Reviewerly/exHarmony huggingface dataset:
LaSQuE: Improved Zero-Shot Classification from Explanations Through Quantifier Modeling and Curriculum Learning

A hallmark of human intelligence is the ability to learn new concepts purely from language. Several recent approaches have explored training machine learning models via natural language supervision. However, these approaches fall short in leveraging linguistic quantifiers (such as 'always' or 'rarely') and mimicking humans in compositionally learning complex tasks. Here, we present LaSQuE, a method that can learn zero-shot classifiers from language explanations by using three new strategies - (1) modeling the semantics of linguistic quantifiers in explanations (including exploiting ordinal strength relationships, such as 'always' > 'likely'), (2) aggregating information from multiple explanations using an attention-based mechanism, and (3) model training via curriculum learning. With these strategies, LaSQuE outperforms prior work, showing an absolute gain of up to 7% in generalizing to unseen real-world classification tasks.
Let's Negotiate! A Survey of Negotiation Dialogue Systems

Negotiation is one of the crucial abilities in human communication, and there has been a resurgent research interest in negotiation dialogue systems recently, which goal is to empower intelligent agents with such ability that can efficiently help humans resolve conflicts or reach beneficial agreements. Although there have been many explorations in negotiation dialogue systems, a systematic review of this task has to date remained notably absent. To this end, we aim to fill this gap by reviewing contemporary studies in the emerging field of negotiation dialogue systems, covering benchmarks, evaluations, and methodologies. Furthermore, we also discuss potential future directions, including multi-modal, multi-party, and cross-cultural negotiation scenarios. Our goal is to provide the community with a systematic overview of negotiation dialogue systems and to inspire future research.
Managing Risk of Business Records: Gg Port (Malaysia) Bhd

To reduce any unwanted risk, an organization should manage risk that may occur and to effectively align it with the management procedures on business records in the company. A few problems have been identified in the study: the awareness between the management in the organization still being one of the questions to the researcher, the suitable policies and procedures for the business nature location, and the proper facilities should be placed in business records storage. This study uses qualitative methods involving structured interviews. Population sampling has been chosen to accord with the accuracy and relevance justification of the study. Keywords: Business Records, Facilities, Records Management, Risk eISSN: 2398-4287 © 2022. The Authors. Published for AMER ABRA cE-Bs by E-International Publishing House, Ltd., UK. This is an open-access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under the responsibility of AMER (Association of Malaysian Environment-Behavior Researchers), ABRA (Association of Behavioral Researchers on Asians), and cE-Bs (Centre for Environment-Behavior Studies), Faculty of Architecture, Planning &amp; Surveying, Universiti Teknologi MARA, Malaysia.
Unsupervised Discontinuous Constituency Parsing with Mildly Context-Sensitive Grammars

We study grammar induction with mildly context-sensitive grammars for unsupervised discontinuous parsing. Using the probabilistic linear context-free rewriting system (LCFRS) formalism, our approach fixes the rule structure in advance and focuses on parameter learning with maximum likelihood. To reduce the computational complexity of both parsing and parameter estimation, we restrict the grammar formalism to LCFRS-2 (i.e., binary LCFRS with fan-out two) and further discard rules that require O(n^6) time to parse, reducing inference to O(n^5). We find that using a large number of nonterminals is beneficial and thus make use of tensor decomposition-based rank-space dynamic programming with an embedding-based parameterization of rule probabilities to scale up the number of nonterminals. Experiments on German and Dutch show that our approach is able to induce linguistically meaningful trees with continuous and discontinuous structures
Introduction to the special issue “Higher education during and after COVID in Central and Eastern Europe”

Is there a need for more research on the effects of COVID-19 on education? While there is research suggesting that the impacts and “the troubling effects of the pandemic on research and higher education… might be felt for years to come” (The Lancet Editorial, 2020), there is also discernible pandemic fatigue, not only among the population, but researchers and subjects of research as well (Patel, Webster, Greenberg, Weston, &amp; Brooks, 2020). Indeed, there has been an overabundance of published research articles in nearly all disciplines about the pandemic. The purpose of the current special issue is not to repetitively take stock of what happened during the pandemic but rather explore how the pandemic transformed the education system while rethinking certain practices and strategies engrained in the education system. To some extent, then, taking stock of what we know about the pandemic should help us prepare for the era some call the “new normal post-COVID-19 era” (Cahapay, 2020).
CEDCES: A Cost Effective Deadline Constrained Evolutionary Scheduler for Task Graphs in Multi-Cloud System

Many scientific workflows can be modeled as a Directed Acyclic Graph (henceforth mentioned as DAG) where the nodes represent individual tasks and the directed edges represent data and control flow dependency between two tasks. Due to large computational resource requirements, a single cloud cannot meet the requirements of the workflow. Hence, a multi-cloud system, where multiple cloud providers pool their resources together becomes a good solution. The major objectives considered while scheduling the tasks present in a task graph include execution cost and makespan. In this paper, we present Cost Effective Deadline Constrained Evolutionary Scheduler (henceforth mentioned as CEDCES) which aims to minimize the execution cost under a given deadline constraint. CEDCES contains Particle Swarm Optimization-based (henceforth mentioned as PSO) method in its core, however includes novel initialization, crossover, and mutation schemes. Extensive simulation experiments on real-world workflows show that CEDCES outperforms the state-of-art algorithms, in particular, 60.41% on average in terms of execution cost. In cases where the deadline is violated, CEDCES gives the least overshoot in execution time and outperforming the others by 10.96% on average.
Online higher education during the pandemic: The case of Hungary

The coronavirus pandemic has profoundly affected Hungarian higher education (HEI). As campuses around the globe were forced to shut down, online distance learning became an everyday reality for all students. At the same time, faculty were rushed to adjust their teaching content and methods, as well as forms of assessment, to online teaching. As a result, this transition exposed flaws in technological abilities, digital preparedness and flexibilities within universities. In this work, my goal is to analyze how the Hungarian HEIs adopted online educational practices during the pandemic and how they were able to address some of the difficulties while adjusting their teaching and creating new forms of support for both students and faculty. To that end, the article first discusses the prevalence of distance and digital learning before the pandemic, then evaluates the steps and challenges of transition to remote online education during the pandemic, and finally highlights some lessons learnt and best practices of some Hungarian universities.
Analisis Kesalahan Berbahasa Tataran Fonologi dan Tataran Morfologi Ragam Krama pada Karangan Deskripsi Karya Siswa Kelas X

During the Field Experience Practice (PPL), many language errors were found in students' essays. This study describes the errors in Javanese language in the descriptive essay of the work of class X students of SMA N 12 Semarang. The approach used is a qualitative descriptive approach combined with the theory of language error analysis. The research data are in the form of descriptive essays in Javanese variety of manners. The source of this research data is the students of Class X SMA N 12 Semarang which contains language errors. The data were collected using the listening and note-taking technique, while the data analysis techniques used in this study were sorting and dressing techniques. The results showed that there were 4 language errors at the phonological level, namely 78% vowel phoneme errors, 17% consonant phoneme errors, 3% consonant addition errors, and 2% consonant reduction errors. Meanwhile, at the morphological level there are 6 errors, namely errors because prefixes are separated from basic words as much as 17%, errors due to suffixes being separated from basic words as much as 5%, errors in prefixing as much as 11%, ater-ater hanuswara errors 21%, diction errors as much as 24%, and reduplication errors as much as 22%.&#x0D; Keywords: descriptive essay; Javanese manners; language error
OCCAM-v2: Combining Static and Dynamic Analysis for Effective and Efficient Whole-program Specialization

OCCAM-v2 leverages scalable pointer analysis, value analysis, and dynamic analysis to create an effective and efficient tool for specializing LLVM bitcode. The extent of the code-size reduction achieved depends on the specific deployment configuration. Each application that is to be specialized is accompanied by a manifest that specifies concrete arguments that are known a priori, as well as a count of residual arguments that will be provided at runtime. The best case for partial evaluation occurs when the arguments are completely concretely specified. OCCAM-v2 uses a pointer analysis to devirtualize calls, allowing it to eliminate the entire body of functions that are not reachable by any direct calls. The hybrid analysis feature can handle cases that are challenging for static analysis, such as input loops, string processing, and external data (in files, for example). On the suite of evaluated programs, OCCAM-v2 was able to reduce the instruction count by 40.6 percent on average, taking a median of 2.4 seconds.
An Extension of Fisher's Criterion: Theoretical Results with a Neural Network Realization

Fisher's criterion is a widely used tool in machine learning for feature selection. For large search spaces, Fisher's criterion can provide a scalable solution to select features. A challenging limitation of Fisher's criterion, however, is that it performs poorly when mean values of class-conditional distributions are close to each other. Motivated by this challenge, we propose an extension of Fisher's criterion to overcome this limitation. The proposed extension utilizes the available heteroscedasticity of class-conditional distributions to distinguish one class from another. Additionally, we describe how our theoretical results can be casted into a neural network framework, and conduct a proof-of-concept experiment to demonstrate the viability of our approach to solve classification problems.
APOLLO: A Simple Approach for Adaptive Pretraining of Language Models for Logical Reasoning

Logical reasoning of text is an important ability that requires understanding the information present in the text, their interconnections, and then reasoning through them to infer new conclusions. Prior works on improving the logical reasoning ability of language models require complex processing of training data (e.g., aligning symbolic knowledge to text), yielding task-specific data augmentation solutions that restrict the learning of general logical reasoning skills. In this work, we propose APOLLO, an adaptively pretrained language model that has improved logical reasoning abilities. We select a subset of Wikipedia, based on a set of logical inference keywords, for continued pretraining of a language model. We use two self-supervised loss functions: a modified masked language modeling loss where only specific parts-of-speech words, that would likely require more reasoning than basic language understanding, are masked, and a sentence-level classification loss that teaches the model to distinguish between entailment and contradiction types of sentences. The proposed training paradigm is both simple and independent of task formats. We demonstrate the effectiveness of APOLLO by comparing it with prior baselines on two logical reasoning datasets. APOLLO performs comparably on ReClor and outperforms baselines on LogiQA. The code base has been made publicly available.
Explaining Classifications to Non Experts: An XAI User Study of Post Hoc Explanations for a Classifier When People Lack Expertise

Very few eXplainable AI (XAI) studies consider how users understanding of explanations might change depending on whether they know more or less about the to be explained domain (i.e., whether they differ in their expertise). Yet, expertise is a critical facet of most high stakes, human decision making (e.g., understanding how a trainee doctor differs from an experienced consultant). Accordingly, this paper reports a novel, user study (N=96) on how peoples expertise in a domain affects their understanding of post-hoc explanations by example for a deep-learning, black box classifier. The results show that peoples understanding of explanations for correct and incorrect classifications changes dramatically, on several dimensions (e.g., response times, perceptions of correctness and helpfulness), when the image-based domain considered is familiar (i.e., MNIST) as opposed to unfamiliar (i.e., Kannada MNIST). The wider implications of these new findings for XAI strategies are discussed.
Comparing two cohomological obstructions for contextuality, and a generalised construction of quantum advantage with shallow circuits

We present two results on the subject of quantum contextuality and cohomology, and non-locality and quantum advantage with shallow circuits. Abramsky et al. showed that a range of examples of quantum contextuality is detected by a cohomological invariant based on \v{C}ech cohomology. However, the approach does not give a complete cohomological characterisation of contextuality. A different cohomological approach to contextuality was introduced by Okay et al. Their approach exploits the algebraic structure of the Pauli operators and their qudit generalisations known as Weyl operators. We give an abstract account of this structure, then generalise their approach to any example of contextuality with this structure. We prove at this general level that the approach does not give a more complete characterisation of contextuality than the \v{C}ech cohomology approach. Bravyi, Gosset, and K\"{o}nig (BGK) gave the first unconditional proof that a restricted class of quantum circuits is more powerful than its classical analogue. The result, for the class of circuits of bounded depth and fan-in (shallow circuits), exploits a particular family of examples of contextuality. BGK's quantum circuit and computational problem are derived from a family of non-local games related to the well-known GHZ non-local game. We present a generalised version of their construction. A systematic way of taking examples of contextuality and producing unconditional quantum advantage results with shallow circuits.
Robustness of quantum reinforcement learning under hardware errors

Variational quantum machine learning algorithms have become the focus of recent research on how to utilize near-term quantum devices for machine learning tasks. They are considered suitable for this as the circuits that are run can be tailored to the device, and a big part of the computation is delegated to the classical optimizer. It has also been hypothesized that they may be more robust to hardware noise than conventional algorithms due to their hybrid nature. However, the effect of training quantum machine learning models under the influence of hardware-induced noise has not yet been extensively studied. In this work, we address this question for a specific type of learning, namely variational reinforcement learning, by studying its performance in the presence of various noise sources: shot noise, coherent and incoherent errors. We analytically and empirically investigate how the presence of noise during training and evaluation of variational quantum reinforcement learning algorithms affect the performance of the agents and robustness of the learned policies. Furthermore, we provide a method to reduce the number of measurements required to train Q-learning agents, using the inherent structure of the algorithm.
Reference-based Image and Video Super-Resolution via C2-Matching

Reference-based Super-Resolution (Ref-SR) has recently emerged as a promising paradigm to enhance a low-resolution (LR) input image or video by introducing an additional high-resolution (HR) reference image. Existing Ref-SR methods mostly rely on implicit correspondence matching to borrow HR textures from reference images to compensate for the information loss in input images. However, performing local transfer is difficult because of two gaps between input and reference images: the transformation gap (e.g., scale and rotation) and the resolution gap (e.g., HR and LR). To tackle these challenges, we propose C2-Matching in this work, which performs explicit robust matching crossing transformation and resolution. 1) To bridge the transformation gap, we propose a contrastive correspondence network, which learns transformation-robust correspondences using augmented views of the input image. 2) To address the resolution gap, we adopt teacher-student correlation distillation, which distills knowledge from the easier HR-HR matching to guide the more ambiguous LR-HR matching. 3) Finally, we design a dynamic aggregation module to address the potential misalignment issue between input images and reference images. In addition, to faithfully evaluate the performance of Reference-based Image Super-Resolution under a realistic setting, we contribute the Webly-Referenced SR (WR-SR) dataset, mimicking the practical usage scenario. We also extend C2-Matching to Reference-based Video Super-Resolution task, where an image taken in a similar scene serves as the HR reference image. Extensive experiments demonstrate that our proposed C2-Matching significantly outperforms state of the arts on the standard CUFED5 benchmark and also boosts the performance of video SR by incorporating the C2-Matching component into Video SR pipelines.
On the Density of Codes over Finite Chain Rings

We determine the asymptotic proportion of free modules over finite chain rings with good distance properties and treat the asymptotics in the code length n and the residue field size q separately. We then specialize and apply our technique to rank metric codes and to Hamming metric codes.
Co-designing for a Hybrid Workplace Experience in Software Development

With increasing demands for flexible work models, many IT organizations have adapted to hybrid work that promises enhanced team productivity as well as work satisfaction. To achieve productive engineering practice, collaborative product innovation, and effective mentorship in the ensuing hybrid work, we introduce a workshop approach on co-designing for a hybrid workplace experience and provide implications for continuously improving collaborative software development at scale.
Explanation Regeneration via Information Bottleneck

Explaining the black-box predictions of NLP models naturally and accurately is an important open problem in natural language generation. These free-text explanations are expected to contain sufficient and carefully-selected evidence to form supportive arguments for predictions. Due to the superior generative capacity of large pretrained language models, recent work built on prompt engineering enables explanation generation without specific training. However, explanation generated through single-pass prompting often lacks sufficiency and conciseness. To address this problem, we develop an information bottleneck method EIB to produce refined explanations that are sufficient and concise. Our approach regenerates the free-text explanation by polishing the single-pass output from the pretrained language model but retaining the information that supports the contents being explained. Experiments on two out-of-domain tasks verify the effectiveness of EIB through automatic evaluation and thoroughly-conducted human evaluation.
Pseudonymization at Scale: OLCF's Summit Usage Data Case Study

The analysis of vast amounts of data and the processing of complex computational jobs have traditionally relied upon high performance computing (HPC) systems. Understanding these analyses' needs is paramount for designing solutions that can lead to better science, and similarly, understanding the characteristics of the user behavior on those systems is important for improving user experiences on HPC systems. A common approach to gathering data about user behavior is to analyze system log data available only to system administrators. Recently at Oak Ridge Leadership Computing Facility (OLCF), however, we unveiled user behavior about the Summit supercomputer by collecting data from a user's point of view with ordinary Unix commands. Here, we discuss the process, challenges, and lessons learned while preparing this dataset for publication and submission to an open data challenge. The original dataset contains personal identifiable information (PII) about OLCF users which needed be masked prior to publication, and we determined that anonymization, which scrubs PII completely, destroyed too much of the structure of the data to be interesting for the data challenge. We instead chose to pseudonymize the dataset to reduce its linkability to users' identities. Pseudonymization is significantly more computationally expensive than anonymization, and the size of our dataset, approximately 175 million lines of raw text, necessitated the development of a parallelized workflow that could be reused on different HPC machines. We demonstrate the scaling behavior of the workflow on two leadership class HPC systems at OLCF, and we show that we were able to bring the overall makespan time from an impractical 20+ hours on a single node down to around 2 hours. As a result of this work, we release the entire pseudonymized dataset and make the workflows and source code publicly available.