Reviewerly/exHarmony huggingface dataset:
Application of Heuristic Algorithms in Improving Performance of Soft Computing Models for Prediction of Min, Mean and Max Air Temperatures

Traditionally, climate conditions have been one of the influential factors in population growth in worldwide.Hence, predicting these conditions can be an important step to improve life conditions in worldwide.In this study, application of genetic algorithm (GA) and particle swarm algorithm (PSO) were considered as alternatives to available algorithms for training artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) in order to predict air temperature.Therefore, monthly minimum, average and maximum air temperatures of Tehran-Iran station at 64-years (1951-2014) were selected as predicted time-series.First, the most appropriate inputs were selected for the models using sensitivity analysis.After that, long-term air temperatures (1 month, 1-, 2-and 3-year ahead) were modeled.Results showed that: 1) the given algorithms had acceptable results in improving the models' performance in forecasting minimum, mean and maximum air temperatures.Also, they could improve the performance of ANN and ANFIS in most of the prediction intervals, 2) ANFIS-GA was selected as the most suitable model so that its average determination coefficient (R 2 ), root mean square errors (RMSE) and mean absolute errors (MAE) were 0.88, 1.41 and 2.52, respectively, 3) the sensitivity analysis provided suitable results in selecting the most appropriate model inputs for forecasting the minimum, mean and maximum air temperatures in different intervals.
Exchangeable Generative Models with Flow Scans

In this work, we develop a new approach to generative density estimation for exchangeable, non-i.i.d. data. The proposed framework, FlowScan, combines invertible flow transformations with a sorted scan to flexibly model the data while preserving exchangeability. Unlike most existing methods, FlowScan exploits the intradependencies within sets to learn both global and local structure. FlowScan represents the first approach that is able to apply sequential methods to exchangeable density estimation without resorting to averaging over all possible permutations. We achieve new state-of-the-art performance on point cloud and image set modeling.
Secure IoT Data Outsourcing With Aggregate Statistics and Fine-Grained Access Control

With the rapid development of the Internet of Things (IoT) and the dramatic increase of IoT devices, the desire to outsource huge amounts of IoT data to the cloud becomes more urgent than ever.In order to ensure the confidentiality, IoT data are usually encrypted before they are outsourced to the cloud, which will inevitably hinder the statistical analysis of them.Homomorphic encryption is an alternative to achieve the computation of encrypted data, but its inefficiency makes it not practical in the IoT environment.Another problem comes with the encryption is how to enable IoT data to be accessed by users who possess a certain set of attributes defined by data owners.In this paper, we propose a novel and practical IoT data outsourcing scheme based on Corrigan-Gibbs et al. 's computation of aggregate statistics and the ciphertextpolicy attribute-based encryption (CP-ABE).It supports both secure aggregation and fine-grained access control of outsourced IoT data.Users only have to bear a small amount of computation in the process of data upload and recovery.Security analysis demonstrates that our scheme well protects the confidentiality of IoT data.A thorough and detailed performance comparison shows that our scheme enjoys a better performance on both the client side and the fog server side.
Side Information Dependence as a Regularizer for Analyzing Human Brain Conditions across Cognitive Experiments

The increasing of public neuroimaging datasets opens a door to analyzing homogeneous human brain conditions across datasets by transfer learning (TL). However, neuroimaging data are high-dimensional, noisy, and with small sample sizes. It is challenging to learn a robust model for data across different cognitive experiments and subjects. A recent TL approach minimizes domain dependence to learn common cross-domain features, via the Hilbert-Schmidt Independence Criterion (HSIC). Inspired by this approach and the multi-source TL theory, we propose a Side Information Dependence Regularization (SIDeR) learning framework for TL in brain condition decoding. Specifically, SIDeR simultaneously minimizes the empirical risk and the statistical dependence on the domain side information, to reduce the theoretical generalization error bound. We construct 17 brain decoding TL tasks using public neuroimaging data for evaluation. Comprehensive experiments validate the superiority of SIDeR over ten competing methods, particularly an average improvement of 15.6% on the TL tasks with multi-source experiments.
Knowledge management overview of feature selection problem in high-dimensional financial data: cooperative co-evolution and MapReduce perspectives

The term “big data” characterizes the massive amounts of data generation by the advanced technologies in different domains using 4Vs – volume, velocity, variety, and veracity - to indicate the amount of data that can only be processed via computationally intensive analysis, the speed of their creation, the different types of data, and their accuracy. High-dimensional financial data, such as time-series and space-time data, contain a large number of features (variables) while having a small number of samples, which are used to measure various real-time business situations for financial organizations. Such datasets are normally noisy, and complex correlations may exist between their features, and many domains, including financial, lack the al analytic tools to mine the data for knowledge discovery because of the high-dimensionality. Feature selection is an optimization problem to find a minimal subset of relevant features that maximizes the classification accuracy and reduces the computations. Traditional statistical-based feature selection approaches are not adequate to deal with the curse of dimensionality associated with big data. Cooperative co-evolution, a meta-heuristic algorithm and a divide-and-conquer approach, decomposes high-dimensional problems into smaller sub-problems. Further, MapReduce, a programming model, offers a ready-to-use distributed, scalable, and fault-tolerant infrastructure for parallelizing the developed algorithm. This article presents a knowledge management overview of evolutionary feature selection approaches, state-of-the-art cooperative co-evolution and MapReduce-based feature selection techniques, and future research directions.
End-to-End Thorough Body Perception for Person Search

In this paper, we propose an improved end-to-end multi-branch person search network to jointly optimize person detection, re-identification, instance segmentation, and keypoint detection. First, we build a better and faster base model to extract non-highly correlated feature expression; Second, a foreground feature enhance module is used to alleviate undesirable background noise in person feature maps; Third, we design an algorithm to learn the part-aligned representation for person search. Extensive experiments with ablation analysis show the effectiveness of our proposed end-to-end multi-task model, and we demonstrate its superiority over the state-of-the-art methods on two benchmark datasets including CUHK-SYSU and PRW.
If Nothing Is Accepted -- Repairing Argumentation Frameworks

Conflicting information in an agent's knowledge base may lead to a semantical defect, that is, a situation where it is impossible to draw any plausible conclusion. Finding out the reasons for the observed inconsistency (so-called diagnoses) and/or restoring consistency in a certain minimal way (so-called repairs) are frequently occurring issues in knowledge representation and reasoning. In this article we provide a series of first results for these problems in the context of abstract argumentation theory regarding the two most important reasoning modes, namely credulous as well as sceptical acceptance. Our analysis includes the following problems regarding minimal repairs/diagnoses: existence, verification, computation of one and enumeration of all solutions. The latter problem is tackled with a version of the so-called hitting set duality first introduced by Raymond Reiter in 1987. It turns out that grounded semantics plays an outstanding role not only in terms of complexity, but also as a useful tool to reduce the search space for diagnoses regarding other semantics.
Syntactically Look-Ahead Attention Network for Sentence Compression

Sentence compression is the task of compressing a long sentence into a short one by deleting redundant words. In sequence-to-sequence (Seq2Seq) based models, the decoder unidirectionally decides to retain or delete words. Thus, it cannot usually explicitly capture the relationships between decoded words and unseen words that will be decoded in the future time steps. Therefore, to avoid generating ungrammatical sentences, the decoder sometimes drops important words in compressing sentences. To solve this problem, we propose a novel Seq2Seq model, syntactically look-ahead attention network (SLAHAN), that can generate informative summaries by explicitly tracking both dependency parent and child words during decoding and capturing important words that will be decoded in the future. The results of the automatic evaluation on the Google sentence compression dataset showed that SLAHAN achieved the best kept-token-based-F1, ROUGE-1, ROUGE-2 and ROUGE-L scores of 85.5, 79.3, 71.3 and 79.1, respectively. SLAHAN also improved the summarization performance on longer sentences. Furthermore, in the human evaluation, SLAHAN improved informativeness without losing readability.
End-to-End Trainable Non-Collaborative Dialog System

End-to-end task-oriented dialog models have achieved promising performance on collaborative tasks where users willingly coordinate with the system to complete a given task. While in non-collaborative settings, for example, negotiation and persuasion, users and systems do not share a common goal. As a result, compared to collaborate tasks, people use social content to build rapport and trust in these non-collaborative settings in order to advance their goals. To handle social content, we introduce a hierarchical intent annotation scheme, which can be generalized to different non-collaborative dialog tasks. Building upon TransferTransfo (Wolf et al. 2019), we propose an end-to-end neural network model to generate diverse coherent responses. Our model utilizes intent and semantic slots as the intermediate sentence representation to guide the generation process. In addition, we design a filter to select appropriate responses based on whether these intermediate representations fit the designed task and conversation constraints. Our non-collaborative dialog model guides users to complete the task while simultaneously keeps them engaged. We test our approach on our newly proposed AntiScam dataset and an existing PersuasionForGood dataset. Both automatic and human evaluations suggest that our model outperforms multiple baselines in these two non-collaborative tasks.
Block-Based Arnold Chaotic Map for Image Encryption

This study proposes an encryption method in Arnold chaotic map based imagery. The uniqueness of the proposed method is to divide the image into smaller blocks, then each image is encrypted with the same chaotic map formula. Visually the results of image encryption are quite unique because they make the form of image encryption blocks that look different but actually have the same iteration. The chaotic map method is famous for its resistance to differential attacks. Therefore, the results of encryption from the proposed method need to be measured for performance against differential attacks. To measure the performance of the proposed method of measurement using measuring devices and UACI NPCR. Based on the results of the test we found a significant increase in the assessment of the UACI value but there was a decrease in the NPCR value. These results can be used as preliminary research for the development of methods in order to increase the value of UACI and NPCR at once.
Solving Sum-of-Costs Multi-Agent Pathfinding with Answer-Set Programming

Solving a Multi-Agent Pathfinding (MAPF) problem involves finding non-conflicting paths that lead a number of agents to their goal location. In the sum-of-costs variant of MAPF, one is also required to minimize the total number of moves performed by agents before stopping at the goal. Not surprisingly, since MAPF is combinatorial, a number of compilations to Satisfiability solving (SAT) and Answer Set Programming (ASP) exist. In this paper, we propose the first family of compilations to ASP that solve sum-of-costs MAPF over 4-connected grids. Unlike existing compilations to ASP that we are aware of, our encoding is the first that, after grounding, produces a number of clauses that is linear on the number of agents. In addition, the representation of the optimization objective is also carefully written, such that its size after grounding does not depend on the size of the grid. In our experimental evaluation, we show that our approach outperforms search- and SAT-based sum-of-costs MAPF solvers when grids are congested with agents.
Learning Saliency-Free Model with Generic Features for Weakly-Supervised Semantic Segmentation

Current weakly-supervised semantic segmentation methods often estimate initial supervision from class activation maps (CAM), which produce sparse discriminative object seeds and rely on image saliency to provide background cues when only class labels are used. To eliminate the demand of extra data for training saliency detector, we propose to discover class pattern inherent in the lower layer convolution features, which are scarcely explored as in previous CAM methods. Specifically, we first project the convolution features into a low-dimension space and then decide on a decision boundary to generate class-agnostic maps for each semantic category that exists in the image. Features from Lower layer are more generic, thus capable of generating proxy ground-truth with more accurate and integral objects. Experiments on the PASCAL VOC 2012 dataset show that the proposed saliency-free method outperforms the previous approaches under the same weakly-supervised setting and achieves superior segmentation results, which are 64.5% on the validation set and 64.6% on the test set concerning mIoU metric.
Is Big Data About to Retire Expert Knowledge? A Predictive Maintenance Study

In this contribution, a data-driven approach towards the prediction of maintenance for the critical component of an injection molding machine is presented. We present our path from exploring and cleaning the data towards the implementation of a prediction algorithm based on kernel density estimation. We give first analytical evidence of the algorithms potential. Moreover, we compare the approach described here with our previous work where we went a model-based approach and present advantages and disadvantages of the two approaches. We try to contribute to a non-comprehensive guide on the implementation of predictive maintenance systems for industrial mass production facilities.
Analytical Design of the Human Bronchial Tree for Healthy Patients and Patients with Obstructive Pulmonary Diseases

The study is aimed at the analytical design of the full human bronchial tree for healthy patients and patients with obstructive pulmonary diseases. Analytical formulas for design of the full bronchial tree are derived. All surfaces of the bronchial tree are matched with the second-order smoothness (there are no acute angles or ribs). The geometric characteristics of the human bronchial tree in the pathological case are modeled by a “starry” shape of the inner structure of the bronchus; the pathology degree is defined by two parameters: bronchus constriction level and degree of distortion of the cylindrical shape of the bronchus. Closed analytical formulas allow the human bronchial tree of an arbitrary complexity (up to alveoli) to be designed; moreover, the parametric dependences make it possible to specify any desirable degree of airway obstruction.
Asking the Right Questions to the Right Users: Active Learning with Imperfect Oracles

Active learning algorithms automatically identify the salient and exemplar samples from large amounts of unlabeled data and tremendously reduce human annotation effort in inducing a machine learning model. In a traditional active learning setup, the labeling oracles are assumed to be infallible, that is, they always provide correct answers (in terms of class labels) to the queried unlabeled instances. However, in real-world applications, oracles are often imperfect and provide incorrect label annotations. Oracles also have diverse expertise and while they may be noisy, certain oracles may provide accurate annotations to certain specific instances. In this paper, we propose a novel framework to address the challenging problem of active learning in the presence of multiple imperfect oracles. We pose the optimal sample and oracle selection as a constrained optimization problem and derive a linear programming relaxation to select a batch of (sample-oracle) pairs, which can potentially augment maximal information to the underlying classification model. Our extensive empirical studies on 9 challenging datasets (from a variety of application domains) corroborate the usefulness of our framework over competing baselines.
A topic-based sentence representation for extractive text summarization

We examine the effect of probabilistic topic model-based word representations, on sentence-based extractive summarization.We formulate the task of sentence selection as a binary classification problem, and we test a variety of machine learning algorithms, exploring a range of different settings for classification and modelling.A preliminary investigation via a wide experimental evaluation on the MultiLing 2015 MSS dataset illustrates that topicbased representations can prove beneficial to the extractive summarization process, compared to a TF-IDF baseline, with Quadratic Discriminant Analysis and Gradient Boosting providing the best results for micro and macro F1 score, respectively.
Persistent Fault Injection in FPGA via BRAM Modification

The feasibility of persistent fault analysis relies on special faults which can persist in all the rounds of block ciphers. This prerequisite can be positioned as a good fit into the FPGA scenario, which however has not been carefully exploited ever before. In this paper, we propose the persistent fault attack on the block cipher AES-128 implemented in FPGA where a new type of persistent fault is induced with the technique of Block RAM (BRAM) modification. The details of persistent fault injection are elaborated, especially on how the target bits of AES in BRAM can be identified and how they can be altered. Our experimental results show that: with the proposed attack, a simple statistical analysis can extract the secret key of AES-128 with S-Box implemented in BRAMs and protected by the countermeasure of inversive decryption based dual modular redundancy.
Explicitly Modeling Word Translations in Neural Machine Translation

In this article, we show that word translations can be explicitly incorporated into NMT effectively to avoid wrong translations. Specifically, we propose three cross-lingual encoders to explicitly incorporate word translations into NMT: (1) Factored encoder, which encodes a word and its translation in a vertical way; (2) Gated encoder, which uses a gated mechanism to selectively control the amount of word translations moving forward; and (3) Mixed encoder, which stitchingly learns a word and its translation annotations over sequences where words and their translations are alternatively mixed. Besides, we first use a simple word dictionary approach and then a word sense disambiguation (WSD) approach to effectively model the word context for better word translation. Experimentation on Chinese-to-English translation demonstrates that all proposed encoders are able to improve the translation accuracy for both traditional RNN-based NMT and recent self-attention-based NMT (hereafter referred to as Transformer ). Specifically, Mixed encoder yields the most significant improvement of 2.0 in BLEU on the RNN-based NMT, while Gated encoder improves 1.2 in BLEU on Transformer . This indicates the usefulness of an WSD approach in modeling word context for better word translation. This also indicates the effectiveness of our proposed cross-lingual encoders in explicitly modeling word translations to avoid wrong translations in NMT. Finally, we discuss in depth how word translations benefit different NMT frameworks from several perspectives.
DHA: Supervised Deep Learning to Hash with an Adaptive Loss Function

Hashing, which refers to the binary embedding of high-dimensional data, has been an effective solution for fast nearest neighbor retrieval in large-scale databases due to its computational and storage efficiency. Recently, deep learning to hash has been attracting increasing attention since it has shown great potential in improving retrieval quality by leveraging the strengths of deep neural networks. In this paper, we consider the problem of supervised hashing and propose an effective model (i.e., DHA), which is able to generate compact and discriminative binary codes while preserving semantic similarities of original data with an adaptive loss function. The key idea is that we scale and shift the loss function to avoid the saturation of gradients during training, and simultaneously adjust the loss to adapt to different levels of similarities of data. We evaluate the proposed DHA on three widely-used benchmarks, i.e., NUS-WIDE, CIFAR-10, and MS COCO. The state-of-the-art image retrieval performance clearly shows the effectiveness of our method in learning discriminative hash codes for nearest neighbor retrieval.
E-CRM Mobile Applications To Improve Customer Loyalty (Case Study: PT Supermal Karawaci)

Disruptive Technology is one of the backbones of Indutry4.0, In this era the company's large size is no longer a guarantee. Speed ​​in adapting to the development and behavior of consumers is the main key. Therefore, companies must be sensitive and do self-introspection so they can detect their position in the midst of the development of science and technology in order to maintain their existence and have the opportunity to dominate the market. PT Supermall Karawaci, which is engaged in the retail business, needs to adapt and understand this rapid technological development, so for this business sector requires interaction with customers. The current system is good, but in some procedures that make customer service less than optimal and will be better if there is an effort so that customer loyalty can increase and of course the company's profit will increase. Therefore, the writers make this problem as a foundation in research and application development (E-CRM), the writers use the method of observation, elicitation, SWOT, Unified Modeling Language (UML), prototype and black box as methods of data collection, analysis, design and testing. For design, programming, web server and database, the writer uses Adobe XD applications, Android Studio, PHP, XAMPP and Microsoft SQL Server.