Reviewerly/exHarmony huggingface dataset:
Age-Suitability Prediction for Literature Using a Recurrent Neural Network Model

Digital media holds a strong presence in society today. Providers of digital media may choose to have their content undergo an age-suitability analysis before being published. These analyses provide ratings that denote to which age group(s) a particular media item is appropriate. Content rating systems exist in many countries for television, music, video games, and mobile applications. These systems allow consumers to quickly determine whether or not a given media item is suitable to their age or preference. Literature, on the other hand, remains devoid of a comparable rating system. If a new, human-driven rating system for literature were to be implemented, it would be impeded by the fact that literary content is produced far more rapidly than are other forms of digital media; human working within such a system simply would not be able to read and analyze literature at its current rate of production. Thus, to provide fast, automated age-suitability ratings to works of literature (i.e., books), we propose a computer-driven rating system which predicts a book's content rating within each of eight categories: (a) crude humor/language; (b) drug, alcohol, and tobacco use; (c) kissing; (d) profanity; (e) nudity; (f) sex and intimacy; (g) violence and horror; and (h) gay/lesbian characters given the text of that book. Our computer-driven system circumvents the major hindrance to any theoretical human-driven rating system previously mentioned, i.e., infeasibility in time spent.
Heresy: A Serverless Web Application to Store Compressed and Encrypted Document in the Form of URL

In this study, we propose Heresy, a web application to generate a document that solely exists in the form of a Uniform Resource Locator (URL) fragment. That document is compressed using Lempel-Ziv-Markov algorithm (LZMA) and encrypted using one of four symmetric-key cryptography algorithms: AES, Rabbit, RC4, and Triple DES. This allows a user to create a secured document using markdown markup language, and then share or save the generated URL. In order to view the document, a valid passphrase is needed. All operations are performed in the client machine (serverless). We analyzed the capability and performance of Heresy in several devices to find out the best cryptography algorithm to secure a document.
Design and Implementation of AES on FPGA for Security of IOT Data

As we know that the world is been surrounded by the internet and interconnected to it and the different things or we can say the edge devices which helps us to connect to network and communicate to the different alternatives with the surroundings. In this paper we would be discussing about the different algorithms and their and disadvantages and how the advanced standard algorithm is been applied we has an great effect on cryptography and the different process carried out in the encryption and decryption process. In this paper the encryption and the decryption would be given in 128 bit format in hexadecimal, binary or any other format and the key expansion for getting the cipher text. in cryptography their are three classification for the expansion of the key which can be symmetric, asymmetric and the hash function in this paper we have discussed and the symmetric key is been given for both encryption and decryption process.as we know that we are using the 128 bit length which has 10 rounds and the initial round includes the state and the different transformation in the cryptograpy process which is the symmetric key expansion in which the same key is been applied to both encryption and decryption in binary, hexadecimal, etc. As it is lossless operation many applications can be derived using this algorithm as we are using symmetric key block cipher. We are limiting our paper with the 128 bit length of data in verilog language. For encryption and decryption the proposed paper describes the cryptograpy which would be having the fixed size. In this algorithm where we are using the symmetric key block cipher where the key would be given same with respect to encryption. Many applications it can be used as it is lossless operation. We limit our focus on 12b8 bit AES encryption and decryption where coded in VHDL coding. The proposed paper describes the private key cryptosystems which has a key with fixed size.
An Iterative unsupervised Person Search Algorithm on Natural Scene Images

Person search is a challenging task due to the different requirements of annotations between person detection and Re-identification. In general, person search methods use the supervised person Re-identification methods, where abundant identity labels of the bounding boxes are essential. However, most person images are unlabeled in the real-world scenario and it is unpractical to annotate the abundant fine-grained labels for unlabeled images. Obviously, the existing supervised methods are not appropriate with the real-world scenario. Therefore, we propose an unsupervised learning method for person search in this paper, which contacts two parts: one is unsupervised person detection and the other is unsupervised person Re-identification. The experimental results on two well-known datasets, CUHK-SYSU and PRW, indicate that proposed method achieves competitive performance than the state-of-art unsupervised methods. Note that proposed method has greater practical significance even though it does not get the results as good as the general supervised methods.
Detecting Sentences that May be Harmful to Children with Special Needs

Children and adults with special needs may find it difficult to recognize danger and threats as well as socially complex situations. Thus, they are under the risk of being victims of exploitation, violence and attacks. In addition, they may find themselves unintendedly insulting their friends, relatives or caregivers. In this paper, we propose an autonomous agent to assist the special needs person (child or adult) in the goal of recognizing risky or insulting situations. The autonomous agent will detect these situations and will signal them to the user (by text, speech, or other signaling forms). We composed a dataset containing 13,490 sentences, categorized into one of four classes: a 'normal' sentence, an insulting sentence, a negative sentence about a different person, or a risky sentence that may indicate a dangerous situation for the special needs person, which requires immediate intervention. We used several machine learning methods, and we found that the most accurate methods were the random forest method with 100 estimators, a voting method using several classifiers, and a convolutional neural network (CNN) with embedding. All of these mechanisms reached an accuracy close to 70% in classifying the sentences in the test set. Finally, using an ensemble method comprising a panel of the 5 best CNN based methods, improves the accuracy of the results and the F1-score. Our results demonstrate the feasibility of building an assisting agent that will accompany the special needs children and adults, and assist them in their daily social interactions.
Research on Text Classification Method of LDA- SVM Based on PSO optimization

The LDA (Latent Dirichlet Allocation) model is an unsupervised learning model that can extract potential topics in the corpus. It is widely used in natural language processing applications. The combination of SVM (Support Vector Machines) and LDA has better text classification effect. However, because the mature kernel function of SVM and its parameter selection can only be selected according to experience, with certain randomness, it has a great influence on classification accuracy. This paper proposes an optimized LDA-SVM text classification method using Particle Swarm optimization (PSO). The algorithm (PSO) optimizes the SVM error penalty parameter C to improve the LDA-SVM text classification algorithm. The experimental results show that the LDA-PSO-SVM text classification algorithm proposed in this paper has higher accuracy and better classification performance than other algorithms.
Ant Miner

In data mining the task of extracting classification rules from large data is an important task and is gaining considerable attention. This article presents a novel ant miner for classification rule mining. The ant miner is inspired by researches on the behaviour of real ant colonies, simulated annealing, and some data mining concepts as well as principles. This paper presents a Pittsburgh style approach for single objective classification rule mining. The algorithm is tested on a few benchmark datasets drawn from UCI repository. The experimental outcomes confirm that ant miner-HPB (Hybrid Pittsburgh Style Classification) is significantly better than ant-miner-PB (Pittsburgh Style Classification).
Elliptic Arc Fitting for the Shape Control System of Silicon Single Crystal Growth

In the vision-based shape control system of silicon single crystal (SSC) growth, shape parameters are used as the control variables to make the grown SSC approximate to a perfect cylinder. A highlight halo at the junction of the solid crystal and the liquid crysta is a distinctive feature that reflects the current shape of SSC, and the edge of the captured halo is an elliptic arc that consistently changes with the shape of the halo. To estimate the shape parameters through the elliptic arc, we propose a two-side (TS) constraint method to fit the elliptic arc. Firstly, the error correction parameter is introduced into the second-order polynomial model to make the model more robust in the case of noise disturbing, and the resultant non-convex TS constraint model is constructed. Then, two weighted functions, which give the data out of TS constraint a small weight coefficient to resist the large level noise disturbing, are brought into the TS constraint optimization problem tactfully. Simulation and real SSC data examples show that the proposed method can obtain the elliptic arc parameters effectively.
Food Image Retrieval with Gray Level Co-Occurrence Matrix Texture Feature and CIE L*a*b* Color Moments Feature

The following topics are dealt with: pattern classification; mobile computing; learning (artificial intelligence); Internet; feature extraction; support vector machines; computer aided instruction; nearest neighbour methods; data mining; image segmentation.
Bye Audit! A Novel Blockchain-Based Automated Data Processing Scheme for Bank Audit Confirmation

An audit confirmation letter is an inquiry that an auditor sends to a third party to verify the contents of accounting records of the entity that is being audited. Traditional process for delivering the paper confirmation is unnecessarily inefficient, relying much on manual processes and other outdated technologies, making it also vulnerable to fraudulent activities. In this paper, we adopt blockchain technology to address the low-efficiency and fraud risk in conventional bank confirmation process. Our solution involves two processes: Authorization Process and Data Acquisition Process. We integrate smart contract into these processes for automated authorization and automated data acquisition. We implemented our solution based on the open source FISCO-BCOS platform and used simulation of auditing process to prove its feasibility. Our evaluation benchmarks include latency and storage efficiency. The result shows that our system can support long-term stable operation.
Categorical Data Classification based on Fuzzy K-Nearest Neighbor Approach

To facilitate organizing complaints, improve the efficiency of administrators in sorting and determining categories of fields and work units, an intelligent system is needed to classify the documents. Complaints have an unbalanced number of categories of categorical data, which is known as imbalanced text in text classification. Complaints also have sentence ambiguity that allows a complaint to be categorized into several categories of fields and work units. The use of fuzzy theory in K-Nearest Neighbor can handle the ambiguity of a sentence. In the final stage, testing is done using a multiclass confusion matrix table to find the value of accuracy, precision, and recall. The test results showed optimum accuracy values for field classifications of 96.528% with K = 11 in the 80 (train) data percentage: 20 (test) and the classification of work units at 97.375% with K = 9 in 70 data train (train): 30 ( test). K-Nearest Neighbor can be an alternative in classifying data categories, because it produces a high level of accuracy, although its level of precision and recall still needs improvement.
Design of Evaluation and Decision-making Algorithms for Multi-satellite Scheduling Schemes

Aiming at the evaluation requirements of multi-satellite scheduling schemes, a fuzzy comprehensive evaluation algorithm is designed based on the analytic hierarchy process this paper. An index weights is set using analytic hierarchy process, and this index fuzzy vectors is calculated by fuzzy comprehensive evaluation. The gray analytic hierarchy process is used as a comparative evaluation algorithm, and the multi-satellite scheduling simulation experiment is carried out. The simulation result shows that the evaluation conclusions of those two algorithms are consistent. The correctness of the fuzzy comprehensive evaluation algorithm verifies based on the analytic hierarchy process.
EAC: Efficient Associative Classifier for Classification

Mining of large datasets which generally leads to the generation of a huge volume of rules and redundancy which Associative Classifiers find very difficult to handle. Thus, most Associative Classification algorithms like CPAR and CMAR, only mine frequent itemsets, which are then processed using additional algorithms in a greedy manner. This adds overhead in running time and makes the process more complex. In this paper, we proposed a new Associative Classification algorithm, called Efficient Associative Classifier (EAC). EAC deals with redundant association rules through an effective and simple pruning technique which also helps in cutting down the number of rules which finally form a part of the classifier. The classifiers are built in a two-phased manner so as to achieve the maximum accuracy and maximum representation of all possible class labels involved in the domain. In the first phase, association rule mining (ARM) is performed globally by taking global values of support and confidence. The rule set is pruned on the basis of information gain. Similarly, second phase deals with the ARM processed locally for each class label followed by pruning. The optimal pruned rule set is sorted based in certain parameters and supplied to the classifier. The proposed method used the information gain and entropy to build a set of optimal rules. In spite of being an efficient associative classifier, EAC achieves very good accuracy and tops the accuracy comparison charts for most of the UCI-Machine learning datasets that we used for testing.
Human Computer Interaction Strategies — Designing the User Interface

A user-interface is a significant component of any device that is used to communicate efficiently with a particular end-user. Within any specific system, a UI exists as an intermediate component between any given user and the system-in-itself, which thereby enables users to efficiently interact with the system. The UI is, for any given group of users, the primary component of the system that the end-users communicate with. A natural dialog construction between the user and the primary device, like a computer, is one of the most significant challenges faced by UI-designers, as the end-user and the system interact with one another while communicating and completing any set of tasks. However, any single set of strategies cannot be used for building efficient User Interfaces. This is because - different devices may have different purposes, and hence their UI will largely vary. Although the UI is vastly thought of the connection between a user and a device, it has been understood that there are various emotional aspects that are dependable on the design of a UI for the end-user. This research study puts forth a brief base and framework of various design strategies and methods for implementation of rules and principles to achieve the objectives for an optimal UI. Effective UI-Design strategy has been integral in the development of rich user-experiences, and in contrary, poor design planning and strategy has led to unsatisfied performance and user-attitude. In this paper, the study provides a brief literature review of a number of design techniques to build User-Interfaces. The research also puts forth various techniques that enable us to build a UI that is user-friendly and supportive.
THE RELATION BETWEEN SCHOOL CULTURE SUB-CATEGORIES AND EXPECTED RESULTS OF LEARNING PROCESS

The aim of the research was to find out what factors of school culture affect the expected results of teaching and learning processes in basic schools. The research evaluated current and desired school culture in selected basic schools to identify culture gaps. Research using School Culture Inventory explored relations among selected sub-categories, namely between the sub-categories focused on shared objectives, trust in school leadership, and on managerial approach and the important sub-categories focused on innovation process and expected results of teaching and learning processes. The independent evaluators were final year students of the Faculties of Education from two universities in the Czech Republic who assessed schools after one month of their position at the selected schools (the research sample included 182 basic schools). The findings provided the evidence of current culture gaps and of a positive association between higher positive evaluations in the sub-category leadership and management and the sub-category expected results of teaching and learning processes. The results brought by the research will help school leaders when they plan to shape or change the culture of the schools they manage. Keywords: culture gaps, school culture, school culture inventory, shaping school culture.
Towards A Catalog of Design Patterns Variants

With increasing applications of design patterns for the development of quality software applications, the accurate recovery of patterns from software applications remained active research area in the last one and half decade. The implementation variations for standard patterns hamper the accuracy of existing pattern recovery techniques and tools. There is no standard catalog on all variants of Gang of Four(GoF) design patterns in the literature. We present the first catalog on variations of GoF design patterns that are discussed in different publications, blogs and discussion forums at different levels of abstraction. A catalog with 107 variants of standard patterns might serve as a knowledge base for the design pattern research community and it might be helpful to specify GoF patterns and their variants. The critical analysis and observations on GoF design patterns and their variants are presented based on the whole study and our experience.
A Low-Cost Cognitive Assistant

In this paper, we present in depth the hardware components of a low-cost cognitive assistant. The aim is to detect the performance and the emotional state that elderly people present when performing exercises. Physical and cognitive exercises are a proven way of keeping elderly people active, healthy, and happy. Our goal is to bring to people that are at their homes (or in unsupervised places) an assistant that motivates them to perform exercises and, concurrently, monitor them, observing their physical and emotional responses. We focus on the hardware parts and the deep learning models so that they can be reproduced by others. The platform is being tested at an elderly people care facility, and validation is in process.
Revisiting the Wrong-Key-Randomization Hypothesis

Linear cryptanalysis is considered to be one of the strongest techniques in the cryptanalyst’s arsenal. In most cases, Matsui’s Algorithm 2 is used for the key recovery part of the attack. The success rate analysis of this algorithm is based on an assumption regarding the bias of a linear approximation for a wrong key, known as the wrong-key-randomization hypothesis. This hypothesis was refined by Bogdanov and Tischhauser to take into account the stochastic nature of the bias for a wrong key. We provide further refinements to the analysis of Matsui’s Algorithm 2 by considering sampling without replacement. This paper derives the distribution of the observed bias for wrong keys when sampling is done without replacement and shows that less data are required in this scenario. It also develops formulas for the success probability and the required data complexity when this approach is taken. The formulas predict that the success probability may reach a peak and then decrease as more pairs are considered. We provide a new explanation for this behavior and derive the conditions for encountering it. We empirically verify our results and compare them to previous work.
A Novel Iterative Method for Automatic Avian Red Blood Cell Counting

Broiler is among Thai major industries. Red blood cell (RBC) count is one of the basic health screening protocols. The counting is often done manually. The mammalian blood analyzer cannot be used, because the shapes of mammalia and avian RBCs are different. RBC's color also varies among staining methods. Furthermore, RBCs can be grouped together. In this paper, we propose the automatic method for counting avian RBC. Otsu's multiple thresholding is used to extract the nucleus of RBC. The problems of the varying color of and the conglomerated RBC are tackled by applying iterative process. Only one parameter needs to be set manually. The experiment on five different staining types demonstrated that our proposed method provided less than 5% counting error for the staining that did not have the dark RBC's boundary.
Big-Data Applications as Self-Adaptive Systems of Systems

Virtualization technologies have enabled a new way of thinking of computing resources and cloud computing frameworks offer many pay-per-use solutions for renting these resources. Conventional physical servers had to be acquired, provisioned, and configured beforehand; virtual resources can be allocated on demand, and changes can be managed quickly. Deploying systems on virtualized resources allows one to allocate resources given the actual workload and KPIs of interest, but it requires that resource management be part of the system itself. Traditional application components must be augmented with probes and actuators to sense the application behavior and provision resources accordingly. Big data applications are a prominent example of these modern systems, and the paper discusses dynaSpark, that is, the work done by the authors to extend Spark standalone -a well-known framework widely used for parallel processing and big data applications- and augment it with resource management capabilities. It also introduces the key problems the integration and the particular batch applications bring in, and identifies additional aspects that are still to be taken into account and that would lead to a better solution.