Reviewerly/exHarmony huggingface dataset:
Movie Recommendation System Using k-clique and Association Rule Mining

Various recommender systems have been presented in an effort to get better preciseness. In order to further improve more accuracy, the k-clique methodology, which is used to analyze social networks was introduced in the recommendation system and the result shows the k-clique method is effective in improving accuracy. In this paper, we propose a recommendation system using k-cliques and association rule mining with the best accuracy. To estimate the performance, the maximal clique method, collaborative filtering methods are monitored using the k nearest neighbors, the k-clique method, and the k-clique and association rule mining are used to evaluate the MovieLens data. The performance outputs show that the k-cliques and association rule mining get better the preciseness of the movie recommendation system than any other methods used in this experiment.
A Novel Structure for a Multi-Bernoulli Filter without a Cardinality Bias

The original multi-target multi-Bernoulli (MeMBer) filter for multi-target tracking (MTT) is shown analytically to have a significant bias in its cardinality estimation. A novel cardinality balance multi-Bernoulli (CBMeMBer) filter reduces the cardinality bias by calculating the exact cardinality of the posterior probability generating functional (PGFl) without the second assumption of the original MeMBer filter. However, the CBMeMBer filter can only have a good performance under a high detection probability, and retains the first assumption of the MeMBer filter, which requires measurements that are well separated in the surveillance region. An improved MeMBer filter proposed by Baser et al. alleviates the cardinality bias by modifying the legacy tracks. Although the cardinality is balanced, the improved algorithm employs a low clutter density approximation. In this paper, we propose a novel structure for a multi-Bernoulli filter without a cardinality bias, termed as a novel multi-Bernoulli (N-MB) filter. We remove the approximations employed in the original MeMBer filter, and consequently, the N-MB filter performs well in a high clutter intensity and low signal-to-noise environment. Numerical simulations highlight the improved tracking performance of the proposed filter.
Networking Self-Organising Maps and Similarity Weight Associations

Using a Self-Organising Map (SOM), the structure of a data set can be explored when analysing patterns between data that are multivariate, nonlinear and unlabelled in nature. As a SOM alone cannot be used to explore patterns between different data sets, a similarity weighting scheme was previously introduced to associate different SOMs in a network fashion and approximate output patterns for given inputs. This approach uses a global weight association method on the combination of all SOMs specified for a network. However, there has been a difficulty in defining the association when changing the SOM network structure. Furthermore, it has always produced the same output weight distribution for different input data that have the same best matching unit. In an attempt to overcome the issues, we propose a new approach in this paper for locally associating a pair of SOMs as a basic network building block and approximating individually associated weight distribution. The experiments using ecological data demonstrate that the proposed approach effectively associates a pair of input and output SOMs for structural flexibility of the SOM network with better approximation of output weight distributions for individual input data.
A Portable Finger Language Translator Based on Deep Learning with Leap Motion

Hearing impaired people are less able to communicate than ordinary people, and experience many inconveniences in everyday life, hospitals, and government offices. To solve this problem, various geophysical translators and sign language translators are being developed. However, most of the wearable translator has the disadvantage that the accuracy of output data is low. Thus, we try to solve this problem by using leap motion with smart phone, which are hand motion devices, instead of wearable translator. Particularly, we tried to increase the recognition rate of geographical interpreters by applying a deep learning model with multiple perceptron. Experimental results show that the average recognition rate is almost 94.9% separately.
Widget Detection Network: widget detection in mobile screenshot with region-based attention networks

We propose an architecture to automatically detect widgets in mobile screenshots, considering only visual cues. Even though traditional object detection methods perform well on common objects in natural scene images, they are unable to deal with the screenshot images with complex widget layout. Therefore, we propose region-based Widget Detection Network (WDN), which introduces regularities in the screenshot images as the regularizations. First, we design a scale-aware attention structure to make the backbone network sensitive to widget scales so that the salient features of the interest regions could be captured. Second, a strategy of horizontal region generation is proposed to fully utilize the aligned property of widget arrangement, which generates all the region candidates in a horizontal line at once. Finally, a variant of online hard example mining is employed to alleviate the problem of imbalance samples, which explicitly restricts the ratio of foreground and background to achieve better balance. We conduct experiments on a proposed benchmark dataset. The quantitative results and qualitative analysis on the benchmark dataset show that WDN achieves impressive performance, which outperforms the common object detection methods in the widget detection task.
Application-Oriented Network Scheduling With Metaflow

Distributed applications usually feature a set of correlated flows between two consecutive computation stages. The scheduling of these flows has a crucial influence on job completion time. Coflow improves performance by optimizing the finish time of the entire set of flows. However, the flows and computing tasks in one application have more complex relationships that exceed the coflow's barrier assumption. In this context, scheduling via coflow abstraction may hurt application performance. Accordingly, we propose metaflow, a traffic abstraction derived from the computation graph of the application. Metaflow reveals the detailed flow requirements of the application and makes it easier to reduce the job completion time. Based on the metaflow, we first develop a mathematical model and formulate the scheduling problem as an integer linear programming (ILP) problem. We further prove that it has an equivalent linear programming (LP) problem through rigorous theoretical analysis in order to solve this ILP problem efficiently. To demonstrate the effectiveness of scheduling with metaflow, we have conducted extensive simulations with both synthetic single jobs and production traces containing multiple jobs. The simulation results verify that our new scheduler adapts well to different jobs and can achieve a significant increase in an average speed of 2.87× on a real-life workload, compared to the state-of-the-art coflow scheduler.
Enhanced XOR-Based Progressive Visual Secret Sharing Using Multiple Decryptions

This paper proposes XOR-based visual secret sharing rule using random grids with the abilities of OR as well as XOR-based visual cryptographic schemes. This scheme is different from previously proposed schemes as this paper proposes a lossless (k, n) threshold-based vcs with progressive recovery having advantages of multiple decryptions. Although previously proposed schemes were progressive with OR operator but in case of XOR their progressive nature was lost when the number of shares “t” is between “k” and “n”, i.e., k < t < n. Our scheme overcomes this problem by proposing a truly progressive scheme, completely independent of the values of “k” and “n.” In absence of a processing device the secret information can be simply reconstructed by OR-ing a threshold number of shares and in case, if a device having computational ability of XOR is available, then the secret information encoded is reconstructed lossless for (k, n) threshold, when all the “n” shares are collected. There is no pixel expansion, no requirement of codebook design and hence all the shares were generated of the same size of the original secret image.
NTRU-Like Secure and Effective Congruential Public-Key Cryptosystem Using Big Numbers

We propose RCPKC, a random congruential public key cryptosystem working on integers modulo q, such that the norm of a two-dimensional vector formed by its private key, (f, g), is greater than q. RCPKC works similar to NTRU, the fastest and secure PKC. NTRU, uses high order, N, polynomials and is susceptible to the lattice basis reduction attack (LBRA) taking time exponential in N. RCPKC is a secure version of insecure CPKC proposed by NTRU authors and easily attackable by LBRA since CPKC uses small numbers for the sake of the correct decryption. RCPKC specifies a range from which the random numbers shall be selected, it provides correct decryption for valid users and incorrect decryption for an attacker using Gaussian Lattice Reduction (GLR). Because of its resistance to LBRA, RCPKC is more secure, and, due to the use of big numbers instead of high order polynomials, about 24 (7) times faster in encryption (decryption) than NTRU. Also, RCPKC is more than 3 times faster than the most effective known NTRU variant, BQTRU.
The Basic Model of Attack Resistance Estimation for Monitoring the Program Code Integrity of the FPGA-Based Systems

An approach to monitoring the program code integrity of FPGA-based systems is considered. The approach is based on the use of a digital watermark to store monitoring information. As part of this approach, an integrity monitoring method is considered, which is characterized by the use of several rounds of the embedding of a digital watermark in a program code. A basic model for estimation the complexity of the attack on the integrity monitoring method is proposed. The model takes into account the possible directions of attack, both related to the storage of monitoring information in the form of a digital watermark, and the direction of attack due to the traditional mechanisms for protection of the method. With the help of the proposed model, the stages of the attack, which appear during the transition from traditional methods of storing control information to its storage in the form of a digital watermark, are shown.
Real-time face identification from video surveillance cameras

In this research work, we present the implementation of a web environment and processing core based on convolutional neural networks, to recognize faces in real time. The environment can work with several video surveillance cameras at the same time. We conclude that ten images of a person are required for recognition to be highly reliable. The environment was tested with 32 volunteers, in 4 different environments: front face, smiling, wearing a cap, and glasses. We obtained 94%, 91%, and 90% in metrics true-positive-rate, precision, and accuracy, respectively.
Towards a pattern language for the masters student

Every year, thousands of new students begin their Masters in STEM-related topics. Despite being regarded as a common occurrence by the faculty, it represents the culmination of years of studying and preparation for their professional life. Notwithstanding, these students face well-known recurrent problems: how to choose a topic, how to choose an advisor, how to start researching, and how to deal with all the unknowns associated with academic research. Although there are several books on how to write a thesis, most of them avoid prescriptive recommendations on topics beyond research per se or focus on doctoral students, for which the duration and motivation are significantly different. In this paper, we draft a pattern language comprised of thirty patterns that we have observed from supervising over a hundred masters students within the last decade.
New cryptanalytic results upon prime power moduli N = prq

In this paper we propose three attacks on the prime power modulus N = prq for r ≥ 2. The first attack is based on the equation eX − NY +(qr + pru)Y = Z for suitable positive integer u. Using continued fraction we show that YX can be recovered among the convergents of the continued fraction expansion of eN. Also we show that the number of such exponents is at least N5r−76(r+1)−ε where ε ≥ 0 is arbitrarily small for large N. Hence one can factor the prime power modulus N = prq in polynomial time. For i = 1,…,k, with k ≥ 2 and r ≥ 2 the second and third attacks works when attacks k RSA public keys (Ni, ei) are such that there exist k relations of the form eix−Niyi+(qir+piru)yi=zi or of the shape eixi−Niy+(qir+piru)y=zi where the parameters x, xi, y, yi, zi are suitably small in terms of the prime factors of the moduli. Based on LLL algorithm we show that our attack enable us to simultaneously factor the k prime power RSA moduli Ni.
Adaptive Musical Soundtracks

We describe the challenges of creating location-based adaptive musical soundtracks, which we try to address via the iterative development of daoplayer, a tool to create such media experiences. Daoplayer is broadly modelled on the functionality of computer game middleware, functionality we argue is missing from most tools freely available for location-based experiences. We chart the development progress and use in practice, identifying the emerging challenges and outlining our responses to these along the way. Our focus is broad, taking in technical concepts and infrastructure, in addition to the creative concerns of the composer. We reveal that our tool was largely successful in delivering fine grained 'musical' soundtracks, which on the one hand highlights the similarities between computer game soundtracks and location-based soundtracks, but on the other hand we note a number of key distinctions.
Test Driven Development: Process for AUTOSAR Software Development

Abstract The layered architecture of AUTOSAR while making it highly modular, also makes it complex to debug when the software is tested directly on the hardware. This leads to iterations in the development process which sometimes can trace back to changes in the system architecture resulting in overheads and delays in the software development process. This paper discusses how test‐driven development (TDD) methodology can be adopted for AUTOSAR compliant software in enterprises which can reduce the iterations in the development process. This also makes it well suited for Agile workflow.
Bivariate negative binomial regression model with excess zeros and right censoring: an application to Indonesian data

We propose a bivariate hurdle negative binomial (BHNB) regression model with right censoring to model correlated bivariate count data with excess zeros and few extreme observations. The parameters of the BHNB regression model are obtained using maximum likelihood with conjugate gradient optimization. The proposed model is applied to actual survey data where the bivariate outcome is number of days missed from primary activities and number of days spent in bed due to illness during the 4-week period preceding the inquiry date. We compared the right censored BHNB model to the right censored bivariate negative binomial (BNB) model. A simulation study is conducted to discuss some properties of the BHNB model. Our proposed model demonstrated superior performance in goodness-of-fit of estimated frequencies.
Deep Learning-Based Algorithm for Object Identification in Multimedia

As the number of multimedia information such as video and images increases in daily life, researches for efficient identification and retrieval of vast multimedia data have shown great results. In this paper, we propose an algorithm based on YOLO framework to accurately identify and retrieve the object that the user wants to find. The proposed deep learning algorithm based on the YOLO framework identifies objects by clearly grasping the intention of users in the multimedia using the web, and provides more accurate image search by supplementing the limitations of the existing CBIR search. These functions can be expected to increase the use of large-capacity video files such as CCTV and automobile black box in recent years, and it is unnecessary to search for a scene where an object desired to be searched by a user is watched for unnecessary time.
Efficient inference in state-space models through adaptive learning in online Monte Carlo expectation maximization

Abstract Expectation maximization (EM) is a technique for estimating maximum-likelihood parameters of a latent variable model given observed data by alternating between taking expectations of sufficient statistics, and maximizing the expected log likelihood. For situations where sufficient statistics are intractable, stochastic approximation EM (SAEM) is often used, which uses Monte Carlo techniques to approximate the expected log likelihood. Two common implementations of SAEM, Batch EM (BEM) and online EM (OEM), are parameterized by a “learning rate”, and their efficiency depend strongly on this parameter. We propose an extension to the OEM algorithm, termed Introspective Online Expectation Maximization (IOEM), which removes the need for specifying this parameter by adapting the learning rate to trends in the parameter updates. We show that our algorithm matches the efficiency of the optimal BEM and OEM algorithms in multiple models, and that the efficiency of IOEM can exceed that of BEM/OEM methods with optimal learning rates when the model has many parameters. Finally we use IOEM to fit two models to a financial time series. A Python implementation is available at https://github.com/luntergroup/IOEM.git .
Developing A Problem-Based Learning/PBL Model for Improve Students’ English Speaking Problem Solving Ability and Self Confidence at Economic Department of University

Abstract This research was based on the preliminary studies in Economic Department of University in studing English. From this preliminary studies, the researcher want to find out the analysis, the effectivity, and the practicality of her products in designing and implementing PBL Model at Economic of Islamic University of Riau. The design of this research is R &amp; D approach by using ADDIE Model. The sample was selected from one institutions in Pekanbaru namely Islamic University of Riau. The quantitative data were collected through a questionnaire and statistically analyzed by using SPSS program. Based on the reearch findings that there was the high association between Problem Solving Ability, Self Confidence, and student performed positive opinion in English speaking toward PBL Model. This conclusion is in line with educational policy to continually improve the quality especially for Economic students. For further researchers, this research suggests to conduct similar studies to develop PBL model in other program of education.
Improvement Increasing Students’ Senses of Reality and Effect in the Classes of Engineering Ethics

The awareness of the importance of the education of engineering ethics has been growing in recent years, and therefore it is expected to continuously improve the classes of engineering ethics. This study aims to explain the improvement increasing students’ senses of reality in the classes of engineering ethics in FY2018, and reveal the effect of the improvement. The improvement includes the addition of understandable examples, familiar contents and elements of aspirational ethics, and the communication with students utilizing class support system. Based on the result of questionnaire survey, it was reveled that students’ interest in engineering ethics and awareness of the usefulness of engineering ethics were increased in comparison with FY2017. Accordingly, it was understood that the improvements increasing students’ senses of reality in the classes of engineering ethics were effective.
Feature Ensemble Networks with Re-Ranking for Recognizing Disguised Faces in the Wild

Recognizing a person's face images with intentional/unintentional disguising effects such as make-up, plastic surgery, artificial wearables (hats, eye-glasses) is a challenging task. We propose a Feature EnsemBle Network (FEBNet) for recognizing Disguised Faces in the Wild (DFW). FEBNet encompasses multiple base networks (SE-ResNet50, Inception-ResNet-V1) pretrained on large-scale face recognition datasets (MS-Celeb-1M, VGGFace2) and fine-tuned on DFW training dataset. During the fine-tuning phase, we propose to use two novel objective functions, namely, 1) Category loss, 2) Impersonator Triplet loss along with two prevalent objective functions: Identity loss, Inter-person Triplet loss. To further improve the performance, we apply a state-of-the-art re-ranking strategy as a post-processing step. Extensive ablation studies and evaluation results show that FEBNet significantly outperforms the baseline models.
Method of Fraudster Fingerprint Formation During Mobile Application Installations

This study aimed to develop a method of fraudster fingerprint formation during mobile application installations, based on a fuzzy model for fraudster fingerprint formation and algorithms of its use for fraudster fingerprint formation. This method allows determining the reason of labeling user by a particular class during fraud detection. The use of the developed method in fraud detection tasks makes it possible to correctly identify 99.56% of users in general and 80.43% of correctly determined fraudsters in particular and speed up the fraud detection process.
Cyber Risks: Three Basic Structural Issues to Resolve

The incidence of cyber liability and cyber losses, collectively cyber risks, have increased greatly over the last several years. To add to the problem, cyber risks also expose insureds to statutory liability. The increasing number of incidents has given rise to an important question: “to what extent is liability for data breaches covered by a CGL or other sort of insurance policy?” Insurers have responded by including exclusions to mass data breaches in their CGL policies and offering separate plans (with high premiums) to cover such an event. However, insurers face a problem in drafting these policies because there is a lack of judicial information about how these policies will be interpreted by the courts. Without a thorough case history, insurers cannot confidently draft these policies to exclude (or price in) certain high-risk practices. In this vacuum, several aspects of cyber liability require resolution. A short list of issues will illuminate the problem. This paper addresses the three issues above with the aim of providing a framework for resolution.
Automatic Evaluation of MT Output and Post-edited MT Output for Genealogically Related Languages

The aim of the research is twofold: to evaluate the translation quality of the individual sentences of the MT output and also post-edited MT output on the basis of metrics of automatic MT evaluation from Slovak into the German language; and to compare the quality of MT output and post-edited MT output based on the same automatic metrics of MT evaluation. The icon graphs were used to visualize the results for individual sentences. A significant difference was found in sentence 36 in favor of the post-edited MT output and vice versa in sentence 5 in favor of MT output. Due to the error rate, a significant difference was in sentence 29 and 11 in favor of post-edited MT output and vice versa the sentence 26 in favor of MT output. Based on our results we can state that it is necessary to include into the evaluation of the quality of translation all automatic metrics for each sentence separately.