Reviewerly/exHarmony huggingface dataset:
Inducing Predictive Uncertainty Estimation for Face Recognition

Knowing when an output can be trusted is critical for reliably using face recognition systems. While there has been enormous effort in recent research on improving face verification performance, understanding when a model's predictions should or should not be trusted has received far less attention. Our goal is to assign a confidence score for a face image that reflects its quality in terms of recognizable information. To this end, we propose a method for generating image quality training data automatically from 'mated-pairs' of face images, and use the generated data to train a lightweight Predictive Confidence Network, termed as PCNet, for estimating the confidence score of a face image. We systematically evaluate the usefulness of PCNet with its error versus reject performance, and demonstrate that it can be universally paired with and improve the robustness of any verification model. We describe three use cases on the public IJB-C face verification benchmark: (i) to improve 1:1 image-based verification error rates by rejecting low-quality face images; (ii) to improve quality score based fusion performance on the 1:1 set-based verification benchmark; and (iii) its use as a quality measure for selecting high quality (unblurred, good lighting, more frontal) faces from a collection, e.g. for automatic enrolment or display.
Human Factors in Homograph Attack Recognition

Homograph attack is a way that attackers deceive victims about which website domain name they are communicating with by exploiting the fact that many characters look alike. The attack becomes serious and is raising broad attention when recently many brand domains have been attacked such as Apple Inc., Adobe Inc., Lloyds Bank, etc. We first design a survey of human demographics, brand familiarity, and security backgrounds and apply it to 2,067 participants. We build a regression model to study which factors affect participants’ ability in recognizing homograph domains. We find that for different levels of visual similarity, the participants exhibit different abilities. 13.95% of participants can recognize non-homographs while 16.60% of participants can recognize homographs whose the visual similarity with the target brand domains is under 99.9%; but when the similarity increases to 99.9%, the number of participants who can recognize homographs significantly drops down to only 0.19%; and for the homographs with 100% of visual similarity, there is no way for the participants to recognize. We also find that female participants tend to recognize homographs better the male but male participants tend to able to recognize non-homographs better than females. Security knowledge is a significant factor affecting both homographs and non-homographs; surprisingly, people who have strong security knowledge tend to be able to recognize homographs but not non-homographs. Furthermore, people who work or are educated in computer science or computer engineering do not appear as a factor affecting the ability in recognizing homographs; however, interestingly, right after they are explained about the homograph attack, people who work or are educated in computer science or computer engineering are the ones who can capture the situation the most quickly.
Ability of Covariance Matrix in Bi-Response Multi-Prredictor Penalized Spline Model Through Longitudinal Data Simulation

Abstract: Bi-response longitudinal data is assumed to have a correlation between responses and observations on the same subject. This causes a correlation between errors. To overcome this problem, we can use a penalized spline model that involves weighting. In this study, the weight used is the covariance matrix. Based on longitudinal data simulations that contain two responses and three predictors, a small GCV value is obtained from the penalized spline model that involves the covariance matrix. This value is compared with the penalized spline model without the covariance matrix. This shows that we need to involve a covariance matrix in estimating bi-response multi-predictor longitudinal data with a penalized spline model.
Classically Simulating Quantum Circuits with Local Depolarizing Noise

We study the effect of noise on the classical simulatability of quantum circuits defined by computationally tractable (CT) states and efficiently computable sparse (ECS) operations. Examples of such circuits, which we call CT-ECS circuits, are IQP, Clifford Magic, and conjugated Clifford circuits. This means that there exist various CT-ECS circuits such that their output probability distributions are anti-concentrated and not classically simulatable in the noise-free setting (under plausible assumptions). First, we consider a noise model where a depolarizing channel with an arbitrarily small constant rate is applied to each qubit at the end of computation. We show that, under this noise model, if an approximate value of the noise rate is known, any CT-ECS circuit with an anti-concentrated output probability distribution is classically simulatable. This indicates that the presence of small noise drastically affects the classical simulatability of CT-ECS circuits. Then, we consider an extension of the noise model where the noise rate can vary with each qubit, and provide a similar sufficient condition for classically simulating CT-ECS circuits with anti-concentrated output probability distributions.
Multi-clients Verifiable Computation via Conditional Disclosure of Secrets

In this paper, we explore the connection between two-party conditional disclosure of secrets (CDS) and verifiable computation. Here, the integrity mechanism underlying CDS is leveraged to ensure two-clients verifiable computation, where the computation is outsourced to an external server by two clients that share the input to the function. Basing integrity on CDS enjoys several significant advantages such as non-interactivity, constant rate communication complexity, a simple verification procedure, easily batched, and more. In this work, we extend the definition of plain CDS, considering two additional security properties of privacy and obliviousness that respectively capture input and output privacy. We then show that these extended notions of CDS are useful for designing secure two-party protocols in the presence of an untrusted third party. We complement the above with a sequence of new CDS constructions for a class of predicates of interest, including private set-intersection (PSI) and set-union cardinality, comparison, range predicate, and more. Based on these constructions we design new non-interactive constant-rate protocols for comparing two strings based on symmetric-key cryptography, and without requiring bit-decomposition. We additionally design new protocols for PSI cardinality and PSI based on recent work by Le, Ranellucci, and Gordon (CCS 2019) with similar advantages.
Length Adaptive Regularization for Retrieval-based Chatbot Models

Chatbots aim to mimic real conversations between humans. They have started playing an increasingly important role in our daily life. Given past conversations, a retrieval-based chatbot model selects the most appropriate response from a pool of candidates. Intuitively, based on the nature of the conversations, some responses are expected to be long and informative while others need to be more concise. Unfortunately, none of the existing retrieval-based chatbot models have considered the effect of response length. Empirical observations suggested the existing models over-favor longer candidate responses, leading to sub-optimal performance.
Decentralized robinson list

A Robinson list protects users' privacy against the always more frequent spam calls by allowing them to express a consent or a denial about market operator calls. Nowadays, Robinson lists run as centralised services. In this paper we discuss the possibility of implementing this valuable tool as a decentralised service. We identify the requirements of a decentralised Robinson list and the characteristics of a blockchain technology needed to support an implementation. Then, we present a general solution and a proof-of-concept implementation based on the Algorand technology. A preliminary performance evaluation suggest that also in terms of performance we can achieve satisfactory results. The paper is concluded discussing some fundamental aspects related to privacy and identity management and presenting a number of open research challenges that need to be addressed for a successful development of our solution.
Importance of managing the professional development of young teachers

The article considers the importance of managing the process of professional development of young teachers in the education system of Kazakhstan. The author focuses on the introduction of a modern approach to the implementation of personnel policy in educational organizations, as well as on the method of training young professionals for schools and further development of the competencies of a novice teacher. In addition, the author suggests a number of measures for the successful adaptation of future teachers in the framework of state preparatory programs. The article considers the effectiveness of the "mentoring" function in modern realities, as well as the team approach in the process of becoming future teachers.
Smartwatch-based topic suggestions to enrich casual conversations in awkward encounters

Encounters with casual acquaintances are common in our daily lives. In such situations, people are sometimes unable to find an appropriate topic for conversation, and as such, an awkward silence follows. However, we believe that this awkward encounter can be an opportunity to build a good relationship with the acquaintance through a brief conversation if an appropriate topic is discovered. In this study, we examined a method to enrich casual conversations for an unintended encounter by following three strategies. (1) an online questionnaire survey that involves 10,750 participants to determine how they experience awkward encounters. (2) the design and implementation of a smartwatch-based topic suggestion that relies on finding a commonality in the users' video-viewing histories. (3) demos and semi-structured interviews that involves 15 participants to evaluate this approach. This investigation demonstrates that this novel approach can help users overcome the awkwardness of conversations with casual acquaintances.
Coherent video generation for multiple hand-held cameras with dynamic foreground

Abstract For many social events such as public performances, multiple hand-held cameras may capture the same event. This footage is often collected by amateur cinematographers who typically have little control over the scene and may not pay close attention to the camera. For these reasons, each individually captured video may fail to cover the whole time of the event, or may lose track of interesting foreground content such as a performer. We introduce a new algorithm that can synthesize a single smooth video sequence of moving foreground objects captured by multiple hand-held cameras. This allows later viewers to gain a cohesive narrative experience that can transition between different cameras, even though the input footage may be less than ideal. We first introduce a graph-based method for selecting a good transition route. This allows us to automatically select good cut points for the hand-held videos, so that smooth transitions can be created between the resulting video shots. We also propose a method to synthesize a smooth photorealistic transition video between each pair of hand-held cameras, which preserves dynamic foreground content during this transition. Our experiments demonstrate that our method outperforms previous state-of-the-art methods, which struggle to preserve dynamic foreground content.
CNN Classifier based Low-resolution Face Recognition Algorithm

Faces captured under an uncontrolled environment usually have low-resolution (LR) in addition to poor illumination conditions and uncontrolled poses. These types of images significantly deteriorate the performance of traditional face recognition techniques. Hence, in this paper, a new face recognition algorithm is developed to minimize the problem of matching the LR images with high-resolution gallery images. In the proposed algorithm, the convolutional neural network (CNN) based classification mechanism is used to improve recognition accuracy. Investigational results performed on two standard databases of face images exposes the preeminence of the proposed algorithm over the other comparative existing face recognition models.
Research on the Social Impact of Artificial Intelligence and Government’s Coping Strategies

“Artificial intelligence” is one of the most popular buzzwords in the society at present, and was selected as the “Top Ten Chinese Media Popularity in 2017”. Human society is gradually entering a new era of artificial intelligence. Artificial intelligence is not just a scientific and technological innovation, but will bring about a big change in social life. As the State Council’s “New Generation Artificial Intelligence Development Plan” pointed out: “The rapid development of artificial intelligence will profoundly change human social life and change the world.” In the face of the new situation in the new era, governments at all levels must take the initiative to seek change and change, firmly grasp the major historical opportunities for the development of artificial intelligence, keep abreast of development, research and judge the general trend, actively plan, grasp the direction, seize the opportunities, and lead the world in the development of artificial intelligence. The trend, serving economic and social development and supporting national security, drives the overall leap and leapfrog development of national competitiveness.
Linear Optimal Estimators for Systems With Multiplicative Noises and Time-Correlated Additive Noise

This paper is concerned with the state estimation problem for systems with state multiplicative noise, measurement multiplicative noise and time-correlated additive measurement noise. The multiplicative noises are uncorrelated white noises with zero means. Time-correlated measurement noise is described by a linear model with Gauss white noise. Based on a measurement difference method, the linear optimal estimators including filter, multi-step predictor and multistep smoother are derived in the linear minimum variance (LMV) sense. The proposed estimators are recursive. They do not require the inverse of the state transition matrix. The simulation research verifies the effectiveness of the proposed algorithms.
C-DAG: Community-Assisted DAG Mechanism with High Throughput and Eventual Consistency

Blockchain provides a trust model applied to distributed networks, which can achieve strong consistency under decentralized conditions according to its serial chain structure and consensus algorithm. However, the scalability bottleneck limits practical applications. Directed Acyclic Graph (DAG) technology can greatly improve the scalability of system, but it also inevitably incurs security and consistency issues. For addressing the above issues, this paper proposes Community-assisted DAG (C-DAG) model, which can achieve the following advantages: (1) High throughput. Based on Clauset-Newman-Moore (CNM) community detection algorithm, the closely connected network entity nodes are divided into a community. Through the DAG-based parallel transactions in the community, the throughput is greatly improved. (2) Eventual consistency. POV-combined PBFT consensus adopted inside the community and the event-driven global block sequencing algorithm applied between the communities guarantee the convergence and final consistency of C-DAG. The paper finally verifies the high throughput and low latency of C-DAG consensus based on simulation experiments.
Multi-Sensor-based Predictive Control for Autonomous Parking in Presence of Pedestrians

This paper explores the feasibility of a Multi-Sensor-Based Predictive Control (MSBPC) approach in order to have constraint-based backward non-parallel (perpendicular and diagonal) parking maneuvers capable of dealing with moving pedestrians and, if necessary, performing multiple maneuvers. Our technique relies solely in sensor data expressed relative to the vehicle and therefore no localization is inherently required. Since the proposed approach does not plan any path and instead the controller maneuvers the vehicle directly, the classical path planning related issues are avoided. Real experimentation validates the effectiveness of our approach.
Solar Radiation Estimation and Prediction Methods: a Review and Classification

Various methods used to estimate and predict solar radiation are reviewed. Ground-based solar radiation intensity measurements are not carried out everywhere, despite the fact that the need to estimate the gross (theoretical) potential of solar energy in a region arises quite often. In such cases, approximate estimates of solar energy resources are carried out using various models, the main ones of which are considered in this paper. The availability of an adequately estimated local solar energy potential is important not only for rational distribution of funds in designing, but also for predicting the output of a power plant that uses solar energy. A general classification of methods for estimating and predicting solar radiation by the type of used parameters and estimated quantities (physical and statistical models) is presented. Statistical methods include conventional empirical models, in which the monthly average daily sums of solar radiation on a horizontal surface are estimated with dividing them into 4 categories depending on the input parameters involved, and models that evaluate the sums of the solar radiation diffuse component on horizontal (decomposition models and parametric models) and inclined (isotropic and anisotropic models) receiving areas. Models based on data mining methods are also referred to statistical methods for estimating and predicting solar radiation. The suggested statistical models are based on various parameters such as sunshine duration, maximum ambient temperature, relative humidity, pressure, latitude and longitude of the locality, the day of year ordinal number, cloudiness index, average ambient temperature, altitude, average wind velocity, precipitation, etc. The results of modeling the monthly average daily amounts of total solar radiation on a horizontal surface according to the universal empirical models are evaluated and compared with the long-term average data of ground-based measurements on the territory of Kyrgyzstan and with the data from the NASA POWER database.
1st Place Solution to Google Landmark Retrieval 2020

This paper presents the 1st place solution to the Google Landmark Retrieval 2020 Competition on Kaggle. The solution is based on metric learning to classify numerous landmark classes, and uses transfer learning with two train datasets, fine-tuning on bigger images, adjusting loss weight for cleaner samples, and esemble to enhance the model's performance further. Finally, it scored 0.38677 mAP@100 on the private leaderboard.
Prospects for the solar energy development in housing construction: an assessment of economic efficiency

Abstract Currently, around the world there is an acute question of the new energy sources’ development and implementation. It is known that the most significant of them today are oil, natural gas, coal, electricity. Oil and gas reserves are not unlimited. It is necessary to look for the alternative sources of energy [4, 5, 6, 14]. This is due not only to a shortage of basic energy resources, but to the increasing cost of their extraction and processing, as well as to an aggravation of the environmental situation in the world [1]. In search of the new sources of energy, people are increasingly turning to solar panels. This is an excellent replacement for the generators of various types, some of which may be slightly functional or potentially dangerous for a private house. The solar energy has been known for a long time, it is a subject of debate and discussion among the specialists. Some people think that this is a great prospect for the future, others are sure of the opposite, since on the one hand, solar panels do not require expenses for their operation, but the cost of this equipment is high. Some experts claim that the profit from this project will not be able to cover the costs associated with construction. In contrast, these devices can work for tens and hundreds of years, therefore, with long-term operation, the profit will be obvious [4, 5, 6, 11, 14].
Fast Fusion of Basic Belief Assignments Defined on a Dichotomous Frame of Discernment

In this paper, we propose a new fusion approach to combine basic belief assignments (BBAs) defined on a dichotomous frame of discernment based on their canonical decomposition. In a companion paper, we have already proved that the canonical decomposition of this type of BBA (called dichotomous BBA) is always possible and unique thanks to the proportional conflict redistribution rule No 5 (PCR5). More precisely, any dichotomous BBA is always the PCR5 combination of two simpler basic belief assignments named respectively the pro-evidence, and the contra-evidence. From this interesting canonical decomposition, we present a new way of combining many dichotomous BBAs and we show that the computational time for fusing these dichotomous BBAs based on their canonical decomposition is quasi-linear with the number of sources to combine, contrary to the direct fusion of the dichotomous BBAs altogether.
Using Grammar-Based Genetic Programming for Mining Disjointness Axioms Involving Complex Class Expressions

In the context of the Semantic Web, learning implicit knowledge in terms of axioms from Linked Open Data has been the object of much current research. In this paper, we propose a method based on grammar-based genetic programming to automatically discover disjointness axioms between concepts from the Web of Data. A training-testing model is also implemented to overcome the lack of benchmarks and comparable research. The acquisition of axioms is performed on a small sample of DBpedia with the help of a Grammatical Evolution algorithm. The accuracy evaluation of mined axioms is carried out on the whole DBpedia. Experimental results show that the proposed method gives high accuracy in mining class disjointness axioms involving complex expressions.
A Roadmap to Domain Knowledge Integration in Machine Learning

Many machine learning algorithms have been developed in recent years to enhance the performance of a model in different aspects of artificial intelligence. But the problem persists due to inadequate data and resource. Integrating knowledge in a machine learning model can help to overcome these obstacles up to a certain degree. Incorporating knowledge is a complex task though because of various forms of knowledge representation. In this paper, we will give a brief overview of these different forms of knowledge integration and their performance in certain machine learning tasks.
Setting the Stage for Responsible Design

Designers have responsibility by the very nature of their activities; bringing new products and services into the world of the user.Recently there is also raising interest in specifically addressing social issues by deliberate design interventions.Within the University of Twente we strive to shape this responsibility in the context of the design of Human Technology Relations, combining human-technology interaction with scenario-based, user-oriented product design.The research groups associated with design have each developed their own perspective on how to implement this responsibility in design research and practice.Three different design approaches, each of which are strengthened with methods and tools.Although these three perspectives are complementary, they also have their limitations.In this paper we describe how we broadened the three research strands into a multidisciplinary research agenda through a workshop with a diverse group of participants.Providing the stage for a "Twente School" in responsible design.