Reviewerly/exHarmony huggingface dataset:
A Study of the Chinese spam Classification with Doc2vec and CNN

Abstract Convolution neural network is a kind of neural network, which has been proved to be very effective in image recognition and classification. In recent years, convolution neural networks have gradually shifted to the field of natural language processing and become one of the research hotspots. For the construction of word vector text using convolution neural network, only considering the relationship between word granularity level, not considering the relationship between words, nor considering the relationship between semantics, affecting the classification results. In this paper, a method based on Doc2vec and CNN is proposed to classify spam. Firstly, the spam is preprocessed, then the sentence vectors and word vectors of Chinese text are trained by Doc2vec, and finally the trained text vectors are classified by convolution neural network.
A Local-to-Global Approach for Document Image Binarization

Document image binarization is a preprocessing step in Optical Character Recognition (OCR). This paper presents a novel hybrid approach for degraded document image binarization. An adaptive or local approach is applied to enhance the degraded image that separates the foreground and background considerably. After that, a new global approach for binarization is proposed. A postprocessing techniqueGupta, Deepika is applied after to get the final output. This transition from local-to-global approach results in document image binarization. Exhaustive experimental analysis has been carried out on a series of benchmarkBag, Soumen dataset having variations of degraded document images. Comparative analysis of the proposed method with other existing methods promises the effectiveness of the approach.
Cooperative Search Area optimization using Multiple Unmanned Aerial Vehicles in a GPS-denied Environment

This paper addresses the problem of maximizing surveillance area coverage using multiple Unmanned Aerial Vehicles (UAVs) in an obstacle-laden and Global Positioning System (GPS)-denied environment. The UAVs should achieve this objective using the shortest possible routes while staying inside the designated search space and avoiding the obstacles. To attain a desired area coverage, we divide the NP-hard multi-objective optimization problem of planning optimal routes for all UAVs into 3 parts: (a) optimizing search area coverage, (b) performing obstacle avoidance, and (c) using Cooperative Localization (CL) for state estimation. We demonstrate the efficiency of our algorithm through extensive simulations.
A Comprehensive Analysis of Kernelized Hybrid Clustering Algorithms with Firefly and Fuzzy Firefly Algorithms

In order to handle the problem of linear separability in the early data clustering algorithms, Euclidean distance is being replaced with Kernel functions as measures of similarity. Another problem with the clustering algorithms is the selection of initial centroids randomly, which affects not only the final result but also decreases the convergence rate. Optimal selection of initial centroids through optimization algorithms like Firefly or Fuzzy Firefly algorithms provide partial solution to this problem. In this paper, we focus on two kernels; Gaussian and Hyper-tangent and use both Firefly and Fuzzy Firefly algorithms separately along with algorithms like FCM, IFCM and RFCM and analyse their efficiency using two measures DB and D. Our analysis concludes that RFCM with Hyper-tangent kernel and fuzzy firefly produce the best results with fastest convergence rate. We use the two images; MRI scan of a human brain and blood cancer cells for our analysis.
Analysis of Spelling Error In Dissertation Based on the General Guideline for Indonesian Spelling (Pedoman Umum Ejaan Bahasa Indonesia)

This study aims at describing and classifying forms of misspelling in dissertations based on General Guideline for Indonesian Spelling (Pedoman Umum Ejaan Bahasa Indonesia [PUEBI]). It is a descriptive qualitative study. The type of data obtained in this study is document text, consisting of primary data sourced from dissertation and secondary data sourced from related documents. Data collection is carried out by using text analysis, documentation, and recording techniques. The result of this research shows that spelling errors in dissertation include 10 errors of writing capital letter, 9 errors of writing italic, 14 errors of forming word, 5 errors of using full stop mark, 5 errors of using comma sign, 1 error of using colon sign, and 6 errors of using quotation mark. Maximum errors are in the use of punctuation and the word formation. Based on the result, it can be concluded that there are still many spelling errors in writing dissertation. Therefore, it needs more effort to understand the rules of writing, such as the use of standard words, PUEBI, capital letter, punctuation, and word formation to make a good writing, especially in writing dissertation.
Daily Physical Activity Classification using a Head-mounted device

Nowadays human activity recognition through wearable devices integrating miniaturized sensors (i.e. gyroscopes, accelerometers) represents one of the most popular research areas. Although common placements are represented by trunk and wrist, head-mounted sensors have been recently proposed. This study introduces a new algorithm for daily physical activity recognition using a head-mounted inertial measurement unit (IMU), including a 3-axial accelerometer and a 3-axial gyroscope. The algorithm has been designed to detect body postures (i.e. Sitting and Standing), body transitions (i.e. Sit-to-Stand and Stand-to-Sit) and Walking periods. Data from twelve healthy young volunteers wearing the head-mounted IMU have been acquired in controlled conditions (ten subjects) and real life environments (two subjects). The performance of the proposed algorithm has been evaluated in terms of accuracy, sensitivity, specificity and precision with respect to a tagging system, used as reference and synchronized with the IMU data acquisition. The results showed that the proposed algorithm recognizes the examined daily activities with an average accuracy of 89% in controlled conditions and of 72% in real life environments. Walking has been properly detected in both the test conditions while a lack of accuracy has been reported in the body postures identification in real life contexts, probably due to a more natural and therefore variable body movement characterizing real life context acquisitions. Despite such issues, the results are promising. The present study, in fact, sets the basis for the development of a new head-mounted device allowing to accurately detect and classify user's physical activities in everyday life.
Experimental Study of Intelligent Lighting Control Method for Dark Field Surveillance

A kind of intelligent lighting control method for the dark field surveillance is proposed. The design of it includes an offline stage and an online stage. Regarding the offline stage, first a visual ergonomic experiment is used to accumulate image datasets which have different subjective image quality evaluation degrees (IQEDs) for the typical surveillance application. Second, the objective IQED metrics are computed for these datasets above: the image region contrast, the image edge blur, the image gray deviation, and the image noise. Third, the k-means cluster method is employed to analyze the distribution thresholds of the objective metrics. Regarding the online stage, first the objective IQED metrics are computed for the input image. Then, the computed objective results will be compared with the distribution thresholds gotten in the offline stage. Finally, a kind of optimal lighting control will be performed. A lighting experimental system is built, and many experimental results have verified the correctness of the proposed method.
Assessment of Reading Material with Flow of Eyegaze Using Low-Cost Eye Tracker

Reading text is a complex process involving visual, language, and motor functions. The movement characteristics of eye depend on the understanding of a content. In this paper, we present the methodology of quantifying the difficulty level of a textual content for a silent reading scenario using the eyegaze data captured from low-cost eye-tracking device, EyeTribe. Broadly, two types of contents are chosen such that one is easy to understand and the other is difficult. Standard readability indices are used to benchmark the easiness level of the contents. Experiment is performed with 15 individuals, and eyegaze data is used to quantify the smoothness in the flow of the reading. Statistical features are derived from the adjacency matrix obtained from the successive fixationsSinha, Aniruddha within and adjacent lines. Results demonstrate statistically significant difference between two types of texts, enabling a set of materials to be relatively graded. A scoring metric basedKumar Saha, Sanjoy on a mixture of partial sigmoid functions is proposed to quantify the easiness of the text experienced by an individual. The scores reflect that even within the broad category of easy and difficultBasu, Anupam content, the level of easiness varies between individuals.
Sensorineural Hearing Loss Identification via Discrete Wavelet Packet Entropy and Cat Swarm Optimization

(Aim) Currently, there are many methods to identify sensorineural hearing loss via magnetic resonance imaging. This study aims to develop a more efficient approach. (Methods) Our approach used discrete wavelet packet entropy as the feature-extraction method. It used single-hidden layer feedforward neural network as the classifier model. A bio-inspired algorithm, cat swarm optimization (CSO) is employed to train the weights/biases of this neural network. (Results) Simulation results showed our approach achieved an overall accuracy of 92.33%. Besides, this cat swarm optimization method gives better performance than genetic algorithm, particle swarm optimization, firefly algorithm, chaotic simulated annealing, and adaptive genetic algorithm. The whole hearing loss identification system yields greater accuracy than three state-of-the-art methods. It costs 102.8 ms to predict a new brain image. (Conclusion) The discrete wavelet packet entropy is an efficient feature-extraction method for detecting hearing loss. The CSO algorithm may be applied in other optimization problems.
A Wavelet Based Edge Detection Algorithm

Edge is in the place where image gray scale changes severely, it contains abundant image information. Image edge detection is a hot and difficult research field. Compare and analyze several classic edge detection method, aim at the advantages and disadvantages, respectively, propose a multi-scale edge detection algorithm based on the wavelet. The simulation shows the algorithm obtains an ideal effect in edge location and noise suppression.
Feature matching via guided motion field consensus

In this paper, we proposed a simple yet substantially efficient approach termed as Feature Matching via Guided Motion Field Consensus. The key idea of our approach is to model the transformation between two images by using the motion smooth constraint and use matching results on a small correspondence set with high inlier ratio to guide the matching on the whole image correspondences. In addition, we adopt a new regularization to overcome the overfitting of the matching process. Experiments demonstrate the practicability of our approach, and it is better than the state-of-the-art methods with better accuracy in feature matching.
Stream-based perception for cognitive agents in mobile ecosystems

Cognitive agent abstractions can help to engineer intelligent systems across mobile devices. On smartphones, the data obtained from onboard sensors can give valuable insights into the user's current situation. Unfortunately, today's cognitive agent frameworks cannot cope well with the challenging characteristics of sensor data. Sensor data is located on a low abstraction level and the individual data elements are not meaningful when observed in isolation. In contrast, cognitive agents operate on high-level percepts and lack the means to effectively detect complex spatio-temporal patterns in sequences of multiple percepts. In this paper, we present a stream-based perception approach that enables the agents to perceive meaningful situations in low-level sensor data streams. We present a crowdshipping case study where autonomous, self-interested agents collaborate to deliver parcels to their destinations. We show how situations derived from smartphone sensor data can trigger and guide auctions, which the agents use to reach agreements. Experiments with real smartphone data demonstrate the benefits of stream-based agent perception.
Deformation Samples Generated Network for Robust Visual Tracking

Object tracking has been extensive applied in video surveillance. However, deformation always makes the tracker have a drift problem, how do we learn a tracker classifier that is robust to deformations? We think the performance of existing trackers using deep classification networks is limited by the training data with different challenging factors. Collecting even larger training dataset is the most intuitive paradigm, but it may still can not cover all situations and the positive samples are still monotonous. To address this problem, in this paper, we propose a novel network to generate deformation samples via deformation samples generated network (DSGN). The goal of our tracker is to generate deformation samples which are difficult for the tracker to classify. In our framework both the classifier and DSGN are learned in a join-t manner. Compared with the recent state-of-the-art trackers, our proposed algorithm illustrates outstanding performance relative to existing tracking benchmarks.
JCOMIX: a search-based tool to detect XML injection vulnerabilities in web applications

Input sanitization and validation of user inputs are well-established protection mechanisms for microservice architectures against XML injection attacks (XMLi). The effectiveness of the protection mechanisms strongly depends on the quality of the sanitization and validation rule sets (e.g., regular expressions) and, therefore, security analysts have to test them thoroughly. In this demo, we introduce JCOMIX, a penetration testing tool that generates XMLi attacks (test cases) exposing XML vulnerabilities in front-end web applications. JCOMIX implements various search algorithms, including random search (traditional fuzzing), genetic algorithms (GAs), and the more recent co-operative, co-evolutionary algorithm designed explicitly for the XMLi testing (COMIX). We also show the results of an empirical study showing the effectiveness of JCOMIX in testing an open-source front-end web application.
Towards a Hybrid Approach to Measure Similarity Between UML Models

Several approaches to measure similarity between UML models have been proposed in recent years. However, they usually fall short of what was expected in terms of precision and sensitivity. Consequently, software developers end up using imprecise, similarity-measuring approaches to figure out how similar design models of fast-changing information systems are. This article proposes UMLSim, which is a hybrid approach to measure similarity between UML models. It brings an innovative approach by using multiple criteria to quantify how UML models are similar, including semantic, syntactic, structural, and design criteria. A case study was conducted to compare the UMLSim with five state-of-the-art approaches through six evaluation scenarios, in which the similarity between realistic UML models was computed. Our results, supported by empirical evidence, show that, on average, the UML-Sim presented high values for precision (0.93), recall (0.63) and f-measure (0.67) metrics, excelling the state-of-the-art approaches. The empirical knowledge and insights that are produced may serve as a starting point for future works. The results are encouraging and show the potential for using UMLSim in real-world settings.
Numerical Integration Based Contrast Enhancement Using Simpson’s Method

This paper is based on mathematical numerical integration method and provides an efficient algorithm for improving the contrast of an image. The aim of this unique method is to use neighboring pixel information and process them in accordance with mathematics (Simpson’s $${\frac{1}{3}}$$ rd rule) to improve contrast of images. Simpson’s rule is a numerical integration method for the accurate approximation of definite integrals. It gives the exact results for polynomials of degree three or less. We use the neighboring pixel information, interpolate among them, and produce the enhanced pixel information. Various parametersHalder, Amiya like Root Mean Square Error (RMSE), Peak-Signal-to-Noise-Ratio (PSNR) and Structural Similarity Index (SSIM) are used to measureBhattacharya, Pritam the quality of the images. The experimental results of the proposed method are compared with some existing methods for further validation. Also, the computational time of the proposed methodShah, Nikita is much less as compared to the different existing methods.
A Semantic Approach to Support the Analysis of Abstracts in a Bibliographical Review

A large amount of scientific information is found in digital databases. This fact turns the search for the state of the art an increasingly challenging task. This work has the goal of developing a technological solution to support researchers in bibliographic review using the Latent Dirichlet Allocation. The Sw3T software, developed in Python, analyses the abstracts in scientific publications that interest the researcher. Through the use of semantic analysis, Sw3t supplies themes, which are defined by a group of terms. Themes can subsidize the researcher in the process of identification of complementary terms through the restriction on the number of publications of interest and it contributes to the analysis of the publications selected by their classification by theme as well. Sw3t has shown to be efficient and increased reliability in the search process regarding complementary terms, as well it contributes to the promptness of the detailed analysis of the publications. The results of the bibliographical review, regarding the application of Sw3T, have shown to be consistent and replicable. Future work will address the use of Sw3T for other bibliographic reviews as well as improvements and broadening of its functionalities such as search in DDB automation and previous content processing in abstracts.
Face Identification via Strategic Combination of Local Features

Face identification systems that use a single local descriptor often suffer from lack of well-structured, complementary and relevant facial descriptors. To achieve notable performance, a face identification system is presented, which makes use of multiple representations of a face by means of different local descriptors derived from Local Binary Pattern (LBP) and Local Graph Structure (LGS). Then, max, average and L2 pooling operations are applied on each representation of face image to scale down the features. Further, different representations are combined together and formed a strategically concatenated feature vector. Three classifiers including two correlation based and k-nearest neighbor (kNN) are used to produce matching proximities which are used to characterize a probe user with rank identity. The produced ranks are then fused together using rank level fusion techniques. The experimental results determined on the Extended Yale face B database and the Plastic Surgery database are encouraging and convincing.
Buffered Garbage Collection: An Approach to Collecting Self-Created Garbage Objects

This paper proposes a new garbage-collection (GC) algorithm, named buffered garbage collection. It allows customizing a garbage collector through computational self-reflection. Although self-reflection seems a promising approach, self-reflection has not been well investigated for garbage collection as far as we know. Our buffered garbage collector collects garbage objects while avoiding both infinite regression and unacceptable memory consumption. We implemented a Scheme-subset interpreter supporting buffered garbage collection and evaluated its memory efficiency.
A Systemic Approach for Early Warning in Crisis Prevention and Management

Given the importance of early warning in crisis prevention this paper discusses both knowledge-based and data-driven approaches. Traditional knowledge-based methods are often of limited suitability for use in crisis prevention and management, since they typically use a model which has been designed in advance. Novel data-driven Artificial Intelligence (AI) methods such as Deep Learning demonstrate promising skills to learn implicitly from data alone, but require significant computing capacities and a large amount of annotated, high-quality training data. This paper addresses research results on concepts and methods that may serve as building blocks for realizing a decision support tool based on hybrid AI methods, which combine knowledge-based and data-driven methods in a dynamic way and provide an adaptable solution to mitigate the downsides of each individual approach.
A new reasoning approach combining information systems and interval type-2 fuzzy sets

In this research, we succeeded in introducing a new reasoning procedure which applies interval type-2 fuzzy sets into a rule induction process. Our proposal allows information granulation which resulted in achieving good experimental results. We introduced decision tables with elements assumed as interval type-2 fuzzy sets which greatly generalize information. Next, by applying corresponding rule induction procedure, we introduced the possibility to generate directly from a benchmark data fuzzy rulebases for type-2 fuzzy inference models. We strongly believe that our reasoning approach will be a proper solution for different research issues such as classification or ranking procedures as well as determining knowledge for fuzzy inference models. The method proposed was tested in a classification problem verified by using medical benchmark data.
Semantic Enrichment of Services for Linked Data Provision in SOA

One of the main challenges concerning information systems that deal with heterogeneous services and data consists in achieving semantic interoperability. Several approaches have been proposed to assist in the semantic enrichment of services, by using domain ontologies. Nevertheless, most of the proposals found in the literature focus on the enrichment of the service description, neglecting data representations provided by such services. This work presents an approach to dynamically provide representations in the form of linked data in a service-oriented system. Therefore, we propose an architecture to build/evolve ontologies and to enrich services with concepts defined by such ontologies. As a way to increase the semantic expressiveness of services, the architecture employs ontology matching techniques to explore equivalences in external sources. The results show that the proposed solution can achieve satisfactory precision and coverage levels, as well as better performance compared to other approaches.