Reviewerly/exHarmony huggingface dataset:
Gpt as Knowledge Worker: A Zero-Shot Evaluation of (AI)CPA Capabilities

The global economy is increasingly dependent on knowledge workers to meet the needs of public and private organizations. While there is no single definition of knowledge work, organizations and industry groups still attempt to measure individuals’ capability to engage in it. The most comprehensive assessment of capability readiness for professional knowledge workers is the Uniform CPA Examination developed by the American Institute of Certified Public Accountants (AICPA). In this paper, we experimentally evaluate OpenAI’s text-davinci-003 and prior versions of GPT on both a sample Regulation (REG) exam and an assessment of over 200 multiple-choice questions based on the AICPA Blueprints for legal, financial, accounting, technology, and ethical tasks.First, we find that text-davinci-003 achieves a correct rate of 14.4% on a sample REG exam section, significantly underperforming human capabilities on quantitative reasoning in zero-shot prompts. Second, text-davinci-003 appears to be approaching human-level performance on the Remembering & Understanding and Application skill levels in the Exam absent calculation. For best prompt and parameters, the model answers 57.6% of questions correctly, significantly better than the 25% guessing rate, and its top two answers are correct 82.1% of the time, indicating strong non-entailment. Finally, we find that recent generations of GPT-3 demonstrate material improvements on this assessment, rising from 30% for text-davinci-001 to 57% for text-davinci-003. These findings strongly suggest that large language models have the potential to transform the quality and efficiency of future knowledge work.
Topic Correlation Analysis for Cross-Domain Text Classification

Cross-domain text classification aims to automatically train a precise text classifier for a target domain by using labeled text data from a related source domain. To this end, the distribution gap between different domains has to be reduced. In previous works, a certain number of shared latent features (e.g., latent topics, principal components, etc.) are extracted to represent documents from different domains, and thus reduce the distribution gap. However, only relying the shared latent features as the domain bridge may limit the amount of knowledge transferred. This limitation is more serious when the distribution gap is so large that only a small number of latent features can be shared between domains. In this paper, we propose a novel approach named Topic Correlation Analysis (TCA), which extracts both the shared and the domain-specific latent features to facilitate effective knowledge transfer. In TCA, all word features are first grouped into the shared and the domain-specific topics using a joint mixture model. Then the correlations between the two kinds of topics are inferred and used to induce a mapping between the domain-specific topics from different domains. Finally, both the shared and the mapped domain-specific topics are utilized to span a new shared feature space where the supervised knowledge can be effectively transferred. The experimental results on two real-world data sets justify the superiority of the proposed method over the stat-of-the-art baselines.
Fast Dictionary-Based Compression for Inverted Indexes

Dictionary-based compression schemes provide fast decoding operation, typically at the expense of reduced compression effectiveness compared to statistical or probability-based approaches. In this work, we apply dictionary-based techniques to the compression of inverted lists, showing that the high degree of regularity that these integer sequences exhibit is a good match for certain types of dictionary methods, and that an important new trade-off balance between compression effectiveness and compression efficiency can be achieved. Our observations are supported by experiments using the document-level inverted index data for two large text collections, and a wide range of other index compression implementations as reference points. Those experiments demonstrate that the gap between efficiency and effectiveness can be substantially narrowed.
How big is a small earthquake? Challenges in determining microseismic magnitudes

Earthquake magnitudes are surprisingly difficult to estimate accurately. This is especially true when evaluating small earthquakes: for example, those caused by human activities such as shale gas stimulation, CO2 and waste water storage, and enhanced geothermal systems. Uncertainties are created by a range of issues including: which ‘magnitude’ scale is used; what type of instrument records the earthquake; how the instruments are deployed; and the heterogeneity of the Earth between the source and the receivers. Errors can be larger than an order of magnitude in scale. For very small earthquakes this is not usually of much concern. However, occasionally, larger earthquakes induced by human activity are felt at the surface. This has led to regulatory frameworks that require accurate assessment of earthquake magnitudes before they reach the point of being felt. Hence, to monitor and mitigate felt seismicity there is a need to calculate accurate earthquake magnitudes in real time. Regulatory monitoring of induced seismicity is becoming a key issue in the successful development of reservoir projects that involve stimulation or storage. Here, we discuss the challenges with implementing such reservoir monitoring, and provide a suggested monitoring strategy.
Intelligent problem-solvers externalize cognitive operations

Humans are nature’s most intelligent and prolific users of external props and aids (such as written texts, slide-rules and software packages). Here we introduce a method for investigating how people make active use of their task environment during problem-solving and apply this approach to the non-verbal Raven Advanced Progressive Matrices test for fluid intelligence. We designed a click-and-drag version of the Raven test in which participants could create different external spatial configurations while solving the puzzles. In our first study, we observed that the click-and-drag test was better than the conventional static test at predicting academic achievement of university students. This pattern of results was partially replicated in a novel sample. Importantly, environment-altering actions were clustered in between periods of apparent inactivity, suggesting that problem-solvers were delicately balancing the execution of internal and external cognitive operations. We observed a systematic relationship between this critical phasic temporal signature and improved test performance. Our approach is widely applicable and offers an opportunity to quantitatively assess a powerful, although understudied, feature of human intelligence: our ability to use external objects, props and aids to solve complex problems. Bocanegra and colleagues present a new variation of the Raven intelligence test, an established measure of cognitive function; better performance on this new version, which allows problem-solving to be externalized, is associated with students’ success in exams.
Using Microsaccades to Estimate Task Difficulty During Visual Search of Layered Surfaces

We develop an approach to using microsaccade dynamics for the measurement of task difficulty/cognitive load imposed by a visual search task of a layered surface. Previous studies provide converging evidence that task difficulty/cognitive load can influence microsaccade activity. We corroborate this notion. Specifically, we explore this relationship during visual search for features embedded in a terrain-like surface, with the eyes allowed to move freely during the task. We make two relevant contributions. First, we validate an approach to distinguishing between the ambient and focal phases of visual search. We show that this spectrum of visual behavior can be quantified by a single previously reported estimator, known as Krejtz's IC coefficient. Second, we use ambient/focal segments based on IC as a moderating factor for microsaccade analysis in response to task difficulty. We find that during the focal phase of visual search (a) microsaccade magnitude increases significantly, and (b) microsaccade rate decreases significantly, with increased task difficulty. We conclude that the combined use of IC and microsaccade analysis may be helpful in building effective tools that provide an indication of the level of cognitive activity within a task while the task is being performed.
Deep learning and manual assessment show that the absolute mitotic count does not contain prognostic information in triple negative breast cancer

The prognostic value of mitotic count for invasive breast cancer is firmly established. As yet, however, limited studies have been aimed at assessing mitotic counts as a prognostic factor for triple negative breast cancers (TNBC). Here, we assessed the prognostic value of absolute mitotic counts for TNBC, using both deep learning and manual procedures. A retrospective TNBC cohort (n = 298) was used. The absolute manual mitotic count was assessed by averaging counts from three independent observers. Deep learning was performed using a convolutional neural network on digitized H&E slides. Multivariable Cox regression models for relapse-free survival and overall survival served as baseline models. These were expanded with dichotomized mitotic counts, attempting every possible cut-off value, and evaluated by means of the c-statistic. We found that per 2 mm2 averaged manual mitotic counts ranged from 1 to 187 (mean 37.6, SD 23.4), whereas automatic counts ranged from 1 to 269 (mean 57.6; SD 42.2). None of the cut-off values improved the models' baseline c-statistic, for both manual and automatic assessments. Based on our results we conclude that the level of proliferation, as reflected by mitotic count, does not serve as a prognostic factor for TNBC. Therefore, TNBC patient management based on mitotic count should be discouraged.
Generalizing Unmasking for Short Texts

Janek Bevendorff, Benno Stein, Matthias Hagen, Martin Potthast. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Neural Temporality Adaptation for Document Classification: Diachronic Word Embeddings and Domain Adaptation Models

Language usage can change across periods of time, but document classifiers models are usually trained and tested on corpora spanning multiple years without considering temporal variations.This paper describes two complementary ways to adapt classifiers to shifts across time.First, we show that diachronic word embeddings, which were originally developed to study language change, can also improve document classification, and we show a simple method for constructing this type of embedding.Second, we propose a time-driven neural classification model inspired by methods for domain adaptation.Experiments on six corpora show how these methods can make classifiers more robust over time.
An Efficient Verifiable Threshold Multi-Secret Sharing Scheme With Different Stages

Secret sharing is one of the most important cryptographic primitives used for data outsourcing.The threshold-based secret sharing scheme is one of the well-known secret sharing schemes in cryptography.A single secret sharing scheme has low efficiency and multiple secret sharing schemes can greatly improve the efficiency of secret sharing.We present an efficient verifiable threshold multi-secret sharing scheme with different stages in which multiple secrets are shared among participants during one sharing process.In such a way, some qualified subsets of participants can collectively recover these secrets.The verifiable nature means that the participant can verify his/her own share.Our proposed scheme can share multiple secrets and each secret has its own threshold access structure.Moreover, each participant only keeps one share, and however they can reconstruct all secrets in recovery stages.This scheme is a multi-stage-use secret sharing scheme, in which the size of the participant's share is the same as that of each secret and the amount of information disclosed reaches the optimal lower bound.Compared with the previous verifiable (t, n)-threshold multisecret sharing scheme, this scheme is of stronger computational security and practicability.
Off-Policy Policy Gradient with State Distribution Correction

We study the problem of off-policy policy optimization in Markov decision processes, and develop a novel off-policy policy gradient method. Prior off-policy policy gradient approaches have generally ignored the mismatch between the distribution of states visited under the behavior policy used to collect data, and what would be the distribution of states under the learned policy. Here we build on recent progress for estimating the ratio of the state distributions under behavior and evaluation policies for policy evaluation, and present an off-policy policy gradient optimization technique that can account for this mismatch in distributions. We present an illustrative example of why this is important and a theoretical convergence guarantee for our approach. Empirically, we compare our method in simulations to several strong baselines which do not correct for this mismatch, significantly improving in the quality of the policy discovered.
Superposition with Lambdas

We designed a superposition calculus for a clausal fragment of extensional polymorphic higher-order logic that includes anonymous functions but excludes Booleans. The inference rules work on $$\beta \eta $$ -equivalence classes of $$\lambda $$ -terms and rely on higher-order unification to achieve refutational completeness. We implemented the calculus in the Zipperposition prover and evaluated it on TPTP and Isabelle benchmarks. The results suggest that superposition is a suitable basis for higher-order reasoning.
Practical multi-fidelity Bayesian optimization for hyperparameter tuning

Bayesian optimization is popular for optimizing time-consuming black-box objectives. Nonetheless, for hyperparameter tuning in deep neural networks, the time required to evaluate the validation error for even a few hyperparameter settings remains a bottleneck. Multi-fidelity optimization promises relief using cheaper proxies to such objectives --- for example, validation error for a network trained using a subset of the training points or fewer iterations than required for convergence. We propose a highly flexible and practical approach to multi-fidelity Bayesian optimization, focused on efficiently optimizing hyperparameters for iteratively trained supervised learning models. We introduce a new acquisition function, the trace-aware knowledge-gradient, which efficiently leverages both multiple continuous fidelity controls and trace observations --- values of the objective at a sequence of fidelities, available when varying fidelity using training iterations. We provide a provably convergent method for optimizing our acquisition function and show it outperforms state-of-the-art alternatives for hyperparameter tuning of deep neural networks and large-scale kernel learning.
A novel unsupervised anomaly detection for gas turbine using Isolation Forest

Monitoring gas turbines’ health, in particular, detecting abnormal behaviors in time, is critical in ensuring gas turbine operating safety and in preventing costly unplanned maintenance. One most popular anomaly detection method is to obtain a classification-prediction model by training a classifier using the real-life data of gas turbine. The excellent detection ability of this method is attributed to enough annotated samples, especially enough annotated abnormal samples. Nevertheless, in gas turbine monitoring data, normal data is far more than abnormal data, even no abnormal data. Advanced technologies that can accurately detect the abnormal behaviors in time using the unlabeled data are in great need. Thus, a novel unsupervised anomaly detection based on Isolation Forest is investigated for gas turbine gas path anomaly detection in this paper. Specifically, the monitoring data is grouped by time series for weakening the affection of inevitable performance degradation when gas turbine operating, and then all detected by an isolation forest model with low contamination. Each detected abnormal group is detected again by an isolation forest model with high contamination for obtaining the specific abnormal flight-cycles. Using the real-life monitoring data from 8 different CFM56-7B aeroengines, the detection results show that the method based on Isolation Forest can achieve high accuracy abnormal detection under unlabeled data and small data set.
Question-answering dialogue system for emergency operations

This research aims to develop a dialogue system for eliminating difficulties encountered in the efficient and accurate utilization of information for decision makers at emergency operation centers (EOC). The rapid growth in the amount of data has caused complications in decision making tasks in disaster management. This has increased the difficulty for decision makers when they try to accomplish their mission accurately and efficiently. In our preliminary work, we attempted to develop a chatbot for decision makers and staff at an EOC to assist them in the utilization, selection, and processing of information efficiently and accurately. A user experience test revealed that a better adaption of the dialogue system to the characteristics of disaster management and the EOC was required. Thus, we specifically focused on the development of a technique of language understanding (LU) to be able to analyze the user's demands. As a solution, we developed a framework for a dialogue system combining question answering features, a knowledge base as the knowledge provider, and a search module that can handle the relatively difficult querying tasks. To improve the performance of the system, we focused on enhancing the capability in understanding users' questions. The construction of the question analysis and knowledge base were both adapted to the characteristics of disaster management. The validation shows that our method can analyze and process the user's questions into a machine-acceptable form with a success rate of approximately 70%.
Privacy-Preserving Deep Learning via Additively Homomorphic Encryption

We aim at creating a society where we can resolve various social challenges by incorporating the innovations of the fourth industrial revolution (e.g. IoT, big data, AI, robot, and the sharing economy) into every industry and social life. By doing so the society of the future will be one in which new values and services are created continuously, making people's lives more conformable and sustainable. This is Society 5.0, a super-smart society. Security and privacy are key issues to be addressed to realize Society 5.0. Privacy-preserving data analytics will play an important role. In this talk we show our recent works on privacy-preserving data analytics such as privacy-preserving logistic regression and privacy-preserving deep learning. Finally, we show our ongoing research project under JST CREST “AI”. In this project we are developing privacy-preserving financial data analytics systems that can detect fraud with high security and accuracy. To validate the systems, we will perform demonstration tests with several financial institutions and solve the problems necessary for their implementation in the real world.
Pigeon: A Dynamic and Efficient Serverless and FaaS Framework for Private Cloud

Recently, voice-triggered small cloud functions such as Alexa skills, and cloud mini programs for IoT and smartphone, grow exponentially. These new developments also attract organizations to host their own cloud functions or mini programs in private cloud environment and move from traditional Microservice architecture to Serverless Function-as-a-Service (FaaS) architecture. However, current Serverless FaaS frameworks cannot meet cold start latency, resource efficiency required by short-lived cloud functions and mini programs. In this paper, we build a new Framework - Pigeon that brings Serverless and FaaS programming paradigm into private cloud to enable enterprises to host these applications. Pigeon creates function-oriented Serverless framework by introducing an independent and finer-grained function-level resource scheduler on top of Kubernetes. A new oversubscription-based static pre-warmed container solution is also proposed to effectively reduce function startup latency and increase resource recycling speed for short-lived cloud functions. Empirical results show that Pigeon framework enhances function cold trigger rate by 26% to 80% comparing to AWS Lambda Serverless platform. Comparing to Kubernetes native scheduler based serverless platforms, throughput gets 3 times improvement while handling short-lived functions.
The Retracing Boomerang Attack

Boomerang attacks are extensions of differential attacks, that make it possible to combine two unrelated differential properties of the first and second part of a cryptosystem with probabilities p and q into a new differential-like property of the whole cryptosystem with probability $$p^2q^2$$ (since each one of the properties has to be satisfied twice). In this paper we describe a new version of boomerang attacks which uses the counterintuitive idea of throwing out most of the data in order to force equalities between certain values on the ciphertext side. In certain cases, this creates a correlation between the four probabilistic events, which increases the probability of the combined property to $$p^2q$$ and increases the signal to noise ratio of the resultant distinguisher. We call this variant a retracing boomerang attack since we make sure that the boomerang we throw follows the same path on its forward and backward directions. To demonstrate the power of the new technique, we apply it to the case of 5-round AES. This version of AES was repeatedly attacked by a large variety of techniques, but for twenty years its complexity had remained stuck at $$2^{32}$$. At Crypto’18 it was finally reduced to $$2^{24}$$ (for full key recovery), and with our new technique we can further reduce the complexity of full key recovery to the surprisingly low value of $$2^{16.5}$$ (i.e., only 90, 000 encryption/decryption operations are required for a full key recovery on half the rounds of AES). In addition to improving previous attacks, our new technique unveils a hidden relationship between boomerang attacks and two other cryptanalytic techniques, the yoyo game and the recently introduced mixture differentials.
Proper Attribution for Curation and Maintenance of Research Collections: Metadata Recommendations of the RDA/TDWG Working Group

Research collections are an important tool for understanding the Earth, its systems, and human interaction. Despite the importance of collections, many are not maintained or curated as thoroughly as we would like. Part of the reason for this is the lack of professional reward for collection, curation, or maintenance. To address this gap in attribution metadata, the Research Data Alliance (RDA) and the Biodiversity Information Standards (TDWG) organization co-endorsed a Working Group to create recommendations for the representation of attribution metadata. After 18 months, this Working Group recommended the use of PROV entities and properties to link people (Agent), the curatorial actions they perform (Activity), and the digital or physical objects they are curating (Entity). Assigning a Role to an Agent is optional. These recommendations are discussed in the context of the RDA, TDWG, and existing standards. Future work includes adapting these recommendations to the specific needs of TDWG and developing a pilot application in collaboration with ORCID and the Data Futures project.