pwc-archive/papers-with-abstracts huggingface dataset:
Strategic Evaluation in Optimizing the Internal Supply Chain Using TOPSIS: Evidence In A Coil Winding Machine Manufacturer
Authors: Dilip U Shenoy, Vinay Sharma, Shiva HC Prasad
Date: 2020-07-08 00:00:00
Tasks: Decision Making

Most of the manufacturing firm aims to optimize their Supply Chain in terms of improved profitability of its products through value Addition. This study takes a critical look into the factors that affect the Performance of internal supply chain with respect to specific criteria. Accordingly, ranking these factors to get the critical dimensions of supply chain performance in the manufacturing industry. A semi-structured interview with the pre-defined set of questions used to collect the responses from decision makers of the firm. Multi criteria decision-making tool called TOPSIS is used to evaluate the responses and rank the factors. The results of this indicate that supplier relationship and inventory planning were most principal factors positively influencing on-time delivery of the product, production flexibility, cost savings, additional costs. This study helps to identify and optimize the process parameters using objective and subjective evaluation approach. The combined influence of the thought process of the manager to optimize the internal supply chain is extracted in this work.
Conceptual Modeling of Time for Computational Ontologies
Authors: Sabah Al-Fedaghi
Date: 2020-07-16 00:00:00

To provide a foundation for conceptual modeling, ontologies have been introduced to specify the entities, the existences of which are acknowledged in the model. Ontologies are essential components as mechanisms to model a portion of reality in software engineering. In this context, a model refers to a description of objects and processes that populate a system. Developing such a description constrains and directs the design, development, and use of the corresponding system, thus avoiding such difficulties as conflicts and lack of a common understanding. In this cross-area research between modeling and ontology, there has been a growing interest in the development and use of domain ontologies (e.g., Resource Description Framework, Ontology Web Language). This paper contributes to the establishment of a broad ontological foundation for conceptual modeling in a specific domain through proposing a workable ontology (abbreviated as TM). A TM is a one-category ontology called a thimac (things/machines) that is used to elaborate the design and analysis of ontological presumptions. The focus of the study is on such notions as change, event, and time. Several current ontological difficulties are reviewed and remodeled in the TM. TM modeling is also contrasted with time representation in SysML. The results demonstrate that a TM is a useful tool for addressing these ontological problems.
Identification of Dementia Using Audio Biomarkers
Authors: Rupayan Chakraborty, Meghna Pandharipande, Chitralekha Bhat, Sunil Kumar Kopparapu
Date: 2020-02-27 00:00:00

Dementia is a syndrome, generally of a chronic nature characterized by a deterioration in cognitive function, especially in the geriatric population and is severe enough to impact their daily activities. Early diagnosis of dementia is essential to provide timely treatment to alleviate the effects and sometimes to slow the progression of dementia. Speech has been known to provide an indication of a person's cognitive state. The objective of this work is to use speech processing and machine learning techniques to automatically identify the stage of dementia such as mild cognitive impairment (MCI) or Alzheimers disease (AD). Non-linguistic acoustic parameters are used for this purpose, making this a language independent approach. We analyze the patients audio excerpts from a clinician-participant conversations taken from the Pitt corpus of DementiaBank database, to identify the speech parameters that best distinguish between MCI, AD and healthy (HC) speech. We analyze the contribution of various types of acoustic features such as spectral, temporal, cepstral their feature-level fusion and selection towards the identification of dementia stage. Additionally, we compare the performance of using feature-level fusion and score-level fusion. An accuracy of 82% is achieved using score-level fusion with an absolute improvement of 5% over feature-level fusion.
iNNk: A Multi-Player Game to Deceive a Neural Network
Authors: Jennifer Villareale, Ana Acosta-Ruiz, Samuel Arcaro, Thomas Fox, Evan Freed, Robert Gray, Mathias Löwe, Panote Nuchprayoon, Aleksanteri Sladek, Rush Weigelt, Yifu Li, Sebastian Risi, Jichen Zhu
Date: 2020-07-17 00:00:00

This paper presents iNNK, a multiplayer drawing game where human players team up against an NN. The players need to successfully communicate a secret code word to each other through drawings, without being deciphered by the NN. With this game, we aim to foster a playful environment where players can, in a small way, go from passive consumers of NN applications to creative thinkers and critical challengers.
KubeEdge.AI: AI Platform for Edge Devices
Authors: Sean Wang, Yuxiao Hu, Jason Wu
Date: 2020-07-07 00:00:00
Tasks: Cloud Computing, Edge-computing, Management

The demand for smartness in embedded systems has been mounting up drastically in the past few years. Embedded system today must address the fundamental challenges introduced by cloud computing and artificial intelligence. KubeEdge [1] is an edge computing framework build on top of Kubernetes [2]. It provides compute resource management, deployment, runtime and operation capabilities on geo-located edge computing resources, from the cloud, which is a natural fit for embedded systems. Here we propose KubeEdge.AI, an edge AI framework on top of KubeEdge. It provides a set of key modules and interfaces: a data handling and processing engine, a concise AI runtime, a decision engine, and a distributed data query interface. KubeEdge.AI will help reduce the burdens for developing specific edge/embedded AI systems and promote edge-cloud coordination and synergy.
Visual Explanation for Identification of the Brain Bases for Dyslexia on fMRI Data
Authors: Laura Tomaz Da Silva, Nathalia Bianchini Esper, Duncan D. Ruiz, Felipe Meneguzzi, Augusto Buchweitz
Date: 2020-07-17 00:00:00
Tasks: BIG-bench Machine Learning, Classification, Diagnostic, General Classification

Brain imaging of mental health, neurodevelopmental and learning disorders has coupled with machine learning to identify patients based only on their brain activation, and ultimately identify features that generalize from smaller samples of data to larger ones. However, the success of machine learning classification algorithms on neurofunctional data has been limited to more homogeneous data sets of dozens of participants. More recently, larger brain imaging data sets have allowed for the application of deep learning techniques to classify brain states and clinical groups solely from neurofunctional features. Deep learning techniques provide helpful tools for classification in healthcare applications, including classification of structural 3D brain images. Recent approaches improved classification performance of larger functional brain imaging data sets, but they fail to provide diagnostic insights about the underlying conditions or provide an explanation from the neural features that informed the classification. We address this challenge by leveraging a number of network visualization techniques to show that, using such techniques in convolutional neural network layers responsible for learning high-level features, we are able to provide meaningful images for expert-backed insights into the condition being classified. Our results show not only accurate classification of developmental dyslexia from the brain imaging alone, but also provide automatic visualizations of the features involved that match contemporary neuroscientific knowledge, indicating that the visual explanations do help in unveiling the neurological bases of the disorder being classified.
Interpretable, Multidimensional, Multimodal Anomaly Detection with Negative Sampling for Detection of Device Failure
Authors: John Sipple
Date: 2020-07-12 00:00:00
Proceeding: ICML 2020 1
Tasks: Anomaly Detection, Attribute, Unsupervised Anomaly Detection

Complex devices are connected daily and eagerly generate vast streams of multidimensional state measurements. These devices often operate in distinct modes based on external conditions (day/night, occupied/vacant, etc.), and to prevent complete or partial system outage, we would like to recognize as early as possible when these devices begin to operate outside the normal modes. Unfortunately, it is often impractical or impossible to predict failures using rules or supervised machine learning, because failure modes are too complex, devices are too new to adequately characterize in a specific environment, or environmental change puts the device into an unpredictable condition. We propose an unsupervised anomaly detection method that creates a negative sample from the positive, observed sample, and trains a classifier to distinguish between positive and negative samples. Using the Contraction Principle, we explain why such a classifier ought to establish suitable decision boundaries between normal and anomalous regions, and show how Integrated Gradients can attribute the anomaly to specific variables within the anomalous state vector. We have demonstrated that negative sampling with random forest or neural network classifiers yield significantly higher AUC scores than Isolation Forest, One Class SVM, and Deep SVDD, against (a) a synthetic dataset with dimensionality ranging between 2 and 128, with 1, 2, and 3 modes, and with and without noise dimensions; (b) four standard benchmark datasets; and (c) a multidimensional, multimodal dataset from real climate control devices. Finally, we describe how negative sampling with neural network classifiers have been successfully deployed at large scale to predict failures in real time in over 15,000 climate-control and power meter devices in 145 Google office buildings.
JAMPI: efficient matrix multiplication in Spark using Barrier Execution Mode
Authors: Tamas Foldi, Chris von Csefalvay, Nicolas A. Perez
Date: 2020-06-27 00:00:00

The new barrier mode in Apache Spark allows embedding distributed deep learning training as a Spark stage to simplify the distributed training workflow. In Spark, a task in a stage does not depend on any other tasks in the same stage, and hence it can be scheduled independently. However, several algorithms require more sophisticated inter-task communications, similar to the MPI paradigm. By combining distributed message passing (using asynchronous network IO), OpenJDK's new auto-vectorization and Spark's barrier execution mode, we can add non-map/reduce based algorithms, such as Cannon's distributed matrix multiplication to Spark. We document an efficient distributed matrix multiplication using Cannon's algorithm, which improves significantly on the performance of the existing MLlib implementation. Used within a barrier task, the algorithm described herein results in an up to 24 percent performance increase on a 10,000x10,000 square matrix with a significantly lower memory footprint. Applications of efficient matrix multiplication include, among others, accelerating the training and implementation of deep convolutional neural network based workloads, and thus such efficient algorithms can play a ground-breaking role in faster, more efficient execution of even the most complicated machine learning tasks.
cencro -- Speedup of Video Quality Calculation using Center Cropping.
Authors: Steve Göring, Christopher Krämmer, Alexander Raake
Date: 2019-12-09 00:00:00
Proceeding: IEEE International Symposium on Multimedia 2019 12
Tasks: 4k, 8k

Today's video streaming providers, e.g. Youtube, Netflix or Amazon Prime, are able to deliver high resolution and high-quality content to end users. To optimize video quality and to reduce transmission bandwidth, new encoders and smarter encoding schemes are required. Encoding optimization forms an important part of this effort in reducing bandwidth and results in saving considerable amount of bitrate. For such optimization, accurate and computationally fast video quality models are required. Netflix's VMAF is an example of such an accurate video quality model. However, it is a full-reference (FR) metric, and the calculation of such metrics tend to be slower in comparison to other metrics, due to the enormous amount of data that needs to be processed, especially for high resolutions of 4k and beyond. We introduce an approach to speed up video quality metric calculations in general. We use VMAF as an example with a video database up to 4K resolution videos, to show that our approach works well. Our main idea is that we reduce each frame of the reference and distorted video based on a center crop of the frame, assuming that most important visual information are presented in the middle of most typical videos. In total we analyze 18 different crop settings and compare our results with uncropped VMAF values and subjective scores. We show that this approach -- named cencro -- is able to save up to 95% computation time, with just an overall error of 4% considering a 360p center crop. Furthermore, we checked other full-reference metrics, and show that cencro performs similar good than for VMAF. As a last evaluation, we apply our approach to full-hd gaming videos, also in this scenario cencro can be successfully applied. The idea behind cencro is not restricted to full-reference models and can also be applied to other type of video quality models or datasets, or even for higher resolution videos such as 8K.
AVT-VQDB-UHD-1: A Large Scale Video Quality Database for UHD-1
Authors: Rakesh Rao Ramachandra Rao, Steve Göring, Werner Robitza, Bernhard Feiten, Alexander Raake
Date: 2019-12-09 00:00:00
Proceeding: IEEE International Symposium on Multimedia 2019 12
Tasks: 4k

4K television screens or even with higher resolutions are currently available in the market. Moreover video streaming providers are able to stream videos in 4K resolution and beyond. Therefore, it becomes increasingly important to have a proper understanding of video quality especially in case of 4K videos. To this effect, in this paper, we present a study of subjective and objective quality assessment of 4K ultra-high-definition videos of short duration, similar to DASH segment lengths. As a first step, we conducted four subjective quality evaluation tests for compressed versions of the 4K videos. The videos were encoded using three different video codecs, namely H.264, HEVC, and VP9. The resolutions of the compressed videos ranged from 360p to 2160p with framerates varying from 15fps to 60fps. All the source 4K contents used were of 60fps. We included low-quality conditions in terms of bitrate, resolution and framerate to ensure that the tests cover a wide range of conditions, and that e.g. possible models trained on this data are more general and applicable to a wider range of real world applications. The results of the subjective quality evaluation are analyzed to assess the impact of different factors such as bitrate, resolution, framerate, and content. In the second step, different state-of-the-art objective quality models were applied to all videos and their performance was analyzed in comparison with the subjective ratings, e.g. using Netflix's VMAF. The videos, subjective scores, both MOS and confidence interval per sequence and objective scores are made public for use by the community for further research.
Bitstream-based Model Standard for 4K/UHD: ITU-T P.1204.3 - Model Details, Evaluation, Analysis and Open Source Implementation
Authors: Rakesh Rao Ramachandra Rao, Steve Göring, Peter List, Werner Robitza, Bernhard Feiten, Ulf Wüstenhagen, Alexander Raake
Date: 2020-05-25 00:00:00
Proceeding: Twelfth International Conference on Quality of Multimedia Experience (QoMEX) 2020 5
Tasks: 4k, model

With the increasing requirement of users to view high-quality videos with a constrained bandwidth, typically realized using HTTP-based adaptive streaming, it becomes more and more important to determine the quality of the encoded videos accurately, to assess and possibly optimize the overall streaming quality. In this paper, we describe a bitstream-based no-reference video quality model developed as part of the latest model-development competition conducted by ITU-T Study Group 12 and the Video Quality Experts Group (VQEG), "P.NATS Phase 2". It is now part of the new P.1204 series of Recommendations as P.1204.3. It can be applied to bitstreams encoded with H.264/AVC, HEVC and VP9, using various encoding options, including resolution, bitrate, framerate and typical encoder settings such as number of passes, rate control variants and speeds. The proposed model follows an ensemble-modelling-inspired approach with weighted parametric and machine-learning parts to efficiently leverage the performance of both approaches. The paper provides details about the general approach to modelling, the features used and the final feature aggregation. The model creates per-segment and per-second video quality scores on the 5-point Absolute Category Rating scale, and is applicable to segments of 5-10 seconds duration. It covers both PC/TV and mobile/tablet viewing scenarios. We outline the databases on which the model was trained and validated as part of the competition, and perform an additional evaluation using a total of four independently created databases, where resolutions varied from 360p to 2160p, and frame rates from 15-60fps, using realistic coding and bitrate settings. We found that the model performs well on the independent dataset, with a Pearson correlation of 0.942 and an RMSE of 0.42. We also provide an open-source reference implementation of the described P.1204.3 model, as well as the multi-codec bitstream parser required to extract the input data, which is not part of the standard. Index Terms-bitstream model, video quality, machine learning , HTTP adaptive streaming.
Evaluation of Intra-coding based image compression.
Authors: Steve Göring, Alexander Raake
Date: 2019-10-28 00:00:00
Proceeding: 8th European Workshop on Visual Information Processing 2019 10
Tasks: Image Compression

Considering modern cameras, increasing image resolutions and thousands of images uploaded to sharing platforms there is still reason to have a deeper look into image compression. Especially lossy image compression is always a trade-off between file-size and image quality, where high quality is usually preferred for storage. Beside classical image compression, e.g. JPEG, there is also ongoing development to use video codecs to compress images. We analyze four different video codecs, namely AV1, H.264, H.265 and VP9, in comparison with JPEG. Our evaluation considers classical image quality metrics, e.g. PSNR, and also a modern subjective quality metric, i.e. Netflix's VMAF. We are able to show that modern video codecs can outperform classical JPEG compression both in terms of quality and file-size. For this we used 1133 uncompressed images and applied different encoding settings and estimated image quality.
AVTrack360: An open Dataset and Software recording people's Head Rotations watching 360° Contents on an HMD
Authors: Stephan Fremerey, Ashutosh Singla, Kay Meseberg, Alexander Raake
Date: 2018-06-28 00:00:00
Proceeding: ACM Multimedia Systems Conference 2018 2018 6

n this paper, we present a viewing test with 48 subjects watching 20 different entertaining omnidirectional videos on an HTC Vive Head Mounted Display (HMD) in a task-free scenario. While the subjects were watching the contents, we recorded their head movements. The obtained dataset is publicly available in addition to the links and timestamps of the source contents used. Within this study, subjects were also asked to fill in the Simulator Sickness Questionnaire (SSQ) after every viewing session. Within this paper, at first SSQ results are presented. Several methods for evaluating head rotation data are presented and discussed. In the course of the study, the collected dataset is published along with the scripts for evaluating the head rotation data. The paper presents the general angular ranges of the subjects' exploration behavior as well as an analysis of the areas where most of the time was spent. The collected information can be presented as head-saliency maps, too. In case of videos, head-saliency data can be used for training saliency models, as information for evaluating decisions during content creation, or as part of streaming solutions for region-of-interest-specific coding as with the latest tile-based streaming solutions, as discussed also in standardization bodies such as MPEG.
HTTP Adaptive Streaming QoE Estimation with ITU-T Rec. P.1203 – Open Databases and Software
Authors: Werner Robitza, Steve Göring, Alexander Raake, David Lindegren, Gunnar Heikkilä, Jörgen Gustafsson, Peter List, Bernhard Feiten, Ulf Wüstenhagen, Marie Neige-Garcia, Kazuhisa Yamagishi, Simon Broom
Date: 2018-06-28 00:00:00
Proceeding: ACM Multimedia Systems Conference 2018 2018 6

This paper describes an open dataset and software for ITU-T Rec. P.1203. As the first standardized Quality of Experience model for audiovisual HTTP Adaptive Streaming (HAS), it has been extensively trained and validated on over a thousand audiovisual sequences containing HAS-typical effects (such as stalling, coding artifacts, quality switches). Our dataset comprises four of the 30 official subjective databases at a bitstream feature level. The paper also includes subjective results and the model performance. Our software for the standard was made available to the public, too, and it is used for all the analyses presented. Among other previously unpublished details , we show the significant performance improvements of using bitstream-based models over metadata-based ones for video quality analysis, and the robustness of combining classical models with machine-learning-based approaches for estimating user QoE.
Hahaha
Authors: jayan
Date: 2020-07-10 00:00:00
Proceeding: ahhahha 2020 7

ahasduasascuja
An investigation of phone-based subword units for end-to-end speech recognition
Authors: Weiran Wang, Guangsen Wang, Aadyot Bhatnagar, Yingbo Zhou, Caiming Xiong, Richard Socher
Date: 2020-04-08 00:00:00
Tasks: Decoder, Language Modeling, Language Modelling, speech-recognition, Speech Recognition

Phones and their context-dependent variants have been the standard modeling units for conventional speech recognition systems, while characters and subwords have demonstrated their effectiveness for end-to-end recognition systems. We investigate the use of phone-based subwords, in particular, byte pair encoder (BPE), as modeling units for end-to-end speech recognition. In addition, we also developed multi-level language model-based decoding algorithms based on a pronunciation dictionary. Besides the use of the lexicon, which is easily available, our system avoids the need of additional expert knowledge or processing steps from conventional systems. Experimental results show that phone-based BPEs tend to yield more accurate recognition systems than the character-based counterpart. In addition, further improvement can be obtained with a novel one-pass joint beam search decoder, which efficiently combines phone- and character-based BPE systems. For Switchboard, our phone-based BPE system achieves 6.8\%/14.4\% word error rate (WER) on the Switchboard/CallHome portion of the test set while joint decoding achieves 6.3\%/13.3\% WER. On Fisher + Switchboard, joint decoding leads to 4.9\%/9.5\% WER, setting new milestones for telephony speech recognition.