Reviewerly/exHarmony huggingface dataset:
Succinct Functional Commitment for a Large Class of Arithmetic Circuits

A succinct functional commitment (SFC) scheme for a circuit class $$\mathbf {CC}$$ enables, for any circuit $$\mathcal {C}\in \mathbf {CC}$$ , the committer to first succinctly commit to a vector $$\varvec{\alpha }$$ , and later succinctly open the commitment to $$\mathcal {C}(\varvec{\alpha }, \varvec{\beta })$$ , where the verifier chooses $$\varvec{\beta }$$ at the time of opening. Unfortunately, SFC commitment schemes are known only for severely limited function classes like the class of inner products. By making non-black-box use of SNARK-construction techniques, we propose a SFC scheme for the large class of semi-sparse polynomials. The new SFC scheme can be used to, say, efficiently (1) implement sparse polynomials, and (2) aggregate various interesting SFC (e.g., vector commitment and polynomial commitment) schemes. The new scheme is evaluation-binding under a new instantiation of the computational uber-assumption. We provide a thorough analysis of the new assumption.
Accurate People Counting Based on Radar: Deep Learning Approach

In this study, a novel radar-based people counting (PC) method is presented using the deep learning (DL) approach. The DL algorithm is a great tool that enables the automatic formation of the optimal features; however, it must be utilized carefully, considering the domain knowledge to prevent the concerns of learning unnecessary information, followed by overfitting. To address the problem and successfully apply the DL framework to the radar-based PC, we propose three novel solutions. First, we establish the preprocessing pipelines to transform the raw signals into a suitable form for network inputs. Second, a network architecture is newly proposed considering the radar signal characteristics and PC application. Finally, we propose several data augmentation strategies to artificially increase the size of training data. It was observed from experiments using real measured data that the proposed DL-based PC approach outperforms the conventional PC methods.
Characterisation of a multi-view fringe projection system based on the stereo matching of rectified phase maps

Abstract Multi-view fringe projection systems can be effective solutions to address the limitations imposed by the limited field of view, line-of-sight issues and occlusions when measuring the geometry of complex objects, associated with single camera–projector systems. However, characterisation of a multi-view system is challenging since it requires the cameras and projectors to be in a common global coordinate system. We present a method for characterising a multi-view fringe projection system which does not require the characterisation of the projector. The novelty of the method lies in determining the correspondences in the phase domain using the rectified unwrapped phase maps and triangulating the matched phase values to reconstruct the three-dimensional shape of the object. A benefit of the method is that it does not require registration of the point clouds acquired from multiple perspectives. The proposed method is validated by experiment and comparison with a conventional system and a contact coordinate measuring machine.
Towards Deep Machine Reasoning: a Prototype-based Deep Neural Network with Decision Tree Inference

In this paper we introduce the DMR - a prototype-based method and network architecture for deep learning which is using a decision tree (DT)- based inference and synthetic data to balance the classes. It builds upon the recently introduced xDNN method addressing more complex multi-class problems, specifically when classes are highly imbalanced. DMR moves away from a direct decision based on all classes towards a layered DT of pair-wise class comparisons. In addition, it forces the prototypes to be balanced between classes regardless of possible class imbalances of the training data. It has two novel mechanisms, namely i) using a DT to determine the winning class label, and ii) balancing the classes by synthesizing data around the prototypes determined from the available training data. As a result, we improved significantly the performance of the resulting fully explainable DNN as evidenced on the well know benchmark problem Caltech-101. Furthermore, we also achieved high results in terms of accuracy for the well known Caltech-256 dataset, as well as surpassed the results of other approaches on Faces-1999 problem. In summary, we propose a new approach specifically advantageous for imbalanced multi-class problems on well known hard benchmark datasets. Moreover, DMR offers full explainability, does not require GPUs and can continue to learn from new data by adding new prototypes preserving the previous ones but not requiring full retraining.
Image Processing Framework for Performance Enhancement of Low-Light Image Sensors

Night vision devices are used for capturing images under low light conditions ( <; 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> lux), which are significantly different from images under standard illumination. The night vision devices based on image intensifier tube have power and cost advantages over other night vision devices, such as thermal imaging for short-range surveillance. However, these vision devices suffer from scintillation noise, which makes the resulting images noisy under very low light conditions. This paper presents a comprehensive methodology that uses a consolidated image processing module to improve the performance of low-light images acquired using image-intensifier-based night vision devices. The proposed module is a combination of a two-stage image processing framework. First, a spatio-temporal filter (based on wavelet and recursive frame averaging) is used to minimize inter-frame and intra-frame noise. Then, image enhancement is performed followed by residual noise reduction using a local spatial filter before applying image tone adjustment for display mapping. The results of the proposed module demonstrate performance enhancement of night vision devices under controlled simulated light conditions as well as field conditions. Objective and subjective analysis also reveals that the proposed module can effectively reduce noise and significantly improve visual image quality by 20-30%.
Concept Drift Detection on Unlabeled Data Streams: A Systematic Literature Review

Dynamic data streams applications are bound to potential changes in data distribution, of which in the context of data stream mining, will cause concept drift. Data stream mining model must have the capability to adapt to concept drift, otherwise, risk the deterioration of its learning performance. Most of the existing surveys on concept drift detection methods focused on labeled data streams, that may be inapplicable to scenario where true labels are unavailable. The aim of this systematic literature review is to study the existing concept drift detection methods on unlabeled data streams, focusing on the learning process and the way concept drift is monitored in the data stream mining model. A total of 15 articles are selected for final analysis, and it is found that most of the drift detection methods were applied in a supervised learning setting. An experimental evaluation of the methods can be performed in the future work to investigate their performance in unsupervised learning setting.
Evaluation of an all sky imager based nowcasting system for distinct conditions and five sites

All sky imager (ASI) based nowcasting system can provide spatially and temporally highly resolved solar irradiance information for the next minutes ahead. Nowcasts, which capture the intra-hour variability of the incoming downward shortwave solar irradiance, have the potential to optimize the operation of solar power plants as well as electrical grids. Such automatized optimizations require a deep understanding of the accuracy in the nowcasting system at any given moment. State of the art validation procedures of ASI based nowcasting systems use scalar error metrics without regards to the actual weather conditions. Yet, the performance of nowcasting systems varies strongly with the prevailing weather conditions. Deviations increase for more complex and variable conditions, for which it is more challenging to detect and model the clouds in the sky. Thus, depending on the used data set such validation results may not be meaningful to describe the expected accuracy in realistic and individual optimization situations. A novel validation procedure is presented in this work, which discretizes the validation data set in distinct temporal DNI variability classes. Individual error metrics are determined as function of the lead time and DNI variability class. This approach is applied for a two ASI based nowcasting system as operated on five distinct sites distributed in Spain, Portugal and Germany, over a combined period of more than 4.5 years. The obtained validation results emphasize that the novel classification method enables a comparison in nowcast performance between the sites despite of distinct local meteorological conditions. The presented method allows the estimation of the overall accuracy of nowcasting systems at a new site if DNI data in 1 min resolution are available.
Evaluation Of Digital Library's Usability Using the System Usability Scale Method of (A Case Study)

The benefit of Information Technology (IT) has spread to almost all fields, including in libraries. IT applications in libraries are the automation of libraries and digital libraries. Digital library or digital library system is the concept of using the internet and information technology in library management. Many libraries have implemented digital libraries, one of them is E-Perpus belongs to Depok City. E-Perpus contains digital books that can be accessed online 24 hours, anytime and everywhere. However, when an application has been launched, it is necessary to evaluate to find out how effective, efficient, and satisfying the application according to the user, what obstacles they might face and the feedback given later can be a benchmark for improving the performance of the application so that it is better in the future. Therefore, in this study, we will conduct an evaluation of usability using the System Usability Scale(SUS) to find out the effectiveness of SUS to evaluate the usability of the system. The results of the evaluation show that the SUS score of E-Perpus is 69.6. It is indicated that the application was included in the Marginal High, OK category, and a D predicate. In addition, for its relation to the Net Promoter Score, it shows that users have the potential to become Detractors where they feel less satisfied with the product or service and can have a negative effect on the brand by a negative review.
An Efficient Renewable Energy-Based Scheduling Algorithm for Cloud Computing

The global growth of cloud computing services is witnessing a continuous surge, starting from storing data to sharing information with others. It makes cloud service providers (CSPs) efficiently utilize the existing resources of datacenters to increase adaptability and minimize the unexpected expansion of datacenters. These datacenters consume enormous amounts of energy generated using fossil fuels (i.e., non-renewable energy (NRE) sources), and emit a substantial amount of carbon footprint and heat. It drastically impacts the environment. As a result, CSPs are pledged to decarbonize the datacenters by adopting renewable energy (RE) sources, such as solar, wind, hydro and biomass. However, these CSPs have not completely ditched fossil fuels as RE sources are subjected to inconsistent atmospheric conditions. Recent studies have suggested using both NRE and RE sources by the CSPs to meet user requirements. However, these studies have not considered flexible duration, nodes and utilization of the user requests (URs) with respect to datacenters. Therefore, we consider these URs’ properties and propose a RE-based scheduling algorithm (RESA) to efficiently assign the URs to the datacenters. The proposed algorithm determines both the earliest completion time and energy cost, and takes their linear combination to decide a suitable datacenter for the URs. We conduct extensive simulations by taking 1000 to 16000 URs and 20 to 60 datacenters. Our simulation results are compared with other algorithms, namely round-robin (RR) and random, which show that RESA is able to reduce the overall completion time (i.e., makespan (M)), energy consumption (EC), overall cost (OC) and the number of used RE (|URE|) resources.
Bayesian Learning for Deep Neural Network Adaptation

A key task for speech recognition systems is to reduce the mismatch between training and evaluation data that is often attributable to speaker differences. Speaker adaptation techniques play a vital role to reduce the mismatch. Model-based speaker adaptation approaches often require sufficient amounts of target speaker data to ensure robustness. When the amount of speaker level data is limited, speaker adaptation is prone to overfitting and poor generalization. To address the issue, this paper proposes a full Bayesian learning based DNN speaker adaptation framework to model speaker-dependent (SD) parameter uncertainty given limited speaker specific adaptation data. This framework is investigated in three forms of model based DNN adaptation techniques: Bayesian learning of hidden unit contributions (BLHUC), Bayesian parameterized activation functions (BPAct), and Bayesian hidden unit bias vectors (BHUB). In the three methods, deterministic SD parameters are replaced by latent variable posterior distributions for each speaker, whose parameters are efficiently estimated using a variational inference based approach. Experiments conducted on 300-hour speed perturbed Switchboard corpus trained LF-MMI TDNN/CNN-TDNN systems suggest the proposed Bayesian adaptation approaches consistently outperform the deterministic adaptation on the NIST Hub5'00 and RT03 evaluation sets. When using only the first five utterances from each speaker as adaptation data, significant word error rate reductions up to 1.4% absolute (7.2% relative) were obtained on the CallHome subset. The efficacy of the proposed Bayesian adaptation techniques is further demonstrated in a comparison against the state-of-the-art performance obtained on the same task using the most recent systems reported in the literature.
Motion-Aware Structured Matrix Factorization for Foreground Detection in Complex Scenes

Foreground detection is one of the key steps in computer vision applications. Many foreground and background models have been proposed and achieved promising performance in static scenes. However, due to challenges such as dynamic background, irregular movement, and noise, most algorithms degrade sharply in complex scenes. To address the problem, we propose a motion-aware structured matrix factorization approach (MSMF), which integrates the structural and spatiotemporal motion information into a unified sparse-low-rank matrix factorization framework. Technologically, it has three main contributions: First, a variant of structured sparsity-inducing norm is proposed to constrain both structure and sparsity of foreground. The model is robust to the statistical variability of the underlying foreground pixels in complex scenes. Second, to capture the ambiguous pixels, a spatiotemporal cube-based motion trajectory is extracted for assisting matrix factorization. Finally, to solve the optimization problem of structured matrix factorization, we develop an augmented Lagrange multiplier method with the alternating direction strategy and Douglas-Rachford monotone operator splitting algorithm. Experiments demonstrate that the proposed approach achieves impressive performance in separating irregular moving foreground while suppressing the dynamic background and the noise, and outperforms some state-of-the-art algorithms.
End-to-end improved convolutional neural network model for breast cancer detection using mammographic data

Any disease is curable if it is diagnosed at the early stages with the help of a little human effort. The disease breast cancer is the second leading cause of death among women after lung cancer. Mammography is one of the most mainstream clinical imaging modalities that are utilized for early recognition of breast cancer. Early breast cancer detection helps to alleviate unnecessary treatments as well as saving women’s lives. The speedy development in deep learning and some of the strategies of machine learning have invigorated abundant enthusiasm for their application to clinical imaging issues. This paper presents an improved convolutional neural network (CNN) model that consists of three convolutional layers where the starting layer searches for low-level features and the ending layer searches for high-level features. Two activation functions, that is, the Rectified Linear Unit and sigmoid functions, are utilized for the detection of breast cancer using digitized film mammograms from the Digital Database for Screening Mammography. The proposed convolutional neural system for identifying breast malignancy on mammogram imaging achieved praiseworthy execution on examination with prior models. The experimentation found that the model achieved has a true positive rate of 99% (accuracy = 97.20%, precision = 99%, true negative rate = 96%, F-score = 0.99, balanced classification rate = 0.975, Youden’s index = 0.95). The proposed improved CNN model can be used as a second opinion of doctors to detect breast cancer.
Learning Saliency from Single Noisy Labelling: A Robust Model Fitting Perspective

The advances made in predicting visual saliency using deep neural networks come at the expense of collecting large-scale annotated data. However, pixel-wise annotation is labor-intensive and overwhelming. In this paper, we propose to learn saliency prediction from a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">single noisy labelling</i> , which is easy to obtain (e.g., from imperfect human annotation or from unsupervised saliency prediction methods). With this goal, we address a natural question: Can we learn saliency prediction while identifying clean labels in a unified framework? To answer this question, we call on the theory of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">robust model fitting</i> and formulate deep saliency prediction from a single noisy labelling as robust network learning and exploit model consistency across iterations to identify inliers and outliers (i.e., noisy labels). Extensive experiments on different benchmark datasets demonstrate the superiority of our proposed framework, which can learn comparable saliency prediction with state-of-the-art fully supervised saliency methods. Furthermore, we show that simply by treating ground truth annotations as noisy labelling, our framework achieves tangible improvements over state-of-the-art methods.
Cloud Gaming: A QoE Study of Fast-paced Single-player and Multiplayer Gaming

Cloud computing offers an attractive solution for modern computer games. By moving the increasingly demanding graphical calculations (e.g., generation of real-time video streams) to the cloud, consumers can play games using small, cheap devices. While cloud gaming has many advantages and is increasingly deployed, not much work has been done to understand the underlying factors impacting players' user experience when moving the processing to the cloud. In this paper, we study the impact of the quality of service (QoS) factors most affecting the players' quality of experience (QoE) and in-game performance. In particular, these relationships are studied from multiple perspectives using complementing analysis methods applied on the data collected via instrumented user tests. During the tests, we manipulated the players' network conditions and collected low-level QoS metrics and in-game performance, and after each game, the users answered questions capturing their QoE. New insights are provided using different correlation/auto-correlation/cross-correlation statistics, regression models, and a thorough breakdown of the QoS metric most strongly correlated with the users' QoE. We find that the frame age is the most important QoS metric for predicting in-game performance and QoE, and that spikes in the frame age caused by large frame transfers can have extended negative impact as they can cause processing backlogs. The study emphasizes the need to carefully consider and optimize the parts making up the frame age, including dependencies between the processing steps. By lowering the frame age, more enjoyable gaming experiences can be provided.
On-Device Deep Personalization for Robust Activity Data Collection

One of the biggest challenges of activity data collection is the need to rely on users and keep them engaged to continually provide labels. Recent breakthroughs in mobile platforms have proven effective in bringing deep neural networks powered intelligence into mobile devices. This study proposes a novel on-device personalization for data labeling for an activity recognition system using mobile sensing. The key idea behind this system is that estimated activities personalized for a specific individual user can be used as feedback to motivate user contribution and improve data labeling quality. First, we exploited fine-tuning using a Deep Recurrent Neural Network to address the lack of sufficient training data and minimize the need for training deep learning on mobile devices from scratch. Second, we utilized a model pruning technique to reduce the computation cost of on-device personalization without affecting the accuracy. Finally, we built a robust activity data labeling system by integrating the two techniques outlined above, allowing the mobile application to create a personalized experience for the user. To demonstrate the proposed model’s capability and feasibility, we developed and deployed the proposed system to realistic settings. For our experimental setup, we gathered more than 16,800 activity windows from 12 activity classes using smartphone sensors. We empirically evaluated the proposed quality by comparing it with a baseline using machine learning. Our results indicate that the proposed system effectively improved activity accuracy recognition for individual users and reduced cost and latency for inference for mobile devices. Based on our findings, we highlight critical and promising future research directions regarding the design of efficient activity data collection with on-device personalization.
Algorithmic Bureaucracy: Managing Competence, Complexity, and Problem Solving in the Age of Artificial Intelligence

In recent years, local government is undergoing changes which are strongly influenced by the growing digitization of governmental operations. Key features of Digital Era Governance (DEG) appear to be displacing features of the New Public Management (NPM) and are also challenging some underlying aspects of Weberian bureaucracy in public administration. In this paper, we expand on the concepts in DEG and its successor, Essentially Digital Government (EDGE), by introducing the concept of Algorithmic Bureaucracy, which looks at the impacts of Artificial Intelligence (AI) on rationalization and the socio-technical nature of public administration. We report on a mixed-method study, which focused on how the growth of data science is changing the ways that local government works in the UK. Special emphasis is put on how algorithms can build citizen and administrator competence and deal with complexity. Algorithmic bureaucracy, like traditional bureaucracy, is impartial in its application, but can be predictably sensitive to context. Society needs to determine the ends to which it is put and how to assign accountability in this context. We find that algorithmic bureaucracy is in its infancy in local government, so there is space to develop an appropriate ethical framework to harness the technology and enhance social problem solving.