Reviewerly/exHarmony huggingface dataset:
Constraint learning for control tasks with limited duration barrier functions

When deploying autonomous agents in unstructured environments over sustained periods of time, adaptability and robustness oftentimes outweigh optimality as a primary consideration. In other words, safety and survivability constraints play a key role and in this paper, we present a novel, constraint-learning framework for control tasks built on the idea of constraints-driven control. However, since control policies that keep a dynamical agent within state constraints over infinite horizons are not always available, this work instead considers constraints that can be satisfied over some finite time horizon T>0, which we refer to as limited-duration safety. Consequently, value function learning can be used as a tool to help us find limited-duration safe policies. We show that, in some applications, the existence of limited-duration safe policies is actually sufficient for long-duration autonomy. This idea is illustrated on a swarm of simulated robots that are tasked with covering a given area, but that sporadically need to abandon this task to charge batteries. We show how the battery-charging behavior naturally emerges as a result of the constraints. Additionally, using a cart–pole simulation environment, we show how a control policy can be efficiently transferred from the source task, balancing the pole, to the target task, moving the cart to one direction without letting the pole fall down.
Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses

Despite achieving remarkable success in various domains, recent studies have uncovered the vulnerability of deep neural networks to adversarial perturbations, creating concerns on model generalizability and new threats such as prediction-evasive misclassification or stealthy reprogramming. Among different defense proposals, stochastic network defenses such as random neuron activation pruning or random perturbation to layer inputs are shown to be promising for attack mitigation. However, one critical drawback of current defenses is that the robustness enhancement is at the cost of noticeable performance degradation on legitimate data, e.g., large drop in test accuracy. This paper is motivated by pursuing for a better trade-off between adversarial robustness and test accuracy for stochastic network defenses. We propose Defense Efficiency Score (DES), a comprehensive metric that measures the gain in unsuccessful attack attempts at the cost of drop in test accuracy of any defense. To achieve a better DES, we propose hierarchical random switching (HRS), which protects neural networks through a novel randomization scheme. A HRS-protected model contains several blocks of randomly switching channels to prevent adversaries from exploiting fixed model structures and parameters for their malicious purposes. Extensive experiments show that HRS is superior in defending against state-of-the-art white-box and adaptive adversarial misclassification attacks. We also demonstrate the effectiveness of HRS in defending adversarial reprogramming, which is the first defense against adversarial programs. Moreover, in most settings the average DES of HRS is at least 5X higher than current stochastic network defenses, validating its significantly improved robustness-accuracy trade-off.
Memories of Carvalhal’s Palace: Haunted Encounters, a Museum Experience to Engage Teenagers

While museums are making great efforts in updating their communication and engagement strategies to include a wide variety of publics, teenagers (15–19) are still identified as an audience group that is often excluded from a museum’s curatorial strategies. As consequence, this audience appears to be generally disinterested in what museums might offer. Our installation, deployed at the Natural History Museum of Funchal (NHMF), in Portugal, makes use of mobile interactive technologies and gaming strategies to promote more engaging museum experiences for teenage visitors. Memories of Carvalhal’s Palace: Haunted Encounters is a location-based game that prompts teenagers to uncover the science in the museum, through investigating the site, which is presented as haunted. In order to complete the quest, the audience needs to find and collect scientific information about specific exhibits while interacting with their Augmented Reality (AR) three-dimensional (3D) models. The audience’s interactions with the museum exhibits are rewarded with pieces of a map, which when completed, will guide them to the hidden scientific library where they can finally unlock the mysteries they have been trying to solve.
How YouTube led to Google's cloud-gaming service: The tech that made YouTube work everywhere promises to do the same for games - [News]

When Google's executives floated a vision for the Stadia cloud-gaming service, which could make graphically intensive gaming available on any device, they knew the company wouldn't have to build all the necessary technology from scratch. Instead, the tech giant planned to leverage its expertise in shaping Internet standards and installing infrastructure to support its YouTube video service for more than a billion people worldwide.
An agent-based intelligent tutoring systems review

This paper presents the current state of research in the fields of intelligent tutoring systems and intelligent agents. The architecture of intelligent tutoring systems typically consists of four major components: the domain knowledge module, the learner diagnosis module, the tutoring module, and the communication module. An agent-based approach to intelligent tutoring systems research and development is applied as the set of agents that constitute the general architecture, and as animated characters - pedagogical agents. Therefore, the paper focuses on the components of intelligent tutoring systems that can be implemented as agents, as well as on functions that these intelligent agents can perform in each intelligent tutoring system component. Also, we present the recent endeavours in the intelligent tutoring systems community that moves from standalone systems focused on a single instructional domain to adaptive instructional systems that comprise learners, intelligent tutoring systems, and external (non-adaptive) environment.
Low-Rank HOCA: Efficient High-Order Cross-Modal Attention for Video Captioning

This paper addresses the challenging task of video captioning which aims to generate descriptions for video data.Recently, the attention-based encoder-decoder structures have been widely used in video captioning.In existing literature, the attention weights are often built from the information of an individual modality, while, the association relationships between multiple modalities are neglected.Motivated by this observation, we propose a video captioning model with High-Order Cross-Modal Attention (HOCA) where the attention weights are calculated based on the high-order correlation tensor to capture the frame-level cross-modal interaction of different modalities sufficiently.Furthermore, we novelly introduce Low-Rank HOCA which adopts tensor decomposition to reduce the extremely large space requirement of HOCA, leading to a practical and efficient implementation in real-world applications.Experimental results on two benchmark datasets, MSVD and MSR-VTT, show that Low-rank HOCA establishes a new state-of-the-art.
Siamese Ballistics Neural Network

Firearm identification is crucial in many investigative scenario. The crime scene often contains traces left by firearms in terms of bullets and cartridges. Traces analysis is a fundamental step in the Forensics Ballistics Analysis Process to identify which firearm fired a specific cartridge. In this paper we present a fully automated technique to compare cartridges represented as a set of 3D point-clouds. The overall approach is based on Siamese Neural Network learning paradigm that we use to build a suitable embedding space where the 3D point-cloud of the cartridges are compared. The proposed approach has been assessed by considering the NBTRD dataset. Obtained results support the exploitation of the proposed technique in ballistic analysis.
An application of artificial intelligence on auditing

Purpose-The purpose of this study is to examine the effects of technological developments in artificial intelligence on auditing.In this study, artificial intelligence examples used in independent auditing and the contribution of artificial intelligence to the audit sector were examined.Methodology-With the technological developments affecting the independent auditing sector, it has been observed that the efficiency of artificial intelligence has increased and the field of application of artificial intelligence has varied.In this study, artificial intelligence applications and the artificial intelligence projects to be implemented to the auditing sector in the future were analyzed and evaluated.Findings-According to the findings; In order to adapt artificial intelligence to businesses, it is revealed that entities need to develop their ERP systems and that there are potential difficulties for the design of artificial intelligence for each company to be audited. Conclusion-In this study, it is thought that auditing with artificial intelligence can be performed full time and will be more effective.However, in order to spread artificial intelligence and increase productivity, it is thought that business executives should follow the developments in the world and implement them by combining them with their corporate culture.In addition, it is seen that entities need to develop ERP softwares and increase the use of digitalization and integrated programs.
Learning robust visual representations using data augmentation invariance

Deep convolutional neural networks trained for image object categorization have shown remarkable similarities with representations found across the primate ventral visual stream. Yet, artificial and biological networks still exhibit important differences. Here we investigate one such property: increasing invariance to identity-preserving image transformations found along the ventral stream. Despite theoretical evidence that invariance should emerge naturally from the optimization process, we present empirical evidence that the activations of convolutional neural networks trained for object categorization are not robust to identity-preserving image transformations commonly used in data augmentation. As a solution, we propose data augmentation invariance, an unsupervised learning objective which improves the robustness of the learned representations by promoting the similarity between the activations of augmented image samples). Our results show that this approach is a simple, yet effective and efficient (10 % increase in training time) way of increasing the invariance of the models while obtaining similar categorization performance.
Library Space of the CIS Countries as a Self-organizing System: Research Methodology

The article reveals the research methodology of the library space of the Commonwealth of Independent States (CIS) based on the theory of self-organization (synergetics). Synergetics as the direction of post-non-classical science was developed at the end of the last — the beginning of the new Millennium in the works of a number of researchers who proved the universality of this methodology and its applicability to the study of processes occurring in society. However, until the 2000s, this methodology did not get worthy application in the library science. The relevance of this study is that the modern library science requires a new worldview-methodological approach to the investigation of processes taking place in the cultural and library sphere, which will help to develop and enrich the successfully used method of system analysis. Based on this, the author sets the task to study the library space of the CIS as a self-organizing system that is in the process of evolution over a long historical period. The article considers the library space as a dynamically developing system of social nature. The author traces its cause-and-effect relations, conditions and factors in historical perspective and modern aspects. The concept of “library space of the CIS countries” arose after the disintegration of the Soviet Union in 1991 and as a result of formation of the CIS to refer to the form of interstate library interaction and cooperation. The article studies the library space of the CIS countries as a system of interaction of libraries formed in the post-Soviet period, the basis of which is their belonging to the Commonwealth of Independent States, within which the interstate relations of libraries are developing, basing on long historical and cultural ties between them; and the Russian language is the language of professional and interpersonal communication. The author presents the descriptive model of interaction system of libraries of the CIS countries with the main features and principles of construction. The article establishes that the CIS library space is characterized by features of a complex, open, dynamic self-organizing system. The methodological nature and theoretical philosophical significance of the article make it interesting for librarians, science theorists and cultural scientists.
A Character Level Convolutional BiLSTM for Arabic Dialect Identification

In this paper, we describe the contribution of CU-RAISA team to the 2019 Madar shared task 2 1 , which focused on Twitter User finegrained dialect identification.Among participating teams, our system ranked the 4th (with 61.54%) F1-Macro measure.Our system is trained using a character level convolutional bidirectional long-short-term memory (BiL-STM) network trained on approximately 2k users' data.We show that training on concatenated user tweets as input is further superior to training on user tweets separately and assign user's label on the mode of user's tweets' predictions.
Emerging role of eHealth in the identification of very early inflammatory rheumatic diseases

Digital health or eHealth technologies, notably pervasive computing, robotics, big-data, wearable devices, machine learning, and artificial intelligence (AI), have opened unprecedented opportunities as to how the diseases are diagnosed and managed with active patient engagement. Patient-related data have provided insights (real world data) into understanding the disease processes. Advanced analytics have refined these insights further to draw dynamic algorithms aiding clinicians in making more accurate diagnosis with the help of machine learning. AI is another tool, which, although is still in the evolution stage, has the potential to help identify early signs even before the clinical features are apparent. The evolving digital developments pose challenges on allowing access to health-related data for further research but, at the same time, protecting each patient's privacy. This review focuses on the recent technological advances and their applications and highlights the immense potential to enable early diagnosis of rheumatological diseases.
The Broad Optimality of Profile Maximum Likelihood

We study three fundamental statistical-learning problems: distribution estimation, property estimation, and property testing. We establish the profile maximum likelihood (PML) estimator as the first unified sample-optimal approach to a wide range of learning tasks. In particular, for every alphabet size $k$ and desired accuracy $\varepsilon$: $\textbf{Distribution estimation}$ Under $\ell_1$ distance, PML yields optimal $\Theta(k/(\varepsilon^2\log k))$ sample complexity for sorted-distribution estimation, and a PML-based estimator empirically outperforms the Good-Turing estimator on the actual distribution; $\textbf{Additive property estimation}$ For a broad class of additive properties, the PML plug-in estimator uses just four times the sample size required by the best estimator to achieve roughly twice its error, with exponentially higher confidence; $\boldsymbol{\alpha}\textbf{-R\'enyi entropy estimation}$ For integer $\alpha>1$, the PML plug-in estimator has optimal $k^{1-1/\alpha}$ sample complexity; for non-integer $\alpha>3/4$, the PML plug-in estimator has sample complexity lower than the state of the art; $\textbf{Identity testing}$ In testing whether an unknown distribution is equal to or at least $\varepsilon$ far from a given distribution in $\ell_1$ distance, a PML-based tester achieves the optimal sample complexity up to logarithmic factors of $k$. Most of these results also hold for a near-linear-time computable variant of PML. Stronger results hold for a different and novel variant called truncated PML (TPML).
Incorporating Contextual and Syntactic Structures Improves Semantic Similarity Modeling

Semantic similarity modeling is central to many NLP problems such as natural language inference and question answering.Syntactic structures interact closely with semantics in learning compositional representations and alleviating long-range dependency issues.However, such structure priors have not been well exploited in previous work for semantic modeling.To examine their effectiveness, we start with the Pairwise Word Interaction Model, one of the best models according to a recent reproducibility study, then introduce components for modeling context and structure using multi-layer BiLSTMs and TreeLSTMs.In addition, we introduce residual connections to the deep convolutional neural network component of the model.Extensive evaluations on eight benchmark datasets show that incorporating structural information contributes to consistent improvements over strong baselines.
Modeling Graph Structure in Transformer for Better AMR-to-Text Generation

Recent studies on AMR-to-text generation often formalize the task as a sequence-to-sequence (seq2seq) learning problem by converting an Abstract Meaning Representation (AMR) graph into a word sequence. Graph structures are further modeled into the seq2seq framework in order to utilize the structural information in the AMR graphs. However, previous approaches only consider the relations between directly connected concepts while ignoring the rich structure in AMR graphs. In this paper we eliminate such a strong limitation and propose a novel structure-aware self-attention approach to better modeling the relations between indirectly connected concepts in the state-of-the-art seq2seq model, i.e., the Transformer. In particular, a few different methods are explored to learn structural representations between two concepts. Experimental results on English AMR benchmark datasets show that our approach significantly outperforms the state of the art with 29.66 and 31.82 BLEU scores on LDC2015E86 and LDC2017T10, respectively. To the best of our knowledge, these are the best results achieved so far by supervised models on the benchmarks.
Stratified Feature Sampling for Semi-Supervised Ensemble Clustering

Ensemble Clustering (EC), which seeks to generate a consensus clustering by integrating multiple base clusterings, has attracted increasing attentions. However, traditional EC methods typically have three main limitations: (1) High dimensional data present a huge challenge to ensemble clustering methods. (2) Most EC algorithms can not use prior information, e.g., pairwise constraints, to enhance the clustering performance. (3) Even in existing semi-supervised ensemble clustering methods, prior information is not sufficiently used, e.g., only used in generating base clusterings. To alleviate these problems, we propose Stratified Feature Sampling for Semi-Supervised Ensemble Clustering (SFS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> EC). Firstly, we develop a novel stratified feature sampling method, which can cope with high dimensional data, guarantee the diversity of base clusterings, and reduce the risk that some features are not selected at the same time. Secondly, semi-supervised clustering, i.e., constraint propagation, is applied to obtain base clusterings. Finally, we propose to utilize prior information in both the base clustering generating process and the consensus process, which guarantees that prior information is sufficiently used. We conduct a series of experiments on ten real-world data sets to demonstrate the effectiveness of the proposed model.
QAInfomax: Learning Robust Question Answering System by Mutual Information Maximization

Standard accuracy metrics indicate that modern reading comprehension systems have achieved strong performance in many question answering datasets. However, the extent these systems truly understand language remains unknown, and existing systems are not good at distinguishing distractor sentences, which look related but do not actually answer the question. To address this problem, we propose QAInfomax as a regularizer in reading comprehension systems by maximizing mutual information among passages, a question, and its answer. QAInfomax helps regularize the model to not simply learn the superficial correlation for answering questions. The experiments show that our proposed QAInfomax achieves the state-of-the-art performance on the benchmark Adversarial-SQuAD dataset.
Non-discriminative data or weak model? On the relative importance of data and model resolution

We explore the question of how the resolution of the input image (input resolution) affects the performance of a neural network when compared to the resolution of the hidden layers (internal resolution). Adjusting these characteristics is frequently used as a hyperparameter providing a trade-off between model performance and accuracy. An intuitive interpretation is that the reduced information content in the low-resolution input causes decay in the accuracy. In this paper, we show that up to a point, the input resolution alone plays little role in the network performance, and it is the internal resolution that is the critical driver of model quality. We then build on these insights to develop novel neural network architectures that we call \emph{Isometric Neural Networks}. These models maintain a fixed internal resolution throughout their entire depth. We demonstrate that they lead to high accuracy models with low activation footprint and parameter count.
Big Data and Trust in Public Policy Automation

Abstract Big data is everywhere, both in and out of public policy. Though a rich data source, what is the impact of big data beyond the research community? We suggest such that invoking big data-related terms acts as a heuristic for assumed algorithmic quality. Such an assumption leads to greater trust in automation in public policy decision-making. We test this “big-data-effect” expectation using four tests including a conjoint experiment embedded in a recently fielded survey experiment. We find strong evidence that indeed, big data-related terms act as powerful signals of assumed quality where respondents consistently prefer algorithms with bigger data behind them, absent any mention of predictive accuracy or definitions of key terms (e.g. “training features”). As we expect this big-data-effect is likely not beholden to public policy, we encourage more research in this vein to deepen an understanding of the influence of big data on modern society.
Unit traffic classification and analysis on P2P video delivery using machine learning

This paper proposes a new traffic classification method for peer-to-peer-based video streaming (P2PTV) by using machine learning. Since P2PTV is a promising technology for video delivery, it is important to comprehend its traffic characteristics. Our previous studies classified and analyzed traffic obtained in viewing P2PTV on a per content basis. However, users’ participation would be dynamic, and then the traffic characteristics may change momentarily. In the proposed method, we divide per-content traffic into short-time data units and then classify them by machine learning. From the experimental results on 80 contents data, six types of unit traffic patterns appear. Moreover, we also analyze their occurrence patterns.