Reviewerly/exHarmony huggingface dataset:
LIMIS: Locally Interpretable Modeling using Instance-wise Subsampling

Understanding black-box machine learning models is crucial for their widespread adoption. Learning globally interpretable models is one approach, but achieving high performance with them is challenging. An alternative approach is to explain individual predictions using locally interpretable models. For locally interpretable modeling, various methods have been proposed and indeed commonly used, but they suffer from low fidelity, i.e. their explanations do not approximate the predictions well. In this paper, our goal is to push the state-of-the-art in high-fidelity locally interpretable modeling. We propose a novel framework, Locally Interpretable Modeling using Instance-wise Subsampling (LIMIS). LIMIS utilizes a policy gradient to select a small number of instances and distills the black-box model into a low-capacity locally interpretable model using those selected instances. Training is guided with a reward obtained directly by measuring the fidelity of the locally interpretable models. We show on multiple tabular datasets that LIMIS near-matches the prediction accuracy of black-box models, significantly outperforming state-of-the-art locally interpretable models in terms of fidelity and prediction accuracy.
Effectiveness of online capacity building programs in wholistic development of faculties: an empirical analysis

Purpose Faculties have a vital role to play in ensuring that their graduates are trained with relevant skills. Formally arranged capacity building programs (CBPs) can aid in training and retraining the faculties with relevant contemporary skills so that they in turn can do justice to their students. The purpose of this study is to analyze the efficacy of such public-funded online CBPs in enhancing faculties’ research and teaching capabilities. Design/methodology/approach Ascientifically designed questionnaire was sent to faculties who attended 12-day public-funded online capacity building workshop. Binomial logistic regression models were constructed to analyze as to how effective the online workshop was in enhancing teaching and research skills of the faculties involved. Findings From the research study, the authors were able to infer that although faculties were able to understand theoretical concepts of qualitative nature relatively easily, the authors felt that its value addition in enhancing the research output was rather limited. The study also found that the faculties felt teaching concepts outside their “syllabus” to be counterintuitive. A significant finding of the study was that research had an important role in enhancing their teaching efficacy. Originality/value Studies which are undertaken to test the efficacy of online capacity building workshops are scarce. This domain is going to gain importance in near future as technology is evolving at a rapid rate and online training of faculties helps in optimizing scarce resources.
Metasystem organization of subject determinants of professional activity (article two)

Theoretical-methodological and empirical-phenomenological materials are presented, revealing and explaining the specifics of the organization of the most important category of subjective determinants of labor activity - professional competencies. It is shown that its main principle is the principle of metasystem organization. From these positions, a new solution is given to the key problem of activity psychology - the problem of the correlation of two main classes of professional competencies, designated by the concepts of «hard» and «soft» skills (hard-skills and soft-skills). It is shown and explained how and on the basis of which specific operational means this principle is implemented in relation to the system of professional competencies. The main ones are the basic functions that implement soft-skills in relation to hard-skills, thereby regulating their determinative influence on activity.
Empathy as a method of comprehension of the psychic (the empirical aspect)

The article is a continuation of the theoretical aspect, where an attempt was made to consider empathy through the prism of the structural-level approach of M. S. Rogovin. Currently, it is necessary to study mental phenomena in the understanding of their multidimensionality, non-linearity of construction and development. It was found that empathy has a «splitting effect», described in detail by M. A. Kholodnaya on the example of intellectual abilities, on productive and destructive components.
Determining the Dimension and Structure of the Subspace Correlated Across Multiple Data Sets

Detecting the components common or correlated across multiple data sets is challenging due to a large number of possible correlation structures among the components. Even more challenging is to determine the precise structure of these correlations. Traditional work has focused on determining only the model order, i.e., the dimension of the correlated subspace, a number that depends on how the model-order problem is defined. Moreover, identifying the model order is often not enough to understand the relationship among the components in different data sets. We aim at solving the complete modelselection problem, i.e., determining which components are correlated across which data sets. We prove that the eigenvalues and eigenvectors of the normalized covariance matrix of the composite data vector, under certain conditions, completely characterize the underlying correlation structure. We use these results to solve the model-selection problem by employing bootstrap-based hypothesis testing.
Machine Learning based Quality Analysis on Inicial Coin Offering (ICO)

Today society is a place where people are looking for improvement.Therefore, people are eager to increase their income in simple ways.Crypto currency is an upcoming topic in the world.Therefore, the number of investments on crypto currency are keep increasing.One of the primary ways to earn Crypto is by Initial Coin Offering (ICO).Crypto currency ICO is an easy and modern way to make money.But nowadays, many people are fooled by so many fake ICOs that they are risking their wealth for nothing.This is becoming a major issue and there are so many ongoing researches that are trying to address this problem.The aim of my research is to address this situation in a unique approach.A classifier algorithm will be developed using machine learning techniques and the accuracy of the proposed algorithm will be calculated against some existing approaches.Also, a novel database will be released to the research community as well.
Embracing Disciplinary Diversity in Visualization

Visualization is inherently diverse and is employed in countless domains to enable meaningful interactions with data. There is tremendous opportunity in embracing disciplinary diversity to widen the pool of contributions to visualization design, research, and practice. We describe a few examples of diverse approaches: scientific method, design studies, tool building, participatory research and co design with communities, data storytelling, and autographic design. We discuss opening the aperture, pushing back on what we, as a community, deem acceptable and rigorous, and what can be gained through greater inclusivity of approaches.
Beyond Heisenberg Limit Quantum Metrology through Quantum Signal Processing

Leveraging quantum effects in metrology such as entanglement and coherence allows one to measure parameters with enhanced sensitivity. However, time-dependent noise can disrupt such Heisenberg-limited amplification. We propose a quantum-metrology method based on the quantum-signal-processing framework to overcome these realistic noise-induced limitations in practical quantum metrology. Our algorithm separates the gate parameter $\varphi$~(single-qubit Z phase) that is susceptible to time-dependent error from the target gate parameter $\theta$~(swap-angle between |10> and |01> states) that is largely free of time-dependent error. Our method achieves an accuracy of $10^{-4}$ radians in standard deviation for learning $\theta$ in superconducting-qubit experiments, outperforming existing alternative schemes by two orders of magnitude. We also demonstrate the increased robustness in learning time-dependent gate parameters through fast Fourier transformation and sequential phase difference. We show both theoretically and numerically that there is an interesting transition of the optimal metrology variance scaling as a function of circuit depth $d$ from the pre-asymptotic regime $d \ll 1/\theta$ to Heisenberg limit $d \to \infty$. Remarkably, in the pre-asymptotic regime our method's estimation variance on time-sensitive parameter $\varphi$ scales faster than the asymptotic Heisenberg limit as a function of depth, $\text{Var}(\hat{\varphi})\approx 1/d^4$. Our work is the first quantum-signal-processing algorithm that demonstrates practical application in laboratory quantum computers.
A Closer Look at Learned Optimization: Stability, Robustness, and Inductive Biases

Learned optimizers -- neural networks that are trained to act as optimizers -- have the potential to dramatically accelerate training of machine learning models. However, even when meta-trained across thousands of tasks at huge computational expense, blackbox learned optimizers often struggle with stability and generalization when applied to tasks unlike those in their meta-training set. In this paper, we use tools from dynamical systems to investigate the inductive biases and stability properties of optimization algorithms, and apply the resulting insights to designing inductive biases for blackbox optimizers. Our investigation begins with a noisy quadratic model, where we characterize conditions in which optimization is stable, in terms of eigenvalues of the training dynamics. We then introduce simple modifications to a learned optimizer's architecture and meta-training procedure which lead to improved stability, and improve the optimizer's inductive bias. We apply the resulting learned optimizer to a variety of neural network training tasks, where it outperforms the current state of the art learned optimizer -- at matched optimizer computational overhead -- with regard to optimization performance and meta-training speed, and is capable of generalization to tasks far different from those it was meta-trained on.
On the Heritage of Crypto Assets -- Tales From the Crypt Protocol

We discuss some issues to the inheritance of crypto assets. We propose a distributed, privacy preserving, protocol to establish a consensus on the death of the owner of crypto assets: the Tales From the Crypt Protocol. Until the actual death of the owner no link can be made between public information and the corresponding crypto assets. This protocol is generic and could be incorparated into any arbitrary crypto platform.
Perceived Security of E-Learning Portal

Information technology has made e-learning possible and available on a large scale. Learning management system (LMS) has been widely used and is accessible through the Internet. However, LMS are exposed to various threats. Proper understanding of the threats is required. Furthermore strategy and best practices countermeasures will ensure a safe learning environment. Therefore, this study looks into the information security aspect of LMS. Specifically, there are two main purposes of this study. First, this study provides a review of information security in e-learning environments and explains the importance of information security. Second, this study looks at how student perceived the security of their e-learning portal. A total of 497 students responded to a survey questionnaires. Frequencies analysis was conducted to show the profile of the respondent. Overall, respondent has strong positive perceptions towards security of their LMS. This study serve as an introduction which help LMS administrator to understand the issues and possibilities related to the safety of LMS.
Open quantum system dynamics of $X$-states: Entanglement sudden death and sudden birth

The origin of disentanglement for two specific sub-classes of $X$-states namely maximally nonlocal mixed states (MNMSs) and maximally entangled mixed states (MEMSs) is investigated analytically for a physical system consisting of two spatially separated qubits interacting with a common vacuum bath. The phenomena of entanglement sudden death (ESD) and the entanglement sudden birth (ESB) are observed, but the characteristics of ESD and ESB are found to be different for the case of two photon coherence and single photon coherence states. The role played by initial coherence for the underlying entanglement dynamics is investigated. Further, the entanglement dynamics of MNMSs and MEMSs under different environmental noises namely phase damping, amplitude damping and RTN noise with respect to the decay and revival of entanglement is analyzed. It's observed that the single photon coherence states are more robust against the sudden death of entanglement indicating the usability of such states in the development of technologies for the practical implementation of quantum information processing tasks.
Universal updates of Dyck-nest signatures

Let $0<k\in\mathbb{Z}$. The anchored Dyck words of length $n=2k+1$ (obtained by prefixing a 0-bit to each Dyck word of length $2k$ and used to reinterpret the Hamilton cycles in the odd graph $O_k$ and the middle-levels graph $M_k$ found by M\"utze et al.) represent in $O_k$ (resp., $M_k$) the cycles of an $n$- (resp., $2n$-) 2-factor and its cyclic (resp., dihedral) vertex classes, and are equivalent to Dyck-nest signatures. A sequence is obtained by updating these signatures according to the depth-first order of a tree of restricted growth strings (RGS's), reducing the RGS-generation of Dyck words by collapsing to a single update the time-consuming $i$-nested castling used to reach each non-root Dyck word or Dyck nest. This update is universal, for it does not depend on $k$.
MLCN: Metric Learning Constrained Network for Whole Slide Image Classification with Bilinear Gated Attention Mechanism

Whole Slide Image (WSI) classification is an important part of pathological diagnosis. Although previous approaches (such as DSMIL and CLAM) have achieved good results, the classification performance is still unsatisfactory because the learned features of WSI lack discrimination and the correlation among sub-characteristics of tumor images are ignored. In this paper, we proposed a Metric Learning Constraint Network (referred to as MLCN). Particularly, MLCN benefits from two aspects: 1) It enhances the discriminative power of features by enlarging inter-class distance and narrowing intra-class distance in both slide-level and patch-level. 2) It learns a more powerful feature aggregator by proposing the bilinear gated attention mechanism to capture relations among sub-characteristics of tumor issues. Experiments on CAMELYON16 and TCGA Kidney datasets validate the effectiveness of our approach, and we achieved state-of-the-art performance compared to other popular methods. The codes will be available soon.
Deep Learning on Home Drone: Searching for the Optimal Architecture

We suggest the first system that runs real-time semantic segmentation via deep learning on a weak micro-computer such as the Raspberry Pi Zero v2 (whose price was \$15) attached to a toy-drone. In particular, since the Raspberry Pi weighs less than $16$ grams, and its size is half of a credit card, we could easily attach it to the common commercial DJI Tello toy-drone (<\$100, <90 grams, 98 $\times$ 92.5 $\times$ 41 mm). The result is an autonomous drone (no laptop nor human in the loop) that can detect and classify objects in real-time from a video stream of an on-board monocular RGB camera (no GPS or LIDAR sensors). The companion videos demonstrate how this Tello drone scans the lab for people (e.g. for the use of firefighters or security forces) and for an empty parking slot outside the lab. Existing deep learning solutions are either much too slow for real-time computation on such IoT devices, or provide results of impractical quality. Our main challenge was to design a system that takes the best of all worlds among numerous combinations of networks, deep learning platforms/frameworks, compression techniques, and compression ratios. To this end, we provide an efficient searching algorithm that aims to find the optimal combination which results in the best tradeoff between the network running time and its accuracy/performance.
Prompting for a conversation: How to control a dialog model?

Dialog modelling faces a difficult trade-off. Models are trained on a large amount of text, yet their responses need to be limited to a desired scope and style of a dialog agent. Because the datasets used to achieve the former contain language that is not compatible with the latter, pre-trained dialog models are fine-tuned on smaller curated datasets. However, the fine-tuning process robs them of the ability to produce diverse responses, eventually reducing them to dull conversation partners. In this paper we investigate if prompting can mitigate the above trade-off. Specifically, we experiment with conditioning the prompt on the query, rather than training a single prompt for all queries. By following the intuition that freezing the pre-trained language model will conserve its expressivity, we find that compared to fine-tuning, prompting can achieve a higher BLEU score and substantially improve the diversity and novelty of the responses.
Learning Agile Flight Maneuvers: Deep SE(3) Motion Planning and Control for Quadrotors

Agile flights of autonomous quadrotors in cluttered environments require constrained motion planning and control subject to translational and rotational dynamics. Traditional model-based methods typically demand complicated design and heavy computation. In this paper, we develop a novel deep reinforcement learning-based method that tackles the challenging task of flying through a dynamic narrow gate. We design a model predictive controller with its adaptive tracking references parameterized by a deep neural network (DNN). These references include the traversal time and the quadrotor SE(3) traversal pose that encourage the robot to fly through the gate with maximum safety margins from various initial conditions. To cope with the difficulty of training in highly dynamic environments, we develop a reinforce-imitate learning framework to train the DNN efficiently that generalizes well to diverse settings. Furthermore, we propose a binary search algorithm that allows online adaption of the SE(3) references to dynamic gates in real-time. Finally, through extensive high-fidelity simulations, we show that our approach is robust to the gate's velocity uncertainties and adaptive to different gate trajectories and orientations.
Calculating Datastructures

Abstract Where do datastructures come from? This paper explores how to systematically derive implementations of one-sided flexible arrays from a simple reference implementation. Using the dependently typed programming language Agda, each calculation constructs an isomorphic—yet more efficient—datastructure using only a handful of laws relating types and arithmetic. Although these calculations do not generally produce novel datastructures they do give insight into how certain datastructures arise and how different implementations are related.
Clustering-Based Multi-instance Learning Network for Whole Slide Image Classification

Automated and accurate classification of Whole Slide Image (WSI) is of great significance for the early diagnosis and treatment of cancer, which can be realized by Multi-Instance Learning (MIL). However, the current MIL method easily suffers from over-fitting due to the weak supervision of slide-level labels. In addition, it is difficult to distinguish discriminative instances in a WSI bag in the absence of pixel-level annotations. To address these problems, we propose a novel Clustering-Based Multi-Instance Learning method (CBMIL) for WSI classification. The CBMIL constructs feature set from phenotypic clusters to augment data for training the aggregation network. Meanwhile, a contrastive learning task is incorporated into the CBMIL for multi-task learning, which helps to regularize the feature aggregation process. In addition, the centroid of each phenotypic cluster is updated by the model, and the weights of the WSI patches are calculated by their similarity to the phenotypic centroids to highlight the significant patches. Our method is evaluated on two public WSI datasets (CAMELYON16 and TCGA-Lung) for binary tumor and cancer sub-types classification and achieves better performance and great interpretability compared with the state-of-the-art methods. The code is available at: https://github.com/wwu98934/CBMIL .
A Machine Learning Approach to Detect Differential Treatment of Anonymous Users

Anonymous Internet use is essential to safeguard against mass surveillance and to protect privacy online. Tor Browser and the Tor anonymity network provide an effective and convenient way to browse the Internet anonymously. However, many websites make browsing inconvenient for anonymous users either by blocking access entirely, by blocking access to some functionality, or by using CAPTCHAs to make access more difficult. Prior work has relied on heuristics to study the extent to which anonymous users are treated differently. However, these heuristics either underestimated blocking or required extensive manual labeling. To address these shortcomings, here we propose a machine learning approach to detect when anonymous users are treated differently. We train binary and multi-class classifiers based on six feature sets and find that they perform very well on our test data (F1 scores 100%–94% for binary and 97%–84% for multi-class classifiers). Applying these classifiers to data collected from browsing 1,000 sites, including visits to subsites and executing search functionality, via 100 Tor exit nodes we find that 16.7% of landing pages inconvenience anonymous users, compared with 15.2% of subsites and 3.8% of search result pages. In particular, we find that websites hosted by Akamai, EdgeCast, and Cloudflare have significantly higher block rates than average, and that blocking of search results is dominated by Google which displays a block page or CAPTCHA for 39.8% of search result pages.
Counterfactual Explanations Using Optimization With Constraint Learning

To increase the adoption of counterfactual explanations in practice, several criteria that these should adhere to have been put forward in the literature. We propose counterfactual explanations using optimization with constraint learning (CE-OCL), a generic and flexible approach that addresses all these criteria and allows room for further extensions. Specifically, we discuss how we can leverage an optimization with constraint learning framework for the generation of counterfactual explanations, and how components of this framework readily map to the criteria. We also propose two novel modeling approaches to address data manifold closeness and diversity, which are two key criteria for practical counterfactual explanations. We test CE-OCL on several datasets and present our results in a case study. Compared against the current state-of-the-art methods, CE-OCL allows for more flexibility and has an overall superior performance in terms of several evaluation metrics proposed in related work.