Reviewerly/exHarmony huggingface dataset:
Closing the Reality Gap with Unsupervised Sim-to-Real Image Translation for Semantic Segmentation in Robot Soccer.

Deep learning approaches have become the standard solution to many problems in computer vision and robotics, but obtaining proper and sufficient training data is often a problem, as human labor is often error prone, time consuming and expensive. Solutions based on simulation have become more popular in recent years, but the gap between simulation and reality is still a major issue. In this paper, we introduce a novel model for augmenting synthetic image data through unsupervised image-to-image translation by applying the style of real world images to simulated images with open source frameworks. This model intends to generate the training data as a separate step and not as part of the training. The generated dataset is combined with conventional augmentation methods and is then applied to a neural network capable of running in real-time on autonomous soccer robots. Our evaluation shows a significant improvement compared to networks trained on simulated images without this kind of augmentation.
On Incentive Compatible Role-based Reward Distribution in Algorand

Algorand is a recent, open-source public or permissionless blockchain system that employs a novel proof-of-stake byzantine consensus protocol to efficiently scale the distributed transaction agreement problem to billions of users. In addition to being more democratic and energy-efficient, compared to popular protocols such as Bitcoin, Algorand also touts a much high transaction throughput. This paper is the first attempt in the literature to study and address this problem. By carefully modeling the participation costs and rewards received within a strategic interaction scenario, we first empirically show that even a small number of nodes defecting to participate in the protocol tasks due to insufficiency of the available incentives can result in the Algorand network failing to compute and add new blocks of transactions. We further show that this effect can be formalized by means of a mathematical model of interaction in Algorand given its participation costs and the current (or planned) reward distribution/sharing approach envisioned by the Algorand Foundation. Specifically, on analyzing this game model we observed that mutual cooperation under the currently proposed reward sharing approach is not a Nash equilibrium. This is a significant result which could threaten the success of an otherwise robust distributed consensus mechanism. We propose a novel reward sharing approach for Algorand and formally show that it is incentive-compatible, i.e., it can guarantee cooperation within a group of selfish Algorand users. Extensive numerical and Algorand simulation results further confirm our analytical findings. Moreover, these results show that for a given distribution of stakes in the network, our reward sharing approach can guarantee cooperation with a significantly smaller reward per round.
Off-Policy Policy Gradient Algorithms by Constraining the State Distribution Shift.

Off-policy deep reinforcement learning (RL) algorithms are incapable of learning solely from batch offline data without online interactions with the environment, due to the phenomenon known as \textit{extrapolation error}. This is often due to past data available in the replay buffer that may be quite different from the data distribution under the current policy. We argue that most off-policy learning methods fundamentally suffer from a \textit{state distribution shift} due to the mismatch between the state visitation distribution of the data collected by the behavior and target policies. This data distribution shift between current and past samples can significantly impact the performance of most modern off-policy based policy optimization algorithms. In this work, we first do a systematic analysis of state distribution mismatch in off-policy learning, and then develop a novel off-policy policy optimization method to constraint the state distribution shift. To do this, we first estimate the state distribution based on features of the state, using a density estimator and then develop a novel constrained off-policy gradient objective that minimizes the state distribution shift. Our experimental results on continuous control tasks show that minimizing this distribution mismatch can significantly improve performance in most popular practical off-policy policy gradient algorithms.
Natural Language Processing for Scam Detection. Classic and Alternative Analysis Techniques

We have seen, over the past decades, an overwhelming increase in the volumes of information being generated, distributed and shared, specially but not only, though social media networks. It has led to an exponential growth on the importance of data in the decision taking processes of most industries and economic sectors, proving the criticality of ensuring the quality of the information we are gathering and using. Whilst most of this information is, or at least is intended to be, true, a non-negligible portion of it contains false information. Miss-information campaigns have played an important role in recent and critical decision-taking processes such as the Brexit referendum or the 2016 U.S. presidential elections.The current spread of incorrect information constitutes a meaningful potential risk on information systems’ management. This problem becomes even greater when considering decision taking automatic algorithms. As a matter of fact, social media and opened access to data may constitute a way to break the information’s asymmetry that has traditionally affected areas such as the financial industry.This paper will propose different techniques of natural language processing, from the more traditional ones to a brief approach to more recently developed techniques on deep learning approaches. They are all intended to enable an automatic texts’ classification in different discussion forums and constructing procedures to pursue users or groups of users’ classifications as an open gate to generate attribution procedures for information sources.
Computationally Intelligent: Crockett is Educating the Next Generation [Women to Watch]

The arrival and future iterations of artificial intelligence and machine learning have been hot topics for some time now, but perhaps less well known is the cadre of academics and industry practitioners that sits behind the machines, researching and experimenting with how to model, govern, and engage true human behavior within these systems. This field, known as computational intelligence, is composed of three main areas: fuzzy systems, evolutionary computation, and neural networks.
Comparative analysis of swarm intelligence algorithms for multi-agent systems coordination

This study aims to describe the most commonly used swarm intelligence algorithms: particle swarm optimization method, ant colony optimization algorithm and bee colony optimization algorithm. The principal features of swarm intelligence algorithms that allow their usage for robotic technology systems design were considered. The algorithms operating principles were described and illustrated by block diagrams. In addition, the main pros and cons of each algorithm were reported. A comparative analysis of the algorithms with the description of their main advantages and application fields was done.This study aims to describe the most commonly used swarm intelligence algorithms: particle swarm optimization method, ant colony optimization algorithm and bee colony optimization algorithm. The principal features of swarm intelligence algorithms that allow their usage for robotic technology systems design were considered. The algorithms operating principles were described and illustrated by block diagrams. In addition, the main pros and cons of each algorithm were reported. A comparative analysis of the algorithms with the description of their main advantages and application fields was done.
Sampled-data Fuzzy Observer Design for State Estimation of Uncertain Systems Under Imperfect Premise Matching

In this paper, we propose a sampled-data fuzzy observer design technique for estimating the state variables of a nonlinear system with model uncertainty. It is assumed that the IF-THEN rules of the fuzzy system contains immeasurable premise variables, which complicates the observer design. In this paper, the observer is assumed not to share the same premise part with that of the system in order to deal with the immeasurable premise condition. After then, the error between the observer and the system including model uncertainty is represented by the Takagi-Sugeno (T-S) fuzzy model. In order to minimize the impact of imperfect premise matching, model uncertainty, and disturbances on the state estimation, an HSUB∞/SUB performance criterion is defined. Based on the fuzzy Lyapunov function, we derive a sufficient condition in the form of linear matrix inequality to ensure that the error dynamics is asymptotically stable and satisfy the HSUB∞/SUB condition. Finally, a simulation example verifies the superiority of the proposed method.
Elementos de teoría de la percepción en la musicología de Pierre Souvtchinsky: El principio de desdoblamiento y concentración en Igor Stravinsky

espanolEn sus escritos musicales mas destacados, Pierre Souvtchinsky, alude de diversas formas a un mecanismo de desdoblamiento y concentracion en el ambito perceptivo. Estos conceptos y la reflexion sobre el tiempo musical (cronos), cuestion central en el filosofo, aparecen de manera particular en sus textos dedicados a Igor Stravinsky. En la primera parte de este articulo se expone y analiza dicho mecanismo, hasta ahora inexplorado en el pensamiento de Souvtchinsky. Tratando de articular estos conceptos y el modo en que el filosofo aborda la musica, el tiempo musical y la figura del compositor, en la segunda parte se propone la aplicacion de los mismos para iluminar algunas particularidades del pensamiento musical de Stravinsky. Por ultimo, ponderando la posible categorizacion de este mecanismo como principio estructural de la percepcion, se sugiere la hipotesis de que pueda constituir una herramienta novedosa en el analisis del pensamiento musical. EnglishIn his most outstanding musical writings, Pierre Souvtchinsky alludes in various ways to a mechanism of unfolding and concentration in the perceptual field. These concepts and the reflection on musical time (chronos), a central issue in the philosopher, appear in a particular way in his texts dedicated to Igor Stravinsky. Considering, among others, some partially unpublished source, in the first part of this article is exposed and analyzed this mechanism, until now unexplored in the thinking of Souvtchinsky. Trying to articulate these concepts and the way in which the philosopher approaches the music, the musical time and the figure of the composer, in the second part is proposed its application to illuminate some peculiarities of Stravinsky's musical thought. Finally, pondering the possible categorization of this mechanism as a structural principle of perception, we suggest the hypothesis that it can be an appropriate tool for the analysis of a musical thought.
A Formal Treatment of Deterministic Wallets.

In cryptocurrencies such as Bitcoin or Ethereum, users control funds via secret keys. To transfer funds from one user to another, the owner of the money signs a new transaction that transfers the funds to the new recipient. This makes secret keys a highly attractive target for attacks, and has lead to prominent examples where millions of dollars worth in cryptocurrency have been stolen. To protect against these attacks, a widely used approach are so-called hot/cold wallets. In a hot/cold wallet system, the hot wallet is permanently connected to the network, while the cold wallet stores the secret key and is kept without network connection. In this work, we propose the first comprehensive security model for hot/cold wallets and develop wallet schemes that are provable secure within these models. At the technical level our main contribution is to provide a new provably secure ECDSA-based hot/cold wallet scheme that can be integrated into legacy cryptocurrencies such as Bitcoin. Our construction and security analysis uses a modular approach, where we show how to generically build secure hot/cold wallets from signature schemes that exhibit a rerandomizing property of the keys.
Impact on Mental Health of Women Victims of Cyber Violence: A Case Analysis

In one of the shocking and bizarre incident recently a eight year girl, Asifa was gang-raped by a group of men before she was killed in Kathua, located in Jammu and Kashmir. This incidence created huge protest in the social media and many celebrities as well as common people took to social media to display their anger and disgust. Violence against women takes place in various forms which cannot be confined to one state or a nation. It is not bound by any religion, caste or social status. Abusing Woman is a regular phenomenon which occurs in many homes which many choosing to ignore considering it to be a trivial matter. “Without anyone's knowledge she gets heartbroken and falls mentally sick sometimes leading to suicides or even murder thereby affecting her physical and mental health which ultimately leads to disastrous consequences on children and family. Rights of a woman are restricted and she is confined within the vicious circle of blames, humiliation and segregation. Various studies, research and surveys are being conducted on regular basis on gender violence”1. With the advancement of technology and development of the smart phones, women are falling prey to online violence at an alarming rate. The studies show women are victims of various types of violence and social media contributes to an increasing rate of violence against women falling prey to trolling, body shaming etc. This paper would aim at making a theoretical study from the following angles: An Introduction of the Social Networking sites and Gender Based Violence, An analysis of some shocking incidents of Gender Violence that made it to the headlines, How do women fall prey to social networking sites, remedial actions to be taking for preventing such measures and a conclusion.
Stochastic Cloning for Robust Fusion of Multiple Relative and Absolute Measurements.

Fusing multiple sensors is vital for the precise online localization of mobile robots. The Extended Kalman filter is well suited for this purpose and allows to directly integrate multiple absolute measurements in the filter. However, multiple relative state measurements cannot be fused directly into the filter because they measure differences between a past state and the current state, which introduces correlations. These interdependencies can be modeled by stochastic cloning, which introduces a state augmentation by cloning the respective state estimates connected by a relative measurement. This paper investigates the impact of multiple mixed relative and absolute updates on the estimated state. An approach to feed back information from absolute updates to the cloned state by explicit cloning is presented and compared to existing implicit methods.
Impossibility Theorems for Domain Adaptation

In many scientific areas, we often seek to obtain general conclusions about the behavior of an object of interest and different phenomena related to its behavior. The conclusions that we are usually aiming for mainly concern the conditions under which these phenomena occur as well as assumptions that prevent them from happening. In mathematics, for instance, a statement can be proved or rejected: while first result is obviously what one is looking for, the second is also extremely valuable for understanding the studied properties of an object. A very direct analogy to this can also be drawn regarding the main subject of this book. In the previous chapters, we have already reviewed the problem of domain adaptation from the theoretical point of view in a variety of different paradigms. This analysis provided us with strong evidence about the necessary conditions that lead to efficient adaptation. They include (i) proper discrepancy minimization between the source and the target domains and (ii) the existence of a good hypothesis that leads to a low error for two domains. These conditions are very important because they give us a thorough theoretical understanding of what domain adaptation is and guide us in designing new effective adaptation algorithms.
A Multilayered and Data-Driven Method for Exploring Arabic in Multilingual Settings

Scholars in Arabic dialectology are widely concerned with the linguistic effects of societal bi-and multilingualism. The present chapter intends to illustrate a non-aprioristic and computer-assisted method for the study of Arabic in multilingual settings. Taking examples from two different sociolinguistic situations, we will illustrate new solutions for annotating and analyzing plurilingual corpora by means of a multilayered annotation system based on JAXE.
Defensive Few-shot Adversarial Learning.

The robustness of deep learning models against adversarial attacks has received increasing attention in recent years. However, both deep learning and adversarial training rely on the availability of a large amount of labeled data and usually do not generalize well to new, unseen classes when only a few training samples are accessible. To address this problem, we explicitly introduce a new challenging problem -- how to learn a robust deep model with limited training samples per class, called defensive few-shot learning in this paper. Simply employing the existing adversarial training techniques in the literature cannot solve this problem. This is because few-shot learning needs to learn transferable knowledge from disjoint auxiliary data, and thus it is invalid to assume the sample-level distribution consistency between the training and test sets as commonly assumed in existing adversarial training techniques. In this paper, instead of assuming such a distribution consistency, we propose to make this assumption at a task-level in the episodic training paradigm in order to better transfer the defense knowledge. Furthermore, inside each task, we design a task-conditioned distribution constraint to narrow the distribution gap between clean and adversarial examples at a sample-level. These give rise to a novel mechanism called multi-level distribution based adversarial training (MDAT) for learning transferable adversarial defense. In addition, a unified $\mathcal{F}_{\beta}$ score is introduced to evaluate different defense methods under the same principle. Extensive experiments demonstrate that MDAT achieves higher effectiveness and robustness over existing alternatives in the few-shot case.
Domain Adaptation Theory Based on Algorithmic Properties

In this chapter, we first review the work of Mansour and Schain, where they derive generalization bound based on the algorithm with regard to the algorithmic robustness of Xu and Mannor recalled in Chapter 1.
Action-Point in Human Driving

When following a vehicle, drivers often change their acceleration at so called action-points (AP), and keep it constant in between them. By investigating a large data-set of car-following data, the state- and time-distributions of the APs is analyzed. In the state-space spanned by speed-difference and distance to the lead vehicle, this distribution is mostly proportional to the distribution of all data-points, with small deviations from this. Therefore, the APs are not concentrated around certain thresholds as is claimed by psycho-physical car-following models. Instead, small distances indicate a slightly higher probability of finding an AP than is the case for large distances. The time-differences between subsequent action-points follow a one-humped distribution that is comparable with a log-normal-distribution. Both results lend support to the hypothesis that APs are not triggered by the crossing of certain perception thresholds.
Collection Development and Management for 21<sup>st</sup> Century Library Collections: An Introduction, 2<sup>nd</sup> ed.

The second edition of this textbook describes the processes librarians use to select, acquire and maintain resources in multiple formats. It discusses the differences between various types of libra...
Cyber Security Education Using Integrative Learning Module for an Optimum Learning Experience

Since our world is getting more connected through information technology, home internet users, businesses, and the country’s security and defences are prone to threats by cyber-attacks. Therefore, it is important to improve the nation’s cyber security preparedness, increase public awareness of cyber security issues, and increase the number of trained cyber security specialists. For these reasons, cyber security education is essential in preparing computer users with the knowledge and skills that will significantly improve security and lower the risks related to the negative effect of digital ecosystems. To deliver an effective cyber security education, it is important to translate conceptual knowledge into practice and promote lifelong learning in cyber security. Scholars have proven that exposure to knowledge and practice is effective in eliminating the gaps between perceived concepts and actual knowledge. Thus, there is a need for a cyber security education system using an integrative learning module for an optimum learning experience from an early age. Therefore, this paper presents a student-centric hands-on system for learning about cyber security. The interactive system was developed based on the ADDIE model to provide lifelong cyber security education using an integrative learning module for an optimum learning experience for secondary students.
Meta Decision Trees for Explainable Recommendation Systems

We tackle the problem of building explainable recommendation systems that are based on a per-user decision tree, with decision rules that are based on single attribute values. We build the trees by applying learned regression functions to obtain the decision rules as well as the values at the leaf nodes. The regression functions receive as input the embedding of the user's training set, as well as the embedding of the samples that arrive at the current node. The embedding and the regressors are learned end-to-end with a loss that encourages the decision rules to be sparse. By applying our method, we obtain a collaborative filtering solution that provides a direct explanation to every rating it provides. With regards to accuracy, it is competitive with other algorithms. However, as expected, explainability comes at a cost and the accuracy is typically slightly lower than the state of the art result reported in the literature.
학생부종합전형 공정성에 대한 교사의 인식과 개선방안: 비평준화지역 일반계고등학교에 관한 질적사례연구

The purpose of this study is to find out the improvement based on the perception of high school teachers about the fairness of the comprehensive school card admission system in the special situation and context of general high school in non-equilibrium area. To do this, I conducted a qualitative cas...
Reasoning Over Paths via Knowledge Base Completion

Reasoning over paths in large scale knowledge graphs is an important problem for many applications. In this paper we discuss a simple approach to automatically build and rank paths between a source and target entity pair with learned embeddings using a knowledge base completion model (KBC). We assembled a knowledge graph by mining the available biomedical scientific literature and extracted a set of high frequency paths to use for validation. We demonstrate that our method is able to effectively rank a list of known paths between a pair of entities and also come up with plausible paths that are not present in the knowledge graph. For a given entity pair we are able to reconstruct the highest ranking path 60% of the time within the the top 10 ranked paths and achieve 49% mean average precision. Our approach is compositional since any KBC model that can produce vector representations of entities can be used.