Reviewerly/exHarmony huggingface dataset:
Efficient Acceleration of CNNs for Semantic Segmentation on FPGAs

We present a Vector Processing Engine (VPE) designed for the acceleration of Convolutional Neural Networks (CNNs) for semantic segmentation. Most CNN accelerators focus on classification. However, CNNs for semantic segmentation incorporate special layer types. Our accelerator supports not only regular convolutional layers, but also dilated convolutions and convolutions in combination with down- or up-sampling. These features are implemented in dedicated address generators which load the corresponding input vector from an input line buffer. The VPE is designed as a 64x64-array where up to 64 output features and 64 input features of a convolutional layer can be unrolled in parallel. The array has a peak performance of 4.12 TOp/s and achieves 3.85 TOp/s on a CNN for semantic segmentation - resulting in an average utilization of 93 %. The design is prototypically implemented on a Virtex UltraScale+ device with a clock rate of 250 MHz. In addition to the overall architecture, we present the two-hot quantization scheme. A value in two-hot quantization can be regarded as a combination of two power-of-two values. Hence, instead of bulky multipliers, two small bit-shifts are implemented. We design and implement dedicated arithmetic engines for this quantization scheme. Additionally, we evaluate this quantization scheme on the rather complex task of semantic segmentation. We show that the performance of an 8 bit two-hot quantization scheme is marginally lower in comparison to a regular 8 bit fixed-point variant.
Faster, Smaller, and Simpler Model for Multiple Facial Attributes Transformation

There are many existing models that are capable of changing hair color or changing facial expressions. These models are typically implemented as deep neural networks that require a large number of computations in order to perform the transformations. This is why it is challenging to deploy on a mobile platform. The usual setup requires an internet connection, where the processing can be done on a server. However, this limits the application's accessibility and diminishes the user experience for consumers with low internet bandwidth. In this paper, we develop a model that can simultaneously transform multiple facial attributes with lower memory footprint and fewer number of computations, making it easier to be processed on a mobile phone. Moreover, our encoder-decoder design allows us to encode an image only once and transform multiple times, making it faster as compared to the previous methods where the whole image has to be processed repeatedly for every attribute transformation. We show in our experiments that our results are comparable to the state-of-the-art models but with $4\times $ fewer parameters and $3\times $ faster execution time.
Explicit and Implicit Measures in Video Quality Assessment

This work investigates the relation between subjective Video Quality Assessment (VQA) metrics and psychophysiological measures of human interaction assessment such as gaze tracking, electroencephalography and facial expression recognition. Subjective quality assessment is based on deliberate judgement attributions of perceived quality and processes that human perceivers are not consciously aware of. Traditional VQA methods ask participants to deliberately assign a quality score to videos in terms of the perceptual video quality. A methodology combining psychophysiological measures with traditional VQA methods is rarely used in the literature. This paper describes a model of video quality assessment which takes into account both explicit and implicit measures of subjective quality, by addressing two questions: (1) Do traditional video quality assessment methods correlate with unaware/implicit psychophysiological measures of quality perception assessment? (2) What can the main psychophysiological methods add to traditional video quality assessment? Findings show that (1) psychophysiological measures are able to measure differences of perceptual quality in compressed videos in terms of number of fixations and that (2) both VQA methods and psychophysiological assessment methods combined are able to provide additional information about cognitive and affective processes of attribution of the affective factors that underlie the attribution of quality.
Understanding of Non-linear Parametric Regression and Classification Models: A Taylor Series based Approach

Machine learning methods like classification and regression models are specific solutions for pattern recognition problems. Subsequently, the patterns ’found’ by these methods can be used either in an exploration manner or the model converts the patterns into discriminative values or regression predictions. In both application scenarios it is important to visualize the data-basis of the model, because this unravels the patterns. In case of linear classifiers or linear regression models the task is straight forward, because the model is characterized by a vector which acts as variable weighting and can be visualized. For non-linear models the visualization task is not solved yet and therefore these models act as ’black box’ systems. In this contribution we present a framework, which approximates a given trained parametric model (either classification or regression model) by a series of polynomial models derived from a Taylor expansion of the original non-linear model’s output function. These polynomial models can be visualized until the second order and subsequently interpreted. This visualization opens the ways to understand the data basis of a trained non-linear model and it allows estimating the degree of its non-linearity. By doing so the framework helps to understand non-linear models used for pattern recognition tasks and unravel patterns these methods were using for their predictions.
Template based Human Pose and Shape Estimation from a Single RGB-D Image

Estimating the 3D model of the human body is needed for many applications. However, this is a challenging problem since the human body inherently has a high complexity due to self-occlusions and articulation. We present a method to reconstruct the 3D human body model from a single RGB-D image. 2D joint points are firstly predicted by a CNN-based model called convolutional pose machine, and the 3D joint points are calculated using the depth image. Then, we propose to utilize both 2D and 3D joint points, which provide more information, to fit a parametric body model (SMPL). This is implemented through minimizing an objective function, which measures the difference of the joint points between the observed model and the parametric model. The pose and shape parameters of the body are obtained through optimization and the final 3D model is estimated. The experiments on synthetic data and real data demonstrate that our method can estimate the 3D human body model correctly.
Data Center Modeling Using a Cyber-Physical Systems Lens

Data center systems must adopt end-to-end resource management, covering both cyber and physical components. Modeling data center components can help to estimate and predict key performance indicators under various assumptions. This paper describes a data center modeling approach using a cyber-physical systems lens. The approach is aimed at facilitating the modeling of quality of service and energy consumption parameters to evaluate data centers under different workload and IT resources conditions, and to then compare them to one another, or compare different scenarios under which data centers operate. Results of the model are also a way to communicate and better understand data center behavior given the data center workload, and to help evaluate areas of improvement.
Multi-baseline extended particle filtering phase unwrapping algorithm based on amended matrix pencil model and quantized path-following strategy

This paper proposes a new multi-baseline extended particle filtering phase unwrapping algorithm which combines an extended particle filter with an amended matrix pencil model and a quantized path-following strategy.The contributions to multibaseline synthetic aperture radar (SAR) interferometry are as follows: a new recursive multi-baseline phase unwrapping model based on an extended particle filter is built, and the amended matrix pencil model is used to acquire phase gradient information with a higher precision and lower computational cost, and the quantized path-following strategy is introduced to guide the proposed phase unwrapping procedure to efficiently unwrap wrapped phase image along the paths routed by a phase derivative variance map.
Topic Spotting using Hierarchical Networks with Self Attention

Pooja Chitkara, Ashutosh Modi, Pravalika Avvaru, Sepehr Janghorbani, Mubbasir Kapadia. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Purpose-driven Assessment of Cataloging and Metadata Services: Transforming Broken Links into Linked Data

In response to the impact of emerging technologies, this article examines the relationship between strategic planning and the assessment methodologies of cataloging and metadata services and explores (or “suggests” if appropriate) how to assess cataloging and metadata activities more effectively and relevantly. Besides literature review, the author conducted surveys for more than seventy librarians and follow-up interviews with three library administrators. The results indicate the gap between strategic planning and assessment for cataloging and metadata services, and identify the significance of impact and users’ needs in strategic planning for the assessment of cataloging and metadata services. These elements are critical in transforming the broken links in the current cataloging assessment into a purpose-driven assessment corresponding to users’ needs and evolving technologies.
Survey on A Smart Health Monitoring System Based on Context Awareness Sensing

Wearable health monitoring systems, with a context-awareness sensing ability, can greatly improve the way such devices work, both in terms of its accuracy and efficiency. This paper summarises related work previously performed, their advantages and disadvantages, and how they can be incorporated in future work. The related work covers approaches using devices such as smartphones used in isolation, smartphones combined with wristbands and ARM based microcontrollers interfaced with different sensors. Different approaches for optimizing power usage in wireless systems are also investigated. For context awareness, various papers are analyzed to determine existing activity recognition patterns, and approaches to solve common problems experienced in this field. Furthermore, this paper proposes a system that can be considered for future work, combining methods that are found to be meet a certain set of criteria, to develop a device that would be applicable for use by underground mine workers.
On the Randomness Cost of Linear Secure Computation : (Invited Presentation)

We consider the problem of secure computation, where K users, each holding an independent message, wish to compute a function on the messages without revealing any additional information. We show that to compute M generic linear independent combinations of the messages securely (i.e., for the linear secure computation problem), it suffices to use $\min\left(\left\lceil\frac{K-M-1}{2}\right\rceil,~M\right)$ randomness symbols per message symbol (i.e., the randomness cost is no larger than $\min\left(\left\lceil\frac{K-M-1}{2}\right\rceil,~M\right)$). The optimality of the achieved randomness cost remains open.
Towards a Scalable EA-Based Optimization of Digital Circuits

Scalability of fitness evaluation was the main bottleneck preventing adopting the evolution in the task of logic circuits synthesis since early nineties. Recently, various formal approaches have been introduced to this field to overcome this issue. This made it possible to optimise complex circuits consisting of hundreds of inputs and thousands of gates. Unfortunately, we are facing to the another problem – scalability of representation. The efficiency of the evolutionary optimization applied at the global level deteriorates with the increasing complexity. In this paper, we propose to apply the concept of local resynthesis. Resynthesis is an iterative process based on extraction of smaller sub-circuits from a complex circuit that are optimized locally and implanted back to the original circuit. When applied appropriately, this approach can mitigate the problem of scalability of representation. Our evaluation on a set of nontrivial real-world benchmark problems shows that the proposed method provides better results compared to global evolutionary optimization. In more than 60% cases, substantially higher number of redundant gates was removed while keeping the computational effort at the same level.
The continuous wavelet transform on ultradistribution spaces

The wavelet transform of ultradistributions in $\mathscr {Z}_{\omega }^{\prime }$ is defined. The corresponding inversion formula is established by interpreting convergence in the weak distributional sense.
An Efficient Outlier Detection Approach Over Uncertain Data Stream Based on Frequent Itemset Mining

Outlier detection is essential in data-based science, it aims at detecting the itemsets that with a significant difference to other data. With the limiting of equipment precision and network transmission, the uncertain data is more common in daily life. However, the traditional outlier detection methods are not applicable for uncertain data stream and the large volume of data makes the outlier detecting occupy large memory usage and time cost, moreover, the multiple scanning times on data stream for Apriori-like methods are unrealistic. In this paper, the matrix structure is constructed to store the information of uncertain data stream and the following mining process is conducted with matrix structure, therefore, the whole data stream only need to be scanned for only one times. Then, the “upper cap” concept is used in FIM-UDS method to mine the frequent itemsets more effectively to support outlier detecting. Moreover, two outlier factors and outlier detection method that called FIM-UDSOD are designed to detect the potential outliers. Finally, two public datasets are used to verify the efficiency of FIM-UDS method and one synthetic dataset is used for evaluating FIM-UDSOD method, the experimental results show that our proposed FIM-UDSOD method is more effective in outlier detecting.
Enhanced Approach Using Reduced SBTFD Features and Modified Individual Behavior Estimation for Crowd Condition Prediction

Sensor technology provides the real-time monitoring of data in several scenarios that contribute to the improved security of life and property. Crowd condition monitoring is an area that has benefited from this. The basic context-aware framework (BCF) uses activity recognition based on emerging intelligent technology and is among the best that has been proposed for this purpose. However, accuracy is low, and the false negative rate (FNR) remains high. Thus, the need for an enhanced framework that offers reduced FNR and higher accuracy becomes necessary. This article reports our work on the development of an enhanced context-aware framework (EHCAF) using smartphone participatory sensing for crowd monitoring, dimensionality reduction of statistical-based time-frequency domain (SBTFD) features, and enhanced individual behavior estimation (IBEenhcaf). The experimental results achieved 99.1% accuracy and an FNR of 2.8%, showing a clear improvement over the 92.0% accuracy, and an FNR of 31.3% of the BCF.
Penerapan Sistem Absensi Sekolah Menggunakan Fingerprint Terintegrasi Dengan Smartphone Android

The need of student attendance information in schools is a major factor in success of the academic process. Often there are validation and identification errors in the student attendance process (human error or manipulation process) as well as the slowness of information obtained, since the attendance is recorded manually in each class. This has an impact on success of the academic process directly and the community at large. Because the importance of attendance information, various studies have offered the system in the process of student attendance, with various technologies. One popular technology and believed to be validating and identifying well is the fingerprint. Therefore, this paper proposes the introduction of attendance system in schools by utilizing fingerprint technology integrated with android smartphone so that information can be delivered quickly. This study use Fingerprint X-100C as a means of identification of student data at the time of absence then the attendance data will be sent to the system database by SOAP (Simple Object Access Protocol) Web Service. The system will validate and transmit that information on the android Smartphone via the newly created XML Service. The success rate of this system in processing absence reach around 90,47% with delay time of sending information 0.0024 s This system has been tested and applied to SMK N 1 Indramayu.
Adversarial Learning With Knowledge of Image Classification for Improving GANs

Generating realistic images with fine details are still challenging due to difficulties of training GANs and mode collapse. To resolve this problem, our main idea is that leveraging the knowledge of an image classification network, which is pre-trained by a large scale dataset (e.g. ImageNet), would improve a GAN. By using the gradient of the network (i.e. discriminator) with high discriminability during training, we can, therefore, guide the gradient of a generator gradually toward the real data region. However, excessive negative feedback of the powerful classifier often prevents a generator from producing diverse images. Based on the main idea, we design a GAN including the added discriminator and propose a novel energy function in order to transfer the pre-trained knowledge to a generator and control the feedback of the added discriminator. We also present an incremental learning method to prevent the density of the generator to be the low-entropy distribution when training our GAN with respect to the energy function. We incorporate our method to DCGAN and demonstrate the ability to enhance the image quality even in high resolution on several datasets compared to DCGAN. In addition, we compare our method with recent GANs for the diversity of generated samples on CIFAR-10 and STL-10 datasets and provide the extensive ablation studies to prove the benefits of our method.
An Efficient Solution Towards Secure Homomorphic Symmetric Encryption Algorithms

In this paper, we consider Homomorphic Encryption (HE) to process over encrypted data in order to achieve user privacy. We present a framework solution to provide a high level of security for the symmetric HE algorithms. The proposed solution introduces a dynamic structure and dynamic diffusion’s primitives that enhance existing symmetric HE algorithms and overcome their weaknesses. We apply this solution to a well known symmetric homomorphic approach, the PORE (Polynomial Operation for Randomization and Encryption) approach. The security analysis of the proposed solution shows that it ensures a high level of security without performance degradation. It is also evaluated against different attacks. This leads to secure and efficient HE Algorithms for practical implementations.
Assessing Software Quality of Agile Student Projects by Data-mining Software Repositories

Group student software projects are important in computer science education. Students are encouraged to self-organize and learn technical skills, preparing them for real life software development. However, the projects contribute to multiple learning objectives, making coaching students a time consuming task. Thus, it is important to have a suitable best practice development process. For providing better insights for the students, the resulting software has to be of value and meet quality requirements, including maintainability, as in real life software development. Using source code quality metrics and by data mining repository data like commit history, we analyze six student projects, measuring their quality and identifying contributing factors to success or failure of a student project. Based on the findings, we formulate recommendations to improve future projects for students and researchers alike.
Validation of a self-perceived media competence instrument for pre-service teachers / Validación de un instrumento de autopercepción de competencia mediática para docentes en formación

Key to guaranteeing the fundamental human rights of freedom of expression and information is the development of media competence, particularly in schools. Training teachers in the area of media competence is particularly important for developing these skills in the broader citizenry. In order to provide educators and educational policymakers with a useful diagnostic tool, we have designed a self-perception questionnaire. The questionnaire was applied to a sample of Peruvian education students (N = 501). On the one hand, the results confirm the construct’s coherence, validity and instrumental reliability. On the other hand, they allow us to propose a theoretically based update to the way in which this construct is interpreted and applied, confirming the multidimensionality of media competence. Finally, we use this empirical evidence to discuss the integral, transversal and ecological status of media competence.
Information Adapted Machine Learning Models for Prediction in Clinical Workflow.

In a database of electronic health records, the amount of available information varies widely between patients. In a real-time prediction scenario, a machine learning model may receive limited information for some patients.Our aim was to evaluate the influence of missing data on real-time prediction of delirium, and detect changes in prediction performance when training separate models for patients with missing data.We compared a model trained specifically on data with missing values to the currently implemented model predicting delirium. Also, we simulated five test data sets with different amount of missing data and compared the prediction results to the prediction on complete data set when using the same model.For patients with missing laboratory and nursing assessment data, a model trained especially for this scenario performed significantly better than the implemented model. The combination of procedure data and demographic data achieved the closest results to a prediction with a complete data set.An ongoing evaluation of real-time prediction is indispensable. Additional models adapted to the information available might improve prediction performance.
Investigation into the Android-based Mobile Learning System

With the rapid development of information technology and the wide usage of portable mobile devices, such as, smart phones, tablet personal computers, and the like, reforms on teaching method and learning system are going on more intensely. Due to more and more e-products available, a new round revolution has sounded the long horn. Mobile learning system has come to be a smart learning system which is getting more and more popular among people. Mobile learning system has set people free from the limitation of learning time and space, which is bound to improve the learning efficiency. This essay has analyzed the learning situation of non-English majors in Medical University, stated the technological support of this learning system , analyzed the feasibility of the system, predicted the potential obstacles, and put forward the advantages and the necessity of the wide usage of the mobile learning system based on Android.