Reviewerly/exHarmony huggingface dataset:
Artificial Bee Colony Inspired Density Based Clustering with efficient Parameter Selection

In this article, we have suggested an Artificial Bee Colony (ABC) inspired density based clustering. It can effectively deal with the issue of parameter selection as well as representation of varying densities of general density based clustering approaches. We plot the average density of the neighbours of each object in the data set. This unknown density distribution is then approximated using kernel density estimation. This helps us to identify the threshold, so that we can divide the density distribution of the data set into core and noise and we can also identify the cluster transitions. Including the parameter selection, the overall process is optimized using ABC. The superiority of the proposed approach has been delineated considering various data sets and by using different measurement techniques.
A Version Space Perspective on Differentially Private Pool-Based Active Learning

We analyze pool-based active learning under a differential privacy guarantee. At every active learning step, some samples are selected to be labeled by an oracle, and the new labels are used to update the classifier. We want to preserve differential privacy during both the sample selection step and the classifier update step. To study the evolution of the active learner, we use the concept of a version space of possible hypotheses (classifiers). This concept helps establish a principled notion of the informativeness of a pool sample: When informative samples are labeled and used for training, the version space shrinks, yielding classifiers consistent with the labeled samples. To provide privacy, we query the oracle with both informative and non-informative samples using a simple randomized sampling scheme. We prove the privacy guarantee, and characterize the increase in label complexity resulting from our randomized sampling strategy. To examine how our theoretical analysis manifests in practice, we built an SVM-based active learner, and measured the accuracy and label complexity achieved with and without privacy.
Adaptive Differential Privacy of Character and Its Application for Genome Data Sharing

Genome sharing leads to privacy concerns of individual, because genome data can uniquely identify an individual and closely associate with inheritance, health, phenotype, and kinship. Existing differential privacy methods cannot achieve expected privacy preserving and expected data utility of genome data. To tackle the problem, we proposed privacy preserving model of genome data sharing satisfying expected privacy preserving and expected data utility. First, we proposed the definition of adaptive differential privacy of character achieving expected privacy preserving and expected data utility, and we gave the adaptive differential privacy mechanisms of character. And also, we theoretically analyzed the properties of definition of adaptive differential privacy of character and its mechanisms. Second, we constructed privacy preserving model of genome data sharing based on adaptive differential privacy mechanisms of character. Finally, our theoretical and experimental analysis showed that the proposed privacy preserving model of genome data sharing using adaptive differential privacy mechanisms of character satisfies expected privacy preserving and expected data utility. Our proposal makes a guideline on designing privacy preserving method of genome data sharing achieving expected privacy preserving and expected data utility
Multi-Modal ISAR Object Recognition using Adaptive Deep Relation Learning

An efficient complex multi-modal ISAR (Inverse-Synthetic-Aperture-Radar) object recognition method, based on an Adaptive Deep Relation Learning (ADRL), is proposed. (1) Adaptive Dilated Deformable Convolution Neural Network (ADDCNN) is proposed to greatly improve CNNs' multimodal sampling and transformation ability, and increase output feature maps' resolution. Particularly, an adaptive related multimodal regions ranking and selection strategy are proposed to rank and select related regions. (2) DARGL (Deep Attribute-Relation Graph Learning) is proposed to jointly estimate large numbers of heterogeneous multi-attributes, and leverage the inter-modal associations among features and attributes. Extensive experiments are performed on two real-world multi-modal ISAR datasets, showing the proposed method outperforms state-of-the-art methods.
Survey on Image Retrieval Based on Rotation, Translation and Scaling Invariant Features

How to retrieve information from the massive image database in time is a “bottleneck” faced by network information processing in recent years and has become a research hotspot at home and abroad. For unstructured image data, we have studied the traditional text-based retrieval method is inefficient, and a content-based image retrieval technique. Compared with standard color, texture and shape features, the image features with the same scale of rotation and translation can get better retrieval results. Therefore, image retrieval based on invariant features has broad research prospects. In this paper, the algorithm of image retrieval based on invariant features is studied. Combined with the principle of integral invariant construction of geometric transformation group and the extraction principle of scale-invariant feature points, two new rotation, translation, and scale-invariant features are presented. That is, the rotation, scaling and translation (RST) invariant feature extraction method and these features are applied to image retrieval.
Data Mining Task Optimization with Soft Computing Approach

The data mining task optimization (DMTO) is one of the emerging research areas in the branch of data mining. We discuss the data mining tasks as classification and clustering. The optimization of these tasks is done using soft computing methods. The soft computing is also a new term coined for optimization problems. The classification task is achieved using the support vector machine and results optimized with particle swarm optimization and simplified swarm optimization with exchange local strategy (SSO with ELS). The clustering task is achieved using the kernel fuzzy c-means (KFCM) and results optimized using particle swarm optimization (PSO) and intelligent firefly algorithm (IFA). The results of both the tasks are worked out in the same paper. The results obtained outperform the existing approach SVM with PSO with cuckoo search (CS) and PSO with social spider optimization (SSO) for classification, KFCM with PSO with Bacteria Foraging Optimization (BFO) for clustering in data mining task optimization framework.
An Improved Method for Facial Expression Recognition Based on Extended Local Binary Pattern and Differential Features

In our study, we made an effort to combine the Extended Local Binary Pattern(ELBP) and Principal Component Analysis (PCA) to weigh out the robust facial features of a person from an image. We set our goal as to develop and perform a low-computational approach on two most widely used databases - JAFFE and Cohn-Kanade based on the differences in the feature vectors to classify the human expressions shown in the images into seven basic emotions-anger, disgust, fear, happiness, sadness, surprise, and neutral. Moreover, we have also proposed a completely unique idea of feature vector generation based on differential method. Our approach yielded test accuracies of 88.29% and 81.04% on the mentioned databases respectively. Furthermore, we have also trained and tested our model on a custom database by mixing up both of these databases and it held an accuracy of 86.89%.
Automatic System for Evaluating the Quality of the Display of Goods in a Smart Store Based on Cascading Neural Networks

In a new smart store, profit depends on the quality of the goods displayed on the shelf. If there is a product, but it is not laid out on shelves, the store suffers losses. In the paper, a control system is being developed for the timely display of goods on store shelves and for tracking in real-time the voids formed. To implement an automatic system for assessing the quality of filling store shelves with goods, we used cascading neural networks that performed two roles: a segmentation and a classifier, supplemented by an algorithmic solution to identify areas of potential voids. To train the classifier using the new algorithm, a training sample of 30 thousand images was created, 3 thousand images were used for validation, quality control on 10 thousand images. The proposed algorithm allowed us to obtain a quality of 96.7%. The developed system for assessing the quality of goods laying out on store shelves allows real-time assessment of the condition of shelves.
A User Oriented Image Retrieval Using Feature Extracted By Dot diffusion Block Truncation Coding

In the field of Image Processing image Retrieval is an key research area. In many applications such as logo identification, crime investigation it necessary to identify or search the particular image. For that purpose image retrieval is mostly used. To retrieve the more similar image from the large image dataset number of techniques are available, out of which DDBTC is used in this papaer. Four number of image features are derived using DDBTC. Image features are obtained using color quantizers and Bitmap image. The first two features such as Color Co-occurrence Features (CCF) and Color Histogram Features (CHF) are obtained using color quantizers but other two features such as Bit Pattern Feature (BPF) and Bit Histogram Feature (BHF) are derived from Bitmap image. The five different distance metrics are used to measure the similarity between two images. The simulated results shows proposed Technique can shows the better result in the form of Average Precision rate (APR) and Average Recall Rate (ARR) as compared to other techniques.
Programmer's Search Engine

Developers, researchers, and students spend a tremendous amount of time on generic search engines like Google during software development for finding relevant resources. This paper presents the design of a dedicated programmer's search engine to reduce the time required for finding relevant resources on the Internet. Web pages containing tutorials, examples, problem solutions, etc of different programming languages are crawled, indexed based on keywords available in different programming languages. Web pages relevant to user queries are ranked using parameters like number of examples/solutions, number of lines of code per example. Experimental results support the effectiveness of the presented approach over generic search engines.
Optimal Interval Type 2 Fuzzy PID Controller and its Application in Inverted Pendulum System

In this paper, we propose an interval 2 fuzzy PID controller (it2f-pid) to operate the uncertain model in inverted pendulum. There are two important problems in the construction of type 2 fuzzy logic system, one is type reduction and the other is rule redundancy. The former one problem aims at calculating the center of mass of a type 2 fuzzy set. For the problem of type reduction, most of the reduction methods are computation-intensive, and an improved EKM algorithm is proposed. This algorithm improves EKM algorithm in two ways. First, change the initialization conditions at the switch point. Then, the method of searching the switch point is improved, so that it can search up and down. When it comes to the later problem, i.e., rule redundancy, the concept of normalized difference should be given first. It is presented to describe the variation of adjacent singular values to reflect the essential difference between redundant rules and important rules. Then, the number of effective singular number is determined according to the critical point of the effective singular number, and the rule is determined by TLS decomposition to construct the 2-type fuzzy model. Finally, the it2f-pid controller of the new method is compared with the it2f-pid controller of the uncertain boundary method, the simplified it2f-pid controller, and the type 1 fuzzy pid controller. Simulation and experimental results show that the performance of the controller has been significantly improved compared to the tlf-pid controller.
Rescheduling with Controllable Processing Times and Job Rejection in the Presence of New Arrival Jobs and Deterioration Effect

This chapter considers a dynamic multi-objective machine scheduling problem in response to continuous arrival of new jobs with deterioration effect, under the assumption that jobs can be rejected and job processing time is controllable by allocating extra resources. By deterioration effect, we mean that each job’s processing time may be subject to change due to the capability deterioration with machine’s usage, i.e., the actual processing time of a job becomes longer if the job starts processing later. The operational cost and the disruption cost need to be optimized simultaneously. To solve these dynamic scheduling problems, a directed search strategy (DSS) is introduced into the elitist non-dominated sorting genetic algorithm (NSGA-II) to enhance its capability of tracking changing optimums while maintaining fast convergence. The DSS consists of a population re-initialization mechanism (PRM) to be adopted upon the arrival of new jobs and an offspring generation mechanism (OGM) during evolutionary optimization. PRM re-initializes the population by repairing the non-dominated solutions obtained before the disturbances occur, modifying randomly generated solutions according to the structural properties, as well as randomly generating solutions. OGM generates offspring individuals by fine-tuning a few randomly selected individuals in the parent population, employing intermediate crossover in combination with Gaussian mutations to generate offspring, and using intermediate crossover together with differential evolution based mutation operator. Both PRM and OGM aim to strike a good balance between exploration and exploitation in solving the dynamic multi-objective scheduling problem. Comparative studies are performed on a variety of problem instances of different sizes and with different changing dynamics. Experimental results demonstrate that the proposed DSS is effective in handling the dynamic scheduling problems under investigation.
Faithful Multi-Hop Qubit Transmission Based on GHZ States

Quantum teleportation faithfully transfers quantum bits (qubits) between distant nodes, which enables global quantum communication network. In this paper, a scheme for faithful multi-hop qubit transmission is proposed employing parallel quantum measurements. In terms of communication delay, the proposed scheme transports a qubit in time almost the same as the one-hop quantum teleportation does regardless of the number of hops between the source and destination.
A Monitor Method based on Adaptive Frequency for Self-Adaptive Software

Self-Adaptive Systems (SASs) are required to adapt to the complex changes with different characteristics by frequent monitor. The problem with gathering and updating changes information frequently at runtime is that it may cause computing and communication overhead, which affects the real-time of SASs. However, if the frequencies are reduced, it is difficult to ensure the accuracy of changes identifying. So it is necessary to solve the trade-off between the accuracy and expensive overhead. Existing methods based on data processing improve accuracy by reducing potential uncertainties. Other methods based on adaptive frequency lack quantification and are difficult to ensure accuracy. We expect to combine them and break through the latter. To this purpose, this paper proposes a method based on adaptive frequency for SASs. The method can ensure the accuracy of adaptive adjustment by comprehensively analyzing the influencing factors of monitoring frequency, and can quantify the monitoring frequency in real time by calculating these factors according to the monitoring data. Finally, we exemplify these methods with an e-commerce System.
Construction of New Efficient Classroom Teaching Model in Mobile Environment

This paper aims at “teachers teach through mobile terminal, students learn through Mobile Terminal”, this paper expounds the effective ways to carry out in-class and out-of-class teaching activities through the Mobile Internet terminal under the mobile environment, to construct a new teaching mode, and to improve the teaching efficiency and teaching quality.
Novel Prototype for Handling Arabic Natural Language Processing: Smart Morphological Analyser

Arabic stemming is a technique that finds the stem or lexical root for Arabic words through the process of eliminating affixes attached to their roots. Many studies have been conducted to generate the stem of Arabic words according to the desirable level of analysis, i.e., rootbased approach, stem-based approach and statistical approach. Arabic language, which is the focus of this paper, is a Semitic language which means that it is a derivational rather than a concatinative language. In this paper we implemented an Arabic triliteral Morphological Analyser that is capable of analysing the classical and modern standard Arabic (MSA) effectively and able to analyse vowelised, semi-vowelised and nonvowelised text. the system is integratable with other applications. one shortcomming for the developed system is that the output obtained from the morphological analyser may contain several alternative solutions which leads to extraction ambiguity.
Hand Gesture Recognition Based on Saliency and Foveation Features Using Convolutional Neural Network

The rapid growth in multimedia technology opened new possibilities for effective human–computer interaction (HCI) using multimedia devices. Gesture recognizing is an important task in the development of an effective human–computer interaction (HCI) system and this work is an attempt in this direction. This work presents an approach for recognizing hand gestures using deep convolutional neural network architecture from features derived from saliency and foveation information. In contrast to the classical approach of using raw video stream as input, low-level features driven by saliency and foveation information are extracted from depth video frames are considered for gesture recognition. The temporal variation of these features is given as input to a convolutional neural network for recognition. Due to the discriminative feature learning capability of deep learning models and the invariance of depth information to visual appearance, high recognition accuracy was achieved by this model. The proposed approach achieved an accuracy of 98.27% on SKIG hand gesture recognition dataset.
A GRU-Based Neural Machine Translation Followed by Proper Noun Transliteration

Narang, Hitesh Gharpure, PrachiNeural machine translation has drastically improved the accuracy of machine translation in recent years. The issue of translating out-of-vocabulary proper nouns (OOV-NNP) is still a hindrance to the betterment of machine translation. In this paper, we introduce neural machine translation followed by Proper Noun Transliteration (NMT-NNPT) to address this issue. We explore the idea of transliteration as a post-processing task on the result of neural machine translation using English–Hindi language pair. This approach further improves the translation accuracy and can be used with any language pair.
Comparison of A* Algorithm and Time Bounded A Algorithm on Maze Chase Game NPC

The application of artificial intelligence in computer games has increased in recent years. Maze chase game is a game that takes place in a maze. The aim of the game is that the player must take all the points in the maze. Inside the labyrinth there are four Non-Playable Characters (NPCs) that move to chase the player. The shortest path algorithm is applied to the NPC in order to determine the shortest path from the current position of the NPC to the player position. In this study the author will compare the optimal level of path retrieval and the length of time needed in selecting the shortest path using the A* algorithm and the Time-Bounded A* algorithm. With the implementation of the A* algorithm and the TBA* algorithm on the Maze Chase game NPCs, the authors found that the A* algorithm has a faster travel time of 0.3% when compared to the TBA* algorithm, while the TBA* algorithm expands the nodes 73.2% less compared to the A* algorithm.
Improving Natural Language Parser Accuracy by Unknown Word Replacement

Natural language parsers are the basis for further understanding the content written in natural language. Parsers for natural language have been shown to be effective in many NLP tasks, such as, machine translation, sentiment analysis and classification of documents. The existing state-of-the-art parsers, such as Charniak [9], Collins [11], Stanford, OpenNLP, have been shown to have F Score ranging from 85 to 92 percent. The accuracy of the parsers is hampered to a major extent by unknown and unseen words. In this paper we show a novel method on improving the accuracy by incorporating knowledge about the unknown words from external source. Experimental results show our technique improves accuracy. The improvement depends on number of known words present in the model during training. We show that we achieve above one percent improvement on some parsers.
A Provable Secure Server Friendly Two-Party SM2 Singing Protocol for Blockchain IoT

The blockchain transaction protocol of IETF shows us an emerging paradigm of blockchain based Internet of Things (BIoT). In this paradigm, a constraint node sends transactions including their device data. Different from a wallet in a computing device of a user, an IoT node could be controlled by an adversary for a while and the private keys in the device may be revealed. This paper proposes a two- party SM2 signing protocol so that each transaction has to be signed by two parties collaboratively. The server of the protocol only needs to compute one scalar multiplication and two finite filed operations for an online signing, which makes it suitable for large scale BIoT applications. We also give a proof about the security of our singing procedure.
A Device Pose Estimation Method Based on Monocular Visual Odometer

When the robot detects the unknown environment, it is necessary to estimate the position and posture of the device. Traditional device pose estimation methods include GPS positioning and laser detection, which are of high cost and not available in some special occasions. With the development of machine learning and 5G technology, visual positioning has gradually become the mainstream of robot navigation. Recently, visual odometry has been applied to the pose estimation of robots equipped with monocular or stereo cameras, because of its high accuracy, low cost and excellent real-time performance. In this paper, we implement a device pose estimation method based on monocular visual odometer, which can restore the self-motion of camera from a continuous sequence of images. Firstly, the ORB algorithm is applied to extracting the feature points from the input images. Secondly, we introduced the FLANN algorithm to match the extracted feature points from the adjacent frames. Finally, a pole-constraint equation is established according to the geometric relationship between the matched feature points, and the pose transition can be restored by solving this equation. Specifically, we introduce the binary particle swarm optimization (BPSO) algorithm to optimize the image processing, and analyze the calculation speed and precision of the SIFT and ORB algorithm in the same scenario. Compared with the previous work, the proposed method can achieve better real-time performance on various image datasets.