Reviewerly/exHarmony huggingface dataset:
Towards Scalable Abnormal Behavior Detection in Automated Surveillance

This study presents a scalable automated video surveillance framework that (1) automatically detects the occurrences of abnormal behavior patterns by both pedestrians and vehicles, and (2) directs the focus of the security personnel to the relevant camera view, thereby providing global situational awareness. Powered by deep learning, our methodology can detect both vision and location-based abnormalities, including the events of vandalism, violence, loitering, scouting, and speeding. The proposed framework requires a low initial investment cost and features both real-time detection of various abnormal behaviors and post-crime analysis in scalable form, by enabling wide-area multi-camera networks with person/object re-identification. By combining multiple functionalities in an efficient framework, the proposed system opens up new possibilities for surveillance.
F-Divergences and Cost Function Locality in Generative Modelling with Quantum Circuits

Generative modelling is an important unsupervised task in machine learning. In this work, we study a hybrid quantum-classical approach to this task, based on the use of a quantum circuit Born machine. In particular, we consider training a quantum circuit Born machine using $f$-divergences. We first discuss the adversarial framework for generative modelling, which enables the estimation of any $f$-divergence in the near term. Based on this capability, we introduce two heuristics which demonstrably improve the training of the Born machine. The first is based on $f$-divergence switching during training. The second introduces locality to the divergence, a strategy which has proved important in similar applications in terms of mitigating barren plateaus. Finally, we discuss the long-term implications of quantum devices for computing $f$-divergences, including algorithms which provide quadratic speedups to their estimation. In particular, we generalise existing algorithms for estimating the Kullback-Leibler divergence and the total variation distance to obtain a fault-tolerant quantum algorithm for estimating another $f$-divergence, namely, the Pearson divergence.
Genetic Algorithm Based Quantum Circuits Optimization for Quantum Computing Simulation

Quantum computing simulation platform can simulate the computation results of the quantum computer based on traditional computers, which is an effective way to promote the development of quantum computing software, algorithms and hardware at the current immature stage of the real quantum computer. Since quantum computers have exponential calculation acceleration compared with traditional computers, the main problems in implementing quantum computing simulation on traditional computers are low computational efficiency and long time-consuming. A quantum circuit which is a sequence of quantum gates acting on a collection of qubits is the general quantum computing model. So by the means of quantum circuit optimization, the calculation speed can be significantly increased while keeping the calculation result unchanged. The existing empirical rules of quantum circuit optimization methods have limitations and there is no common and automatic quantum circuit optimization method. In this paper, a general and automatic quantum circuit optimization method based on the genetic algorithm is proposed, by which the equivalent optimal quantum circuit is obtained through a finite number of searching in a large searching space. This method is not limited by the hardware of the quantum computing simulation and the composition of the quantum circuit. The experimental results show that for the QFT algorithm of 29 qubits, the running time can be shortened by 41.4% and for the variational circuit of 6 qubits, the running time can be shortened by 18.8% compared with the state-of-the-art quantum circuit optimization method. So this method can improve the quantum computing simulation capability and operating efficiency and provide a rapid development way for quantum algorithms and applications.
Does Data Repair Lead to Fair Models? Curating Contextually Fair Data To Reduce Model Bias

Contextual information is a valuable cue for Deep Neural Networks (DNNs) to learn better representations and improve accuracy. However, co-occurrence bias in the training dataset may hamper a DNNmodel’s generalizabil- ity to unseen scenarios in the real world. For example, in COCO [26], many object categories have a much higher cooccurrence with men compared to women, which can bias a DNN’s prediction in favor of men. Recent works have focused on task-specific training strategies to handle bias in such scenarios, but fixing the available data is often ignored. In this paper, we propose a novel and more generic solution to address the contextual bias in the datasets by selecting a subset of the samples, which is fair in terms of the co-occurrence with various classes for a protected attribute. We introduce a data repair algorithm using the coefficient of variation( c <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">v</inf> ), which can curate fair and contextually balanced data for a protected class(es). This helps in training a fair model irrespective of the task, architecture or training methodology. Our proposed solution is simple, effective and can even be used in an active learning setting where the data labels are not present or being generated incrementally. We demonstrate the effectiveness of our algorithm for the task of object detection and multi-label image classification across different datasets. Through a series of experiments, we validate that curating contextually fair data helps make model predictions fair by balancing the true positive rate for the protected class across groups without compromising on the model’s overall performance. Code: https://github.com/sumanyumuku98/contextual-bias
Personalized Digital Fitness Coach

The Corona crisis limits the mobility of people and it proves that many people have an increased body mass index. A lot of people want to increase their fitness and to boost their immune system. There is a run-on fitness schools and fitness equipment for the home environment. At fitness schools, fitness coaches are available for personalized instructions and supervision. Fitness in the home environment lacks personalized instruction and supervision and can cause serious injuries. In this paper a digital coach will be introduced providing digital support and supervision. The digital coach offers personalized fitness program, supervises athletes during fitness exercises and provides a supporting system concerning injuries. A prototype of the developed system can be downloaded as an app on a smart phone.
A general elimination strategy for camera motion estimation

Camera motion estimation, such as relative pose estimation and absolute pose estimation, are fundamental problems in computer vision and robotics. To obtain the motion parameters, classical methods rely on studying the properties of the geometric matrices, e.g., rotation matrix, essential matrix, homography matrix. The well known five-point algorithm was successfully derived using the singular constraint and trace constraints on the essential matrix. However, finding all the algebraic constraints is not always trivial for some recent problems. In this paper, we propose a simple and general technique to find complete algebraic constraints so that we can derive efficient algorithms. We show that using the quaternion to formulate the rotation matrix we can eliminate any unknowns from the original equations and obtain constraints on the rest of the unknowns based on Gröbner basis. We demonstrate that this approach can be applied to almost all the camera motion estimation and show its improvement compared to the existing methods. Further more, based on this elimination technique, we exploit new constraints for the relative pose estimation with gravity prior, and derive a new globally optimal algorithm to this problem. We compare our algorithm with the state-of-the-art methods on both synthetic and real-world data, and show the benefits including accuracy and efficiency.
New horizons of book science and bibliography

Abstract : In contrast to the sciences, that were formed gradually and in many ways spontaneously, the development of bibliological disciplines was based on their theoretical comprehension without the expectation that they would all arise naturally. The central question remains unclear: is bibliology and its derivatives (library science, bibliography science) a complex of scientific disciplines or a complex discipline? In most cases, their union is limited to separate concepts that unite an established range of problems. This article aims to consider theoretical questions about the book and the bibliography as properties of living systems: what is their purpose, how do they “work” in the body, how do they arise in the course of evolution and how do they behave? Based on the achievements of neuroscience, first of all, on the research of Academician of the Russian Academy of Sciences K. V. Anokhin and his colleagues, the author considers the book as an external cognitome. It is a complete, alienated system of subjective experience formed in an organism in the process of evolution, individual development and learning. In other words, the book is already in us, it is already “embedded” in the body. The author must not invent it, he must translate it. Therefore bibliography can be viewed as a superstructure over an external cognitome. This is a network over a network, i. e. a hypernet. The main general theory is the understanding of the mechanism of interaction of three elements: books (kniga in Russian – K) and (in Russian– I) a bibliography (bibliografiya in Russian – B) – these stands for KIB, where I (and) is the key element. The article considers the issues of reading as a challenge for the brain and thinking in the digital age. Particular attention is paid to “distant reading” (F. Moretti) by using Digital Humanities. F. Moretti's predecessor was the Russian researcher B. I. Yarkho (1889–1942). It concludes that the general theory of science about a book and bibliography should be based on three components: 1) understanding the mechanism of the relationship between a book as an external cognitome and bibliography as a hypertext (KIB); 2) exploring the study of close and distant reading in the digital age; 3) the formation of the foundations of the theory exact study of bibliography.
Overview and Current Status of Advanced Land Observing Satellite-3 (ALOS-3)

The Advanced Land Observing Sattelite-3 (ALOS-3, “Daichi-3”) is a successor to the optical mission of ALOS, “Daichi”. The mission objectives of ALOS-3 are (1) creating and updating geo-spatial information and (2) contributing to the realization of safe and secure society including provisions for natural disaster. To meet these main objectives and respond to other various needs, ALOS-3 equips Wide-Swath and High-resolution Imager (WISH) and has five observation modes. All subsystems/components for ALOS-3 has been completed, and currently the proto-flight tests of the satellite system are underway. ALOS-3 will be launched in JFY 2021 from the Tanegashima Space Center, Japan.
Function-Private Conditional Disclosure of Secrets and Multi-evaluation Threshold Distributed Point Functions

Conditional disclosure of secrets (CDS) allows multiple parties to reveal a secret to a third party if and only if some pre-decided condition is satisfied. In this work, we bolster the privacy guarantees of CDS by introducing function-private CDS wherein the pre-decided condition is never revealed to the third party. We also derive a function secret sharing scheme from our function-private CDS solution. The second problem that we consider concerns threshold distributed point functions, which allow one to split a point function such that at least a threshold number of shares are required to evaluate it at any given input. We consider a setting wherein a point function is split among a set of parties such that multiple evaluations do not leak non-negligible information about it.
Fundamental Constructs in Programming Languages

When a new programming language appears, the syntax and intended behaviour of its programs need to be specified. The behaviour of each language construct can be concisely specified by translating it to fundamental constructs (funcons), compositionally. In contrast to the informal explanations commonly found in reference manuals, such formal specifications of translations to funcons can be precise and complete. They are also easy to write and read, and to update when the language evolves.The PLanCompS project has developed a large collection of funcons. Each funcon is defined independently, using a modular variant of structural operational semantics. The definitions are available online, along with tools for generating funcon interpreters from them.This paper introduces and motivates funcons. It illustrates translation of language constructs to funcons, and funcon definition. It also relates funcons to the notation used in some previous language specification frameworks, including monadic semantics and action semantics.
A Course Recommendation System Oriented to Multiple Condition Constraints

E-Learning recommendation is an important application in the Self-Adopting Learning. The recommendation problem in the education field is special, and it cannot be separated from subjective factors such as students' preference, expectations of performance and so on. The target of e-learning recommendation should be to enhance students' learning enthusiasm. In this work, we propose a novel course recommendation method for learners with multiple objectives and constraints. This method achieves the purpose of recommendation by generating a recommended course list, and employs the genetic algorithm to balance the constraints between various conditions to optimize the generated recommended course list. Experiments confirm that our approach has more advantages than baselines oriented to multiple constraints.
Integrating Individual Preferences into Collective Argumentation

In the field of collective argumentation, multiple agents may have distinct knowledge representations and individual preferences. In order to obtain reasonable collective outcome for the group, either individual frameworks should be merged or individual preference should be aggregated. However, framework merging and preference aggregation are different procedures, leading to disagreements on collective outcome. In this paper, we figure out a solution to combine framework merging, argumentative reasoning and incomplete preference aggregation together. Furthermore, a couple of rational postulates are proposed to be the criteria for the reasonability of collective outcome obtained based on our approach.
Efficient public-key encryption with equality test from lattices

Public-key encryption with equality (PKEET) test enables testing if two ciphertexts, possibly under two different public keys, encrypt the same messages. Recent research on PKEET considers the setting where the testing ability is delegated to semi-trusted parties to negate unfettered chosen-plaintext attacks. In this work, we revise and enhance the PKEET security model, and introduce a new property of unmaskability which further prevents an attacker from skirting the test. We then propose a simple and efficient PKEET system with adaptive chosen-ciphertext security, provably secure under our revised security model, from either plain or ring lattice assumptions. The construction adopts a direct approach which significantly departs from the existing way of building such systems. Compared with existing literature, our system relies on weaker learning-with-errors assumptions while also being more efficient and providing better security.
The Effect of Fatigue on the Performance of Online Writer Recognition

Background: The performance of biometric modalities based on things done by the subject, like signature and text-based recognition, may be affected by the subject state. Fatigue is one of the conditions that can significantly affect the outcome of handwriting tasks. Recent research has already shown that physical fatigue produces measurable differences in some features extracted from common writing and drawing tasks. It is important to establish to which extent physical fatigue contributes to the intra-person variability observed in these biometric modalities and also to know whether the performance of recognition methods is affected by fatigue. Goal: In this paper we assess the impact of fatigue on intra-user variability and on the performance of signature-based and text-based writer recognition approaches encompassing both identification and verification. Methods: Several signature and text recognition methods are considered and applied to samples gathered after different levels of induced fatigue, measured by metabolic and mechanical assessment and, also by subjective perception. The recognition methods are Dynamic Time Warping and Multi Section Vector Quantization, for signatures, and Allographic Text-Dependent Recognition for text in capital letters. For each fatigue level, the identification and verification performance of these methods is measured. Results: Signature shows no statistically significant intra-user impact, but text does. On the other hand, performance of signature-based recognition approaches is negatively impacted by fatigue whereas the impact is not noticeable in text-based recognition, provided long enough sequences are considered.
Review of Unsupervised Learning Techniques

Unsupervised learning methods, as one of the important machine learning methods, have been developing rapidly, receiving more and more attention since they can automatically classify the data according to their attributes. However, most current studies of the unsupervised learning are focused on specific techniques and application scenarios, while few summarize its development and typical algorithms systematically. This paper is devoted to a comprehensive summarization of the unsupervised learning methods. According to different data processing methods, unsupervised learning can be divided into dimensionality reduction, clustering and deep learning-based methods. The methods of dimensionality reduction focus on reducing the complexity and removing redundant features of the data, while keeping the original data structure as much as possible. Clustering methods can automatically classify data according to the data features, which is useful for data analysis. As for deep learning-based methods, deep neural network is used to train the data to achieve higher data processing performance. For each category of the unsupervised learning methods, the typical algorithms and their applications are explained and the recent researches are summarized. Finally, the future development of the unsupervised learning is provided.
A theoretical and computational analysis of full strong-branching

Full strong-branching (henceforth referred to as strong-branching) is a well-known variable selection rule that is known experimentally to produce significantly smaller branch-and-bound trees in comparison to all other known variable selection rules. In this paper, we attempt an analysis of the performance of the strong-branching rule both from a theoretical and a computational perspective. On the positive side for strong-branching, we identify vertex cover as a class of instances where this rule provably works well. In particular, for vertex cover we present: (1) an upper bound on the size of the branch-and-bound tree using strong-branching as a function of the additive integrality gap; (2) show how the Nemhauser-Trotter property of persistency, which can be used as a pre-solve technique for vertex cover, is being recursively and consistently used throughout the strong-branching tree; (3) and finally provide an example of a vertex cover instance where not using strong-branching leads to a tree that has at least exponentially more nodes than the branch-and-bound tree based on strong-branching. On the negative side for strong-branching, we identify another class of instances where the strong-branching tree is exponentially larger than another branch-and-bound tree for solving these instances. On the computational side, we conduct experiments on various types of instances, like the lot-sizing problem and its variants, packing integer programs (IP), covering IPs, chance constrained IPs, vertex cover, etc., to understand how much larger is the size of the strong-branching based branch-and-bound tree in comparison to the optimal branch-and-bound tree. The main take-away from these experiments (on small instances) is that for all these instances the size of the strong-branching tree is within a factor of two of the size of the optimal branch-and-bound tree.
Capability Assessment of Software Product Line Processes in Defense Industry

The Software Product Line (SPL) approach is used to enhance reuse, increase quality, and reduce cost and the project completion time of the developed products. To gain these benefits, companies want to assess their current SPL capability/maturity level and to achieve guidance for improvement. The Software Product Line Capability Maturity Model (SPL-CMM) aims to provide a generic assessment methodology that different companies can use. This research aims to apply SPL-CMM in a defense company that has already been studying the SPL transformation. The SPL-CMM has two dimensions: process and capability. The process dimension includes 16 SPL-speciflc processes. The case study includes assessing these 16 SPL-speciflc processes performed in the Company, determining their current process capability levels, and derivation of a road-map for improving to the next capability level. The results support that SPL-CMM is applicable and usable in the defense industry.
Predicting Disparity Distributions

We investigate a novel deep-learning-based approach to estimate uncertainty in stereo disparity prediction networks. Current state-of-the-art methods often formulate disparity prediction as a regression problem with a single scalar output in each pixel. This can be problematic in practical applications as in many cases there might not exist a single well defined disparity, for example in cases of occlusions or at depth-boundaries. While current neural-network-based disparity estimation approaches obtain good performance on benchmarks, the disparity prediction is treated as a black box at inference time. In this paper we show that by formulating the learning problem as a regression with a distribution target, we obtain a robust estimate of the uncertainty in each pixel, while maintaining the performance of the original method. The proposed method is evaluated both on a large-scale standard benchmark, as well on our own data. We also show that the uncertainty estimate significantly improves by maximizing the uncertainty in those pixels that have no well defined disparity during learning.