Reviewerly/exHarmony huggingface dataset:
UnDispNet: Unsupervised Learning for Multi-Stage Monocular Depth Prediction

Despite advances in single view depth estimation, most existing techniques treat the task in a supervised manner. Recent approaches utilize the possibility of learning without ground truth depth, by minimizing the photometric error. In this paper, we propose a deep framework that refines predicted depth from a single image using a two-stage process, exploiting sub-pixel convolutions for depth super resolution. The first stage uses a pyramidal input and learns depth at 4 scales, utilizing depth super resolution. The second stage uses warp errors, reconstructed images, predicted depth along with original left input to refine the depth predicted by the first stage. We use data augmentation by varying color, scale and incorporating left-right flipped images in our data. We train our network in a completely unsupervised way on photoconsistency imposing occlusion, left right consistency and disparity smoothness constraints. We transform the learning process into optimally distributed steps, varying the combination of scales and losses to minimize over-regularization of depth maps. We evaluate our model for monocular inputs on KITTI driving benchmark. Our depth predictions surpass state-of-the art self-supervised approaches for monocular depth prediction.
Multiple Point Light Estimation from Low-Quality 3D Reconstructions

We address the problem of light source estimation in environments containing multiple, in-scene illuminants. While there have been several recent advances in this domain, current approaches are either ineffectual in the absence of specific visual cues or assume full knowledge of surface reflectance. We develop a more general framework, which works directly on noisy, colored 3D reconstructions, as produced by RGB-D video sequences. To model multiple light sources, we employ a large set of potential lights. Using physically-based rendering techniques, we calculate each light's capacity to illuminate the scene. From this assessment, we formulate a non-linear, least squares optimization problem to determine which lights to activate. In contrast with traditional approaches in which photometric error is minimized, our objective function resembles that found in intrinsic image decomposition. This obviates estimating reflectance as an intermediate step. We evaluate our framework on both real-world and synthetic datasets, illuminated by up to three light sources, and show it is capable of modeling complex lighting scenarios with high-fidelity.
Using Map Matching for DeIdentification of Connected Vehicle Locations

We introduce a location deidentification procedure that uses road network structure to protect against certain types of inference-based attacks. Our target is large databases containing vehicle locations. Previous anonymization approaches are inappropriate because location generalization and perturbation of geopositions could negatively affect development of safety-critical applications that require precise position information. Furthermore, k-anonymity-based clustering approaches would lead to significant data suppression. Our algorithm attempts to balance privacy protection and data utility, while protecting against re-identification attacks. Our data is from the first connected vehicle model deployment in the United States.
A performance analysis for real-time flood monitoring using image-based processing

&lt;span style="font-size: 9pt; font-family: 'Times New Roman', serif;"&gt;Nowadays, various image-based methods have been used in the area of monitoring. Whereas the precision of detection objects and real-time processing are the key issues for many applications. Considering the limitation of the working environment, the higher correctness and faster operating time can guarantee the work efficiency. In this paper, the image-based methods have been studied to monitoring the state of the flood in the real-time system. The performance of each image processing technique has been evaluated based on accuracy and processing time. In the flood monitoring system, the variation of important parameters can cause the change of performance and the effect of the variable parameters has been demonstrated from the experiment results. After comparing to the other image-based techniques, canny edge detection presents the best one, which also has better repeatability with the source image from different locations. Consequently, the improved canny edge detection method has been proved that can work very well on the real hardware in the outdoor environment.&lt;/span&gt;
Quantum Speedup for Graph Sparsification, Cut Approximation, and Laplacian Solving

Graph sparsification underlies a large number of algorithms, ranging from approximation algorithms for cut problems to solvers for linear systems in the graph Laplacian.In its strongest form, ``spectral sparsification"" reduces the number of edges to near-linear in the number of nodes, while approximately preserving the cut and spectral structure of the graph.In this work we demonstrate a polynomial quantum speedup for spectral sparsification and many of its applications.In particular, we give a quantum algorithm that, given a weighted graph with n nodes and m edges, outputs a classical description of an \epsilon -spectral sparsifier in sublinear time \widetil O( \surd mn/\epsilon ).This contrasts with the optimal classical complexity \widetil O(m).We also prove that our quantum algorithm is optimal up to polylog-factors.The algorithm builds on a string of existing results on sparsification, graph spanners, quantum algorithms for shortest paths, and efficient constructions for k-wise independent random strings.Our algorithm implies a quantum speedup for solving Laplacian systems and for approximating a range of cut problems such as min cut and sparsest cut.
EXPANSION OF INFORMATION CAPACITY OF TRADITIONAL MAPS WITH USING QR-CODES

Despite a good enough degree so traditional printed maps, however you can still find new ways to increase information card capacity and thus partially resolve the existing contradiction between the clarity and readability of the maps. One such way is to use QR code technology. The article describes the basic features of QR codes, to ensure that the interest in this technology. The research identified a number of advantages of using QR codes when creating traditional cards. Technology research with the use of QR codes in cartography has both scientific and practical value in terms of creating traditional cards for various content and destination.
Contagious defaults in a credit portfolio: a Bayesian network approach

The robustness of credit portfolio models is of great interest for financial institutions and regulators, since misspecified models translate into insufficient capital buffers and a crisis-prone financial system. In this paper, the authors propose a method to enhance credit portfolio models based on the model of Merton by incorporating contagion effects. While, in most models, the risks related to financial interconnectedness are neglected, the authors use Bayesian network methods to uncover the direct and indirect relationships between credits while maintaining the convenient representation of factor models. A range of techniques to learn the structure and parameters of financial networks from real credit default swaps data are studied and evaluated. Their approach is demonstrated in detail in a stylized portfolio, and the impact on standard risk metrics is estimated.
Mining Algorithm for Weighted FP-Growth Frequent Item Sets based on Ordered FP-Tree

FP-growth algorithm is a classic algorithm of mining frequent item sets, but there exist certain disadvantages for mining the weighted frequent item sets. Based on the weighted downward closure property of the weighted model, this paper proposed a method to reduce the use of storage space by constructing a weight ordered FP-tree, so as to improve the generation efficiency of weighted frequent item sets.
Evaluation of Faculty Performance of Higher Education Institution Using Principal Component Analysis

Education is one of the drivers of economy and the role of higher education institutions (HEIs) as knowledge contributors to the nation's economy is significant.Educational organisations being service organizations quality of service depends directly on the capability, commitment, and motivation of faculty who provide it and ensuring quality is a challenge for education managers.One method of ensuring quality is by assessing the performance of faculty and ranking them based on their performance against set standards-Academic Performance Indicators.Teachers of modern education system have to carry out multiple tasks-administrative, teaching, research, societal engagement, mentoring, extra-curricular activities and so on.Hence, setting standards for each of these activities and measuring them on the same yardstick may not yield desired results.This is especially true in multidisciplinary institutions wherein faculty have different tasks and roles as per their specialization and discipline.Therefore, conventional assessment criteria may not suffice the decision makers of educational institutions.Principal Component analysis (PCA) is a standard statistical technique that can be used to reduce the dimensionality of a data set by assessing the dimensional structure of a dataset (Dunteman, 1989) and reducing a large number of variables into a smaller set of linear combinations (components).PCA is a variable reduction method that can be used to reduce the multiple variables in the performance appraisal criteria and result in smaller dataset for further analysis.In this study, the faculty performance scores (API) as per UGC format were analysed using PCA and the most important components were identified contributing to the performance of faculty.
A NEW DENSITY BASED SAMPLING TO ENHANCE DBSCAN CLUSTERING ALGORITHM

DBSCAN is one of the efficient density-based clustering algorithms. It is characterized by its ability to discover clusters with different shapes and sizes, and to separate noise and outliers. However, when the dataset contain different densities, DBSCAN clustering will be inefficient. In this paper, we propose an approach to enable DBSCAN to cluster dataset having different densities by preprocess the dataset to make it with one density level. This system composed of four stages: firstly, a new approach to separate dataset based on density is presented. Secondly, a new density biased sampling technique is proposed. Thirdly, the resulted sparse data from the last two stages is clustered with DBSCAN. Finally, the remaining data from sampling will be clustered with KNN. The experimental results on synthetic and real datasets on average show that the clustering of the proposed algorithm is better than that of DBSCAN by more than 7% and retains time complexity of DBSCAN
Main barriers to ICT teacher training and disability

From the earliest ages, the commitment to an inclusive education needs the impulse of an educational system that opens its schools to all students. This reality would not be possible today without taking into account the educational support provided by Information and Communication Technologies (ICT) to the inclusive process, since they constitute the scaffolding that will allow participation and the realization of tasks adjusted to the possibilities and interests of the people. In the process of integration of ICT in the classroom, teachers are a key element since, as has been shown in numerous studies, one of the critical factors for the success of educational systems is the existence of trained, motivated teachers. The purpose is to know the main barriers or obstacles for the development of teacher training plans in ICT for people with disabilities, as well as the priority aspects for training. In the empirical part of the study, 241 interviews with professionals from the Spanish educational sector (members of management teams, ICT coordinators, directors and technological advisors of teacher training centers) were analyzed. Among the conclusions, we can highlight that the main barriers that hinder the implementation of ICT training activities and disability in most of the autonomous communities are determined in the first place by economic factors, lack of time, and teachers’ attitude. The conclusions of the study make it necessary to adopt important measures in the initial training of teachers in order to be trained in the incorporation and appropriate use of ICT in teaching people with different types of disabilities, especially taking into account that the reality that we face every time has more innovative and enriching technological tools that offer us a wide range of applications and adequate tools for this purpose.
A General-Purpose Algorithm for Constrained Sequential Inference

Inference in structured prediction involves finding the best output structure for an input, subject to certain constraints.Many current approaches use sequential inference, which constructs the output in a left-to-right manner.However, there is no general framework to specify constraints in these approaches.We present a principled approach for incorporating constraints into sequential inference algorithms.Our approach expresses constraints using an automaton, which is traversed in lockstep during inference, guiding the search to valid outputs.We show that automata can express commonly used constraints and are easily incorporated into sequential inference.When it is more natural to represent constraints as a set of automata, our algorithm uses an active set method for demonstrably fast and efficient inference.We experimentally show the benefits of our algorithm on constituency parsing and semantic role labeling.For parsing, unlike unconstrained approaches, our algorithm always generates valid output, incurring only a small drop in performance.For semantic role labeling, imposing constraints using our algorithm corrects common errors, improving F 1 by 1.5 points.These benefits increase in low-resource settings.Our active set method achieves a 5.2x relative speedup over a naive approach. 1O
Fearless steps: Taking the next step towards advanced speech technology for naturalistic audio

Over the past two decades, machine learning technologies have been targeting real-world problems in the Speech-Language (SLT) domain. Speech corpora developed under diverse environments have been paramount to progress, though most are simulated/controlled scenarios. Success in Machine Learning for SLT requires new innovative challenges posed by multi-speaker naturalistic audio. The UTDallas-CRSS led Fearless Steps (FS) initiative over the past 6 years has made significant strides through the development of the Fearless Steps Corpus, and multiple novel schemes to address core speech tasks. The next steps taken through this initiative aim at motivating further research on these data through worldwide collaborative efforts. To achieve this, the Inaugural Fearless Steps Challenge held in 2019 saw the release of 11 000 h of Apollo-11 audio data and diarization transcripts, made freely available to public online. An additional 100 h of manually annotated data was released as a Challenge corpus, available to researchers interested in working on any of the five core speech tasks: Speech-Activity-Detection, Speaker Diarization and Identification, Speech Recognition, and Sentiment Detection. FS Challenge resulted in over 150 participants worldwide developing novel task-specific algorithms. Going forward, the FS initiative aims at digitizing and releasing over 150 000 h of the remaining Apollo Missions in conjunction with follow-on Challenge Tasks focused on developing multi-channel and conversational speech systems.
Clustering topic groups of documents using K-Means algorithm: Australian Embassy Jakarta media releases 2006-2016

Introduction. The Australian Embassy in Jakarta is storing a wide array of media release document. Analyzing particular and vital patterns of the documents collection is imperative as it will result in new insights and knowledge of significant topic groups of the documents.Methodology. K-Means was used algorithm as a non-hierarchical clustering method which partitioning data objects into clusters. The method works through minimizing data variation within cluster and maximizing data variation between clusters. Data Analysis. Of the documents issued between 2006 and 2016, 839 documents were examined in order to determine term frequencies and to generate clusters. Evaluation was conducted by nominating an expert to validate the cluster result.Results and discussions. The result showed that there were 57 meaningful terms grouped into 3 clusters. “People to people links”, “economic cooperation”, and “human development” were chosen to represent topics of the Australian Embassy Jakarta media releases from 2006 to 2016.Conclusions. Text mining can be used to cluster topic groups of documents. It provides a more systematic clustering process as the text analysis is conducted through a number of stages with specifically set parameters.
Performance Evaluation of Machine Learning Techniques for Identifying Forged and Phony Uniform Resource Locators (URLs)

Since the invention of Information and Communication Technology (ICT), there has been a great shift from the erstwhile traditional approach of handling information across the globe to the usage of this innovation. The application of this initiative cut across almost all areas of human endeavours. ICT is widely utilized in education and production sectors as well as in various financial institutions. It is of note that many people are using it genuinely to carry out their day to day activities while others are using it to perform nefarious activities at the detriment of other cyber users. According to several reports which are discussed in the introductory part of this work, millions of people have become victims of fake Uniform Resource Locators (URLs) sent to their mails by spammers. Financial institutions are not left out in the monumental loss recorded through this illicit act over the years. It is worth mentioning that, despite several approaches currently in place, none could confidently be confirmed to provide the best and reliable solution. According to several research findings reported in the literature, researchers have demonstrated how machine learning algorithms could be employed to verify and confirm compromised and fake URLs in the cyberspace. Inconsistencies have however been noticed in the researchers’ findings and also their corresponding results are not dependable based on the values obtained and conclusions drawn from them. Against this backdrop, the authors carried out a comparative analysis of three learning algorithms (Naïve Bayes, Decision Tree and Logistics Regression Model) for verification of compromised, suspicious and fake URLs and determine which is the best of all based on the metrics (F-Measure, Precision and Recall) used for evaluation. Based on the confusion metrics measurement, the result obtained shows that the Decision Tree (ID3) algorithm achieves the highest values for recall, precision and f-measure. It unarguably provides efficient and credible means of maximizing the detection of compromised and malicious URLs. Finally, for future work, authors are of the opinion that two or more supervised learning algorithms can be hybridized to form a single effective and more efficient algorithm for fake URLs verification.Keywords: Learning-algorithms, Forged-URL, Phoney-URL, performance-comparison
Survey on assistive technologies for people with Dementia

Today, in developed societies, life expectancy is noticeably increasing due to improved healthcare systems and medical advances. Along with a longer aging period, falling birth rates, and other concomitant social factors, the age distribution is evidencing a shift towards the demographic expansion of older population stages. Epidemiological studies about this demographic aging distribution of the world population indicate that older adults are experiencing a variety of age-related problems, including Dementia. Dementia is overwhelming and frightening for both loved persons who live with Dementia, and also their caregivers. The number of people living with Dementia in societies is now rising at an unprecedented rate. Neither decisive treatment, nor effective medicines have been introduced to help society cope with such age associated pathologies. However, one promising alternative to this emerging crisis has been the adoption of Assistive Technologies (AT). Therefore, for this study, a systematic literature review is conducted to provide an overview about recent findings in this field of study and also address some available technologies that have potential application for Dementia sufferers. This work makes a report on existing technologies in the field, thus becoming a survey on existing supportive technologies for People with Dementia (PwD). The findings of this work provide an insight into the types and percentages of suggested technologies for each classification that aim to be useful for those researching the field or who want to know state of the art in this presently relevant domain. Concluding points and discussion are presented subsequently.
The ecology of cybercrime

Cybersecurity remains a puzzle. While human societies are in the midst of a Digital Revolution that is proving as transformative and disruptive as the two Industrial Revolutions that preceded it, the complex problem of how to safeguard new emerging techno-social assemblages against a broad range of manufactured online risks proves extremely challenging. This chapter proposes to adopt an ecological approach compatible with Norbert Elias’ concept of figuration to analyze the cybersecurity ecosystem and its interdependencies. This approach breaks down the ecosystem into three discrete but interconnected communities that each display very different goals, capacities and rationalities: the industrial, criminal and security communities. These communities comprise organizational actors that range from multinational corporations to criminal networks, public law enforcement organizations and NGOs. The interactions within each community and between communities mainly belong to one of the three following categories: competitive, predatory and cooperative interactions. The application of these ecological concepts to emerging techno-social assemblages can help us identify intervention and regulatory strategies to address the growing cybercrime problem.
Vectorial Boolean Functions with Very Low Differential-Linear Uniformity Using Maiorana-McFarland Type Construction

The differential-linear connectivity table (DLCT) of a vectorial Boolean function was recently introduced by Bar-On et al. at EUROCRYPT’19. In this paper we construct a new class of balanced vectorial Boolean functions with very low differential-linear uniformity and provide a combinatorial count of hardware gates which is required to implement such circuits. Here, all the coordinate functions are constructed by modifying the Maiorana-McFarland bent functions. Further, we derive some properties of DLCT and differential-linear uniformity of modified inverse functions.
TCEC Cup 4

This is a report of the 'TCEC Cup 4' computational experiment, the 4th TCEC knockout event and the second major part of TCEC’s 16th season. It started on 16th October 2019 and featured the best 32 engines of the ‘TCEC 16’ league event and the games were held at Blitz tempo, The different format called for different optimal settings in the engines, thereby enabling a comparison with performance at slower tempi. STOCKFISH retrieved the title from LEELA CHESS ZERO, defeating it when their perspectives diverted on move forty of game eight. All decisive games in the supplementary data have been played out by FRITZ17 at search depth 24 to enable and benchmark endgame practice.