Reviewerly/exHarmony huggingface dataset:
Bipartite Threshold Multi-Secret Sharing Scheme Based on Hypersphere

To address the problem that existing bipartite secret sharing scheme is short of dynamic characteristic, and to solve the problem that each participant can only use secret share once, this paper proposed a bipartite ( )-threshold multi-secret sharing scheme which combined cryptography and hypersphere geometry.In this scheme, we introduced a bivariate function and a coordinate function over finite field Z p to calculate the derived points of secret share, which can reconstruct the shared secrets by producing the intersection point of hypernormal plane and normal line on the hypertangent plane.At the initial stage the secret dealer distributes to each participant a secret share that can be kept secret based on the intractability of discrete logarithm problem and need not be changed with updating the shared secrets.Each cooperative participant only needs to submit a derived point calculated from the secret share without exposing this secret share during the process of reconstructing the shared secret.Analyses indicate that the proposed scheme is not only sound and secure because of hypersphere geometric properties and the difficulty of discrete logarithm problem, but also efficient because of its well dynamic behavior and the invariant secret share.Therefore, this bipartite threshold multi-secret sharing scheme is easy to implement and is applicable in practical settings.
Video Analysis Based on Human Pose for Unsupervised Summarization and Retrieval

Finding good representations for videos is becoming increasingly more important to enable an efficient analysis and comparison, with potential applications in sports, surveillance, news, or web services. This paper proposes a new representation of videos based on human pose. Rather than looking at conventional features, our method relies only on human pose detections to characterize the video. This approach provides a powerful tool for the efficient analysis of videos of human activities, particularly for video summarization and retrieval. We evaluate the proposed representation on the following tasks: 1) computing video statistics, such as the main poses and viewpoint preferences; 2) partitioning videos into a collection of short clips that will compose the video summary; and 3) retrieving frames or scenes with specific poses from videos. Results show that the proposed approach is able to successfully perform these tasks.
Perancangan Teknik Kriptografi Block Cipher Berbasis Pola Permainan Tradisional Rangku Alu

Data security is one of the most important factors in the world of Information Technology today. One way to secure data is by Block Cipher Cryptography technique. However, some cryptographic techniques have been successfully solved by the cryptanalysis so that new cryptographic algorithms need to be created. The design of Cryptography algorithms based on traditional game patterns of Rangku Alu from East Nusa Tenggara is a design of Cryptographic Block Cipher algorithms that operate in the form of bits designed with 10 rounds where each cycle contains four processes. In each round there are four patterns for the plaintext process and four patterns for the key. The fourth process is transformed with an S-Box table to get a more random ciphertext. Tests were also carried out using Avalance Effect which reached 49,38% and the correlation value where there was a change in character reached 73,44%. So the result designing Cryptographic algorithms can be used for encryption and description in text files.
Research on Predicting the Short-term Output of Photovoltaic (PV) Based on Extreme Learning Machine Model and Improved Similar Day

In this paper, an extreme learning machine model based on the improved similar day to predict the short-term output of photovoltaic (PV) is presented. The Pearson correlation coefficient is used to analyze the influence of various meteorological factors on the output of photovoltaic power generation, so as to find out the meteorological factors that have a greater impact on photovoltaic output. And then the prediction model is to be established with combining the fuzzy clustering method by improving the similar day selection method. The multi-day photovoltaic output data with the highest correlation about the day to be tested is used to train the extreme learning machine neural network which is then used to predict the PV output of the day to be measured. Finally, the experimental results show that the proposed method has higher prediction accuracy and shorter calculation time than the traditional prediction method, and what's more, the algorithm is simple and the prediction cost is low. It has a wide application value and research space.
A Novel Deep Convolutional Neural Network Structure for Off-line Handwritten Digit Recognition

Handwritten digit recognition is widely used nowadays because traditional methods require the extraction of feature vectors from the original data by hand. The deep convolutional neural network (DCNN) can solve this problem efficiently, and the recognition effects are better than the traditional methods. However, deep learning requires lots of data set to be trained, and a suitable learning algorithm is required to adjust a lot of parameters. We analyze the difference between convolution neural network (CNN) network and traditional neural network (NN) in the recognition of handwritten digital datasets. CNN show the obvious superiority to the digital prediction rate, and then we study the applicability of DCNN in handwritten digital datasets. A deep convolutional neural network (DCNN) based on Alex network (AlexNet) is designed and the recognition accuracy can reach to 99%. The confusing digital set are sort out, called Confuse Numbers (CN) based on MNIST. In the field of handwritten digit recognition, most of the work is focusing on the processing of the whole MNIST data set. As a supplement, the CN dataset is helpful to make the progress on handwritten digit recognition technology.
Effects of Training on Employee Performance in Ga East Hotels

The hospitality industry, from which hotels are a main part, is service intensive and consequently relies heavily on its human resources [1].The hospitality industry is one of the oldest indus-
Evaluation of Visualisation of Scanpath Trend Analysis (ViSTA) Tool

Eye tracking plays a key role in user behaviour understanding and usability studies. We previously proposed an algorithm called STA (Scanpath Trend Analysis) that analyses multiple individual scanpaths on a web page to discover their trending path in terms of the areas of interest (AOIs). This algorithm provides the most representative path of multiple users and compared to other algorithms (i.e., provides the most similar path to individual scanpaths). However, its current implementation has no graphical user interface and provides a sequence of characters that represent AOIs. Some external modules should also be installed in advance to run it. In our previous work, we presented the first web-based visualisation tool for the STA algorithm called ViSTA along with its initial evaluation. This tool allows to visualise individual scanpaths on a particular web page with gaze plots, visually draw AOIs, apply the STA algorithm, and visualise the result of the algorithm. In this paper, we present the extended version of ViSTA with a follow up user evaluation. The first version of ViSTA uses the STA algorithm which identifies trending AOIs based on all individual scanpaths. However, the extended one uses the STA algorithm with the tolerance level parameter which means trending elements can be identified based on a subset of individual scanpaths for discovering a more representative path. Both of our initial and follow up evaluations show that the workload in terms of NASA Task Load Index (TLX) is lower with ViSTA compared to the current implementation of the STA algorithm.
Arabic Sign Language Translator

Development of systems that can recognize the gestures of Arabic Sign language (ArSL) provides a method for hearing impaired to easily integrate into society.This paper aims to develop a computational structure for an intelligent translator to recognize the isolated dynamic gestures of the ArSL.In our proposed system we build a datasets for ArSL from scratch of, we used 100-sign vocabulary from ArSL, we have applied 1500 video files for these signs.These signs were divided into five types of signs, recognizing a sign language gestures from dynamic gestures could be a difficult analysis issue.This paper solves the problem using gradient based key frame extraction technique.These key frames are useful for splitting continuous language gestures into sequence of signs for removing uninformative frames.After splitting of gestures every sign has been treated as isolated gesture.Then features of pre-processed gestures are extracted using Intensity Histogram by integrating with Gray Level Co-occurrence Matrix (GLCM) features.Experiments are performed on our own ArSL dataset and the matching between the ArSL and Arabic text is tested by Euclidian distance.The evaluation of the proposed system for the automatic recognition and translation for isolated dynamic ArSL gestures has proven to be effective and highly accurate.The experimental results show that the proposed system recognizes signs with a precision of 95.8%.
A Hierarchical Framwork with Improved Loss for Large-scale Multi-modal Video Identification

This paper introduces our solution for iQIYI Celebrity Video Identification Challenge. After analyzing the iQIYI-VID-2019 dataset, we find the distribution of the dataset is very unbalanced and there are many unlabeled samples in the validation set and the test set. For these challenge, we propose a hierarchical system which combines different models and fuses base classifiers. For the false detections and low-quality features in the dataset, we use a simple and reasonable strategy to fuse features. In order to detect videos more accurately, we choose an improved loss function for the learning of base classifiers. Experiment results show that our framework performs well and evaluation conducted by the organizers shows that our final result gets the ninth place online and mAP 88.08%.
Security and Privacy Cloud Storage as a Personal Digital Archive Storage Media

Increasing the utilization of internet technology gave birth to the new challenges in the protection of privacy and personal data, especially with the increasing collection practices, the utilization and dissemination of one's personal data. Regulatory instruments and failed to become one of the trigger mechanism of the weak protection of privacy and personal data in particular in the use of cloud storage technology. Cloud storage is a technology which uses the internet and a central server that much to keep or manage user data. Cloud storage allows users to use the application without the install so that personal files can be accessed anywhere and at any time through internet access. This technology enables efficiency by concentrating storage, data processing and memory. However, on the other hand, cloud storage has raised new legal issues, namely how the personal data of users of cloud storage are protected from a variety of disclosure and distribution by cloud storage service providers to third parties.
Entanglement criteria for two strongly interacting ensembles

Two interacting atomic ensembles display a Dicke-like quantum phase transition above a critical coupling strength. We show that an ensemble-ensemble entanglement accompanies the quantum phase transition. We derive entanglement criteria, which can witness the entanglement of the two interacting ensembles. We observe that all criteria are successful in the thermodynamic limit, while only the newly introduced ones~(number squeezing-like criteria) can witness the ensemble-ensemble entanglement for a finite number of particles. We also mention about implementations of these criteria to two-component condensate systems and nanoscale quantum plasmonics.
Theme Based Instruction Model in Teaching Extensive Reading

The purpose of this study is to investigate in detail the implementation of theme based instruction model in teaching extensive reading. This study is classroom action research conducted at the fourth semester students’ English Educational Sciences, Educational Sciences Faculty, Universitas Muhammadiyah Jakarta with participant number are 40 students. Data obtained from the result of the test in each cycle. The findings showed that the learning process using theme based instruction model in teaching extensive reading makes the students more participative and active in their classroom reading learning process, it is showed by their high participation during the teaching is increased. Another finding revealed that there is significant influence toward the students’ extensive reading learning process from preliminary to cycle II stage in the research process. The average score of students at the preliminary stage is only 68 then it increased to 72 in the first cycle and increased significantly in cycle II with the amount of 83. It can be concluded that classroom action research is done by using of theme based instruction model in teaching extensive reading in the fourthsemester students of English Educational Sciences significantly helps the students in increasing about 21% improvement from pre-cycle to cycle II in their Extensive Reading course. This study recommends the continuation of using various teaching models for university students to improve their English language skills, especially English reading skill.
Extracting Mapping Relations for Mobile User Interface Transformation

The development of mobile apps has become the current mantra for any business' success. The rise of many types of mobile devices and mobile OS has instantly created the need to develop multiple versions for the same app. In order to grasp as much market share as possible, it is desirable to have all the versions of an app demonstrate similar user interface (UI) appearances, to make users feel comfortable when switching from one platform to another and more likely to stick to the app. However, to ensure consistent UIs among cross-platform versions can be a challenging and costly endeavor, since different platforms have their own UI controls and programming languages. In this paper, we propose an automatic approach to transforming mobile app UIs across platforms, and illustrate our approach by transforming the UIs of iOS apps to Android ones. We leverage the enormous existing apps carefully designed by developers to achieve similar UI effects between iOS and Android versions, since these apps contain valuable knowledge of mapping relations between the iOS and Android UI controls. Starting from the reverse engineering of these apps, our approach separates each user interface into modules of adequate sizes. Then it maps the modules from both versions that contribute to the same visual and functional effect, and automatically mines the mapping relations. By applying the mined relations, our approach has successfully transformed the iOS app UIs into Android app UIs, as confirmed by a series of experiments.
Student Profile Toword Course Activities At PGSD FKIP University Widya Dharma Klaten

The research was backgrounded by a decrease in the number of students who enrolled in Unwidha Klaten's PGSD Study Program. The study focused on the profile of students towards the PGSD FKIP Klaten Widya Dharma University lecture activities. Data collection uses an instrument in the form of a questionnaire. Processing data using editing, explanation information, diagrams, and percentage explanations. The research conclusions are explained as follows. The majority of Unwidha Klaten's PGSD students in 2017 have self-explanations such as being born in Klaten before the millennium, having a productive age, having traits, and predominantly female characters. The majority of Unwidha Klaten's PGSD students in 2017 have family information such as having to struggle to get more information about bachelor diplomas, civil servants, private teachers, love their siblings, be an example, and a good example in the family. The majority of Unwidha Klaten's PGSD students in 2017 have health information such as being able to maintain their weight, height, eye health, hearing health, should not be considered arrogant because they do not have recurrence of disease and accident problems. The majority of Unwidha Klaten's PGSD students in 2017 have lecture information such as having the desire to become an elementary school teacher, taking a distance of more than 1 kilometer with up to 1 hour, choosing the subjects they like and the hardest courses, namely PGSD courses.
APLIKASI ASISTEN UNTUK LANSIA DENGAN MEMANFAATKAN SMARTPHONE BERBASIS ANDROID

This study aims to design and build an android based elderly assistant application using Android Studio Software to help the elderly in performing its activities. There are two features in this elderly assistant application that are Exercise feature and Map Feature. Exercise feature aims to assist elderly in exercising by utilizing accelerometer and gyroscope sensors to read and calculate exercise movement performed by elderly and the Map feature aims to track the location where the elderly are snd also receive the notification when the elderly out of radius. From the results of trials of elderly assistant applications, accelerometer and gyroscope sensors on exercise features have been able to calculate and read the exercise movement that performed by the elderly and the map feature have been able to track the elderly location using GPS on Smartphone.
Optimized memory model for hadoop map reduce framework

Map Reduce is the preferred computing framework used in large data analysis and processing applications. Hadoop is a widely used Map Reduce framework across different community due to its open source nature. Cloud service provider such as Microsoft azure HDInsight offers resources to its customer and only pays for their use. However, the critical challenges of cloud service provider is to meet user task Service level agreement (SLA) requirement (task deadline). Currently, the onus is on client to compute the amount of resource required to run a job on cloud. This work present a novel memory optimization model for Hadoop Map Reduce framework namely MOHMR (Optimized Hadoop Map Reduce) to process data in real-time and utilize system resource efficiently. The MOHMR present accurate model to compute job memory optimization and also present a model to provision the amount of cloud resource required to meet task deadline. The MOHMR first build a profile for each job and computes memory optimization time of job using greedy approach. Experiment are conducted on Microsoft Azure HDInsight cloud platform considering different application such as text computing and bioinformatics application to evaluate performance of MOHMR of over existing model shows significant performance improvement in terms of computation time. Experiment are conducted on Microsoft Azure HDInsight cloud. Overall, good correlation is reported between practical memory optimization values and theoretical memory optimization values.
Tingkat Adiksi Game Online Siswa SMPN 13 Padang

The incidence of online game addiction in teenagers of Indonesia is increasing every year. This lead to the impact of teenager’s behavior. This study aims to determine online game addiction level of teenagers in SMPN 13 Padang. The design of this study uses the descriptive analyst. The sample in this study is the students of SMP N 13 Padang with the number of respondent who had online games experience is 88 teenagers by using stratified random sampling. The research instrument used is GASA (Game Addiction Scale Assessment). The results showed that the average online game addiction average score was 63,32 in intermediate level of addiction. This means that the teenagers of SMPN 13 Padang need to be given an intervention to decrease their addiction level and prevent further negative effect for their productive life. It is suggested that mental nurses can collaborate with the school and the education office to conduct cognitive behavior therapy on an ongoing basis in an effort to prevent online game addiction.
Parts of Speech Tagging for Kannada

Parts of speech (POS) tagging is the process of assigning the part of speech tag to each and every word in a sentence.In this paper, we have presented POS tagger for Kannada, a low resource south Asian language, using Condition Random Fields.POS tagger developed in the work uses novel features native to Kannada language.The novel features include Sandhi splitting, where a compound word is broken down into two or more meaningful constituent words.The proposed model is trained and tested on the tagged dataset which contains 21 thousand sentences and achieves a highest accuracy of 94.56%.
High level speaker specific features modeling in automatic speaker recognition system

Spoken words convey several levels of information. At the primary level, the speech conveys words or spoken messages, but at the secondary level, the speech also reveals information about the speakers. This work is based on the high-level speaker-specific features on statistical speaker modeling techniques that express the characteristic sound of the human voice. Using Hidden Markov model (HMM), Gaussian mixture model (GMM), and Linear Discriminant Analysis (LDA) models build Automatic Speaker Recognition (ASR) system that are computational inexpensive can recognize speakers regardless of what is said. The performance of the ASR system is evaluated for clear speech to a wide range of speech quality using a standard TIMIT speech corpus. The ASR efficiency of HMM, GMM, and LDA based modeling technique are 98.8%, 99.1%, and 98.6% and Equal Error Rate (EER) is 4.5%, 4.4% and 4.55% respectively. The EER improvement of GMM modeling technique based ASR systemcompared with HMM and LDA is 4.25% and 8.51% respectively.
Sistem Deteksi Wajah Pada Kamera Realtime dengan menggunakan Metode Viola Jones

In general, human are given the mind and mind to be able to determine or be able to didtinguish individuals who appear either human, animal, plant, and other objects that are known or unknown. And it is possible for human to recognize these object from their sight and from their brain memory. Especially on the human face, human can recognize whether the object is human or not human, and can recognize the object very well through his own eyes.face detection system in human becomes very important in the development of science of digital image processing. The research has been done with many advantages and disadvantages. From a face many information features that can be read, such as eyes, nose, and mouth. The detection system uses Viola Jones method as an object detection method. The Viola Jones method is known to have considerable Speed and accuracy as it combines several concepts (Haar feature, Integral image, Adaboost, Cascade classifier) into a main method for detecting objects.Based on tests conducted on face identification under conditions that may affect face detection results, the results show an accuracy of 67,6 % to detect the face.