Reviewerly/exHarmony huggingface dataset:
Probability Paths and the Structure of Predictions over Time

In settings ranging from weather forecasts to political prognostications to financial projections, probability estimates of future binary outcomes often evolve over time. For example, the estimated likelihood of rain on a specific day changes by the hour as new information becomes available. Given a collection of such probability paths, we introduce a Bayesian framework -- which we call the Gaussian latent information martingale, or GLIM -- for modeling the structure of dynamic predictions over time. Suppose, for example, that the likelihood of rain in a week is 50%, and consider two hypothetical scenarios. In the first, one expects the forecast is equally likely to become either 25% or 75% tomorrow; in the second, one expects the forecast to stay constant for the next several days. A time-sensitive decision-maker might select a course of action immediately in the latter scenario, but may postpone their decision in the former, knowing that new information is imminent. We model these trajectories by assuming predictions update according to a latent process of information flow, which is inferred from historical data. In contrast to general methods for time series analysis, this approach preserves the martingale structure of probability paths and better quantifies future uncertainties around probability paths. We show that GLIM outperforms three popular baseline methods, producing better estimated posterior probability path distributions measured by three different metrics. By elucidating the dynamic structure of predictions over time, we hope to help individuals make more informed choices.
What Makes a Scientific Paper be Accepted for Publication

Despite peer-reviewing being an essential component of academia since the 1600s, it has repeatedly received criticisms for lack of transparency and consistency. We posit that recent work in machine learning and explainable AI provide tools that enable insights into the decisions from a given peer-review process. We start by simulating the peer-review process using an ML classifier and extracting global explanations in the form of linguistic features that affect the acceptance of a scientific paper for publication on an open peer-review dataset. Second, since such global explanations do not justify causal interpretations, we propose a methodology for detecting confounding effects in natural language and generating explanations, disentangled from textual confounders, in the form of lexicons. Our proposed linguistic explanation methodology indicates the following on a case dataset of ICLR submissions: a) the organising committee follows, for the most part, the recommendations of reviewers, and b) the paper’s main characteristics that led to reviewers recommending acceptance for publication are originality, clarity and substance.
Distributed Zero-Order Optimization under Adversarial Noise

We study the problem of distributed zero-order optimization for a class of strongly convex functions. They are formed by the average of local objectives, associated to different nodes in a prescribed network of connections. We propose a distributed zero-order projected gradient descent algorithm to solve this problem. Exchange of information within the network is permitted only between neighbouring nodes. A key feature of the algorithm is that it can query only function values, subject to a general noise model, that does not require zero mean or independent errors. We derive upper bounds for the average cumulative regret and optimization error of the algorithm which highlight the role played by a network connectivity parameter, the number of variables, the noise level, the strong convexity parameter of the global objective and certain smoothness properties of the local objectives. When the bound is specified to the standard undistributed setting, we obtain an improvement over the state-of-the-art bounds, due to the novel gradient estimation procedure proposed here. We also comment on lower bounds and observe that the dependency over certain function parameters in the bound is nearly optimal.
A Free Format Legal Question Answering System

We present an information retrieval-based question answer system to answer legal questions. The system is not limited to a predefined set of questions or patterns and uses both sparse vector search and embeddings for input to a BERT-based answer re-ranking system. A combination of general domain and legal domain data is used for training. This natural question answering system is in production and is used commercially.
Digital trust – asystematic literature review

Digital technologies, such as social networks, smartphones, block chainsorbig data, have become an indispensable part of our lives in recent years. They have a significant impact on our daily lives, even in crucial areas such as health, finance, and education. Recognising this, organisations presently face a difficult challenge – to create consumer digital trust. It should be noted that most studies have focused on the establishment of digital trust without identifying and considering the basic definition of “digital trust”. The purpose of this article is to provide insights regarding the state of the art of digital trust, and to suggest areas for future research. Using a systematic literature review, this paper provides an overview of the literature. The most important conclusionto be derived from this study is that there is no single generally accepted definition of digital trust. Although the early research on “digital trust” can be traced back to 1996, it was only after 2016 that the number of papers on this topic increased substantially.
Legal Terminology Extraction with the Termolator

Domain-specific terminology is ubiquitous in legal documents. Despite potential utility in populating glossaries and ontologies or as arguments in information extraction and document classification tasks, there has been limited work done for legal terminology extraction. This paper describes some work to remedy this omission. In the described research, we make some modifications to the Termolator, a high-performing, open-source terminology extractor which has been tuned to scientific articles. Our changes are designed to improve the Termolator’s results when applied to United States Supreme Court decisions. Unaltered and using the recommended settings, the original Termolator provides a list of terminology with a precision of 23% and 25% for the categories of economic activity (development set) and criminal procedures (test set) respectively. These were the most frequently occurring broad issues in Washington University in St. Louis Database corpus, a database of Supreme Court decisions that have been manually classified by topic. Our contribution includes the introduction of several legal domain-specific filtration steps and changes to the web search relevance score; each incrementally improved precision culminating in a combined precision of 63% and 65%. We also evaluated the baseline version of the Termolator on more specific subcategories and on broad issues with fewer cases. Our results show that a narrowed scope as well as smaller document numbers significantly lower the precision. In both cases, the modifications to the Termolator improve precision.
Robust stereo infrared camera calibration for 3D depth estimation and measurement

Thermal imaging can be used to characterize heated objects in numerous applications, including security and surveillance, where it is vital to locate an object within a 3D space. This can be achieved with stereoscopic vision, where optical sensors are used to construct a 3D image of the space it is looking at. In order to enhance computer vision research in the thermal modality, stereo thermal-infrared camera calibration must be precise and effective. This research presents a calibration board for thermal stereo vision systems that allows for visual thermal contrast and corner detection within the pattern. In an unstructured and dynamic environment, this thermal stereo vision provides a 3D volumetric measurement for depth estimation of the whole scene within the optical sensors’ combined field of view. Experiments are carried out in the lab, with a mean calibration re-projection error of 0.4 pixel and it is estimated the accuracy of 97.26% at a distance of around 6m for 3D depth measurement.
Facial Micro-expression Recognition Algorithm Based on Big Data

Abstract The 21st century is the era of big data. All aspects of society, from facial expressions to national defense and military, will generate massive amounts of data. Facial expression recognition technology, as a new technology spawned in the era of big data, has broad applications The prospects are widely used in intelligent transportation, assisted medical care, distance education, interactive games and public safety. In recent years, it has attracted more scholars’ attention and has become another research hotspot in the field of computer vision and machine learning. The purpose of this article is to study the facial micro-expression recognition algorithm based on big data. This time, big data technology is used to analyze the algorithm. Big data can better solve the small changes in face recognition and complex data processing. This paper firstly summarizes the basic theory of big data, derives the core technology of big data, and analyzes its shortcomings and shortcomings based on the current research status of facial micro-expression in my country, and finally discusses the big data based on big data. Research on facial micro-expression recognition algorithm under the following. This article takes the research situation of the face micro-expression recognition by related companies as the survey object, and analyzes it through the literature data method, questionnaire survey method, mathematical statistics method and other research methods. Experimental results show that the lower the dimensionality reduction, the less classification time is used. When the dimensionality reduction is 45 dimensions, the recognition rate of facial expressions is the highest.
Application of Data Mining for Product Purchase Pattern Analysis with Frequent Pattern Growth (FP-Growth) Algorithm on Sales Transaction Data

The rapid growth of pet products, especially in Indonesia, makes the business competition in this field more even strict. In these conditions, a good data processing technique is needed, one of them is called the data mining technique. The algorithm used in this study is FP-Growth to generate frequent itemset that will later be used in the decision-making process that can result in a choice. This study took an object of sales transaction data from a pet shop named Ciwo Pet Shop, and the transaction data is processed by using the FP-Growth algorithm. From the results of the tests, it is found that a rule that has the best confidence value is 100% with a combination of pattern products fragrant cat sand 25 liters and equilibria kitten 7.5 kg + 750 gr, It will most certainly buy Whiskas cat food from 85 gr.
Why Do Pretrained Language Models Help in Downstream Tasks? An Analysis of Head and Prompt Tuning

Pretrained language models have achieved state-of-the-art performance when adapted to a downstream NLP task. However, theoretical analysis of these models is scarce and challenging since the pretraining and downstream tasks can be very different. We propose an analysis framework that links the pretraining and downstream tasks with an underlying latent variable generative model of text -- the downstream classifier must recover a function of the posterior distribution over the latent variables. We analyze head tuning (learning a classifier on top of the frozen pretrained model) and prompt tuning in this setting. The generative model in our analysis is either a Hidden Markov Model (HMM) or an HMM augmented with a latent memory component, motivated by long-term dependencies in natural language. We show that 1) under certain non-degeneracy conditions on the HMM, simple classification heads can solve the downstream task, 2) prompt tuning obtains downstream guarantees with weaker non-degeneracy conditions, and 3) our recovery guarantees for the memory-augmented HMM are stronger than for the vanilla HMM because task-relevant information is easier to recover from the long-term memory. Experiments on synthetically generated data from HMMs back our theoretical findings.
Visual, tactile and auditory memory: any difference between students?

To recall objects depends on how we receive information in our daily life. There is three-way to receive information: hear, see or touch. Each way reacts differently to memory performance. Thus, the main purpose of this study is to investigate the differences between auditory, visual and tactile information in the memory performance. Besides, this study also aimed to find out the differences between gender and field of study in their memory performance. A total of 60 sciences and social sciences students in university (30 females and 30 males) involved in this study. The tool to record the memory performance was namely A Message from Your Brain. The result has shown that visual and tactile information were more efficient than auditory information in the memory performance. The finding also shows there was a significant difference between male and female students in auditory memory. Females were outperformed in auditory memory performance compared to males. The result also shows that there was a significant difference between science students and social sciences students on visual memory performance. Students from the sciences field have better performance in visual and tactile memory than social sciences students.
Evaluation of Representation Models for Text Classification with AutoML Tools

Automated Machine Learning (AutoML) has gained increasing success on tabular data in recent years. However, processing unstructured data like text is a challenge and not widely supported by open-source AutoML tools. This work compares three manually created text representations and text embeddings automatically created by AutoML tools. Our benchmark includes four popular open-source AutoML tools and eight datasets for text classification purposes. The results show that straightforward text representations perform better than AutoML tools with automatically created text embeddings.
An Intelligent Path Selection Algorithm Based on Deep Reinforcement Learning

Abstract Path selection is the most important algorithm in intelligent devices such as robots. At present, the traditional path-planning algorithm has achieved some results, but it lacks the ability of environmental perception and continuous learning. In order to solve the above problems, this paper proposes an intelligent path selection algorithm based on deep reinforcement learning, which uses the learning ability of deep learning and the decision-making ability of reinforcement learning to realize the autonomous path planning of robots and other equipment. Simulation results show that the proposed algorithm has faster convergence, efficiency and accuracy.
Uncertainty-aware learning for improvements in image quality of the Canada–France–Hawaii Telescope

We leverage state-of-the-art machine learning methods and a decade's worth of archival data from CFHT to predict observatory image quality (IQ) from environmental conditions and observatory operating parameters. Specifically, we develop accurate and interpretable models of the complex dependence between data features and observed IQ for CFHT's wide-field camera, MegaCam. Our contributions are several-fold. First, we collect, collate and reprocess several disparate data sets gathered by CFHT scientists. Second, we predict probability distribution functions (PDFs) of IQ and achieve a mean absolute error of $\sim0.07''$ for the predicted medians. Third, we explore the data-driven actuation of the 12 dome "vents" installed in 2013-14 to accelerate the flushing of hot air from the dome. We leverage epistemic and aleatoric uncertainties in conjunction with probabilistic generative modeling to identify candidate vent adjustments that are in-distribution (ID); for the optimal configuration for each ID sample, we predict the reduction in required observing time to achieve a fixed SNR. On average, the reduction is $\sim12\%$. Finally, we rank input features by their Shapley values to identify the most predictive variables for each observation. Our long-term goal is to construct reliable and real-time models that can forecast optimal observatory operating parameters to optimize IQ. We can then feed such forecasts into scheduling protocols and predictive maintenance routines. We anticipate that such approaches will become standard in automating observatory operations and maintenance by the time CFHT's successor, the Maunakea Spectroscopic Explorer, is installed in the next decade.
Who’s accountable?

Software warranties generally deny all liability for problems. Manufacturers and developers should be required to be more accountable. Everyone needs to be constantly curious about improving systems and reporting problems.
Cycle Self-Training for Domain Adaptation

Mainstream approaches for unsupervised domain adaptation (UDA) learn domain-invariant representations to narrow the domain shift. Recently, self-training has been gaining momentum in UDA, which exploits unlabeled target data by training with target pseudo-labels. However, as corroborated in this work, under distributional shift in UDA, the pseudo-labels can be unreliable in terms of their large discrepancy from target ground truth. Thereby, we propose Cycle Self-Training (CST), a principled self-training algorithm that explicitly enforces pseudo-labels to generalize across domains. CST cycles between a forward step and a reverse step until convergence. In the forward step, CST generates target pseudo-labels with a source-trained classifier. In the reverse step, CST trains a target classifier using target pseudo-labels, and then updates the shared representations to make the target classifier perform well on the source data. We introduce the Tsallis entropy as a confidence-friendly regularization to improve the quality of target pseudo-labels. We analyze CST theoretically under realistic assumptions, and provide hard cases where CST recovers target ground truth, while both invariant feature learning and vanilla self-training fail. Empirical results indicate that CST significantly improves over the state-of-the-arts on visual recognition and sentiment analysis benchmarks.
Collaborating with Humans without Human Data

Collaborating with humans requires rapidly adapting to their individual strengths, weaknesses, and preferences. Unfortunately, most standard multi-agent reinforcement learning techniques, such as self-play (SP) or population play (PP), produce agents that overfit to their training partners and do not generalize well to humans. Alternatively, researchers can collect human data, train a human model using behavioral cloning, and then use that model to train human-aware agents (behavioral cloning play, or BCP). While such an approach can improve the generalization of agents to new human co-players, it involves the onerous and expensive step of collecting large amounts of human data first. Here, we study the problem of how to train agents that collaborate well with human partners without using human data. We argue that the crux of the problem is to produce a diverse set of training partners. Drawing inspiration from successful multi-agent approaches in competitive domains, we find that a surprisingly simple approach is highly effective. We train our agent partner as the best response to a population of self-play agents and their past checkpoints taken throughout training, a method we call Fictitious Co-Play (FCP). Our experiments focus on a two-player collaborative cooking simulator that has recently been proposed as a challenge problem for coordination with humans. We find that FCP agents score significantly higher than SP, PP, and BCP when paired with novel agent and human partners. Furthermore, humans also report a strong subjective preference to partnering with FCP agents over all baselines.
Evaluating ARITE System Usability for Rendering Hands-on Experience to Engineering Undergraduates

Hands-on experience through laboratory tasks is essential in engineering education. It allows students to perform experiments designed in their respective courses, which imbibes particular aptitudes and skills. This paper discusses the use of the ARITE system for teaching Embedded System courses to engineering undergraduates. The ARITE system uses Augmented Reality technology to teach underlying concepts of Embedded Systems to students. In addition, a System Usability (SU) test was conducted in which 36 engineering students of different disciplines participated. The SU score for the ARITE system was evaluated as 67.40%, which implies its potential usage in delivering hands-on experience to engineering students.
Optimal Rates for Nonparametric Density Estimation under Communication Constraints

We consider density estimation for Besov spaces when each sample is quantized to only a limited number of bits. We provide a noninteractive adaptive estimator that exploits the sparsity of wavelet bases, along with a simulate-and-infer technique from parametric estimation under communication constraints. We show that our estimator is nearly rate-optimal by deriving minimax lower bounds that hold even when interactive protocols are allowed. Interestingly, while our wavelet-based estimator is almost rate-optimal for Sobolev spaces as well, it is unclear whether the standard Fourier basis, which arise naturally for those spaces, can be used to achieve the same performance.
Speeding Up Transformer Training By Using Dataset Subsampling - An Exploratory Analysis

Transformer-based models have greatly advanced the progress in the field of the natural language processing and while they achieve state-of-the-art results on a wide range of tasks, they are cumbersome in parameter size. Subsequently, even when pre-trained transformer models are used for fine-tuning on a given task, if the dataset is large, it may still not be feasible to fine-tune the model within a reasonable time. For this reason, we empirically test 8 subsampling methods for reducing the dataset size on text classification task and report the trade-off between metric score and training time. 7 out of 8 methods are simple methods, while the last one is CRAIG, a method for coreset construction for data-efficient model training. We obtain the best result with the CRAIG method, offering an average decrease of 0.03 points in f-score on test set while speeding up the training time on average by 63.93%, relative to the score and time obtained by using the full dataset. Lastly, we show the trade-off between speed and performance for all sampling methods on three different datasets.
Education, Human Capital, Technology and Economic Growth in Malaysia

Abstract : This study aims to determine the impact of education, human resources, and technology on economic growth in Malaysia using annual time series data for the period 1984-2019. This study uses an autoregressive distribution gap to cointegration approach to examine the relationship between human capital education, technology, and economic growth. The estimation results show that human resources in the form of middle and high school participation have a significant positive effect on economic growth. Based on the results of our research, it can be concluded that education has a significant impact on economic growth in Malaysia. Keywords : Education, Human capital, Technology, Economic Growth
Implementation of a Mobile Device Eye-tracking System Using Augmented Reality

Eye-tracking technology is used within various fields of research such as medicine, computer vision and psychology among others. Despite the many application areas and a growing interest, most eye- ...