Miracle-dz/UniFAR-FTUs huggingface dataset:
Realtime Porn Image Censor Method for Preventing Smartphone Users to Take a Pornographic Photo

Nowadays, many pornographic photos are taken by using smartphones. These photos unfortunately can be shared instantly around the world by only using social media. In order to minimize the spread of these kind of photos, this paper proposes a method to prevent smartphones users to take pornographic photo. This paper proposes a built-in camera application that can detect the existence of pornographic aspect in a photo then it can automatically censor the pornographic part in real time. The main approach used in this paper is it recognizes the pornographic part of a photo by using pattern matching method, then it censors those parts automatically. If a smartphone user tries to take a pornographic photo, with this proposed system, then he/she will save a photo including the censors. The key feature of proposed method is its ability to censor only pornographic part of a photo automatically in real time. This proposed method is also implemented in common smartphone. It has been tested to practically run in low resource machine like a smartphone. This paper is designed to overcome the ambiguity of pornographic photo. For example, in some countries the existence of bra can be categorized as a pornographic part of a photo. Meanwhile in other countries, a pornographic part of a photo must contain nipples. The proposed method uses external files as pattern matching templates to find pornographic part in a photo. By using the external files, the proposed method can flexibly adjust its measurement of pornographic part in a photo according to the circumstance that it faces. For example, the proposed method uses different external file to identify pornographic part in a photo taken in West Countries and in East Countries. By using the proposed method, the spread of pornographic photo taken by smartphone can be decreased automatically and flexibly.
Coupling Reduction for a Wideband Circularly Polarized Conformal Array Antenna With a Single-Negative Structure

Decoupling of a wideband circularly polarized (CP) conformal array is studied by a single-negative (SNG) metamaterial (MTM) isolator. The conformal array consists of two wideband CP antennas, which are assembled on an arched carrier, and a wideband SNG structure is placed between the antenna elements for coupling reduction. The SNG structure consists of four wheel-shaped units concentrically. The SNG structure is bianisotropic, and its constitutive parameters are tailored to form a wide isolation band. By using the proposed wideband SNG structure, the mutual coupling between the conformal antenna elements is reduced up to 25 dB at 4.5 GHz, and the mutual coupling is improved in the 3.9–5.05 GHz band. In addition, the loading of the SNG structure has little effect on the axial-ratio bandwidth. Besides, the gain and radiation patterns are improved. This research provides some basis in the decoupling of the conformal array.
Partnership or insanity: why do health partnerships do the same thing over and over again and expect a different result?

Objectives The paper reports on an empirical study of Health and Wellbeing Boards (HWBs) in England. Established by the Health and Social Care Act 2012, HWBs act as place-based hubs for leaders in health, social care, local government and other sectors to come together to address health improvement and the wider determinants of health. Methods We conducted a three-year study of HWBs (2015–2017) in five localities across England. This involved collecting qualitative data from semi-structured interviews with key actors in the HWBs at strategic and operational levels, and focus group sessions with voluntary-sector participants at each HWB. Results HWBs have largely followed the path of previous partnerships in terms of a lack of clear aims and objectives, lack of ownership and accountability by partners, and an absence of any significant impact on health outcomes. Conclusions Many of the features of unsuccessful partnership working were largely displayed by HWBs. Boards require more executive power and ownership from the bottom up if they are to have any real impact.
Unsupervised query by example spoken term detection using features concatenated with Self-Organizing Map distances

In the task of the unsupervised query by example spoken term detection (QbE-STD), we concatenate the features extracted by a Self-Organizing Map (SOM) and features learned by an unsupervised GMM based model at the feature level to enhance the performance. More specifically, The SOM features are represented by the distances between the current feature vector and the weight vectors of SOM neurons learned in an unsupervised manner. After fetching these features, we apply sub-sequence Dynamic Time Warping (S-DTW) to detect the occurrences of keywords in the test data. We evaluate the performance of these features on the TIMIT English database. After concatenating the SOM features and the GMM based features together, we achieve an improvement of 7.77% and 7.74% on Mean Average Precision (MAP) and P@10 on average.
DNN Regression Model and Microphone Array for Noise Reduction System on Real Field

In this study, we proposed a regression model based on the deep neural network (DNN) and a microphone array for noise reduction system on a supermarket robot. The proposed speech enhancement model can be separated into two parts: the training phase and the enhancement phase. A large amount of data is used for DNN model to train the mapping function, and to separate the estimated features of clean speech signal according to the features of noisy speech signal. In addition, convolutional recurrent neural networks are added to enhance the output performance of the original DNN model. The main objective is to take advantage of convolutional networks in feature extraction and to use recurrent networks to have the ability to process temporal models. This proposed system is used to overcome the noise interference in real life. The system can achieve the better speech recognition rate and provide correct sentences for the backend dialogue system. The microphone array is used for collecting audio, which not only to reduce the noise at the front end, but also to enhance the collected audio distance of the system.
Characteristics of the Co-Morbidity of Irritable Bowel Syndrome: A Secondary Analysis of Existing Twin Data

Mary K. Wojczynski: Characteristics of the Co-Morbidity of Irritable Bowel Syndrome: A Secondary Analysis of Existing Twin Data (Under the direction of Kari E. North, Ph.D.) Irritable bowel syndrome (IBS) is a chronic disorder whose manifestations typically fluctuate over time. Prior epidemiologic studies estimate the one-year prevalence as 7-20%, depending on criteria used to define IBS. Individuals with IBS demonstrate a high co-occurrence with common functional somatic syndromes and psychiatric disorders; however, the majority of these associations derive from non-population-based studies. We examined associations between IBS risk factors and Rome II-defined IBS in a U.S. population-based twin registry. Data from 4,591 male and female twins were available for this analysis. Variables representing self-reported presence of IBS, major depressive disorder (MDD), chronic widespread pain (CWP), fatiguing illness (CFS-like illness), Medical Outcomes Study short form (SF-12) scores and other personal characteristics were obtained through questionnaire. Odds ratios (OR) and 95% confidence intervals (CI) were
ORAL MANIFESTATION ON SYSTEMIC LUPUS ERYTHEMATOSUS PATIENTS

Background: Periodontitis worldwide reported increasing 57.3% between 20 years also reported 6 th most prevalence disease around the world. Imune response abnormalities, hyperactivity of production of autoantibodies deposited in human tissue and organ could affect oral cavity condition. Objectives: To find oral manifestation on SLE patients and correlate with SLE severity. Methods: Subjects were 61 patients with SLE (age 17-51 years; diagnosed using SLICC) collected from Dr. Saiful Anwar General Hospital, Malang Indonesia. Oral Manidestation is measured by clinical examination and SLE severity measured using SLEDAI. Result: A total 61 SLE subjects were included in this study. We found that 54 patients (88,53%) subjects with SLE had periodontitis. 7 subject had no periodontitis, 11 mild periodontitis, 43 severe periodontitis. There is correlation between oral condition and SLE severity. Periodontitis and SLEDAI score showed significant (p<0.0001) and strong positive correlation (r=0.948) Discussion : Our study found high rates of gingivitis, periodontitis, bop, low plaque index, and low calculus index. SLE is chronic autoimmune disease develop autoantibodies and immune complexes, because of immune respon abnormalities.It could be forming autoantibodies cause DNA damage, lipid peroxidation, protein. This condition induce collagen breakdown, RANKL, osteoclast stimulation until alveolar bone resorption resulting poor oral condition and periodontitis. Conclusion: Our study showed that oral condition were associated with SLE disease activity Keywords: Oral Manifestation , Periodontitis; SLEDAI; SLE
Disordered Speech Assessment Using Kullback-Leibler Divergence Features with Multi-Task Acoustic Modeling

For acoustical assessment of pathological speech, naturally spoken sentences are believed to be most suitable from the perspectives of both patients and clinicians. This is a challenging problem, as the extraction of pathology-dependent features is not straightforward. Previous research showed that features derived from lattice posteriors and decoding results of automatic speech recognition (ASR) could be used to quantifying various types of speech impairments. This paper describes a novel feature that can be derived from phone posterior probabilities generated by an ASR system. The Kullback-Leibler (KL) divergence is used to measure the phone-level distortion between unimpaired and impaired speakers. A Cantonese ASR system is trained with a combination of normal and impaired speech corpora. The multi-task learning approach is applied in order to incorporate different speech characteristics. Experimental results show that the proposed KL divergence feature is effective in the continuous speech based assessment of different pathologies, including voice disorder and post-stroke aphasia. The KL divergence feature is found to outperform conventional acoustic features and supra-segmental duration features, and is complementary to text features in quantifying language impairment. Index Terms: disordered speech assessment, voice disorders, aphasia, continuous speech, KL divergence, ASR, multi-task learning
Brightness vs. Lightness Enhancement for Image Segmentation

Videos which are taken under low light environment usually contain lots of noise with grainy look. The low light situation likewise yields under-saturated and low contrast video footages. The frames of this kind video footage, if further processed, will be the type of input that are very challenging to deal with. The aim of this work was to choose the best feature candidate, either brightness or lightness, for improving the frames quality which are then fed to the segmentation algorithm. A random residence and a campus parking lot area are the places where the video footages of this research are taken. The first step after gathering the videos is to extract the frames and converting it to 2D images, where the brightness and lightness features of the images are then solely fed into the CLAHE enhancement algorithm. The enhanced images are then fed to the chosen segmentation algorithm, called the Active Contour. From the experiments, it is proven that the enhancement based on the lightness feature outperformed the brightness based enhancement system which is proven by the better segmentation results. Moreover, from the appearance, the output of the lightness based enhancement system looks softer and the resulting image contains less artifacts.
Overview And Preliminary Results For Opinion Mining Using Fuzzy String Searching

One of the important types of information on the Web is the opinions expressed in the user generated content, e.g., customer reviews of products, forum posts, and blogs. In this paper, we focus on customer reviews of products. In particular, we study the problem of determining the semantic orientations (positive, negative or neutral) of opinions expressed on product features in reviews. This problem has many applications, e.g., opinion mining, summarization and search. Here we utilize a list of opinion words(also called opinion lexicon) for the purpose. Opinion words are words that express desirable (e.g., great, amazing, etc.) or undesirable (e.g., bad, poor, etc.) states. Sometime customers writes the wrong spellings for the product property so we solve this problem here by using fuzzy string searching. Approximate string matching (often colloquially referred to as fuzzy string searching) is the technique of finding strings that match a pattern approximately (rather than exactly).
DTreeSim: A new approach to compute decision tree similarity using re-mining

A number of recent studies have used a decision tree approach as a data mining technique; some of them needed to evaluate the similarity of decision trees to compare the knowledge reflected in different trees or datasets. There have been multiple perspectives and multiple calculation techniques to measure the similarity of two decision trees, such as using a simple formula or an entropy measure. The main objective of this study is to compute the similarity of decision trees using data mining techniques. This study proposes DTreeSim, a new approach that applies multiple data mining techniques (classification, sequential pattern mining, and k-nearest neighbors) sequentially to identify similarities among decision trees. After the construction of decision trees from different data marts using a classification algorithm, sequential pattern mining was applied to the decision trees to obtain rules, and then the k-nearest neighbor algorithm was performed on these rules to compute similarities using two novel measures: general similarity and pieced similarity. Our experimental studies compared the results of these novel similarity measures and also compared our approach with existing approaches. Our comparisons indicate that our proposed approach performs better than existing approaches, because it takes into account the values of the branches in the trees through sequential pattern mining.
Resource Reservation and Request Routing for a Cloud-Based Content Delivery Network

Content delivery networks (CDNs) built on clouds constitute a promising content-distribution-as-a-service alternative. Exploiting the advantages of the cloud, such as the pay-as-you-go business model and geographical dispersion of resources, a cloud-based CDN can provide a flexible and cost-effective solution to realize content delivery without investments on installing and maintaining the infrastructures. However, resource reservation and request routing are critical issues to be addressed in such systems due to the geographical diversity of cloud resource prices and the dynamic nature of traffic demands. In this paper, we investigate the optimal resource reservation problem of cloud resources and explore the best trade-off between rental cost and user experience, and formulate the problem and develop a heuristic approach for it. To deal with fluctuating user demands, we further investigate the request routing optimization problem and design an online heuristic algorithm to redirect user requests. The simulation experiments demonstrate the effectiveness of our algorithms.
Comparison of FP-Growth and ID3 by Creating Feature Model for Software Product Line

—Today, software product line engineering (SPLE) is an emerging software engineering paradigm. SPLE is based on the concept of reusing software artifacts gaining from the previous software product lifecycle. For SPLE, many researchers concerns with domain analysis and feature modeling (FM). In this system, the feature model management process is proposed for SPLE about mobile phone environment. To create feature model, there are two processes. The first is feature extraction process that is extracted feature from the tested mobile environment. For feature extraction process, this system uses iterative dichotomiser-3 (ID-3) and frequent pattern growth (FP-growth) algorithms. Moreover, this system compares these two feature extraction algorithms to know which algorithm is more effective for feature extraction process. By using extracted features, this system uses the propositional formula to create feature model for SPLE. This system is tested by using different HTC mobile versions. According to the performance comparison results, FP-growth is faster than ID-3 about feature extraction process.
Characterization of circular array current transducers

This paper is evaluating different aspects affecting the performance of circular arrays of magnetic sensors used for contactless current transducer. Some of these error mechanisms are quantified using analytical expressions to provide designers with theoretical upper limits of achievable accuracy. Novel method of misalignment compensation using multi-axial sensors is presented. Measurements were performed with different sensor arrays to verify theoretical assumptions and determine limiting factors for real-world application of yokeless transducers.
Impact of Body Fat Percent on Heart Rate of Moderate-Intensity Aerobic Activity

PURPOSE: To better understand the relationship between body fat percent (%BF) and aerobic capacity, this study examined how %BF would impact heart rate (HR), maximal HR% (%HRmax) and HR reserve% (%HRR) when walking at 3.0 mph among young adults. METHODS: The three-site skinfold measure was administered to 176 university students (mean age: 20.82±1.49; 102 males and 74 females) in the US and converted to %BF using the conversion tables by Jackson et al. (1985). The ACSM satisfactory ranges of %BF (2014, 10%-22%BF for men and 20%-32%BF for women) were used to divide participants into three %BF groups: Normal, Lean, and Obese. HRmax was calculated with “220-age”, resting HR (after lying on the floor for five minutes) was measured using HR monitors (Sigma PC26.14), and HRR was calculated with “HRmax-resting HR”. Finally, HR at the end of a three-minute treadmill walking at 3.0 mph was measured, which was also used to compute %HRmax (HR ÷ HRmax × 100%) and %HRR [(HR Resting HR) ÷ HRR × 100%] of the walking. One-way MANOVA was used to examine differences in HR, %HRmax, and %HRR of the three-minute walking among the three %BF groups. RESULTS: There were 90 participants in Normal, 64 in Lean, and 22 in Obese group. No age difference (p > .70) was found among the three %BF groups (Normal 20.71±.90, Lean 20.94±2.79, Obese 20.95±1.50). However, significant differences (p values ranged from .000 to .003) were observed in HR, %HRmax, and %HRR for the three-minute walking at 3.0 mph among the three %BF groups. Specifically, significant differences were identified in HR in all the three pairwise comparisons: Lean (95.56±10.27) vs. Obese (112.14±9.82), Lean vs. Normal (104.58±11.24), and Normal vs. Obese; in %HRmax in all the three pairwise comparisons: Lean (48.03±5.29) vs. Obese (56.34±4.91), Lean vs. Normal (52.48±5.64), and Normal vs. Obese; and in %HRR between Lean (22.89±7.35) and Obese (30.22±6.07) and between Lean and Normal (26.85±6.99). CONCLUSIONS: The %BF classified with ACSM %BF ranges has significant impact on HR among young adults when walking at 3.0 mph, a moderate-intensity (3.5-MET) activity. Specifically, when walking at 3.0 mph, lean individuals demonstrate significantly lower HR, %HRmax, and %HRR than normal and obese individuals; and normal individuals show significantly lower HR and %HRmax than obese individuals.
A multi-tenant database framework for software and cloud computing applications

Cloud Computing is a new computing paradigm that transforms accessing computing resources from internal data centres to external service providers. This approach is rapidly becoming a standard for offering cost effective and elastic computing services that are used over the internet. Software as a service (SaaS) is one of the Cloud Computing service models that exploits economies of scale for SaaS service providers by offering the same software and computing environment for multiple tenants. This contemporary multi-tenant service requires a multi-tenant database design that can accommodate data for multiple tenants in one single database schema. Due to multi-tenant database resource sharing in this service, the multi-tenant schema should be highly secured, optimized, configurable, and extendable during runtime execution to fulfil the applications’ requirements of different tenants. However, traditional Relational Database Management Systems (RDBMS) do not support such multi-tenant database schema capabilities, and it is a significant challenge to enable RDBMS to support these capabilities. Therefore, one solution is using an intermediate software layer that mediates multi-tenant applications and RDBMS, to convert multi-tenant queries into regular database queries, and to execute them in a RDBMS. Developing such a multi-tenant software layer to manage and access tenants’ data is a hard and complex problem to solve and has significant complexities that involve longer development lifecycle. There are two main contributions of this thesis. Firstly, a proposal for a novel multi-tenant schema technique called Elastic Extension Tables (EET). Secondly, a proposal for a multi-tenant database framework prototype to implement EET schema in a RDBMS. This approach can be used to develop a software layer that mediates software applications and a RDBMS. This software layer aims to facilitate the development of software applications, and multi-tenant SaaS and Big Data applications for both cloud service providers and their tenants. Extensive experiments were conducted to evaluate the feasibility and effectiveness of EET multi-tenant database schema by comparing it with Universal Table Schema Mapping (UTSM), which is commercially used. Significant performance improvements obtained using EET when compared to UTSM, makes the EET schema a good candidate for implementing multi-tenant databases and multi-tenant applications. Furthermore, the prototype of the EET framework was developed, and several experiments were performed to verify the practicability and the effectiveness of using this framework that based on EET multi-tenant database schema. The results of the experiments indicate that the EET framework is suitable for the development of software applications in general, and multi-tenant SaaS and Big Data applications in particular.
Conceptual Model of the Smart Attendance Monitoring System Using Computer Vision

The monitoring of student attendance remains a fundamental and vital part of any educational institution. Although some automated biometric-based attendance monitoring systems have been proposed in the literature, poor system modeling has negatively affected their performance. There is, hence, a need to address the various needs associated with the development of an AI-based attendance monitoring system. This paper proposes the conceptual model for a smart attendance monitoring system that uses face recognition to monitor students’ attendance during lectures. The front and back-end system architectures, as well as a full view multi-camera structure aimed at effectively capturing and detecting of faces, are presented. Thee different feature extraction techniques - PCA, LDA, LBP and a combination of PCA and LDA were investigated. The model achieved a recognition accuracy of 90%.