Reviewerly/exHarmony huggingface dataset:
Research on the Method of 3D Registration Technology

Abstract Three-dimensional registration technology is the foundation, key and difficult point of constructing augmented reality system. This paper first reviews the three-dimensional registration technology in augmented reality, and compares the advantages and disadvantages of each method. Then, from the improvement of feature descriptor, the combination of registration technology and tracking technology, online learning-based registration technology and deep learning-based registration technology, the improvement strategy of registration method is analysed and summarized, which is the three-dimensional registration technology. In-depth research provides ideas. Finally, the problems existing in the research of 3D registration technology are pointed out, and the future research directions are prospected.
The Application of the K-Nearest Neighbors Method as A Recommendation for The Selection of Departments in Higher Education Based on The Results of Multiple Intelligence Tests

Abstract The problem faced by students after graduating from High School or Vocational High School is the difficulty in deciding whether to work immediately or going to college. The next problem for students who will enter college is to choose which major study based on their type of intelligence. Determination of Departments that are inappropriate often makes students unable to do well in lectures. The problem of this study is whether the selection of Departments for the University of PGRI Madiun in 2019 is in accordance with the type of intelligence or not. The purpose of this study is to recommend the selection of Departments at the University of PGRI Madiun students based on Multiple Intelligence test results. This research is quantitative research and data collection is done qualitatively. The research subjects were students of PGRI Madiun University in 2019. The sampling technique used purposive sampling with a sample of 297 students from 4 Faculties and 22 Departments. The technique of collecting data using the multiple intelligence test questionnaire. The research data were analyzed using the K-Nearest Neighbors method. The results of the study showed that the suitability of the choice of Departments of the University of PGRI Madiun students based on multiple Intelligence test for the Faculty of Health and Science had the highest accuracy of 86.36%, Faculty of Teacher Training and Education (Sains) 63.04%, Faculty of Teacher Training and Education (Social) 32.28%, Faculty of Teacher Training and Education (Language) 58.62%, Faculty of Technology 56.67%, Faculty of Economics and Business 48.84%.
Cloud-based Embedded System for Object Detection and Recognition

Abstract Object detection and recognition techniques require large image datasets, memory, a workstation with specific graphics processing capability to train the algorithm and might have high power consumption. Embedded platforms on the other hand are characterized by portability, low power consumption and space, and energy resources making the deployment of such algorithms on them difficult. In order to overcome these drawbacks, cloud-based processing embedded system for object detection and recognition is proposed in this work. The system consists of an image acquisition device set up using embedded board and camera to capture, process and send images to the remote computer via cloud storage platform. This cloud platform serves as an interface between the embedded board and the remote computer. The detection algorithm of Faster R-CNN is executed on the remote computer and is trained and validated with 3000 images obtained from ImageNet. The training of the algorithm aims to detect five classes of object. The proposed system was validated off-line and have achieved a mean Average Precision (mAP) of 0.67. The performance of entire system procedure took about 45 seconds and have obtained an average confidence score of 0.86.
Secure Data Hiding in Coded Image using Arduino Speech Recognition

Abstract The cover medium is used as an envelope to hide inside information. Embedded data may represent a text, image or video to be transferred in the spread media. In this paper, secret information hiding in coded image is exhibited relying upon quantization level and discrete wavelet transform (DWT). Secret data is encrypted and controlled by Arduino speech recognition board in both transmitter ad receiver side to decrypt these secret data. The color image is utilized as a cover image with the discrete wavelet transform it into another form and Absolute Moment Block Truncation Coding (AMBTC) technique is used for embedding the data. The mapping of secret data and image scrambling is depended on the Henon map, while the logistic map is used to encrypt secret data. The proposed method achieved accurate results in peak signal to noise ratio about 34.0524 at average for tested image.
Detecting application logic vulnerabilities via finding incompatibility between application design and implementation

Logic vulnerabilities are due to defects in the application logic implementation such that the application logic is not the logic that was expected. Indeed, such vulnerabilities pattern depends on the design and business logic of the application. There are no specific and common patterns for application logic vulnerabilities in commercial applications. In this study, a method named FINAD is introduced to detect application logic vulnerabilities using an activity flow graph (AFG) to find the incompatibilities of an implemented application with its design. In this work, the AFG, consisting of the activity diagram (AD) and control flow graph (CFG), is presented for the first time. Investigation of different common types of application logic vulnerabilities indicated that the majority of such vulnerabilities could be detected through conducting a static analysis on an AFG. The FINAD method is independent of the language and can be used for vulnerability detection for any programming language, provided that the AD is available, and the CFG of the program can be created. Implementation of FINAD for PHP language showed its effectiveness in detecting known logic vulnerabilities in CVE vulnerability database.
When to Be Agile: Ratings and Version Updates in Mobile Apps

Lean and agile models of product development organize the flexible capacity to rapidly update individual products in response to customer feedback. While agile operations have been adopted across numerous industries, neither the benefits nor the factors explaining when firms choose to become agile are validated and understood. We study these questions using data on the development of mobile apps, which occurs through the dynamic release of new versions into the mobile app marketplace, and the apps' customer ratings. We develop a structural model estimating the dependence of product versioning on (A) market feedback in the form of customer ratings, against (B) project and work-based considerations, such as development timelines, scale economies, and operational constraints. In contrast to when they actually benefit from operational agility, firms become agile when launching riskier products (in terms of uncertainty in initial customer reception) and less agile when able to exploit scale economies from coordinating development over a portfolio of apps. Agile operations increase firm payoffs by margins of 20-80%, and interestingly partial agility is often sufficient to capture the bulk of these returns. Finally turning to a question of marketplace design, we study how the mobile app marketplace should design the display of ratings to incentivize quality (increasing app categories' average user satisfaction rates by as much as 22%).
Scaling PageRank to 100 Billion Pages

Distributed graph processing frameworks formulate tasks as sequences of supersteps within which communication is performed asynchronously by sending messages over the graph edges. PageRank’s communication pattern is identical across all its supersteps since each vertex sends messages to all its edges. We exploit this pattern to develop a new communication paradigm that allows us to exchange messages that include only edge payloads, dramatically reducing bandwidth requirements. Experiments on a web graph of 38 billion vertices and 3.1 trillion edges yield execution times of 34.4 seconds per iteration, suggesting more than an order of magnitude improvement over the state-of-the-art.
Asymptotic Behavior of Sequence Models

In this paper we study the limiting dynamics of a sequential process that generalizes Pólya's urn. This process has been studied also in the context of language generation, discrete choice, repeat consumption, and models for the web graph. The process we study generates future items by copying from past items. It is parameterized by a sequence of weights describing how much to prefer copying from recent versus more distant locations. We show that, if the weight sequence follows a power law with exponent α ∈ [0, 1), then the sequences generated by the model tend toward a limiting behavior in which the eventual frequency of each token in the alphabet attains a limit. Moreover, in the case α > 2, we show that the sequence converges to a token being chosen infinitely often, and each other token being chosen only constantly many times.
Personal Shopper – Mobile Phone Applications

Abstract This project is focused on the development of personal shopper mobile phone application. The purpose of this system is to help part-timer and full-timer personal shoppers gather in one platform and also for the customer to hire a personal shopper to buys their items. This project will implement a rating system, give reward to the customer, accept customer requests, view customer requested details and contact the customer through Whatsapp application. This application is very flexible for the customer and personal shopper where the customer can become a personal shopper and vice versa. This entire project is developed according to software engineering methodology with the waterfall model. The tool used to create this project is Android Studio with Java, PHP, XAMPP Server, and MySQL database.
Toward the Semantic Data Inter-processing: A Semantic Web Approach and Its Services

Semantic data and information make the applications and its services intelligent by providing reasoning mechanism and inter-processing capability of data and information. Semantic Web research communities have developed standard and more complex use-cases applications that need semantic data inter-processing for better utilization of data and information. Currently, Semantic Web is frequently used to develop the innovative applications in various areas with unbelievable benefits and services, and people quickly agree to adopt the upgraded technology. Hence, this paper will broadly explain the Semantic Web and its supporting technology with their applications and benefits. We will also introduce here about the Web services that will be utilized for the development of innovative applications in various areas which will relay the innovative services to applications developers and users.
How Do We Create a Fantabulous Password?

Although pronounceability can improve password memorability, most existing password generation approaches have not properly integrated the pronounceability of passwords in their designs. In this work, we demonstrate several shortfalls of current pronounceable password generation approaches, and then propose, ProSemPass, a new method of generating passwords that are pronounceable and semantically meaningful. In our approach, users supply initial input words and our system improves the pronounceability and meaning of the user-provided words by automatically creating a portmanteau. To measure the strength of our approach, we use attacker models, where attackers have complete knowledge of our password generation algorithms. We measure strength in guess numbers and compare those with other existing password generation approaches. Using a large-scale IRB-approved user study with 1,563 Amazon MTurkers over 9 different conditions, our approach achieves a 30% higher recall than those from current pronounceable password approaches, and is stronger than the offline guessing attack limit.
Finding the optimal cluster state configuration. Minimization of one-way quantum computation errors

In this paper, we estimate the errors of Gaussian transformations implemented using one-way quantum computations on cluster states of various configurations. From all possible cluster state configurations, we choose those that give the smallest computation error. Furthermore, we evaluate errors in hybrid computational schemes, in which Gaussian operations are performed using one-way computations with additional linear transformations. As a result, we find the optimal strategy for the implementation of universal Gaussian computations with minimal errors.
Dynamic Web Service Composition Using AI Planning Technique: Case Study on Blackbox Planner

The dynamic composition of web services is an important research problem to offer value added services to the end user. As per the demands of the end user, the sequence in which services to be combined as well as participating services are to be decided at run-time. Planners based approach is useful to achieve the dynamic web service composition. Based on the functional parameters—input, output, precondition and effect, various AI planners achieve service composition differently. In this work, we present a AI planning-based dynamic web service composition approach using Blackbox planner. The experimental results show the effectiveness of the proposed approach.
Comparative Study of Different Classifiers Based on Extracted Facial Features for Supervised Age Estimation

Abstract Estimating a person age is a major concern for many researchers due to his importance in many applications, whereas finding a person age is crucial in making specific decision. This paper conducts a comparative study between two classification-algorithms for age estimation which applied on extracted Local Binary Patterns (LBP). It was also divided dataset into 3 classes in order to improve results and increasing accuracy of system, Root Mean Squared Error, Mean Absolute Error and other parameters are used to measure the precision of the system. The proposed methodology of this work is divided into three phases. First phase consists pre-processing methods in which selected image is handled using color to gray scale image conversion and histogram equalization. Result image of which is manipulated by standard Viola-Jones algorithm to detect and crop face area from the whole image. While to ensure same size for all images, detected face image is resized to 256 × 256. Second phase tends to extract LBP features to be fed in the next classification phase. In third phase, two classifiers Logistic Model Tree (LMT) as well as Sequential Minimal Optimization (SMO) have been applied on the extracted features for age estimation.
Design and Implementation of Lifting Wavelet Transform Using Field Programmable Gate Arrays

Abstract Lifting Wavelet transform (LWT) has an extensive usage in different image processing applications as image compression and information hiding. LWT is considered a good solution for hardware designs as it relies only on integer calculations while applying the wavelet transform. In this paper, an FPGA design and implementation of LWT is presented, the implementation is achieved using VHDL coding without importing off-shelf components which make the proposed design applicable to a wide range of devices. The design is based on parallel execution to perform LWT implementation with real time response. The design utilized 421 logic registers of DE2 Cyclone II (EP2C35F672C6) FPGA device with 151.91MHz frequency.
Reasoning about Uncertainty in Empirical Results

Conclusions that are drawn from experiments are subject to varying degrees of uncertainty. For example, they might rely on small data sets, employ statistical techniques that make assumptions that are hard to verify, or there may be unknown confounding factors. In this paper we propose an alternative but complementary mechanism to explicitly incorporate these various sources of uncertainty into reasoning about empirical findings, by applying Subjective Logic. To do this we show how typical traditional results can be encoded as "subjective opinions" -- the building blocks of Subjective Logic. We demonstrate the value of the approach by using Subjective Logic to aggregate empirical results from two large published studies that explore the relationship between programming languages and defects or failures.
Data De-duplication Scheme for File Checksum in Cloud

Data de-duplication is a significant source of security technique in the cloud environment. It accomplishes identical reproductions of the redundant records to save cloud storage. The de-duplication has been used to preserve bandwidth and confidentiality of the user. As de-duplication has many advantages, safety and protection of data is the primary concern as the data of the clients are susceptible to various types of attacks.
Green Credit Risk Assessment under the Background of Water Ecological Civilization City Construction——Based on BP Neural Network Model

Abstract The construction of water ecological civilized cities requires a large amount of capital, but at the same time, the project itself has certain risks. Commercial banks are not only the main providers of credit funds, but also the main bearers of project risks. Strengthening the credit risk management of water ecological civilized urban construction projects and enhancing the financial support of commercial banks for water ecological civilized urban construction projects have become the key to further promoting the construction of water ecological civilized cities. At present, the credit risk assessment of commercial banks in China is based on traditional methods, and it is difficult to achieve the desired effect. This paper applies the AHP method to construct the credit risk index system of water conservancy projects, and establishes a credit risk assessment model through BP neural network technology. Finally, it combines 39 listed companies. The data is empirically analyzed for the model to verify the accuracy of the predictions.
Face Recognition Algorithm for Low-Resolution Images

Recently, in the field of face recognition, deep learning has achieved great success. FaceNet, one of the Google’s deep learning frameworks, obtained nearly 100% accuracy in recognizing high-resolution faces. But, its performance on low-resolution images is unsatisfactory. Here, to justify the above statement, the performance of FaceNet has been evaluated on four well-known facial databases. For improving the performance, the face recognition algorithm for low-resolution images has been designed. Specifically, the algorithm performs image enhancement using a customized error back propagation network, and then the nearest patterns (NP) have been extracted and fed as embeddings to FaceNet. NP is a modified version of dual-cross patterns face descriptor that encodes the texture information of each image pixel in eight directions as unique features. The experimentation done on various databases such as LFW, ORL, EYB, and AR. The results of proposed approaches with NP have shown better performance than the other face descriptors like DCP, LBP, and LTP. Moreover, better results have been obtained on shrinking the Inception-Resnet-v1 network, which resulted in the fast convergence of the FaceNet.
Visual Concept Naming: Discovering Well-Recognized Textual Expressions of Visual Concepts

We propose a task called Visual Concept Naming to associate visual concepts with the corresponding textual expressions, i.e., names of visual concepts found in real-world multimodal data. To tackle the task, we create a dataset consisting of 3.4 million tweets in total in three languages. We also propose a method for extracting candidate names of visual concepts and validating them by exploiting Web-based knowledge obtained through image search. To demonstrate the capability of our method, we conduct an experiment with the dataset we create and evaluate names obtained by our method through crowdsourcing, where we establish an evaluation method to verify the names. The experimental results indicate that the proposed method can identify a wide variety of names of visual concepts. The names we obtained also show interesting insights regarding languages and countries where the languages are used.1
Review on High Utility Rare Itemset Mining

Today’s era is an era of data. Hence, the most attentive field of research and study is to collect and select the data rapidly as per the need nowadays. Data mining is the field which helps industries to overcome the problem of data extraction. Association rule mining (ARM) is one of the major techniques of data mining which identifies the itemsets appear frequently in the dataset known as frequent itemset and generates the association rules. This helps in decision making. The extension of traditional association rule mining has come up with the concept of utility which should be considered while mining; hence, utility mining aims to identify the itemsets which not only have the frequent occurrences but also have considered the utility of the itemset. High utility itemsets mining can be used as an efficient method to discover interesting patterns. Rare items are items that appear fewer frequently in a database. High utility itemsets can be frequent or rare. Even rare itemsets can also be of high or low utility. In this paper, a literature survey of various research works has been discussed on High Utility Rare Itemset Mining. We have thoroughly surveyed High Utility Rare Itemset Mining methods and applications here. Also, we have focused on some open research issues to represent future challenges in this domain.
ResQueue: A Smarter Datacenter Flow Scheduler

Datacenters host a mix of applications: foreground applications perform distributed lookups in order to service user queries and background applications perform batch processing tasks such as data reorganization, backup, and replication. While background flows produce the most load, foreground applications produce the most number of flows. Because packets from both types of applications compete at switches for network bandwidth, the performance of applications is sensitive to scheduling mechanisms. Existing schedulers use flow size to distinguish critical flows from non-critical flows. However, recent studies on datacenter workloads reveal that most flows are small (e.g., most flows consist of only a handful number of packets). In light of recent findings, we make the key observation that because most flows are small, flow size is not sufficient to distinguish critical flows from non-critical flows and therefore existing flow schedulers do not achieve the desired prioritization. In this paper, we introduce ResQueue, which uses a combination of flow size and packet history to calculate the priority of each flow. Our evaluation shows that ResQueue improves tail flow completion times of short flows by up to 60% over the state-of-the-art flow scheduling mechanisms.
Statistical Flow Classification for the IoT

Cirillo, Gennaro Passerone, Roberto Posenato, Antonio Rizzon, LucaThe objective of this work is to analyze packet flows and classify them as traffic that belongs to IoT devices or to traditional non-IoT communication. We employ two methods: a clustering approach, which learns directly from the structure of the dataset, and a classification tree, trained with the collected data and evaluated using 10-fold cross validation. The results show that classification trees outperform clustering on all datasets, and achieve high accuracy on both homogeneous simulated and real deployment traffic data.