Reviewerly/exHarmony huggingface dataset:
Adaptive Rule Discovery for Labeling Text Data

Creating and collecting labeled data is one of the major bottlenecks in machine learning pipelines and the emergence of automated feature generation techniques such as deep learning, which typically requires a lot of training data, has further exacerbated the problem. While weak-supervision techniques have circumvented this bottleneck, existing frameworks either require users to write a set of diverse, high-quality rules to label data (e.g., Snorkel), or require a labeled subset of the data to automatically mine rules (e.g., Snuba). The process of manually writing rules can be tedious and time consuming. At the same time, creating a labeled subset of the data can be costly and even infeasible in imbalanced settings. This is due to the fact that a random sample in imbalanced settings often contains only a few positive instances. To address these shortcomings, we present Darwin, an interactive system designed to alleviate the task of writing rules for labeling text data in weakly-supervised settings. Given an initial labeling rule, Darwin automatically generates a set of candidate rules for the labeling task at hand, and utilizes the annotator's feedback to adapt the candidate rules. We describe how Darwin is scalable and versatile. It can operate over large text corpora (i.e., more than 1 million sentences) and supports a wide range of labeling functions (i.e., any function that can be specified using a context free grammar). Finally, we demonstrate with a suite of experiments over five real-world datasets that Darwin enables annotators to generate weakly-supervised labels efficiently and with a small cost. In fact, our experiments show that rules discovered by Darwin on average identify 40% more positive instances compared to Snuba even when it is provided with 1000 labeled instances.
Iterated multisource exchangeability models for individualized inference with an application to mobile sensor data

Researchers are increasingly interested in using sensor technology to collect accurate activity information and make individualized inference about treatments, exposures, and policies. How to optimally combine population data with data from an individual remains an open question. Multisource exchangeability models (MEMs) are a Bayesian approach for increasing precision by combining potentially heterogeneous supplemental data sources into analysis of a primary source. MEMs are a potentially powerful tool for individualized inference but can integrate only a few sources; their model space grows exponentially, making them intractable for high-dimensional applications. We propose iterated MEMs (iMEMs), which identify a subset of the most exchangeable sources prior to fitting a MEM model. iMEM complexity scales linearly with the number of sources, and iMEMs greatly increase precision while maintaining desirable asymptotic and small sample properties. We apply iMEMs to individual-level behavior and emotion data from a smartphone app and show that they achieve individualized inference with up to 99% efficiency gain relative to standard analyses that do not borrow information.
An Exploratory Study of a NoSQL Database for a Clinical Data Repository

The need to implement a distributed Clinical Data Repository (CDR) at a healthcare facility, rose in large part due to the high volume of data and the discrepancy of their sources. Over the years, Relational Database Management Systems (RDBMS) began to present difficulties in responding to the needs of various organizations when it comes to manipulating a large amount of data and to its scalability. Therefore, it was necessary to explore other techniques to choose the appropriate technology to build the CDR. In this way, NoSQL emerged as a new type of database that is quite useful to work with multiple and different types of data. In addition, NoSQL introduces a number of user-friendly features such as a distributed, scalable, elastic and also fault tolerant system. In this way, Oracle NoSQL Database was the NoSQL solution chosen to develop this case study, using the key-value storage. This article was motivated to propose a CDR architecture based on Oracle NoSQL Database functionalities. A one-single node database was deployed for better comprehension, in order to enhance their features for future implementation.
Structural Temporal Graph Neural Networks for Anomaly Detection in Dynamic Graphs

Detecting anomalies in dynamic graphs is a vital task, with numerous practical applications in areas such as security, finance, and social media. Previous network embedding based methods have been mostly focusing on learning good node representations, whereas largely ignoring the subgraph structural changes related to the target nodes in dynamic graphs. In this paper, we propose StrGNN, an end-to-end structural temporal Graph Neural Network model for detecting anomalous edges in dynamic graphs. In particular, we first extract the $h$-hop enclosing subgraph centered on the target edge and propose the node labeling function to identify the role of each node in the subgraph. Then, we leverage graph convolution operation and Sortpooling layer to extract the fixed-size feature from each snapshot/timestamp. Based on the extracted features, we utilize Gated recurrent units (GRUs) to capture the temporal information for anomaly detection. Extensive experiments on six benchmark datasets and a real enterprise security system demonstrate the effectiveness of StrGNN.
DECISION-MAKING DURING LIMITED NUMBER OF EXPERIMENTS WITH MULTIPLE CRITERIA

Context. The mechanism of decision-making during limited number of experiments with multiple criteria are considered. The investigation object is process decision-making for project or control in complex systems with multiple criteria.Objective. It is necessary to determine optimal (most preferred) parameters of the systems with multiple criteria. It is no the mathematical model of the system, there is limited number of experiments only.Method. A scheme is proposed for constructing a selection mechanism for decision-making in systems with several criteria for which there is a sample of experimental results. The scheme includes the following procedures: an experimental study of a process with several criteria (functions) depending on its parameters; the use of expert evaluation to build a matrix of preferences for individual implementations; building a function of choosing preferred solutions based on a preference matrix by constructing a mathematical model of preference recognition, formulation and solving the problem of generalized mathematical programming as the final step in building the selection mechanism. The decision-making mechanism depends on the expert assessment procedure when comparing a limited set of results with each other, as well as on the statement of conditions when solving the problem of generalized mathematical programming. Comparison of a finite number of experiments is convenient for expert evaluation. Presentation of the final choice as a result of solving the problem of generalized mathematical programming is convenient for using such a mechanism in automatic control systems already without human intervention.Results. The proposed scheme of decision-making during limited number of experiments has been applied to decision-making of project management for pellet burner. Experimental decision-making results are presented in the presence of several criteria for a pellet burner of a tubular heater, which confirm the acceptability of the developed decision-making mechanism.Conclusions. It was proposed the new scheme for constructing a selection mechanism for decision-making in systems with several criteria where there is a sample of experimental results only. The scheme of decision-making is includes the solving the problem of generalized mathematical programming as the final step in building the selection mechanism. For the solving the problem of generalized mathematical programming may be applied the evolution search algorithm.
Aerial infrared target tracking method based on KCF for frequency-domain scale estimation

The kernel correlation filter (KCF) tracking algorithm encounters the issue of tracking accuracy degradation due to large changes in scale and rotation of aerial infrared targets. Therefore, this paper proposes a new scale estimation KCF-based aerial infrared target tracking method, which can extract scale feature information of images in the frequency domain based on the distribution characteristics and change laws of frequency-domain energy. In addition, the proposed method can improve the accuracy of target scale information estimation. First, the KCF tracking algorithm is used to obtain the target position. Then, spectral eigenvalues are calculated as eigenvectors, and frequency-domain rotation scale invariance is adopted to extract the eigenvector between two frames as the target rotation change information. Reverse rotation is performed on the current frame spectrum map for isolating the effects of target rotation on scale information estimation. Then, the current target scale is estimated on the basis of the eigenvectors between the adjacent frames. Finally, the length-to-width ratio and the scale of the tracking box are updated on the basis of the target rotation information, which improves the adaptability of the tracking box to changes in the target scale and rotation. The results indicate that the proposed algorithm is suitable for stable tracking of target scales and rapid changes in attitudes. The average tracking accuracy and the average success rate of the algorithm are 0.954 and 0.782, which represent improvements of 5.3% and 18.9%, respectively, compared with the KCF algorithm. The average tracking success rate is improved by 4.1% compared with the discriminative scale space tracker algorithm, and the average tracking performance is better than that of related filter tracking algorithms based on other scale estimation methods.
Predicting Tolerance to Velocity Mismatch Between Virtual and Physical Head Rotation in CIoud Virtual Reality Systems

The idea of virtual velocity loss strategy is introduced to reduce the viewport quality degradation caused by the limited capacity and bandwidth of current network transmission for cloud virtual reality service. To avoid the negative effect of velocity mismatch, this paper quantitatively models the human perceptual tolerance to the velocity mismatch with regard to various physical rotational velocities and velocity losses through a psychophysiological experiment. The experimental results revealed a decreased tolerance to loss for larger physical velocity. We found that virtual rotational velocity can be tuned at least about 25.8 % less than physical rotational velocity.
Reviewing Data Analytics Techniques in Breast Cancer Treatment

Data mining (DM) or Data Analytics is the process of extracting new valuable information from large quantities of data; it is reshaping many industries including the medical one. Its contribution to medicine is very important particularly in oncology. Breast cancer is the most common type of cancer in the world and it occurs almost entirely in women, but men can get attacked too. Researchers over the world are trying every day to improve, prevention, detection and treatment of Breast Cancer (BC) in order to provide more effective treatments to patients. In this vein, the present paper carried out a systematic map of the use of data mining technique in breast cancer treatment. The aim was to analyse and synthetize studies on DM applied to breast cancer treatment. In this regard, 44 relevant articles published between 1991 and 2019 were selected and classified according to three criteria: year and channel of publication, research type through DM contribution in BC treatment and DM techniques. Of course, there are not many articles for treatment, because the researchers have been interested in the diagnosis with the different classification techniques, and it may be because of the importance of early diagnosis to avoid danger. Results show that papers were published in different channels (especially journals or conferences), researchers follow the DM pipeline to deal with a BC treatment, the challenge is to reduce the number of non-classified patients, and affect them in the most appropriate group to follow the suitable treatment, and classification was the most used task of DM applied to BC treatment.
Teaching quantum information science to high-school and early undergraduate students

We present a simple, accessible, yet rigorous outreach/educational program focused on quantum information science and technology for high-school and early undergraduate students. This program allows students to perform meaningful hands-on calculations with quantum circuits and algorithms, without requiring knowledge of advanced mathematics. A combination of pen-and-paper exercises and IBM Q simulations helps students understand the structure of quantum gates and circuits, as well as the principles of superposition, entanglement, and measurement in quantum mechanics.
Inferring Personality Types for Better Automated Negotiation

Automated negotiation between computational agents or between agents and humans has been a subject of active research with a focus on obtaining better quality solutions within reasonable time frames. The critical issue negotiators face during automated negotiation is that a negotiator may not always know the personality type of the opponent. Studies show that having information about the opponent improves the outcome of negotiation in general. However, unless there is prior knowledge, learning the opponent type in the limited amount of time or number of rounds in a negotiation is a difficult task. In this paper, we use a Partially Observable Markov Decision Process (POMDP) based modeling to perform better modeling of the opponent personality type. In particular, we focus on modeling the opponent into four different types to showcase that a better understanding of personality type can improve the outcome of automated negotiation. Our experiments performed using data sets generated from the IAGO software showcase that we indeed obtain better negotiation outcomes with a higher classification accuracy of the opponent personality type.
Meta-supervision for Attention Using Counterfactual Estimation

Abstract Neural attention mechanism has been used as a form of explanation for model behavior. Users can either passively consume explanation or actively disagree with explanation and then supervise attention into more proper values (attention supervision). Though attention supervision was shown to be effective in some tasks, we find the existing attention supervision is biased, for which we propose to augment counterfactual observations to debias and contribute to accuracy gains. To this end, we propose a counterfactual method to estimate such missing observations and debias the existing supervisions. We validate the effectiveness of our counterfactual supervision on widely adopted image benchmark datasets: CUFED and PEC.
Adversarial Training for Commonsense Inference

We propose an AdversariaL training algorithm for commonsense InferenCE (ALICE). We apply small perturbations to word embeddings and minimize the resultant adversarial risk to regularize the model. We exploit a novel combination of two different approaches to estimate these perturbations: 1) using the true label and 2) using the model prediction. Without relying on any human-crafted features, knowledge bases, or additional datasets other than the target datasets, our model boosts the fine-tuning performance of RoBERTa, achieving competitive results on multiple reading comprehension datasets that require commonsense inference.
Individual assessment procedure and its tools for PBL teamwork

Project Based Learning (PBL) is a methodology that requires students working in teams. Based on the Agile philosophy, this articlepresents a procedure (and its tools) to assess individuals in a team based on, not only the final project, but on the process, whereeach contribution to joint work is better seen. Students are asked to fix SMART objectives, tasks, dates and people in charge foreach one and include it in the project status table. Students make weekly follow up meetings (with or without the teacher) to shareand review information, results and project process. Portfolio including the meetings minutes, peer and self-assessment of teamworkand project status table provides teachers a rich information to evaluate students and give them feedback. This iterative process ofcontinuous review and short-design time frames helps the team to quickly adapt the projects and the teachers to detect teamproblems. We propose two rubrics: one to assess individually teamwork using a peer and self-assessment; another to evaluatelearning outcomes in each the delivery, both individually and within the team. Teachers’ and students’ perceptions while using thisAgile methodology have been collected from a qualitative approach. Proposed rubrics along the whole process give the requiredfeedback to teachers and students making a 360oassessment and introduces individual assessment of learning outcomes. Overall,the results obtained are quite positive in terms of working atmosphere, quality of the work, learning outcomes achieved andassessment. However, a careful planning and assessment is needed.
Coiflet Wavelet-Homotopy Solution of Channel Flow due to Orthogonally Moving Porous Walls Governed by the Navier–Stokes Equations

A newly computational method based on the Coiflet wavelet and homotopy analysis method is developed, which inherits the great nonlinear treatment of the homotopy analysis technique and the local high-precision capability of the wavelet approach, to give solutions to the classic problem of channel flow with moving walls. The basic principle of this suggested technique and the specific solving process are presented in detail. Its validity and efficiency are then checked via rigid comparisons with other computational approaches. It is found that the homotopy-based convergence-control parameter and the wavelet-based resolution level of Coiflet are two effective ways to improve on accuracies of solutions.
Development of Students’ Digital Competences When Studying the Functional Capabilities of Modern DLP-Systems

In the paper, the specifics of training future information security specialists, who are needed to have a formed competency for neutralization of internal and external information security threats are being analyzed.According to the authors, all kinds of corporate information and personal data leaks arising from the actions of so-called "internal intruders" or insiders represent a particular danger, against which the Data Loss Prevention software class (DLP systems) is the most effective remedy.InfoWatch Traffic Monitor 6.11 is a Russian software product certified in Russia FSTEC system and considered as one of the world leaders in this class of software.Based on the license obtained from InfoWatch, in the virtual laboratory of Omsk State Technical University a specialized stand was created, which gives an opportunity to develop one of the most important and modern digital competencies -"Corporate protection from internal threats to information security".Using it as a basis, students gain skills to configure and verify the DLP system, determine security policies, ensure control of information flows in the company and analyze incidents that have been identified.For this purpose, an algorithm for introducing a DLP system into the educational process have been developed, where students could define information security objects and develop appropriate policies to prevent data leakage and identify possible incidents, intercepts confidential information through controlled communication channels.
Invertible Image Rescaling

High-resolution digital images are usually downscaled to fit various display screens or save the cost of storage and bandwidth, meanwhile the post-upscaling is adpoted to recover the original resolutions or the details in the zoom-in images. However, typical image downscaling is a non-injective mapping due to the loss of high-frequency information, which leads to the ill-posed problem of the inverse upscaling procedure and poses great challenges for recovering details from the downscaled low-resolution images. Simply upscaling with image super-resolution methods results in unsatisfactory recovering performance. In this work, we propose to solve this problem by modeling the downscaling and upscaling processes from a new perspective, i.e. an invertible bijective transformation, which can largely mitigate the ill-posed nature of image upscaling. We develop an Invertible Rescaling Net (IRN) with deliberately designed framework and objectives to produce visually-pleasing low-resolution images and meanwhile capture the distribution of the lost information using a latent variable following a specified distribution in the downscaling process. In this way, upscaling is made tractable by inversely passing a randomly-drawn latent variable with the low-resolution image through the network. Experimental results demonstrate the significant improvement of our model over existing methods in terms of both quantitative and qualitative evaluations of image upscaling reconstruction from downscaled images.
Ultrasound Video Summarization using Deep Reinforcement Learning

Video is an essential imaging modality for diagnostics, e.g. in ultrasound imaging, for endoscopy, or movement assessment. However, video hasn't received a lot of attention in the medical image analysis community. In the clinical practice, it is challenging to utilise raw diagnostic video data efficiently as video data takes a long time to process, annotate or audit. In this paper we introduce a novel, fully automatic video summarization method that is tailored to the needs of medical video data. Our approach is framed as reinforcement learning problem and produces agents focusing on the preservation of important diagnostic information. We evaluate our method on videos from fetal ultrasound screening, where commonly only a small amount of the recorded data is used diagnostically. We show that our method is superior to alternative video summarization methods and that it preserves essential information required by clinical diagnostic standards.
Robust Layout-aware IE for Visually Rich Documents with Pre-trained Language Models

Many business documents processed in modern NLP and IR pipelines are visually rich: in addition to text, their semantics can also be captured by visual traits such as layout, format, and fonts. We study the problem of information extraction from visually rich documents (VRDs) and present a model that combines the power of large pre-trained language models and graph neural networks to efficiently encode both textual and visual information in business documents. We further introduce new fine-tuning objectives to improve in-domain unsupervised fine-tuning to better utilize large amount of unlabeled in-domain data. We experiment on real world invoice and resume data sets and show that the proposed method outperforms strong text-based RoBERTa baselines by 6.3% absolute F1 on invoices and 4.7% absolute F1 on resumes. When evaluated in a few-shot setting, our method requires up to 30x less annotation data than the baseline to achieve the same level of performance at ~90% F1.