Reviewerly/exHarmony huggingface dataset:
Use of Constriction Factor-Based Particle Swarm Optimization in Design of Reinforced Concrete Beams

This paper presents a particle swarm optimization based on constriction factor (CFPSO) technique used to perform the design optimization of reinforced concrete beams that satisfies Indian codal requirements of strength and serviceability. Optimal cross-sectional sizing and optimal percentage of reinforcement of an RC beam results in cost saving, but these cannot be standardized for the various factors that influence a given design. The objective function consists of the cost of concrete, rebars, and formwork as prevalent at the place of construction. Successful implementation of the algorithm clearly establishes CFPSO's ability of performance as compared to standard particle swarm optimization with inertia weights in the case of RC beams. A number of examples have been presented to show the effectiveness of this formulation for achieving optimal design.
EFSG: Evolutionary Fooling Sentences Generator

Large pre-trained language representation models (LMs) have recently collected a huge number of successes in many NLP tasks. In 2018 BERT, and later its successors (e.g. RoBERTa), obtained state-of-the-art results in classical benchmark tasks, such as GLUE. Works about adversarial attacks have been published to test their generalization proprieties and robustness. In this study, we propose Evolutionary Fooling Sentences Generator (EFSG), a black-box task-agnostic adversarial attack algorithm designed in an evolutionary fashion to generate false-positive sentences for binary classification tasks. We successfully apply EFSG to single-sentence (CoLA) and sentence-pair (MRPC) classification tasks, on BERT and RoBERTa. Results prove the presence of weak spots in state-of-the-art LMs. To complete the analysis, we perform transferability tests and ablation study. Finally, adversarial training helps as a data augmentation defence approach against EFSG, obtaining stronger improved models with no loss of accuracy.
Vehicle Recommendation System using Hybrid Recommender Algorithm and Natural Language Processing Approach

Owning a vehicle has become a mandatory requirement in the modern world. Automobile industry investing a lot on producing different car models to cater the needs of their customers with different social and economic backgrounds. Thus, Auto makers constantly produce similar car models with different features. In Sri lanka, total number of new vehicles registered at Sri Lanka Registry of Motor Vehicles(RMV) during the period of seven years (from 2008 to 2015) has been increased from 265,199 to 668,907 which is nearly 2.5 times growth. This figure shows the rapid growth of the domestic vehicle market. For a new customer, choosing the most appropriate vehicle requires an extra effort/time and has become a challenging task. For example, matching personal interests and economy with number of available options is a quite complex task. Thus, most of the customers seek support from experts who provide consultancy services. However, customers frequently making complains about the existing services which offers consultancy for new vehicle buyers. The key issues are the people involved in the consultancy are not technically sound and pay minimal attention to customer requirements. Their main focus is to sell the vehicle. Thus, the customers face numerous difficulties before and after buying their vehicle. To address this problem, this research presents a novel vehicle recommender system which guides and gives suggestions to the customers using machine learning technologies. Here, we trained a neural network model using data collected from vehicle users and vehicle sellers. Other than the neural network model, the proposed recommendation system uses natural language processing (NLP) to produce more personalized recommendations. The results shows that the recommendations made by the proposed vehicle recommendation system achieves 96 % accuracy in recommending vehicles.
Conscience as a higher moral capacity

The article is devoted to the psychological analysis of the concept “conscience”, which is considered in connection with the psychological model of spiritual capacities [the author’s previous works of 2010, 2016]. Conscience is described as a higher moral capacity related to the moral component/level of this model. The psychological frame of considering conscience within the psychological model of spiritual capacities allows emphasising its spiritual nature; explaining the dual nature of its manifestation – rationality/irrationality; showing its connection with higher meanings and absolute values, as well as spiritual and moral qualities, spiritual altruistic orientation, moral feelings and moral experience; noting its interaction with the higher capacities of all three components/levels of the model of spiritual capacities – moral, mental, transcendent. Conscience is interpreted as a regulator of moral behaviour, as a spiritual mentor, guiding a person on the path from the egocentric “I” to the spiritual “I”. Thanks to conscience, a person rises to the highest level of personality functioning – a spiritual one, at which it manifests itself as a spiritual personality, focused on the principles of absolute morality.
HoneyHash: Honeyword Generation Based on Transformed Hashes

Since systems using honeywords store a set of decoy passwords together with real passwords of users to confuse adversaries, they are strongly dependent on the algorithm for generating honeywords. However, all of the existing honeyword generating algorithms are based on raw passwords of users and they either need lots of storage space or show weaknesses in flatness or usability. This paper proposes HoneyHash, a new direction of generating honeywords - generating by transforming password hashes. Analyses show that our algorithm attains expected levels of flatness, security, performance and usability.
Item‐to‐item recommender system with simultaneous use of multiple images for image mosaicking creation in dynamic scenes

Image mosaicking is a series of successive algorithms that use a sequence of images or a video of a scene to create a single image with a wider field of view of the whole scene. In most cases, when dynamic objects exist in the input data, issues, such as ghosting, parallax effects or object duplication, are visible in the resulting mosaic. These technical errors appear when the objects in motion aren't properly treated. In order to create good result mosaics, a new method is presented in this paper. The proposed approach uses all the images at the same time to divide the images into different areas by using k-means clustering to create categories, then each category is recommended from the original images with a recommender system. In fact, by considering each image as a user, each pixel as an item, and each item belonging to a category, it is possible to use a recommender system by computing scores with the item profiles. The resulting mosaic will then be a new user to the system. Furthermore, by clustering the images, projection errors are avoided and a better quality mosaic can be created as is seen in the obtained results.
Short-term wind power prediction based on multiple kernel extreme learning machine method

For short-term wind power prediction, a multiple kernel extreme learning machine (MKELM) is proposed based on single kernel extreme learning machine (KELM) and multiple kernel learning (MKL) method. Compared with KELM method, in view of the deficiency of the existing single KELM regression ability, several different kernel functions are weighted combined according to the actual situation, and the unique advantages of different kernel functions are fully utilized to further improve the performance of the network. In order to verify the effectiveness of the MKELM method proposed in this paper, the MKELM method is applied to the research of short-term wind power forecasting in a certain region, and is compared with the existing extreme learning machine (ELM), KELM and support vector machine (SVM) under the same conditions. The experimental results demonstrate that the proposed MKELM method is better than KELM method in prediction accuracy, which shows its effectiveness.
Improving Distant Supervised Relation Extraction with Noise Detection Strategy

Distant supervised relation extraction (DSRE) is widely used to extract novel relational facts from plain text, so as to improve the knowledge graph. However, distant supervision inevitably suffers from the noisy labeling problem that will severely damage the performance of relation extraction. Currently, most DSRE methods are mainly focused on reducing the weights of noisy sentences, ignoring the bag-level noise where all sentences in a bag are wrongly labeled. In this paper, we present a novel noise detection-based relation extraction approach (NDRE) to automatically detect noisy labels with entity information and dynamically correct them, which can alleviate both instance-level and bag-level noisy problems. By this means, we can extend the dataset from the Web tables without introducing more noise. In this approach, to embed the semantics of sentences from corpus and web tables, we firstly propose a powerful sentence coder that employs an internal multi-head self-attention mechanism between the piecewise max-pooling convolutional neural network. Second, we adopt a noise detection strategy, which is expected to dynamically detect and correct the original noisy label according to the similarity between sentence representation and entity-aware embeddings. Then, we aggregate the information from corpus and web tables to make the final relation prediction. Experimental results on a public benchmark dataset demonstrate that our proposed approach achieves significant improvements over the state-of-the-art baselines and can effectively reduce the noisy labeling problem.
Context-specific Adaptation of Subjective Content Descriptions

An agent in pursuit of a task may work with an individual collection of documents, which is known as a corpus. We assume that each document in the corpus is associated with additional location-specific data making the nearby content explicit by providing descriptions, references, or explanations about the content. Manually creating corpus- and location-specific data for documents is a time-consuming task. Thus, we are interested in using already existing data associated to documents in one corpus to enrich documents in another corpus without such data using the existing descriptions. This paper describes the problem for adapting location-specific data of documents in one corpus to documents in another corpus and presents an approach solving the problem. A case study shows the effectiveness of the adaptation approach.
Non-rigid medical image registration using multi-scale residual deep fully convolutional networks

Abstract Medical image registration technology is important in medical image processing, which can establish correspondence between complex medical images and ensure the comparability of image data. Since the deep convolutional neural network model is prone to degradation and the convolution kernel has limited detection range during training, a Multi-scale residual full convolutional network (MS-ResFCN) model is proposed for unsupervised non-rigid medical image registration tasks. The model introduces a residual structure in the Fully Convolutional Network (FCN) model to ensure effective and stable training. At the same time, a hierarchical multi-scale convolution kernel is constructed within a single convolutional layer of the residual structure, which enhances the nonlinear mapping ability of the network. The local texture information and the contextual information of the image are synchronously extracted and fused to enrich the diversity of features. The experiment results on LPBA40 data sets show that the MS-ResFCN model can effectively eliminate the degradation phenomenon of the deep network during the training process and extract multi-scale features, which achieves better feature representation ability and registration accuracy.
A Video Object Detector Based on Deep Reinforcement Learning and Correction Module

The following topics are dealt with: feature extraction; learning (artificial intelligence); neural nets; power grids; image classification; satellite communication; optimisation; convolutional neural nets; error statistics; graph theory.
A novel model based on Sequential Adaptive Memory for English–Hindi Translation

Machine-based language translation has been certainly picking up. Still, machines lag behind the cognitive powers of human beings. Neural Machine Translation (NMT) methods require huge datasets and computational power for high-quality translation. A novel Sequential Adaptive Memory (SAM) cognitive model-based machine translation system for English to Hindi translation, was proposed. This model is an augmented version of the Cortical Learning Algorithm (CLA). The SAM is based on the architecture of the neocortex region of the brain, where speech and language comprehension and production take place. The proposed model is capable of learning with smaller datasets. This model employs the sequence to sequence learning approach, which provides better quality translation. It enables the creation of word pairs, dictionaries, and rules for translation. The results of the proposed approach are compared with the traditional phrase-based SMT approach as well as with the state-of-the-art NMT approach. The results are comparable with the results of the conventional approaches. We illustrate that the limitations of the approaches are won over by the proposed SAM approach. It is observed that SAM is capable of exhibiting satisfactory quality translation for low resource languages as well.
An Artificial Intelligence approach to Social Networks agent task scheduling analysis in map-reduce for Sentiment Opinion Analysis

Now a day’s everyone widely used social networks; it is one part of our daily life activity. In this way, the user can upload and view their profiles text messages, images and video etc. In the way, user can communicate either individual or group in their existing regions and post updated information to circulate all other followers or guest users like working the same as social networks sites. This research work, we propose a novel algorithm to analyze existing resources and predict sentiment analysis and prediction along with this user expectation. These results will be useful in Applied Economics, Expert Systems, Human Brain Analysis, Voting, Stock markets, Live updates on every field, unknown object identifications Etc.
Machine learning explanability method for the multi-label classification model

Multi label classification is the identification of the multiple labels for a sample. There are a number of problems where a single label cannot be assigned to the sample and multiple labels are applicable. To take an action based on the multi label classification model, the model needs to be explained. There are many model agnostic machine learning explainability methods. However, the effectiveness of such methods for multi label classification model is not evaluated and the cognitive load of using the existing algorithms is high. In this paper, we propose a method for multi label model explainability and compare it with the LIME and CXPlain algorithm. We propose a method to compute a single list of ranked features explaining the multi-label model local explanation. The ranked list can be used for a variety of purposes such as, to debug the model misclassification, which provides the explanation quality similar to LIME but the time taken for using the explanation in a cognitive task is significantly lower. We show the results of evaluating the proposed method for a cognitive task on a private dataset and the open source dataset of CMU movie summary dataset. We get the hamming score of 90.75% on the private dataset and an evaluation time of 18 minutes for the sample set, and the hamming score of 65.23% on the public dataset and an evaluation time of 30 minutes.
Design and Application of Preschool Music Teaching System Based on Moodle Platform

The system adopts B/S structure, including user login module, Moodle platform module and database module. The application of the system in music teaching is analyzed. Network teaching not only changes the former teaching method, but also changes people’s concept of expanding knowledge field, and has good effect in the process of practical teaching. However, the network teaching system software on the market has a high price, and the related service and upgrade costs are also high, which has become an embarrassing task for colleges and universities to explore teaching mode and improve teaching effect.
A New Method for Defining Scale to Estimate the Aspects Oriented Sentiment Polarity of the Tweets

The positive or negative polarity of an opinion on a product, event, or activity is not strengthen enough if the tweet of opinion exploring about the vivid aspects of the target objective. In this regard, many of the contributions in contemporary research has portrayed for sentiment analysis using supervised learning. However, the content on twitter, which is social media platform to share the opinion on anything, limits the size of each tweet. Hence, the users intend to express negative polarity of the opinion in their tweets. This trend has not consider much in contemporary contributions of sentiment analysis. In this context, the proposal of this manuscript portrayed "A New Method for Defining Scale to Estimate the Aspects Oriented Sentiment Polarity (SEAOSP) of the Tweets". Experimental study evincing the significance of the proposal that scaled by comparing the performance of other contemporary model with the proposed model.
Study Recycling Effluents of Hospital WWTP with Reverse Osmosis

Abstract Reverse osmosis (RO) membrane technology has been used extensively in seawater desalination, drinking water production, brackish water treatment, and industrial wastewater treatment, whereas in hospital wastewater treatment plants (WWTP) has not been much studied. Effluents of hospital WWTP can still be reprocessed with filtration technology to improve its quality, so that it can replace the role of clean water that has been taken from ground water, and can even save water use by the Regional Water Supply Company. This study aims to examine the performance of reverse osmosis (RO) technology for recycling effluents of hospital WWTP. The results showed that the reverse osmosis (RO) system with a polyamide membrane proved to be effective for recycling effluents of hospital WWTP. The value of the concentration of processed water (product) RO when viewed from the TDS (Total Dissolved Solid) and COD (Chemical Oxygen Demand) parameters has decreased and meets clean water quality standards according to existing regulations. Increased operating time causes a decrease in permeate flux. Increased operating pressure causes increased permeate flux and rejection efficiency. The use of RO systems with polyamide membranes is expected to be the best choice for recycling effluents of hospital WWTP under current conditions.
Utilizing Extended Visual Cryptography for Ensuring Safety and Accuracy of PDF File in Cloud Storage

Nowadays cloud computing (CC) is in endless prosperity position and visual cryptography (VC) is very important part of cloud computing. Many institution and people at every turn use cloud storage but they do not know about its security policies, which is risky for their business. It is a very challenging part for the user and cloud service provider (CSP) to protect data in cloud storage. If secret data is stored in cloud in unencrypted form then the unauthorized people and the CSP can easily access the data. But the algorithm which is standard need computational primitives, storage space and cost. So it is very staple significance to protect cloud data with minimum computation and storage space. Utilizing extended visual cryptography for ensuring safety and accuracy of PDF file in cloud storage (UEVCESAPDFCS) process offer useful storage to maintain data confidentiality and integrity verification. In the previous work the authors used (VC), in the proposed method we also use (VC). But the different between this two works is, in the previous work there is a chance to rearrange the data value, but in our algorithm the condition is like when the uploaded and downloaded data will be same then the algorithm will be finished, so there is no chance to change the data value. In this research our main target was to defense the unauthorized people from recognize the clouds data value.
Non-local Denoising Convolutional Neural Network for Rolling Bearing Vibration Signal

The denoising process for rolling bearing vibration signal could significantly influence the performance of the diagnosis, prognostics and control process of a mechanical system. During the last several years, convolutional neural network(CNN) has been widely applied in the field of image denoising. However, it has been rarely applied to time-series signal denoising. Therefore, in this paper, we construct a novel architecture of CNN for rolling bearing vibration signal denoising. Furthermore, we also adopt non-local means (NLM) which is firstly introduced in the field of image denoising to construct non-local block (NLB), which could improve the performance of our proposed CNN considerably. Moreover, the NLB could be plugged into the denoising architecture at any positions without breaking the original behavior of the network. Based on it, we also propose a non-local denoising convolutional neural network (NL-DeCNN) aiming at rolling bearing vibration signal denoising. In the experiment section, we firstly proposed our original CNN architecture and then test the influence of the single NLB when it is embedded into different layers. Next, we test the influence of the number of NLBs. Lastly, we also compare our proposed CNN based method with other three traditional time-series denoising methods and our NL-DeCNN exhibits the best performance among these methods.
SrSpark: Skew-resilient Spark based on Adaptive Parallel Processing

MapReduce-based SQL processing systems, e.g., Hive and Spark SQL, are widely used for big data analytic applications due to automatic parallel processing on large-scale machines. They provide high processing performance when loads are balanced across the machines. However, skew loads are not rare in real applications. Although many efforts have been made to address the skew issue in MapReduce-based systems, they can neither fully exploit all available computing resources nor handle skews in SQL processing. Moreover, none of them can expedite the processing of skew partitions in case of failures. In this paper, we present SrSpark, a MapReduce-based SQL processing system that can make full use of all computing resources for both non-skew loads and skew loads. To achieve this goal, SrSpark introduces fine-grained processing and work-stealing into the MapReduce framework. More specifically, SrSpark is implemented based on Spark SQL. In SrSpark, partitions are further divided into sub-partitions and processed in sub-partition granularity. Moreover, SrSpark adaptively uses both intra-node and inter-node parallel processing for skew loads according to available computing resources in realtime. Such adaptive parallel processing increases the degree of parallelism and reduces the interaction overheads among the cooperative worker threads. In addition, SrSpark checkpoints sub-partition's processing results periodically to ensure fast recovery from failures during skew partition processing. Our experiment results show that for skew loads, SrSpark outperforms Spark SQL by up to 3.5x, and 2.2x on average, while the performance overhead is only about 4% under non-skew loads.