Reviewerly/exHarmony huggingface dataset:
Film Classification Using HSV Distribution and Deep Learning Neural Networks

The number of films is numerous and complex. A viewer wants to choose a favorite movie is time consuming. This study aims to develop an automatic film classification system. Firstly, a film is sampled frame by frame. The color space in terms of hue, saturation, and brightness value (HSV) in each frame is analyzed. Hence the mean and deviation of the HSV are computed and utilized as classification features for each film. These features are fed into deep learning neural networks. Twenty-five trailers are employed to train the model parameters of neural networks. In the classification phase, twenty-five trailers are classified into five categories, including science fiction, literature-love, action, comedy films, and horror and thrillers. Experimental results show that the proposed method can effectively classify the film types, where the precision rate can reach 93.3%.
An SOA Design Patterns Recommendation System Based on Ontology

Service-Oriented Architecture is an architectural style for building systems based on interacting services. For more performance, Service-oriented architectures (SOA) systems must have some quality requirements (e.g., reliability, availability, and performance). SOA design patterns are proven solutions to specific problems in this context. Given the benefits they offer for software development, the SOA design patterns use is in an increasing expansion. Nonetheless, without assistance, any inexperienced designer may not take advantage of SOA design patterns due to their overwhelming number. In this paper, we propose a new approach that recommends the SOA design pattern that is adequate to the designer’s modeling context. For this purpose, a new ontology created to classify the different SOA patterns problems and their corresponding solutions. Then this ontology will be interrogated by SPARQL to search for the adequate pattern in the repository of SOA patterns and present the appropriate solution.
From the Goldbach Conjecture Cipher to the Han Ge CODE

At present, there is an unprecedented crisis in the world of cryptography and the cryptographers around the world are worried: There is no possibility of avoiding code breaking. Is there any cipher that can be secure, efficient and not cracked? The first thing one might think of is the prime number cipher, but since the big prime numbers are too difficult to obtain, this road is difficult to get through. So, what's the best cipher? The answer is: the combination of Chinese Coding and Goldbach Conjecture Cipher (CCG), called Han Ge Code in Chinese. The CCG code has the advantages of simple materials collection, easy compilation, simple decoding, changeability, wide application, and so on. One of the greatest advantages is that it is the safest code in the world. Moreover, an encrypted CCG code of more than 100,000 digits is hard to decipher for 100 years for even all the scientists in the world who use the most advanced computers. Given this the CCG code must be recognized as the best code in the world.
Authentication Scheme Using Sparse Matrix in Cloud Computing

Cloud computing is boon in technology field which provide on-demand services in IT domain. Security and authentication threats are challenges associated with storage and access of data in cloud environment. Authentication in cloud computing is a major concern with the increase in cloud user base. The authentication schemes in cloud computing are based on hash function, biometric, logic functions to keep the data in secure and safe manner. This paper compares the different methods of authentication schemes in terms of complexity and the strength to prevent attacks and proposes a new improved method using sparse matrix approach performing better than simple matrix approach. Our method improves the usability of full matrix approach converting it to sparse matrix to authenticate client for use of cloud service.
Amelioration of a proxy signature using RSA encryption

A proxy signature scheme permits a specific selected person, named a proxy signer, to sign messages on behalf of the original signer. It is a useful mechanism in several fields. For example, a director of a company can delegate his signing power to a proxy agent when he goes to travel.
Current Situation, Principles and Strategies of Traffic Law Textbooks Writing

This paper explores the blended learning mode of college English through empirical research. This model divides college English teaching into three stages: the first stage is for students to independently conduct web-based self-learning; the second stage is for on-line collaborative learning among learners; the third stage is for teachers to pass classroom teachers through classrooms. Conduct face-to-face teaching for students and interact with teachers and students. The quality of learning at each stage has a corresponding evaluation and monitoring. The study found that the college English mixed learning mode can solve the contradiction between the limited classroom teaching capacity and the students’ individualized learning needs, and effectively improve the quality of college English teaching.
Malayalam to English Translation: A Statistical Approach

The English language is international language and Malayalam is a morphologically rich language. Malayalam sentence consist of root or stem word with extra suffixes. The Malayalam language uses diverse order of word as distinguish to the language English. Due to the morphological richness of Malayalam leads very few approaches for developing translation system. These approaches are divided into rule based approach and corpus based approach. Statistical machine translation model takes large set of bilingual corpus and monolingual corpus for training. The bilingual corpus contains source sentence and corresponding target sentence. The statistical approach contains translation model, language model and decoder. The translation model assign the probability of Malayalam-English sentence pair based on the maximum likelihood estimation. The decoder is the main step of statistical machine translation. It’s find the best target sentence by choosing the highest probability from the product of the translation model and language model.
Multi-modal Behavioral Information-Aware Recommendation with Recurrent Neural Networks

Data sparsity is one of the most challenging problems in recommendation systems. In this paper, we tackle this problem by proposing a novel multi-modal behavioral information-aware recommendation method named MIAR which is based on recurrent neural networks and matrix factorization. First, an interaction context-aware sequential prediction model is designed to capture user-item interaction contextual information and behavioral sequence information. Second, an attributed context-aware rating prediction model is proposed to capture attribution contextual information and rating information. Finally, three fusion methods are developed to combine two sub-models. As a result, the MIAR method has several distinguished advantages in terms of mitigating the data sparsity problem. The method can well perceive diverse influences of interaction and attribution contextual information. Meanwhile, a large number of behavioral sequence and rating information can be utilized by the MIAR approach. The proposed algorithm is evaluated on real-world datasets and the experimental results show that MIAR can significantly improve recommendation performance compared to the existing state-of-art recommendation algorithms.
Modeling of sludge conditioning and dewatering performance based on SVM

To solve the highly non-linear problem of sludge conditioning and dewatering model, the Support Vector Machines (SVM), was used to model the sludge conditioning process and the sample data like organic matter content, dosage ratio of reagent A and reagent B was set as input while the filter index was set as out put. The simulation results indicated that the filtering performance was basically consistent with the actual value of dosage and the model prediction result, and the target value could be well predicted. It shown that the model had a certain value for industrial application.
Automated information foraging for sensemaking

In preparations for Multi-Domain Operations and Battles, All-Source and OSINT intelligence analysts gather, assess, and extract relevant information from operational databases as well as publicly available information. This data, often unstructured text documents, is noisy with relevant snippets buried within the document corpus. The costs of exploratory search and exploitive document analysis required to find these hidden snippets of information often drive searches toward a small subset of documents. Additionally, modern search tools may reinforce the confirmation bias of analysts by providing only those documents that closely match their search query. Due to the potentially high tempo of multi-domain battle, the end result is a decision or hypothesis that is ill-considered and substantiated by potentially biased information. An automated information foraging framework can mitigate these challenges by automatically identifying a wide breadth of topics for the user, extracted directly from a document corpus. A semantic network formed from the constituent entities within a document corpus contains inherently valuable topological structures that can be used to generate topics and also guide the analyst?s information exploration. Leveraging a suite of information retrieval and graph analysis algorithms that analyze the semantic network, a framework is defined for assisting analysts in both exploring and exploiting relevant information from a corpus to support the sensemaking process.
Towards Enabling Internet-Scale Context-as-a-Service: A Position Paper

Deploying context management systems at a global scale comes with a number of challenges and requirements. We argue that the hypermedia model and the agent-oriented paradigm help achieve the vision of Context-as-a-Service. We categorize challenges according to context processing concerns and use a scenario to exemplify how the proposed architectural principles help overcome the challenges.
An iterative SIFT based on intensity and spatial information for remote sensing image registration

Owing to significant geometric distortions and illumination differences, high precision and robust matching of multisource remote sensing image registration poses a challenge. This paper presents a new approach, called iterative scale invariant feature transform (ISIFT) with rectification (ISIFTR), to remote sensing image registration. Unlike traditional SIFT-based methods or modified SIFT-based methods, the ISIFTR includes rectification loops to obtain rectified parameters in an iterative manner. The SIFT-based registration results is updated by rectification loops iteratively and terminated by an automatic stopping rule. ISIFTR works in three stages. The first stage is used to capture consistency feature sets with maximum similarity followed by a second stage to compare the registration parameters between two successive iterations for updating and finally concluded by a third stage to terminate the algorithm. The experimental results demonstrate that ISIFTR performed better registration accuracy than SIFT without rectification. By comparing the iteration curve based on the four different similarity metric, the results illustrate that the RIRMI-based rectification obtains better results than other similarity metrics.
Path Tracking Of a Mobile Robot Based On Interval Analysis

One important problem in autonomous navigation of a robot is following an unknown path. Over the past several years, numerous studies and researches had considered the path tracking issue. Alternative methods have been developed to overcome this problem. This paper proposes a method based on Interval Analysis. The problem is then formulated as a constraint satisfaction problem where a simple algorithm is applied in order to contract the solution. This technique yields a guaranteed and robust online estimation of the robot. Simulation results on a car-like robot corroborate the efficiency of the proposed technique.
Jumping Particle Swarm Optimization Algorithm for Robust Image Watermarking

The tradeoff between the embedding energy of watermark and the perceptual translucence and the image fidelity following attacks represents an important issue in watermarking images. This paper hence proposed a population based method called jumping particle swarm optimization algorithm (JPSO) to improve the ownership and imperceptibility of image. Accordingly, JPSO algorithm is utilized in the maximization the quality of watermarked image, particularly within the extracted watermarks. The former is resolvable via embedding owner's watermark components of into the host image while the latter is determined by the amount of the embedded scaling factor of the principle components. The robustness of watermarking can be improved using JPSO, where the appropriate scaling factor is optimized. JPSO invisible watermarking involves the use of the host's global and local characteristics, besides watermark images within the Singular Value Decomposition (SVD) domain. The obtained results of the recommended technique affirm the reliable identification of the watermark image ownership and even following severe attacks. Comparisons with other comparable techniques affirm the superiority of this study's recommended technique.
An Enhanced Accuracy of a Prediction Model Having a Modified Genetic Algorithm with Cross-Average Crossover

Prediction models helped an organization in decision-making activities. However, enhancing an accuracy of a prediction model is an ongoing subject of research in the field of information technology. The study introduced a new prediction model having a modified genetic algorithm (GA) with Cross Average Crossover operator and rank based selection function to the existing model having k-means segmentation combined with C4.5 algorithms. Comparison of the accuracy of the existing model and the new model is presented in the simulation using the 4,410 records of student leaver's in a university. Simulation results showed that the new prediction model having GA with CAX and rank-based se- lection outperformed the model with generic GA with spliced crossover and roulette wheel selection method with respect to its accuracy.
Strategizing Game Playing Using Evolutionary Approach

Since the inception of evolutionary algorithms, the capabilities of genetic algorithms is showcased by games. This paper proposes the use of a genetic algorithm for the game, Tetris. An evolutionary approach is used to design a Tetris bot. The proposed approach uses a novel set of parameters to decide which move needs to be taken by the Tetris bot for each falling Tetromino. These parameters represent the various genes present in the chromosome. Each individual is being allowed to play the game once. Once the entire population has played, the population undergoes crossover and mutation. In this way, the parameters are evolved to get a better bot. The most evolved bot as per the fitness is allowed to simulate 200 rounds of Tetris during which its actions are recorded. Further, Frequent Pattern Growth algorithm, a data mining technique, is used to extract knowledge from the given stored actions. The extracted knowledge is used for mining association rules and identifying strategies used by the evolved bot to play the game.
GraviTIE: Exploratory Analysis of Large-Scale Heterogeneous Image Collections

We present GraviTIE (Global Representation and Visualization of Text and Image Embeddings, pronounced ”gravity”), an interactive visualization system for large-scale image datasets. GraviTIE operates on datasets consisting of images equipped with unstructured and semi-structured text, relying on multi-modal unsupervised learning methods to produce an interactive similarity map. Users interact with the similarity map through pan and zoom operations, as well as keyword-oriented queries. GraviTIE makes no assumptions about the form, scale, or content of the data, allowing it to be used for exploratory analysis, assessment of unsupervised learning methods, data curation and quality control, data profiling, and other purposes where flexibility and scalability are paramount. We demonstrate GraviTIE on three real datasets: 500k images from the Russian misinformation dataset from Twitter, 2 million art images, and 5 million scientific figures. A screencast video is available at https://vimeo.com/310511187.
POS Tagging and Structural Annotation of Handwritten Text Image Corpus of Devnagari Script

Natural Language Processing (NLP) germaneness required a large benchmark annotated dataset. Handwritten and impressed text corpus plays a momentous role in pattern recognition algorithm for benchmarking. Part-of-speech tagging is very recurrent and subjugated types of annotation. Because POS tagging is significant to many linguistic annotations like lemmatization, syntactic parsing, semantic annotation, etc. Part-of-Speech tagging together with the structural annotations of handwritten text image corpus of Devnagari script of 1300 handwritten form collected from different geographical location and demographics are narrating in this paper.
Heterogeneous Data Integration using Confidence Estimation of Unseen Visual Data for Zero-shot Learning

Zero-shot learning is a learning methodology that can be used to recognize concepts that have never been seen during the training phase. Recently, interest in zero-shot learning has been increased by embedding multi-modal data into common vector space through heterogeneous data integration methodology. However, since the existing methodologies compare heterogeneous data focusing on the similarity between each vector, the performance of zero-shot learning decreases when the number of semantic candidates increases. We propose a heterogeneous data integration methodology using a confidence estimator for unseen visual data which estimates that whether input data is unseen data or not and output confidence measure. The proposed methodology constructs a more efficient zero-shot learning model by applying estimated confidence of input unseen visual data to the visual-semantic distance obtained from heterogeneous data integration model. Experiments have shown that the proposed methodology can improve zero-shot learning performance for unseen data despite a small performance decrease in the seen data.
Stochastic Heuristic Approach to Addition Chain Problem in PKC for Efficiency and Security Effectiveness

This paper shows that stochastic heuristic approach for implicitly solving addition chain problem (ACP) in public-key cryptosystem (PKC) enhances the efficiency of the PKC and improves the security by blinding the multiplications/squaring operations involved against side-channel attack (SCA). We show that while the current practical heuristic approaches being deterministic expose the fixed pattern of the operations, using stochastic method blinds the pattern by being unpredictable and generating diffident pattern of operation for the same exponent at a different time. Thus, if the addition chain (AC) is generated implicitly every time the exponentiation operation is being made, needless for such approaches as padding by insertion of dummy operations and the operation is still totally secured against the SCA. Furthermore, we also show that the stochastic approaches, when carefully designed, further reduces the length of the operation than state-of-the-art practical methods for improving the efficiency. We demonstrated our investigation by implementing RSA cryptosystem using the stochastic approach and the results benchmarked with the existing current methods.
Explainable automatic target recognition (XATR)

An explainable automatic target recognition (XATR) algorithm with part-based representation of 2D and 3D objects is presented. The algorithm employs a two-phase approach. In the first phase, a collection of Convolutional Neural Networks (CNNs) recognizes major parts of these objects, also known as the vocabulary. A Markov Logic Network (MLN) and structure learning mechanism are used to learn the geometric and spatial relationships between the parts in the vocabulary that best describe the objects. The resultant network offers three unique features: 1) the inference results are explainable with qualitative information involving the vocabulary that make up the object; 2) the part-based approach achieves robust recognition performance in cases of partially occluded objects or images of hidden object under canopy; and 3) different object representations can be created by varying the vocabulary and permuting learned relationships.
Performance Evaluation of Tree Ensemble Classification Models Towards Challenges of Big Data Analytics

Big Data Analytics poses challenges like effective and accurate real-time data mining, lack of suitable tools & techniques and in-memory processing problem. Tree-based ensemble methods (machine learning models) are able to perform such kind of large-scale analytical processing in combination with high-performance cluster computing (special kind of distributed computing) using parallel processing. Random Forest (forest of randomized trees, a tree ensemble) algorithm is considered for the performance evaluation, as tree model supports concurrency and all trees are grown simultaneously in it, so it is a suitable parallel approach with good accuracy, noisy & imbalance dataset handling capability and also it never overfit unlike a single tree model for large dataset. However significant notable improvement over the original approach is available, but some limitation still exists regarding performance and streaming dataset such that performance rate decreases on increasing the compute nodes due to a redundant allocation of feature subsets in the hybrid approach of task & data parallelization and inability to handle stream data. So these performance issues are identified and a problem statement is formulated with an objective to achieve the linear scalable speedup and incremental processing capability of random forest algorithm to perform predictive analytics over massive datasets in the cluster environment.
Planning Learning Activities

Planning learning activities prepares students to make the most of workplace learning (WPL). Planning can have the greatest impact on learning because it occurs ahead of time to help students focus on what they would like to achieve, what to expect and how to engage in the workplace (Stirling, Kerr, Banwell, MacPherson, and Heron, 2016). Planning learning activities to harness the affordances of mobile technology in and for WPL requires a collaborative approach that involves academic staff, students and workplace educators. With this chapter, we discuss learning design principles that can help educators and learners decide what can productively be designed ahead of time. We provide a set of activities for educators and students to help them plan for the kinds of opportunities students will have and challenges they will face using mobile technology for WPL. We also suggest approaches to planning these activities that will strengthen students’ critical digital literacy, ‘epistemic fluency’, professional agency and capacity for lifelong learning.
Three Parallelization Syndrome Block for Reed Solomon code RS (255, 239) Used in Digital Video Broadcasting DVB-T

This article presents a Three Parallelization Syndrome Block for Reed Solomon code RS (255,239) Used In Digital Video Broadcasting DVB-T. This Parallelization is compared with the basic circuit used a serial syndrome computation block. In this paper we developed the design of three parallelization syndrome. This parallelization reduces both the consummation of power and the number of iterations. The RS (255,239) code is composed of 255 symbols that are multiples of 3. The Symbols are entered in parallel in the syndrome block.