Reviewerly/exHarmony huggingface dataset:
TaskLint: Automated Detection of Ambiguities in Task Instructions

Clear instructions are a necessity for obtaining accurate results from crowd workers. Even small ambiguities can force workers to choose an interpretation arbitrarily, resulting in errors and inconsistency. Crisp instructions require significant time to design, test, and iterate. Recent approaches have engaged workers to detect and correct ambiguities. However, this process increases the time and money required to obtain accurate, consistent results. We present TaskLint, a system to automatically detect problems with task instructions. Leveraging a diverse set of existing NLP tools, TaskLint identifies words and sentences that might foretell worker confusion. This is analogous to static analysis tools for code ("linters"), which detect possible features in code that might indicate the presence of bugs. Our evaluation of TaskLint using task instructions created by novices confirms the potential for static tools to improve task clarity and the accuracy of results, while also highlighting several challenges.
Weight-2 input sequences of $ 1/n $ convolutional codes from linear systems point of view

&lt;abstract&gt;&lt;p&gt;Convolutional codes form an important class of codes that have memory. One natural way to study these codes is by means of input state output representations. In this paper we study the minimum (Hamming) weight among codewords produced by input sequences of weight two. In this paper, we consider rate $ 1/n $ and use the linear system setting called $ (A, B, C, D) $ input-state-space representations of convolutional codes for our analysis. Previous results on this area were recently derived assuming that the matrix $ A $, in the input-state-output representation, is nonsingular. This work completes this thread of research by treating the nontrivial case in which $ A $ is singular. Codewords generated by weight-2 inputs are relevant to determine the effective free distance of Turbo codes.&lt;/p&gt;&lt;/abstract&gt;
Competitive Strategies and Performance of Pharmaceutical Wholesaler Companies in Kigali, Rwanda

This research dealt with competitive strategies and pharmaceutical wholesaler companies’ performance in Kigali Rwanda. The three specific objectives of this research were to examine effect of cost leadership, focus and differentiation on pharmaceutical wholesaler companies’ performance in Kigali, Rwanda. This research study adopted a descriptive research design with one hundred and thirty six pharmaceutical wholesalers in Kigali city licensed and registered by Rwanda Food and Drug Authority as population. The research used size of the sample which were 101 wholesaler companies, selected using stratified probability sampling method. This study used a quantitative data analysis technique to the collected data by use of questionnaire as research instrument and data were coded and analyzed using SPSS version 22.0. Questionnaires pre-test was used through pilot study with teen respondents where the reliability was tested by use of Cronbach’s Alpha. Descriptive statistics results demonstrated that an overall mean of 1.963 calculated during the analysis of the statements regarding cost leadership implied that a big number of respondents strongly agreed that leadership of cost strategies affected pharmaceutical companies’ performance while the calculated overall mean of 2.019 implied that a big number of respondents agreed that focus strategies affect performance of pharmaceutical companies and the overall mean of 2.029 implied that the big number of respondents agreed that strategies of differentiation affected pharmaceutical companies performance in Rwanda. Inferential statistics results revealed that cost leadership, focus and differentiation strategies effected performance of pharmaceutical companies because the calculated correlation results indicated positive and significant relationship between cost leadership strategies and sales revenue (r=0.296 and sig=0.000), focus strategies and profitability (r=0.391 and sig=0.000), and differentiation strategies and market leadership (r=0.964 and sig=0.000) at 0.01 level of significance; while the regression results revealed that there is a positive and significant effect between cost leadership sales revenue (b=0.065 and sig=.004), between focus strategies and sale revenue (b=0.744 and sig=.000) and differentiation strategies (b=0.086 and sig=.030) at 0.05 level of significance. Thus, these correlation and regression results justified why all null hypotheses were rejected because all predictors of competitive strategies showed a significantly positive effect on pharmaceutical companies’ performance or their indicators. In conclusion, the results indicated that competitive strategies demonstrated a positively significant effect on pharmaceutical companies’ performance in Rwanda. The researcher recommended the pharmaceutical companies to enhance their performance by always updating and creating innovation in their competitive strategies in order to ensure that the company is always on top in regard to performance. Keywords: Competitive Strategies, Pharmaceutical Wholesaler Companies, Performance, Rwanda
The use of information and communication technologies is the key to improving the efficiency of higher education institutions

Abstract Information and telecommunication technologies are confidently included in all spheres of human activity. The introduction of information technology is taking place in almost all areas, but the most acute solution to this issue requires the education system, in particular the organization of school management. The dynamics of development of an educational institution depends on the quality of construction of an integrated information automated management system of higher education institutions. The problem of informatization is the main issue around which the whole system of higher education is built. Solving this problem allows to solve the orders of the information society, to train specialists who are able to apply information technology at a modern level in professional activities, in everyday life. It is difficult to imagine a higher education institution that operates without the use of computers and information technology. Thus, the choice of topic is due to the need to study this issue in order to develop new approaches to the introduction of information and communication technologies in the educational process, its improvement and modernization in the general educational system, and on this basis to identify and disseminate positive experience higher education institution.
Dynamic Token Normalization Improves Vision Transformers

Vision Transformer (ViT) and its variants (e.g., Swin, PVT) have achieved great success in various computer vision tasks, owing to their capability to learn long-range contextual information. Layer Normalization (LN) is an essential ingredient in these models. However, we found that the ordinary LN makes tokens at different positions similar in magnitude because it normalizes embeddings within each token. It is difficult for Transformers to capture inductive bias such as the positional context in an image with LN. We tackle this problem by proposing a new normalizer, termed Dynamic Token Normalization (DTN), where normalization is performed both within each token (intra-token) and across different tokens (inter-token). DTN has several merits. Firstly, it is built on a unified formulation and thus can represent various existing normalization methods. Secondly, DTN learns to normalize tokens in both intra-token and inter-token manners, enabling Transformers to capture both the global contextual information and the local positional context. {Thirdly, by simply replacing LN layers, DTN can be readily plugged into various vision transformers, such as ViT, Swin, PVT, LeViT, T2T-ViT, BigBird and Reformer. Extensive experiments show that the transformer equipped with DTN consistently outperforms baseline model with minimal extra parameters and computational overhead. For example, DTN outperforms LN by $0.5\%$ - $1.2\%$ top-1 accuracy on ImageNet, by $1.2$ - $1.4$ box AP in object detection on COCO benchmark, by $2.3\%$ - $3.9\%$ mCE in robustness experiments on ImageNet-C, and by $0.5\%$ - $0.8\%$ accuracy in Long ListOps on Long-Range Arena.} Codes will be made public at \url{https://github.com/wqshao126/DTN}
On Measures of Biases and Harms in NLP

Recent studies show that Natural Language Processing (NLP) technologies propagate societal biases about demographic groups associated with attributes such as gender, race, and nationality. To create interventions and mitigate these biases and associated harms, it is vital to be able to detect and measure such biases. While existing works propose bias evaluation and mitigation methods for various tasks, there remains a need to cohesively understand the biases and the specific harms they measure, and how different measures compare with each other. To address this gap, this work presents a practical framework of harms and a series of questions that practitioners can answer to guide the development of bias measures. As a validation of our framework and documentation questions, we also present several case studies of how existing bias measures in NLP -- both intrinsic measures of bias in representations and extrinsic measures of bias of downstream applications -- can be aligned with different harms and how our proposed documentation questions facilitates more holistic understanding of what bias measures are measuring.
SODAPOP: Open-Ended Discovery of Social Biases in Social Commonsense Reasoning Models

A common limitation of diagnostic tests for detecting social biases in NLP models is that they may only detect stereotypic associations that are pre-specified by the designer of the test. Since enumerating all possible problematic associations is infeasible, it is likely these tests fail to detect biases that are present in a model but not pre-specified by the designer. To address this limitation, we propose SODAPOP (SOcial bias Discovery from Answers about PeOPle) in social commonsense question-answering. Our pipeline generates modified instances from the Social IQa dataset (Sap et al., 2019) by (1) substituting names associated with different demographic groups, and (2) generating many distractor answers from a masked language model. By using a social commonsense model to score the generated distractors, we are able to uncover the model's stereotypic associations between demographic groups and an open set of words. We also test SODAPOP on debiased models and show the limitations of multiple state-of-the-art debiasing algorithms.
BLOX: Macro Neural Architecture Search Benchmark and Algorithms

Neural architecture search (NAS) has been successfully used to design numerous high-performance neural networks. However, NAS is typically compute-intensive, so most existing approaches restrict the search to decide the operations and topological structure of a single block only, then the same block is stacked repeatedly to form an end-to-end model. Although such an approach reduces the size of search space, recent studies show that a macro search space, which allows blocks in a model to be different, can lead to better performance. To provide a systematic study of the performance of NAS algorithms on a macro search space, we release Blox - a benchmark that consists of 91k unique models trained on the CIFAR-100 dataset. The dataset also includes runtime measurements of all the models on a diverse set of hardware platforms. We perform extensive experiments to compare existing algorithms that are well studied on cell-based search spaces, with the emerging blockwise approaches that aim to make NAS scalable to much larger macro search spaces. The benchmark and code are available at https://github.com/SamsungLabs/blox.
Multi-Task Learning for Joint Semantic Role and Proto-Role Labeling

We put forward an end-to-end multi-step machine learning model which jointly labels semantic roles and the proto-roles of Dowty (1991), given a sentence and the predicates therein. Our best architecture first learns argument spans followed by learning the argument's syntactic heads. This information is shared with the next steps for predicting the semantic roles and proto-roles. We also experiment with transfer learning from argument and head prediction to role and proto-role labeling. We compare using static and contextual embeddings for words, arguments, and sentences. Unlike previous work, our model does not require pre-training or fine-tuning on additional tasks, beyond using off-the-shelf (static or contextual) embeddings and supervision. It also does not require argument spans, their semantic roles, and/or their gold syntactic heads as additional input, because it learns to predict all these during training. Our multi-task learning model raises the state-of-the-art predictions for most proto-roles.
Reinforcement Learning with Unbiased Policy Evaluation and Linear Function Approximation

We provide performance guarantees for a variant of simulation-based policy iteration for controlling Markov decision processes that involves the use of stochastic approximation algorithms along with state-of-the-art techniques that are useful for very large MDPs, including lookahead, function approximation, and gradient descent. Specifically, we analyze two algorithms; the first algorithm involves a least squares approach where a new set of weights associated with feature vectors is obtained via least squares minimization at each iteration and the second algorithm involves a two-time-scale stochastic approximation algorithm taking several steps of gradient descent towards the least squares solution before obtaining the next iterate using a stochastic approximation algorithm.
FOON Creation and Traversal for Recipe Generation

Task competition by robots is still off from being completely dependable and usable. One way a robot may decipher information given to it and accomplish tasks is by utilizing FOON, which stands for functional object-oriented network. The network first needs to be created by having a human creates action nodes as well as input and output nodes in a .txt file. After the network is sizeable, utilization of this network allows for traversal of the network in a variety of ways such as choosing steps via iterative deepening searching by using the first seen valid option. Another mechanism is heuristics, such as choosing steps based on the highest success rate or lowest amount of input ingredients. Via any of these methods, a program can traverse the network given an output product, and derive the series of steps that need to be taken to produce the output.
Mind the Labels: Describing Relations in Knowledge Graphs With Pretrained Models

Pretrained language models (PLMs) for data-to-text (D2T) generation can use human-readable data labels such as column headings, keys, or relation names to generalize to out-of-domain examples. However, the models are well-known in producing semantically inaccurate outputs if these labels are ambiguous or incomplete, which is often the case in D2T datasets. In this paper, we expose this issue on the task of descibing a relation between two entities. For our experiments, we collect a novel dataset for verbalizing a diverse set of 1,522 unique relations from three large-scale knowledge graphs (Wikidata, DBPedia, YAGO). We find that although PLMs for D2T generation expectedly fail on unclear cases, models trained with a large variety of relation labels are surprisingly robust in verbalizing novel, unseen relations. We argue that using data with a diverse set of clear and meaningful labels is key to training D2T generation systems capable of generalizing to novel domains.
Frustratingly Easy Sentiment Analysis of Text Streams: Generating High-Quality Emotion Arcs Using Emotion Lexicons

Automatically generated emotion arcs -- that capture how an individual or a population feels over time -- are widely used in industry and research. However, there is little work on evaluating the generated arcs. This is in part due to the difficulty of establishing the true (gold) emotion arc. Our work, for the first time, systematically and quantitatively evaluates automatically generated emotion arcs. We also compare two common ways of generating emotion arcs: Machine-Learning (ML) models and Lexicon-Only (LexO) methods. Using a number of diverse datasets, we systematically study the relationship between the quality of an emotion lexicon and the quality of the emotion arc that can be generated with it. We also study the relationship between the quality of an instance-level emotion detection system (say from an ML model) and the quality of emotion arcs that can be generated with it. We show that despite being markedly poor at instance level, LexO methods are highly accurate at generating emotion arcs by aggregating information from hundreds of instances. This has wide-spread implications for commercial development, as well as research in psychology, public health, digital humanities, etc. that values simple interpretable methods and disprefers the need for domain-specific training data, programming expertise, and high-carbon-footprint models.
Non-fungible token transactions: data and challenges

Non-fungible tokens (NFT) have recently emerged as a novel blockchain hosted financial asset class that has attracted major transaction volumes. Investment decisions rely on data and adequate preprocessing and application of analytics to them. Both owing to the non-fungible nature of the tokens and to a blockchain being the primary data source, NFT transaction data pose several challenges not commonly encountered in traditional financial data. Using data that consist of the transaction history of eight highly valued NFT collections, a selection of such challenges is illustrated. These are: price differentiation by token traits, the possible existence of lateral swaps and wash trades in the transaction history and finally, severe volatility. While this paper merely scratches the surface of how data analytics can be applied in this context, the data and challenges laid out here may present opportunities for future research on the topic.
A Concise Introduction to Reinforcement Learning in Robotics

One of the biggest hurdles robotics faces is the facet of sophisticated and hard-to-engineer behaviors. Reinforcement learning offers a set of tools, and a framework to address this problem. In parallel, the misgivings of robotics offer a solid testing ground and evaluation metric for advancements in reinforcement learning. The two disciplines go hand-in-hand, much like the fields of Mathematics and Physics. By means of this survey paper, we aim to invigorate links between the research communities of the two disciplines by focusing on the work done in reinforcement learning for locomotive and control aspects of robotics. Additionally, we aim to highlight not only the notable successes but also the key challenges of the application of Reinforcement Learning in Robotics. This paper aims to serve as a reference guide for researchers in reinforcement learning applied to the field of robotics. The literature survey is at a fairly introductory level, aimed at aspiring researchers. Appropriately, we have covered the most essential concepts required for research in the field of reinforcement learning, with robotics in mind. Through a thorough analysis of this problem, we are able to manifest how reinforcement learning could be applied profitably, and also focus on open-ended questions, as well as the potential for future research.
Use of Distance Learning Technologies in the Higher Education System

Abstract: Distance learning is defined by a combination of pedagogical and information technologies. Modern computer networks are able to provide knowledge transfer and access to a variety of educational information on an equal basis, and sometimes even more effectively than traditional learning tools. However, for their effective use in the process of acquiring knowledge and forming intellectual skills, a coordinated development of technological, informational, and didactic and methodological components of the educational process is necessary.
Holistic Sentence Embeddings for Better Out-of-Distribution Detection

Detecting out-of-distribution (OOD) instances is significant for the safe deployment of NLP models. Among recent textual OOD detection works based on pretrained language models (PLMs), distance-based methods have shown superior performance. However, they estimate sample distance scores in the last-layer CLS embedding space and thus do not make full use of linguistic information underlying in PLMs. To address the issue, we propose to boost OOD detection by deriving more holistic sentence embeddings. On the basis of the observations that token averaging and layer combination contribute to improving OOD detection, we propose a simple embedding approach named Avg-Avg, which averages all token representations from each intermediate layer as the sentence embedding and significantly surpasses the state-of-the-art on a comprehensive suite of benchmarks by a 9.33% FAR95 margin. Furthermore, our analysis demonstrates that it indeed helps preserve general linguistic knowledge in fine-tuned PLMs and substantially benefits detecting background shifts. The simple yet effective embedding method can be applied to fine-tuned PLMs with negligible extra costs, providing a free gain in OOD detection. Our code is available at https://github.com/lancopku/Avg-Avg.
Formulation of general dynamical invariants and their unitary relations for time-dependent coupled quantum oscillators

An exact invariant operator of time-dependent coupled oscillators is derived using the Liouville-von Neumann equation. The unitary relation between this invariant and the invariant of two uncoupled simple harmonic oscillators is represented. If we consider the fact that quantum solutions of the simple harmonic oscillator is well-known, this unitary relation is very useful in clarifying quantum characteristics of the original systems, such as entanglement, probability densities, fluctuations of the canonical variables, and decoherence. We can identify such quantum characteristics by inversely transforming the mathematical representations of quantum quantities belonging to the simple harmonic oscillators. As a case in point, the eigenfunctions of the invariant operator in the original systems are found through inverse transformation of the well-known eigenfunctions associated with the simple harmonic oscillators.
Deep PatchMatch MVS with Learned Patch Coplanarity, Geometric Consistency and Adaptive Pixel Sampling

Recent work in multi-view stereo (MVS) combines learnable photometric scores and regularization with PatchMatch-based optimization to achieve robust pixelwise estimates of depth, normals, and visibility. However, non-learning based methods still outperform for large scenes with sparse views, in part due to use of geometric consistency constraints and ability to optimize over many views at high resolution. In this paper, we build on learning-based approaches to improve photometric scores by learning patch coplanarity and encourage geometric consistency by learning a scaled photometric cost that can be combined with reprojection error. We also propose an adaptive pixel sampling strategy for candidate propagation that reduces memory to enable training on larger resolution with more views and a larger encoder. These modifications lead to 6-15% gains in accuracy and completeness on the challenging ETH3D benchmark, resulting in higher F1 performance than the widely used state-of-the-art non-learning approaches ACMM and ACMP.