Reviewerly/exHarmony huggingface dataset:
Lossless JPEG-Huffman Model for Digital Image Compression

The transmission of digital images has been bedevilled with limitation of storage and bandwidth capacities. One of the common strategies to resolving this limitation is to perform pre-transmission compression on the images. In this research, a lossless Joint Photography Expert Group (JPEG) and Huffman algorithms-based model for digital image compression is proposed. The lossless JPEG component of the model was used to perform Differential Pulse Coding Modulation (DPCM) on the pixels while adaptive Huffman coding was used for quality improvement and standardization. The implementation was carried out in an environment characterized by Windows 10 with Visual Basic as frontend on Personal Computer with 4 GB RAM, 500 GB ROM and 2.2 Ghz Core i3 Processor. The experimental images used for testing the algorithms were acquired from Signal and Image Processing Institute in the University of Southern California (USC-SIPI). Camera (Nikon D7000) and Geographical Information System (GIS) images were also used. Quantitative analyses of the experimental results and performance evaluation using Compression Ratio (CR), Bits per pixel (Bpp), Maximum Difference (MD), Mean Square Error (MSE), Root Mean Square Error (RMSE), Peak Signal Noise Ratio (PSNR), Average Difference (AD) and Structural Content (SC) were carried out. The analyses showed good compression rates and ratios for the proposed model. The superiority of the integrated model over some existing and related ones is also established.
FinSeg: Finger Parts Semantic Segmentation using Multi-scale Feature Maps Aggregation of FCN

Image semantic segmentation is in the center of interest for computer vision researchers.Indeed, huge number of applications requires efficient segmentation performance, such as activity recognition, navigation, and human body parsing, etc.One of the important applications is gesture recognition that is the ability to understanding human hand gestures by detecting and counting finger parts in a video stream or in still images.Thus, accurate finger parts segmentation yields more accurate gesture recognition.Consequently, in this paper, we highlight two contributions as follows: First, we propose data-driven deep learning pooling policy based on multi-scale feature maps extraction at different scales (called FinSeg).A novel aggregation layer is introduced in this model, in which the features maps generated at each scale is weighted using a fully connected layer.Second, with the lack of realistic labeled finger parts datasets, we propose a labeled dataset for finger parts segmentation (FingerParts dataset).To the best of our knowledge, the proposed dataset is the first attempt to build a realistic dataset for finger parts semantic segmentation.The experimental results show that the proposed model yields an improvement of 5% compared to the standard FCN network.
Colorectal tumor identification by transferring knowledge from pan-cytokeratin to H&E

Tumor budding is a recently recognized, independent prognostic factor in colorectal cancer, but lacks a standardized assessment methodology. Although staining with pan-cytokeratin has been shown to mitigate the issue of lack of reproducible, intra-observer agreement, usage of this antibody remains expensive and is limited in clinical practice. We propose an automated image analysis framework to take advantage of the visual superiority of pan-cytokeratin and the routine use of H&E to detect and quantify tumor budding. Our framework has demonstrated promising ability to identify tumor regions of colorectal slides – 92.0% accuracy, 94.5% sensitivity, and 85% specificity – across four independent datasets.
Data Communication Security Based on Novel Reversible Mean Shift Steganography Algorithm

With the popularity of node data to the sink-node in wireless sensor networks (WSNs), it is vital to protect the privacy of data and enable the sink-node to easily manage the data at the same time. Under such demands, reversible data steganography in encrypted images attracts more and more researchers' attention. In this paper, we propose a novel reversible mean shift steganography algorithm for improving the security of data communication in WSNs. The algorithm first calculates the l, α, and β sub-components after removing the l component of the original image. At the same time, we use mean shift clustering algorithm to calculate the 3-dimensional regional maximum points based on l, α, and β components and mark them. Finally, the secret information is encoded and hidden in the corresponding extreme points of the Bit plane 0 of the sub-components, and then scrambled with the key. The extensive experimental results show that the proposed algorithm has little influence on the original image, and the histogram is almost the same, and the average error bit ratio (EBR) is only 0.164%.
Consumer responses to resource patch size and architecture: leaf packs in streams

Accumulations of leaf litter in freshwaters can vary from individual leaves to large leaf packs.Past studies have demonstrated that decomposition rates decrease as leaf pack size increases.We considered a set of hypotheses that lower breakdown in larger leaf packs occurs as a consequence of diffusion gradients of oxygen and nutrients, or lower accessibility to larger detritivores to leaf tissue in the middle of these leaf packs.We manipulated leaf pack size in a stream to quantify decomposition rates and the abundances of consumers relative to the amount of detrital mass available.Mass loss rates of beech leaves were lower as leaf pack size increased.We found no differences in fungal biomass across our treatment gradients, or when comparing leaves in the middle of the leaf pack versus those on the outside.The lower decomposition of larger leaf packs would thus not result from a lower quality of the resource based on fungal biomass.Invertebrates per unit mass of leaf packs declined exponentially with size of leaf pack.Smaller invertebrates were less abundant per unit of resource as leaf pack size increased, but abundances of larger invertebrates declined even more dramatically than that of smaller ones.The results are consistent with accessibility within leaf packs decreasing as leaf pack size increases, a factor that is important for the estimation of consumer-resource functions for this patchy resource.
PERSEPSI MAHASISWA TERHADAP PELAYANAN PERPUSTAKAAN UNIVERSITAS TRUNOJOYO MADURA

College Library is one of the main supporting units in the effort to realize the vision and mission of a higher education activity. Based on these conditions, the role of the library is very dominant in order to foster insight into students as prospective graduates and competency of lecturers in order to carry out the Tri Dharma of Higher Education since the library is the heart of Higher Education. This research has described that, students' perception of Trunojoyo Madura University library services includes aspects of service personnel, aspects of facilities and infrastructure (completeness of library material collections, buildings / spaces, other supporting facilities), social conditions of the library's social environment (visitor interaction with library staff and between library visitors) and the security and comfort of the environment is "good". While the factors that influence the low quantity and quality of student visits to the Trunojoyo Madura University Library are in principle more caused by factors of lack of interest in reading students, the existence of other libraries scattered in each regional faculty and library, the availability of reading materials in bookstores , there are no regulations that require students to visit (academic prerequisites). Based on these conditions it is expected that in the future the Trunojoyo Madura University Library can become an international library 'through serious attention in better development efforts so that the vision and mission of the Trunojoyo Madura University can truly be realized.
Hand orientation redundancy filter applied to hand-shapes dataset

We have created a dataset of frames extracted from videos of Irish Sign Language (ISL) for sign language recognition. The dataset was collected by recording human subjects executing ISL hand-shapes and movements. Frames were extracted from the videos producing a total of 52,688 images for the 23 static common hand-shapes. Given that some of the frames were relativity similar we designed a new method for removing redundant frames based on labelling the hand images by using axis of least inertia - Hand Orientation Redundancy Filter (HORF) - and we compare the results with an iterative method - Iterative Redundancy Filter (IRF). This selection process method selects the most different images in order to keep the dataset diverse. The IRF dataset contains 50,000 images whereas the HORF consists of 27,683 images. Finally, we tested two classifiers over the HORF dataset and compared the results with the IRF dataset.
What happens when the assumptions underlying our commitment to free speech no longer hold?

A commitment to freedom of expression means that an individual must be free to speak to others and to hear what others may say, without interference from the state. It is said that the answer to bad or erroneous speech is not censorship, but rather more and better speech. Importantly the listener, and not the speaker, is seen as responsible (as an independent agent) for his or her actions, including harmful actions, whether these actions occur because he or she agrees or disagrees with the speaker’s message.
End-to-End Product Taxonomy Extension from Text Reviews

Product ontologies - consisting of a taxonomic categorization of product types and lists of attributes that types and products have - are invaluable for analyzing sales, opinions and ratings of items on e-commerce sites. Unfortunately, many international and smaller sites lack such ontologies, and instead feature only coarse high-level categories. We present a Siamese neural model which utilizes such coarse categories to learn a fine-grained hierarchical categorization of products, and jointly extract lists of product attributes from text reviews. We show that our model retains a high accuracy on the categorization task for unseen products and unseen category depths, and as a side effect learns to extract useful product attributes.
Deep Learning Machine using Hierarchical Cluster Features

Deep learning of multi-layer computational models allowed processing to recognize data representation at multiple levels of abstraction. These techniques have greatly improved the latest ear recognition technology. PNN is a type of radiative basis for classification problems and is based on the Bayes decision-making base, which reduces the expected error of classification. In this paper, strong features of images are used to give a good result, therefore, SIFT method using these features after adding improvements and developments. This method was one of the powerful algorithms in matching that needed to find energy pixels. This method gives stronger feature on features and gives a large number of a strong pixel, which is considered a center and neglected the remainder of it in our work. &#x0D; Each pixel of which is constant for image translation, scaling, rotation, and embedded lighting changes in lighting or 3D projection. Therefore, the interpretation is developed by using a hierarchical cluster method; to assign a set of properties (find the approximation between pixels) were classified into one.
An Analysis and Speedup of the FALDOI Method for Optical Flow Estimation

We present a detailed analysis of FALDOI, a large displacement optical flow method proposed by P. Palomares et al.This method requires a set of discrete matches, which can be extremely sparse, and an energy functional which locally guides the interpolation from the matches.It follows a two-step minimization method at the finest scale which is very robust to the outliers of the sparse matcher and can capture large displacements of small objects.The results shown in the original paper consistently outperformed the coarse-to-fine approaches and achieved good qualitative and quantitative performance on the standard optical flow benchmarks.In this paper we revise the proposed method and the changes done to significantly reduce its execution time while reporting nearly the same accuracy.Finally, we also compare it against the current state-of-the-art to assess its performance.
App for session monitoring and subjective quality assessment of 2D and 360VR video in real time

We present an app to both monitor the session activity and conduct subjective quality tests of both 2D and 360VR content transmitted through adaptive bitrate streaming techniques in real time in an unsupervised, highly-configurable, minimum-intrusive, integrated fashion. Users carry out the tests within the app environment, thus fostering immersiveness and engagement. Furthermore, monitoring and test results are collected and stored in a cloud database for subsequent analysis.
Implementasi Steganografi LSB Dengan Enkripsi Vigenere Cipher Pada Citra

Message encoding is an art and science to maintain news or data, learn mathematical techniques related to aspects of information security such as data confidentiality, data validity, data integrity, and data authentication. The process of encoding and inserting messages with the Vigenere Cipher and LSB methods is the first step in inputting text and keyword files, then text files and keywords are converted into decimal numbers, then processed according to the Vigenere Cipher formula, the ciphertext is then transformed into binary form it is inserted into the image by means of each bit of the ciphertext in the last bit of the image, so it is expected that the data sent is not easy to read by third parties. The vigenere cipher algorithm and the LSB (Last Significant Bit) technique can be used as a solution for the security of secret messages that are inserted into the image, can be re-revealed exactly the same as the original form and do not experience the slightest damage.
Estimation and Analysis of the Output Gap for the Saudi Economy; Econometric Study (1970-2016)

This study highlights the important role plays by the output gap as guidance to the policymakers and macroeconomic decisions. There are two aims for this paper: firstly, it measures and estimates the output gap and secondly, it identifies and analyses the determinants of the economic output gap for the Saudi Arabian economy over the period 1970-2017. This paper uses the HP filter and the new form of production function methods to measure the output gap as the production function method gives more accurate results in calculating the output gap by basing the GDP gap on the sum of production factors gaps. The Autoregressive Distributed Lag (ARDL) cointegration approach and bounds test was applied to determine the factors responsible for this output gap and the Error Correction Model indicated the convergence towards long-run equilibrium. The findings showed the existence of a positive and negative cointegration relationship in the long run between the output gap and its estimated determinants whereby the public sector investment, import expenditure, and higher secondary enrollment have a positive relationship, while the money supply and export earnings have a negative relationship.
J’ai l’impression que: Lexical Bundles in the Dialogues of Beginner French Textbooks

Formulaic language is notoriously difficult for second language learners of French to master (Edmonds, 2014; Forsberg, 2010). Yet, no study has examined formulaic language in French textbooks despite the fact that in many contexts, textbooks represent a significant proportion of the input that learners receive. The current study addresses this gap. Using a distributional approach (as used in Biber, Conrad, &amp;amp; Cortes, 2004), four-word lexical bundles were extracted from an oral corpus of French. The average number of lexical bundles in oral corpus utterances was compared to the average number of bundles in a corpus of A1-B1 level textbook dialogues. An independent samples t test showed that the average number of lexical bundles per 100,000 words was significantly higher in texts from the oral corpus than the textbook corpus. The average number of stance and referential lexical bundles was also revealed to be higher in the oral corpus. Implications for textbook design are discussed, such as increasing the amount of formulaic language in A2 level textbooks and incorporating more authentic language into textbooks.
A MODIFIED ROUND ROBIN CPU SCHEDULING ALGORITHM WITH DYNAMIC TIME QUANTUM.

CPU scheduling is one of the basic factors for performance measure of multitasking operating system which makes a commuter system more productive by switching the CPU among the processes.The performance of the CPU scheduling algorithms depends on minimizing waiting time, response time, turnaround time and context switching, and maximizing CPU utilization.Round Robin (RR) is the most widely used CPU scheduling algorithm in multitasking operating system.The efficiency of a multitasking systemcomprising with Round Robin CPU scheduling relies onthe selection of the optimal time quantum.If the time quantum is longer, the response time of the processes becomes too high.On the other hand, the shorter time quantum raises the amount of context switch among the processes.In this paper, a modified CPU scheduling algorithm, called Round Robin with Dynamic Time Quantum (RRDTQ) is introduced for enhancing CPU performance using dynamic time quantum with RR.This time quantum is calculated from the burst time of the set of waiting processes in the ready queue.The experimental results show that the proposed algorithm solves the fixed time quantum problem and decreases the average waiting time and turnaround time compared to traditional RR algorithm.
Visual Attention Fusion Framework for Image Retargeting Quality Assessment

To adapt images for diversified digital devices, researchers have presented many image retargeting methods. However, the consistency between results of objective image retargeting quality assessment (IRQA) metrics and subjective perception is still low. In this paper, we propose a visual attention fusion (VAF) framework to assist IRQA metrics in better understanding the features of images such as image saliency, faces, and lines. First, we combine the results of multiple salient object detection algorithms to reduce the limitations of a single algorithm. Second, faces and lines are considered in our framework to measure deformations to these visually sensitive regions. Finally, we propose a saliency enhancement model to simulate human visual attention for IRQA. We combine the proposed VAF framework with some state-of-the-art IRQA metrics. Experimental results show that the proposed VAF framework can improve the consistency between the results of objective IRQA metrics and subjective opinion scores.
Shared visualizations and guided procedure simulation in augmented reality with Microsoft HoloLens

Background: Current display technologies can be suboptimal, even inadequate, when conveying spatially-complex healthcare concepts to patients and providers. This can lead to difficulties in sharing medical information, with potentially deleterious effects on patient outcomes, research, and trainee education. Methods: Investigators used off-the-shelf augmented reality (AR) technologies to facilitate visual communication for healthcare. Using untethered headset devices (Microsoft HoloLens), proof-of-concept work was completed for two use-cases: 1.) multi-user shared AR visualizations and 2.) AR-guided invasive procedural performance. The research team collaborated to create: 1.) a shared AR environment that enabled multiple users to independently visualize and manipulate 3D patient anatomic models with position, rotation, and scale synchronized across users; and 2.) a hybrid [AR-physical] covered box configuration containing CT-scanned targets and custom trajectory guidance system for simulated needle aspiration. As a pilot study exploring technical feasibility and experimental viability of the selected approach, measurements of 1.) size, displacement, angular rotation, and 2.) expert aspiration success were used as primary metrics. Results: The mean difference between AR models and physical objects was 2.0&plusmn;0.4% and 1.7&plusmn;0.4% of all dimensions on two HoloLens devices. One shared model configuration exhibited deviations of 7.8&plusmn;2.8mm in location and 0.5&plusmn;0.9&deg; in angular orientation, and another showed differences of 6.5&plusmn;2.1mm and 0.1&plusmn;0.7&deg; . For AR-guided procedure simulations, two expert surgeons required 3 attempts in 10 minutes and 1 attempt in 3 minutes to successfully aspirate the hybrid targets. Conclusion: AR technologies were used to enable core elements of an interactive shared medical visualization environment and a guided procedure simulation.
Modulation Classification Using a Goodness of Fit Test

In this paper, we propose an innovative approach using Anderson-Darling (A-D) test for signal modulation classification with less signal samples in AWGN channels. The A-D test is a non-parametric approach to measure the goodness of fit test. It is based on Cramer-Von Mises (CVM) test, using the mean square integral of the difference between the empirical cumulative distribution functions (ECDFs) from received signals and cumulative distribution functions (CDFs) of the signal under different candidate modulation format. In order to avoid the default of CVM, such as it is less sensitive at heads and tails of the distributions, more weights are given to heads and tails in A-D test. Massive simulation results show that compared with the Kolmogorov-Smirnov (K-S) classifiers and the traditional high-order cumulant-based classifiers, the A-D classifiers show better classification performance at different SNRs with less signal samples for M-QAM and M-PSK modulations in AWGN channels.
A Multilayered Approach to Automatic Music Generation and Expressive Performance

When analyzing scores, musicologists often use multilayered representations to describe different importance levels of notes and chords, according to hierarchical musical structures. These structures are believed to represent the composer's mental representation as well as the listeners' perception of the piece. Thus, in the context of automated music generation, this kind of information can be of great use to model both the composition itself and its expressive performance. In this paper one computational method to perform this kind of analysis is described. Its implementation is then used to generate short musical phrases according to a hierarchical structure that is also used to model the performance of these melodies.