analysis Methodology for Detecting Changes in Evidence Positioning at Crime Scenes

This paper proposed an improved methodology to assist forensic investigators in detecting positional change of objects due to crime scene contamination. Either intentionally or by accident, crime scene contamination can occur during the investigation and documentation process. This new proposed methodology utilises an ASIFT-based feature detection algorithm that compares pre- and post-contaminated images of the same scene, taken from different viewpoints. The contention is that the ASIFT registration technique is better suited to real world crime scene photography, being more robust to affine distortion that occurs when capturing images from different viewpoints. The proposed methodology was tested with both the SIFT and ASIFT registration techniques to show that (1) it could identify missing, planted and displaced objects using both SIFT and ASIFT and (2) ASIFT is superior to SIFT in terms of error in displacement estimation, especially for larger viewpoint discrepancies between the pre- and post-contamination images. This supports the contention that our proposed methodology in combination with ASIFT is better suited to handle real world crime scene photography.
A fuzzy linguistic supported framework to increase Artificial Intelligence intelligibility for subject matter experts

The application of artificial intelligence (AI) techniques in the decision making processes is more widespread in the industry than ever before. Yet, one of the most critical show-stoppers is the communication gap between the machine learning (ML) models and the experts community. On one hand, the output of ML is often not intelligible for experts, in spite of the latest advances in explainable AI. On the other hand, the expert knowledge, rarely completely present in the available data, but rather in the heads of the experts, needs to be connected to the data-driven insights created by the ML model. In this paper we first identify the most critical situations with a manifest intelligibility gap and then propose a framework supported by fuzzy linguistic modelling techniques to close this gap. In addition, we present its integration into the end-to-end decision making flow, from data gathering to the execution and evaluation and we show the output of our approach with practical examples.
Entity Relationship Extraction Based on BLSTM Model

Entity relationship extraction is an important research topic in the field of NLP, which is to extract the relationship between two entities from natural language texts. Research shows that deep neural networks are feasible in the task of entity relation extraction and are superior to traditional relation extraction methods. In this paper, we compare the performance of the BLSTM model and the Bidirectional LSTM model with Attention under the entity relationship extraction task. The comparison includes the different performance of the model after adding additional NLP features and modifying embedding size. Compared with the BLSTM model, the Bidirectional LSTM model with Attention can achieve similar accuracy under the condition of simpler and less preprocessing. In addition, the larger size of dimension of word embedding and additional features acquired by NLP tools can improve the accuracy of relationship extraction. This article provides a reference standard for engineers to select the optimal model under different conditions.
Conservation Design and Scenario for Flood Mitigation on Arui Watershed, Indonesia

Flooding has been natural disaster in Indonesia and elsewhere. This research is designed to create scenarios and designs conservation to mitigate flooding disaster. Data potential ,vulnerability, and duplicated river covering 0.25% of the targeted flooding area were collected and analysed. Five disain of conservation, natural river as control, river normalization, normalization with gabion stone, river straigtening, and straigtening with gabion stone, are proposed, and main targeted responses of these five scenarios are river current velocity. Effectiveness scenarios were analysed using Anova and Tukey test. The results showed that alignment with gabion stone was the most effective scenario for flooding mitigation since this was the most effective in increasing river current velocity. This could prevent riverbank occurrence of avalanche, accelerate river current, overcome flooding, and prevent future flooding. Other scenarios likes dead clicth ended-hallway, canalization, and riparian reclamation are also possible implemented.
PHOC Descriptor Applied for Mammography Classification

This paper describes experiments with PHOC (Pyramid Histogram of Color) features descriptor in terms of capacity for representing features presented in breast radiograph (also known as mammography). Patches were taken from regions in digital mammographies, representing benign, cancerous, normal tissues and image’s background. The motivation is to evaluate the proposal in perspective of using it for execution in an inexpensive ordinary desktop computer in places located far from medical experts. The images were obtained from DDSM database and processed producing the feature-dataset used for training an Artificial Neural Network, the results were evaluated by analysis of the learning rate curve and ROC curves, besides these graphical analytical tools the confusion matrix and other quantitative metrics (TPR, FPR and Accuracy) were also extracted and analyzed. The average accuracy ≈ 0.8 and the other metrics extracted from results demonstrate that the proposal presents potential for further developments. At the best effort, PHOC was not found in literature for applications in mammographies such as it is proposed here.
Release of Unverified Plaintext: Tight Unified Model and Application to ANYDAE

Authenticated encryption schemes are usually expected to offer confidentiality and authenticity. In case of release of unverified plaintext (RUP), an adversary gets separated access to the decryption and verification functionality, and has more power in breaking the scheme. Andreeva et al. (ASIACRYPT 2014) formalized RUP security using plaintext awareness, informally meaning that the decryption functionality gives no extra power in breaking confidentiality, and INT-RUP security, covering authenticity in case of RUP. We describe a single, unified model, called AERUP security, that ties together these notions: we prove that an authenticated encryption scheme is AERUP secure if and only if it is conventionally secure, plaintext aware, and INT-RUP secure. We next present ANYDAE, a generalization of SUNDAE of Banik et al. (ToSC 2018/3). ANYDAE is a lightweight deterministic scheme that is based on a block cipher with block size n and arbitrary mixing functions that all operate on an n-bit state. It is particularly efficient for short messages, it does not rely on a nonce, and it provides maximal robustness to a lack of secure state. Whereas SUNDAE is not secure under release of unverified plaintext (a fairly simple attack can be mounted in constant time), ANYDAE is. We make handy use of the AERUP security model to prove that ANYDAE achieves both conventional security as RUP security, provided that certain modest conditions on the mixing functions are met. We describe two simple instances, called MONDAE and TUESDAE, that conform to these conditions and that are competitive with SUNDAE, in terms of efficiency and optimality.
Comparison of Dynamic Programming and Genetic Algorithm Approaches for Solving Subset Sum Problems

Albeit Evolutionary Algorithms (EAs) are prominent, proven tools for resolution of optimization problems in the real world, appraisal of their appropriateness in solving wide variety of mathematical problems, from simple to complex, continues to be an active research area in the domain of Computer Science. This paper portrays an evaluation of the relevance of Genetic Algorithm (GA) in addressing the Subset Sum Problem (SSP) of Mathematics and providing empirical results with discussions. A GA with pertinent mutation and crossover operators is designed and implemented to solve SSP. Design of the proposed algorithm are clarified in detail. The results obtained by the proposed GA are assessed among different instances with different initial population by the intermediary solutions obtained and the execution time. This study also adapted the traditional Dynamic Programming (DP) approach, pursuing a bottom-up strategy, to solve the SSP. The findings revealed that the GA approach would be unpreferred on account of its longer execution time.