Reviewerly/exHarmony huggingface dataset:
Not All Poisons are Created Equal: Robust Training against Data Poisoning

Data poisoning causes misclassification of test time target examples by injecting maliciously crafted samples in the training data. Existing defenses are often effective only against a specific type of targeted attack, significantly degrade the generalization performance, or are prohibitive for standard deep learning pipelines. In this work, we propose an efficient defense mechanism that significantly reduces the success rate of various data poisoning attacks, and provides theoretical guarantees for the performance of the model. Targeted attacks work by adding bounded perturbations to a randomly selected subset of training data to match the targets' gradient or representation. We show that: (i) under bounded perturbations, only a number of poisons can be optimized to have a gradient that is close enough to that of the target and make the attack successful; (ii) such effective poisons move away from their original class and get isolated in the gradient space; (iii) dropping examples in low-density gradient regions during training can successfully eliminate the effective poisons, and guarantees similar training dynamics to that of training on full data. Our extensive experiments show that our method significantly decreases the success rate of state-of-the-art targeted attacks, including Gradient Matching and Bullseye Polytope, and easily scales to large datasets.
Addressing the Extreme Cold-Start Problem in Group Recommendation

The task of recommending items to a group of users, a.k.a. group recommendation, is receiving increasing attention. However, the cold-start problem inherent in recommender systems is amplified in group recommendation because interaction data between groups and items are extremely scarce in practice. Most existing work exploits associations between groups and items to mitigate the data scarcity problem. However, existing approaches inevitably fail in extreme cold-start scenarios where associations between groups and items are lacking. For this reason, we design a group recommendation model for EXreme cold-star} in group REcommendation (EXTRE) suitable for the extreme cold start scenario. The basic idea behind EXTRE is to use the limit theory of graph convolutional neural networks to establish implicit associations between groups and items, and the derivation of these associations does not require explicit interaction data, making it suitable for cold start scenarios. The training process of EXTRE depends on the newly defined and interpretable concepts of consistency and discrepancy, other than commonly used negative sampling with pairwise ranking, which can improve the performance of the group recommendation. Extensive experiments validate the efficacy of the proposed model EXTRE.
Learning Fashion Compatibility from In-the-wild Images

Complementary fashion recommendation aims at identifying items from different categories (e.g. shirt, footwear, etc.) that "go well together" as an outfit. Most existing approaches learn representation for this task using labeled outfit datasets containing manually curated compatible item combinations. In this work, we propose to learn representations for compatibility prediction from in-the-wild street fashion images through self-supervised learning by leveraging the fact that people often wear compatible outfits. Our pretext task is formulated such that the representations of different items worn by the same person are closer compared to those worn by other people. Additionally, to reduce the domain gap between in-the-wild and catalog images during inference, we introduce an adversarial loss that minimizes the difference in feature distribution between the two domains. We conduct our experiments on two popular fashion compatibility benchmarks - Polyvore and Polyvore-Disjoint outfits, and outperform existing self-supervised approaches, particularly significant in cross-dataset setting where training and testing images are from different sources.
Gone Quishing: A Field Study of Phishing with Malicious QR Codes

The COVID-19 pandemic enabled "quishing", or phishing with malicious QR codes, as they became a convenient go-between for sharing URLs, including malicious ones. To explore the quishing phenomenon, we conducted a 173-participant study where we used a COVID-19 digital passport sign-up trial with a malicious QR code as a pretext. We found that 67 % of the participants were happy to sign-up with their Google or Facebook credentials, 18.5% to create a new account, and only 14.5% to skip on the sign-up. Convenience was the single most cited factor for the willingness to yield participants' credentials. Reluctance of linking personal accounts with new services was the reason for creating a new account or skipping the registration. We also developed a Quishing Awareness Scale (QAS) and found a significant relationship between participants' QR code behavior and their sign-up choices: the ones choosing to sign-up with Facebook scored the lowest while the one choosing to skip the highest on average. We used our results to propose quishing awareness training guidelines and develop and test usable security indicators for warning users about the threat of quishing.
Transfer learning with affine model transformation

Supervised transfer learning has received considerable attention due to its potential to boost the predictive power of machine learning in scenarios where data are scarce. Generally, a given set of source models and a dataset from a target domain are used to adapt the pre-trained models to a target domain by statistically learning domain shift and domain-specific factors. While such procedurally and intuitively plausible methods have achieved great success in a wide range of real-world applications, the lack of a theoretical basis hinders further methodological development. This paper presents a general class of transfer learning regression called affine model transfer, following the principle of expected-square loss minimization. It is shown that the affine model transfer broadly encompasses various existing methods, including the most common procedure based on neural feature extractors. Furthermore, the current paper clarifies theoretical properties of the affine model transfer such as generalization error and excess risk. Through several case studies, we demonstrate the practical benefits of modeling and estimating inter-domain commonality and domain-specific factors separately with the affine-type transfer models.
Towards Understanding Sharpness-Aware Minimization

Sharpness-Aware Minimization (SAM) is a recent training method that relies on worst-case weight perturbations which significantly improves generalization in various settings. We argue that the existing justifications for the success of SAM which are based on a PAC-Bayes generalization bound and the idea of convergence to flat minima are incomplete. Moreover, there are no explanations for the success of using $m$-sharpness in SAM which has been shown as essential for generalization. To better understand this aspect of SAM, we theoretically analyze its implicit bias for diagonal linear networks. We prove that SAM always chooses a solution that enjoys better generalization properties than standard gradient descent for a certain class of problems, and this effect is amplified by using $m$-sharpness. We further study the properties of the implicit bias on non-linear networks empirically, where we show that fine-tuning a standard model with SAM can lead to significant generalization improvements. Finally, we provide convergence results of SAM for non-convex objectives when used with stochastic gradients. We illustrate these results empirically for deep networks and discuss their relation to the generalization behavior of SAM. The code of our experiments is available at https://github.com/tml-epfl/understanding-sam.
Absolute Expressiveness of Subgraph-based Centrality Measures

In graph-based applications, a common task is to pinpoint the most important or ``central'' vertex in a (directed or undirected) graph, or rank the vertices of a graph according to their importance. To this end, a plethora of so-called centrality measures have been proposed in the literature. Such measures assess which vertices in a graph are the most important ones by analyzing the structure of the underlying graph. A family of centrality measures that are suited for graph databases has been recently proposed by relying on the following simple principle: the importance of a vertex in a graph is relative to the number of ``relevant'' connected subgraphs surrounding it; we refer to the members of this family as subgraph-based centrality measures. Although it has been shown that such measures enjoy several favourable properties, their absolute expressiveness remains largely unexplored. The goal of this work is to precisely characterize the absolute expressiveness of the family of subgraph-based centrality measures by considering both directed and undirected graphs. To this end, we characterize when an arbitrary centrality measure is a subgraph-based one, or a subgraph-based measure relative to the induced ranking. These characterizations provide us with technical tools that allow us to determine whether well-established centrality measures are subgraph-based. Such a classification, apart from being interesting in its own right, gives useful insights on the structural similarities and differences among existing centrality measures.
Transductive CLIP with Class-Conditional Contrastive Learning

Inspired by the remarkable zero-shot generalization capacity of vision-language pre-trained model, we seek to leverage the supervision from CLIP model to alleviate the burden of data labeling. However, such supervision inevitably contains the label noise, which significantly degrades the discriminative power of the classification model. In this work, we propose Transductive CLIP, a novel framework for learning a classification network with noisy labels from scratch. Firstly, a class-conditional contrastive learning mechanism is proposed to mitigate the reliance on pseudo labels and boost the tolerance to noisy labels. Secondly, ensemble labels is adopted as a pseudo label updating strategy to stabilize the training of deep neural networks with noisy labels. This framework can reduce the impact of noisy labels from CLIP model effectively by combining both techniques. Experiments on multiple benchmark datasets demonstrate the substantial improvements over other state-of-the-art methods.
Contrastive Learning for Unsupervised Domain Adaptation of Time Series

Unsupervised domain adaptation (UDA) aims at learning a machine learning model using a labeled source domain that performs well on a similar yet different, unlabeled target domain. UDA is important in many applications such as medicine, where it is used to adapt risk scores across different patient cohorts. In this paper, we develop a novel framework for UDA of time series data, called CLUDA. Specifically, we propose a contrastive learning framework to learn contextual representations in multivariate time series, so that these preserve label information for the prediction task. In our framework, we further capture the variation in the contextual representations between source and target domain via a custom nearest-neighbor contrastive learning. To the best of our knowledge, ours is the first framework to learn domain-invariant, contextual representation for UDA of time series data. We evaluate our framework using a wide range of time series datasets to demonstrate its effectiveness and show that it achieves state-of-the-art performance for time series UDA.
Reversible computation and the causal structure of space-time

Reversible algorithms play a crucial role both in classical and quantum computation. While for a classical bit the only nontrivial reversible operation is the bit-flip, nature is far more versatile in what it allows to do to a quantum bit. The reversible operations that a quantum computer can perform on a qubit are group of linear unitary transformations. However, laws of quantum mechanics prohibit implementation of anti-linear anti-unitary gates, even though they are perfectly reversible. Here we show that such a restriction on possible set of reversible operations is, remarkably, a fundamental constraint of spacetime structure. In particular, it will be shown that construction of any anti-linear anti-unitary gate will lead to violation of a fundamental causal primitive which, as we shall argue, is fundamentally different from the principle of relativistic causality.
Implementation of the Use of Website Media for High Risk Delivery Planning Towards Healthy Mothers and Babies in the Gondangwetan Health Center Area

Health problems that are a priority are maternal and child health problems due to high maternal and infant mortality rates in Indonesia.The attention of pregnant women and their families to implement the Childbirth Planning and Complications Prevention Program is quite low.This study aims to create an integrated service media in the form of the Sehati Mobile Inscription Website (High Risk Delivery Planning Towards Healthy Mothers and Babies).The method used is by using Research and Development media.Data were collected using a questionnaire.The Sehati Mobile Inscription Media can be used easily and helps health workers to find pregnant women with high risk for immediate follow-up treatment.In addition, the Sehati Mobile Inscription provides a lot of information to pregnant women regarding the preparation for childbirth that must be done to prevent complications and even death for the mother and baby.
Design and Development of Higher Vocational English Learning System Based on Visual Studio Platform

Based on Visual Studio platform, in ASP Net framework, using C# language to complete the construction of Higher Vocational English online learning system, and improve the deficiencies in the current higher vocational English education and teaching process with the functions of online learning, key exercises, homework assessment and interactive communication.With the help of network information technology, this paper introduces the online learning mode into the daily teaching plan of Higher Vocational English, breaks through the dependence on teachers and teaching materials under the classroom teaching mode, gives full play to the richness of network resources and the variability of teaching means, and realizes the online and offline integrated education for the purpose of stimulating higher vocational students' learning interest and improving teaching effect.At the same time, higher vocational English online learning system can also become an important auxiliary means for college teachers to carry out teaching.While reducing the workflow and improving work efficiency, it can also further promote the improvement of Higher Vocational English teaching system, and provide reference for the reform of teaching informatization in Colleges and universities.
The Design and Construction of College English Multi-modal Assisted Teaching System Based on Virtual Classroom

In view of the problems of single teaching form, weak students' sense of classroom participation and insufficient educational resources in colleges and universities, the author of this paper uses the characteristics of VR technology with strong interactivity and multi-sensory experience, and combines the multi-modal teaching concept to develop a multi-modal auxiliary teaching system for college English based on virtual classroom.The system is modeled by 3DSMAX, and the function is designed by using unity3D in C# language to complete the system development.It gives full play to the characteristics of virtual reality technology, and designs virtual scenes from the perspectives of classroom, roles and scenes, thus constructing an English classroom teaching system with virtual perception.The virtual space can be created by combining VR hardware with students and teachers, and the human-computer interaction experience can be realized.During the development process, the Control Rotation.cs class is used to realize the function of rotating the viewing angle around the object by 360 degrees, and the Input Tracking class technology is used to obtain the user's eye position.The development of the system has promoted the current educational reform in China to a certain extent and contributed to the educational cause in China.
Design and Implementation of Japanese Online Learning System in Mixed Teaching Mode

This paper uses Web technology to build a Japanese online learning system in mixed mode.The system is student-oriented, student-centered, and improves the one-sided evaluation of learning effect and single teaching mode in the teaching process.Among them, the system effect evaluation function needs to be realized by using tools such as Flume, HDFS, MapReduce, Spark and Java programming language in big data technology.The design of the system is not only conducive to stimulating students' interest in learning, enhancing students' ability to apply knowledge and teamwork spirit, but also conducive to improving students' autonomous learning ability and creating a learning atmosphere, and ultimately improving students' learning quality and comprehensive quality level.At the same time, it can help teachers accurately grasp students' learning situation and help students to form objective learning evaluation.
Research on the Construction of Informative OKR Performance Management System for University Teachers

Nowadays, informatization has been widely used.Especially, its combination with OKR performance appraisal greatly improves management efficiency.Performance assessment based on OKR starts from the teacher's inner drive, combines the development project and strategy of the institute and the secondary college with the individual's self-development and realization, which effectively mobilizes the enthusiasm and creativity of the teacher.This paper explores the theory and the practice of the collegian teachers' performance management, based on the concept of OKR informative construction principle, which applies Java, Processing and other visual performance management system.It makes a relatively accurate evaluation for teachers' performance and greatly improves the efficiency of performance management.Finally, it promotes the leap-forward development of colleges and universities.
STUDENTS’ OPINIONS TOWARD WRITING ACTIVITIES

Problem in this research is how is students’ opinions toward writing activities. This research is a qualitative research. The subject of the research is First semester consisted of 21 students who pragrammed Basic Writing subject in English Study Program at Dayanu Ikhsanuddin University, academic year 2019-2020. The instrument used in this research is questionairre about students’ opinion toward writing activities. The test consisted of 10 numbers with 5 optional degree of feeling, are: Strongly Agree (5), Agree (4), Neither Agree nor Disagree (3), Disagree (2) and Strongly Disagree (1). Data analysis uses the percentage formula as the frequency is divided by the number of the students, then the total number of the percentage is multiplied by 100% (Sugiyono,2010 in Husain, 2012).1 The research outcomes is majority students at the first semester of English Study Program, Teaching Training and Education Faculty, Dayanu Ikhsanuddin University, academic year 2019/2020 presented their positive opinion and responses toward writing activities.
The Effect of Multi-Generational Selection in Geometric Semantic Genetic Programming

Among the evolutionary methods, one that is quite prominent is Genetic Programming, and, in recent years, a variant called Geometric Semantic Genetic Programming (GSGP) has shown to be successfully applicable to many real-world problems. Due to a peculiarity in its implementation, GSGP needs to store all the evolutionary history, i.e., all populations from the first one. We exploit this stored information to define a multi-generational selection scheme that is able to use individuals from older populations. We show that a limited ability to use "old" generations is actually useful for the search process, thus showing a zero-cost way of improving the performances of GSGP.
METGEN: A Module-Based Entailment Tree Generation Framework for Answer Explanation

Knowing the reasoning chains from knowledge to the predicted answers can help construct an explainable question answering (QA) system. Advances on QA explanation propose to explain the answers with entailment trees composed of multiple entailment steps. While current work proposes to generate entailment trees with end-to-end generative models, the steps in the generated trees are not constrained and could be unreliable. In this paper, we propose METGEN, a Module-based Entailment Tree GENeration framework that has multiple modules and a reasoning controller. Given a question and several supporting knowledge, METGEN can iteratively generate the entailment tree by conducting single-step entailment with separate modules and selecting the reasoning flow with the controller. As each module is guided to perform a specific type of entailment reasoning, the steps generated by METGEN are more reliable and valid. Experiment results on the standard benchmark show that METGEN can outperform previous state-of-the-art models with only 9% of the parameters.
WDV: A Broad Data Verbalisation Dataset Built from Wikidata

Data verbalisation is a task of great importance in the current field of natural language processing, as there is great benefit in the transformation of our abundant structured and semi-structured data into human-readable formats. Verbalising Knowledge Graph (KG) data focuses on converting interconnected triple-based claims, formed of subject, predicate, and object, into text. Although KG verbalisation datasets exist for some KGs, there are still gaps in their fitness for use in many scenarios. This is especially true for Wikidata, where available datasets either loosely couple claim sets with textual information or heavily focus on predicates around biographies, cities, and countries. To address these gaps, we propose WDV, a large KG claim verbalisation dataset built from Wikidata, with a tight coupling between triples and text, covering a wide variety of entities and predicates. We also evaluate the quality of our verbalisations through a reusable workflow for measuring human-centred fluency and adequacy scores. Our data and code are openly available in the hopes of furthering research towards KG verbalisation.
Can collaborative learning be private, robust and scalable?

In federated learning for medical image analysis, the safety of the learning protocol is paramount. Such settings can often be compromised by adversaries that target either the private data used by the federation or the integrity of the model itself. This requires the medical imaging community to develop mechanisms to train collaborative models that are private and robust against adversarial data. In response to these challenges, we propose a practical open-source framework to study the effectiveness of combining differential privacy, model compression and adversarial training to improve the robustness of models against adversarial samples under train- and inference-time attacks. Using our framework, we achieve competitive model performance, a significant reduction in model's size and an improved empirical adversarial robustness without a severe performance degradation, critical in medical image analysis.
Polynomial-Time Algorithms for Counting and Sampling Markov Equivalent DAGs with Applications

Counting and sampling directed acyclic graphs from a Markov equivalence class are fundamental tasks in graphical causal analysis. In this paper we show that these tasks can be performed in polynomial time, solving a long-standing open problem in this area. Our algorithms are effective and easily implementable. As we show in experiments, these breakthroughs make thought-to-be-infeasible strategies in active learning of causal structures and causal effect identification with regard to a Markov equivalence class practically applicable.