Reviewerly/exHarmony huggingface dataset:
Genetic programming for borderline instance detection in high-dimensional unbalanced classification

In classification, when class overlap is intertwined with the issue of class imbalance, it is often challenging to discover useful patterns because of an ambiguous boundary between the majority class and the minority class. This becomes more difficult if the data is high-dimensional. To date, very few pieces of work have investigated how the class overlap issue can be effectively addressed or alleviated in classification with high-dimensional unbalanced data. In this paper, we propose a new genetic programming based method, which is able to automatically and directly detect borderline instances, in order to address the class overlap issue in classification with high-dimensional unbalanced data. In the proposed method, each individual has two trees to be trained together based on different classification rules. The proposed method is examined and compared with baseline methods on high-dimensional unbalanced datasets. Experimental results show that the proposed method achieves better classification performance than the baseline methods in almost all cases.
Tracking Interaction States for Multi-Turn Text-to-SQL Semantic Parsing

The task of multi-turn text-to-SQL semantic parsing aims to translate natural language utterances in an interaction into SQL queries in order to answer them using a database which normally contains multiple table schemas. Previous studies on this task usually utilized contextual information to enrich utterance representations and to further influence the decoding process. While they ignored to describe and track the interaction states which are determined by history SQL queries and are related with the intent of current utterance. In this paper, two kinds of interaction states are defined based on schema items and SQL keywords separately. A relational graph neural network and a non-linear layer are designed to update the representations of these two states respectively. The dynamic schema-state and SQL-state representations are then utilized to decode the SQL query corresponding to current utterance. Experimental results on the challenging CoSQL dataset demonstrate the effectiveness of our proposed method, which achieves better performance than other published methods on the task leaderboard.
MTAAL: Multi-Task Adversarial Active Learning for Medical Named Entity Recognition and Normalization

Automated medical named entity recognition and normalization are fundamental for constructing knowledge graphs and building QA systems. When it comes to medical text, the annotation demands a foundation of expertise and professionalism. Existing methods utilize active learning to reduce costs in corpus annotation, as well as the multi-task learning strategy to model the correlations between different tasks. However, existing models do not take task-specific features for different tasks and diversity of query samples into account. To address these limitations, this paper proposes a multi-task adversarial active learning model for medical named entity recognition and normalization. In our model, the adversarial learning keeps the effectiveness of multi-task learning module and active learning module. The task discriminator eliminates the influence of irregular task-specific features. And the diversity discriminator exploits the heterogeneity between samples to meet the diversity constraint. The empirical results on two medical benchmarks demonstrate the effectiveness of our model against the existing methods.
Finite Model Theory of the Triguarded Fragment and Related Logics

The Triguarded Fragment (TGF) is among the most expressive decidable fragments of first-order logic, subsuming both its two-variable and guarded fragments without equality. We show that the TGF has the finite model property (providing a tight doubly exponential bound on the model size) and hence finite satisfiability coincides with satisfiability known to be N2ExpTime-complete. Using similar constructions, we also establish 2ExpTime-completeness for finite satisfiability of the constant-free (tri)guarded fragment with transitive guards.
A Semantic-based Method for Unsupervised Commonsense Question Answering

Unsupervised commonsense question answering is appealing since it does not rely on any labeled task data.Among existing work, a popular solution is to use pre-trained language models to score candidate choices directly conditioned on the question or context.However, such scores from language models can be easily affected by irrelevant factors, such as word frequencies, sentence structures, etc.These distracting factors may not only mislead the model to choose a wrong answer but also make it oversensitive to lexical perturbations in candidate answers.In this paper, we present a novel SEmanticbased Question Answering method (SEQA) for unsupervised commonsense question answering.Instead of directly scoring each answer choice, our method first generates a set of plausible answers with generative models (e.g., GPT-2), and then uses these plausible answers to select the correct choice by considering the semantic similarity between each plausible answer and each choice.We devise a simple, yet sound formalism for this idea and verify its effectiveness and robustness with extensive experiments.We evaluate the proposed method on four benchmark datasets, and our method achieves the best results in unsupervised settings.Moreover, when attacked by TextFooler (Jin et al., 2020) with synonym replacement, SEQA demonstrates much less performance drops than baselines, thereby indicating stronger robustness.
Surprisal Estimators for Human Reading Times Need Character Models

While the use of character models has been popular in NLP applications, it has not been explored much in the context of psycholinguistic modeling.This paper presents a character model that can be applied to a structural parser-based processing model to calculate word generation probabilities.Experimental results show that surprisal estimates from a structural processing model using this character model deliver substantially better fits to self-paced reading, eye-tracking, and fMRI data than those from large-scale language models trained on much more data.This may suggest that the proposed processing model provides a more humanlike account of sentence processing, which assumes a larger role of morphology, phonotactics, and orthographic complexity than was previously thought.
A Unified Multi-Scenario Attacking Network for Visual Object Tracking

Existing methods of adversarial attacks successfully generate adversarial examples to confuse Deep Neural Networks (DNNs) of image classification and object detection, resulting in wrong predictions. However, these methods are difficult to attack models of video object tracking, because the tracking algorithms could handle sequential information across video frames and the categories of targets tracked are normally unknown in advance. In this paper, we propose a Unified and Effective Network, named UEN, to attack visual object tracking models. There are several appealing characteristics of UEN: (1) UEN could produce various invisible adversarial perturbations according to different attack settings by using only one simple end-to-end network with three ingenious loss function; (2) UEN could generate general visible adversarial patch patterns to attack the advanced trackers in the real-world; (3) Extensive experiments show that UEN is able to attack many state-of-the-art trackers effectively (e.g. SiamRPN-based networks and DiMP) on popular tracking datasets including OTB100, UAV123, and GOT10K, making online real-time attacks possible. The attack results outperform the introduced baseline in terms of attacking ability and attacking efficiency.
Deep Learning Data Privacy Protection Based on Homomorphic Encryption in AIoT

With the rapid development of 5G technology, its high bandwidth, high reliability, low delay, and large connection characteristics have opened up a broader application field of IoT. Moreover, AIoT (Artificial Intelligence Internet of Things) has become the new development direction of IoT. Through deep learning of real-time data provided by the Internet of Things, AI can judge user habits more accurately, make devices behave in line with user expectations, and become more intelligent, thus improving product user experience. However, in the process, there is a lot of data interaction between the edge and the cloud. Given that the shared data contain a large amount of private information, preserving information security on the shared data is an important issue that cannot be neglected. In this paper, we combine deep learning with homomorphic encryption algorithm and design a deep learning network model based on secure multiparty computing (MPC). In the whole process, we realize that the cloud only owns the encryption samples of users, and users do not own any parameters or structural information related to the model. In the experimental part, we input the encrypted Mnist and Cifar-10 datasets into the model for testing, and the results show that the classification accuracy rate of the encrypted Mnist can reach 99.21%, which is very close to the result under plaintext. The classification accuracy rate of encrypted Cifar-10 can reach 91.35%, slightly lower than the test result in plaintext and better than the existing deep learning network model that can realize data privacy protection.
Decoder-Side Cross Resolution Synthesis for Video Compression Enhancement

This paper proposes a decoder-side Cross Resolution Synthesis (CRS) module to pursue better compression efficiency beyond the latest Versatile Video Coding (VVC), where we encode intra frames at original high resolution (HR), compress inter frames at a lower resolution (LR), and then super-resolve decoded LR inter frames with the help from preceding HR intra and neighboring LR inter frames. For a LR inter frame, a motion alignment and aggregation network (MAN) is devised to produce temporally aggregated motion representation to best guarantee the temporal smoothness; Another texture compensation network (TCN) is utilized to generate texture representation from decoded HR intra frame for better augmenting spatial details; Finally, a similarity-driven fusion engine synthesizes motion and texture representations to upscale LR inter frames for the removal of compression and resolution re-sampling noises. We enhance the VVC using proposed CRS, showing averaged 8.76% and 11.93% Bjntegaard Delta Rate (BD-Rate) gains against the latest VVC anchor in Random Access (RA) and Low-delay P (LDP) settings respectively. In addition, experimental comparisons to the state-of-the-art super-resolution (SR) based VVC enhancement methods, and ablation studies are conducted to further report superior efficiency and generalization of the proposed algorithm. All materials will be made to public at https://njuvision.github.io/CRS for reproducible research.
Modeling Objects with Uncertain Behaviors.

Modeling the behavior of complex systems that operate in real environments, deal with physical elements, or interact with humans is a challenging task.It involves the explicit representation of aspects of behavioral uncertainty that are inherent in the system, but generally neglected in software models.In this paper, we focus on the explicit representation of the behavior of objects of complex systems, considering their motivations, randomness, and the different types of underlying uncertainty that affect their actions.We show how such uncertain behaviors can be effectively modeled in UML and OCL, and how the specifications produced can be used to simulate and analyze these systems.
On Addressing Heterogeneity in Federated Learning for Autonomous Vehicles Connected to a Drone Orchestrator

In this paper we envision a federated learning (FL) scenario in service of amending the performance of autonomous road vehicles, through a drone traffic monitor (DTM), that also acts as an orchestrator. Expecting non-IID data distribution, we focus on the issue of accelerating the learning of a particular class of critical object (CO), that may harm the nominal operation of an autonomous vehicle. This can be done through proper allocation of the wireless resources for addressing learner and data heterogeneity. Thus, we propose a reactive method for the allocation of wireless resources, that happens dynamically each FL round, and is based on each learner’s contribution to the general model. In addition to this, we explore the use of static methods that remain constant across all rounds. Since we expect partial work from each learner, we use the FedProx FL algorithm, in the task of computer vision. For testing, we construct a non-IID data distribution of the MNIST and FMNIST datasets among four types of learners, in scenarios that represent the quickly changing environment. The results show that proactive measures are effective and versatile at improving system accuracy, and quickly learning the CO class when underrepresented in the network. Furthermore, the experiments show a tradeoff between FedProx intensity and resource allocation efforts. Nonetheless, a well adjusted FedProx local optimizer allows for an even better overall accuracy, particularly when using deeper neural network (NN) implementations.
Utilizing timestamps of longitudinal electronic health record data to classify clinical deterioration events

Abstract Objective To propose an algorithm that utilizes only timestamps of longitudinal electronic health record data to classify clinical deterioration events. Materials and methods This retrospective study explores the efficacy of machine learning algorithms in classifying clinical deterioration events among patients in intensive care units using sequences of timestamps of vital sign measurements, flowsheets comments, order entries, and nursing notes. We design a data pipeline to partition events into discrete, regular time bins that we refer to as timesteps. Logistic regressions, random forest classifiers, and recurrent neural networks are trained on datasets of different length of timesteps, respectively, against a composite outcome of death, cardiac arrest, and Rapid Response Team calls. Then these models are validated on a holdout dataset. Results A total of 6720 intensive care unit encounters meet the criteria and the final dataset includes 830 578 timestamps. The gated recurrent unit model utilizes timestamps of vital signs, order entries, flowsheet comments, and nursing notes to achieve the best performance on the time-to-outcome dataset, with an area under the precision-recall curve of 0.101 (0.06, 0.137), a sensitivity of 0.443, and a positive predictive value of 0. 092 at the threshold of 0.6. Discussion and Conclusion This study demonstrates that our recurrent neural network models using only timestamps of longitudinal electronic health record data that reflect healthcare processes achieve well-performing discriminative power.
Designing the pedagogical design of the educational environment of the university

The relevance of the research is due to the fact that the educational environment of the university is an integral element of the educational process, which is constantly changing, even at a fairly high speed. In this regard, pedagogical design in such conditions concerns not only educational programs, but also the environment itself. The author considers a new category «pedagogical design», defines how the pedagogical designer differs from the methodologist or methodologist; Why is a pedagogical design design mechanism needed? The empirical basis of the study was the data of international studies by Deloitte «Digital Education Survey», Goldman, EFMD, Metaari, Technavio, as well as data from domestic studies by HR-academy and the Sberbank Corporate University. The novelty of the research lies in the fact that the author presents a mechanism for designing the pedagogical design of the educational environment of the university, based on obtaining, comprehending, checking one’s own experience and analyzing best practices. The practical significance of the study lies in the fact that the results obtained will allow educational institutions to structure their work in such a way as to calmly relate to changes, responding to requests from the labor market, while making changes made the training solution the most effective.
A Study on Visually Induced VR Reduction Method for Virtual Reality Sickness

In this paper, we propose a new type of visually induced reduction method for virtual reality (VR) sickness. This method induces a gaze based on a visual guide (VG). Although VGs are used in commercial VR game contents as crosshairs, no studies have been conducted related to VR sickness. VGs can have various properties determined by position, size, shape, color, etc., and it was necessary to investigate which properties affect VR sickness. In particular, size and position of VG are properties that directly affect the user’s gaze movement. Therefore, in this paper, five VR sickness reduction methods with different position and size of VG are proposed. Then, an experiment was conducted to investigate the effectiveness of the proposed VR sickness reduction method. To this end, a SSQ (including nausea, oculomotor discomfort, disorientation, and total score) and a questionnaire on fatigue and immersion were used. From the experimental results, a VG with a size 30% that of the aspect ratio, and a position synchronized to the user’s head movement direction, was most effective in terms of VR sickness reduction and immersion.
Exploring internal business factors and their impact on firm performance: small business perspective in Ethiopia

Abstract Mainly, the study is aimed at exploring the internal business factors and their impact on firm performance in micro- and small-scale enterprises. The study employed a descriptive and explanatory research design and used a quantitative research approach. The study is conducted on Amhara region, North Wollo zone, Lalibela city administration micro- and small-scale enterprise operators, and in this study, the dependent variable is the performance of SMEs while independent variables are managerial factors, workplace factors, and entrepreneurial factors. To achieve the objectives of the study, 199 sample micro and small enterprises are selected from the 395 target populations. It used an explanatory design with stratified and simple random sampling techniques. The study employed descriptive and inferential statistical data analysis methods. The finding shows that the independent variables managerial factors and workplace-related factors have a positive and significant effect on small business performance in the study area. Also, there is an insignificant relationship between entrepreneurial factors and the dependent variable.
Individual Identification of Radar Emitters Based on a One-Dimensional LeNet Neural Network

Specific emitter identification involves extracting the fingerprint features that represent the individual differences of the emitter through processing the received signals. By identifying the extracted fingerprint features, one can also identify the emitter to which the received signals belong. Due to differences in transmitter hardware, this fingerprint cannot be duplicated. Therefore, SEI plays an important role in the field of information security and can reduce the information leakages caused by key theft. This method can also be used in the military field to support communication countermeasures via emitter individual identification. In this paper, empirical mode decomposition is carried out for each radar pulse signal, and then the bispectral features are extracted. Dimensionality reduction is carried out according to the symmetry of the bispectral features. The features after dimensionality reduction are input into a one-dimensional LeNet neural network as the fingerprint features of the emitter, and the identification of 10 radar emitter sources is completed. Based on the verification of real signals, the SEI identification strategy in this paper achieved a recognition rate of 96.4% for 10 radar signals, 98.9% for 10 data emitter signals, and 88.93% for 5 communication radio signals.
Spoken language identification using i-vectors, x-vectors, PLDA and logistic regression

In this paper, i-vector and x-vector is used to extract the features from speech signal from local Indonesia languages, namely Javanese, Sundanese and Minang languages to help classifier identify the language spoken by the speaker. Probabilistic linear discriminant analysis (PLDA) are used as the baseline classifier and logistic regression technique are used because of prior studies showing logistic regression has better performance than PLDA for classifying speech data. Once these features are extracted. The feature is going to be classified using the classifier mentioned before. In the experiment, we tried to segment the test data to three segment such as 3, 10, and 30 seconds. This study is expanded by testing multiple parameters on the i-vector and x-vector method then comparing PLDA and logistic regression performance as its classifier. The x-vector has better score than i-vector for every segmented data while using PLDA as its classifier, except where the i-vector and x-vector is using logistic regression, i-vector still has better accuracy compared to x-vector.
Behavior of Analog Quantum Algorithms.

Analog quantum algorithms are formulated in terms of Hamiltonians rather than unitary gates and include quantum adiabatic computing, quantum annealing, and the quantum approximate optimization algorithm (QAOA). These algorithms are promising candidates for near-term quantum applications, but they often require fine tuning via the annealing schedule or variational parameters. In this work, we explore connections between these analog algorithms, as well as limits in which they become approximations of the optimal procedure.Notably, we explore how the optimal procedure approaches a smooth adiabatic procedure but with a superposed oscillatory pattern that can be explained in terms of the interactions between the ground state and first excited state that effect the coherent error cancellation of diabatic transitions. Furthermore, we provide numeric and analytic evidence that QAOA emulates this optimal procedure with the length of each QAOA layer equal to the period of the oscillatory pattern. Additionally, the ratios of the QAOA bangs are determined by the smooth, non-oscillatory part of the optimal procedure. We provide arguments for these phenomena in terms of the product formula expansion of the optimal procedure. With these arguments, we conclude that different analog algorithms can emulate the optimal protocol under different limits and approximations. Finally, we present a new algorithm for better approximating the optimal protocol using the analytic and numeric insights from the rest of the paper. In practice, numerically, we find that this algorithm outperforms standard QAOA and naive quantum annealing procedures.
Selfit – An Intelligent Tutoring System for Psychomotor Development

Recent advancements in Machine Learning and software development are extending the applications of Intelligent Tutoring Systems (ITS) into non-cognitive skill domains, while proposing novel design architectures and tutoring strategies. In this paper we present our ongoing work on Selfit, an Intelligent Tutoring System for psychomotor development. The system focuses on and was tested for Anatomical Adaptation training, the first phase of training. The tutoring module includes a contextual multi-armed bandit algorithm for online generation of teaching sequences to overcome multiple problems, such as lack of training time, complexity of user characteristics, or management of motivation. First, the system was evaluated in a virtual environment, where populations of trainees follow several personalization strategies in systematic experiments. Second, Selfit is currently being tested by a group of users and a preliminary study revealed that most trainees find the system easy to use, modern, and attractive.