Reviewerly/exHarmony huggingface dataset:
GSGP-CUDA — A CUDA framework for Geometric Semantic Genetic Programming

Geometric Semantic Genetic Programming (GSGP) is a state-of-the-art machine learning method based on evolutionary computation. GSGP performs search operations directly at the level of program semantics, which can be done more efficiently than operating at the syntax level like most GP systems. Efficient implementations of GSGP in C++ exploit this fact, but not to its full potential. This paper presents GSGP-CUDA, the first CUDA implementation of GSGP and the most efficient, which exploits the intrinsic parallelism of GSGP using GPUs. Results show speedups greater than 1,000× relative to the state-of-the-art sequential implementation, during the model training process. Additionally, our implementation allows the user to seamlessly make inferences over new data through the best evolved model, opening the possibility of using GSGP on Big Data problems.
Productive Method as the Basis for Soft Skills Development in Engineering Foreign Language Education

The modern world requires engineering specialists with excellent hard skills as well as soft skills that contribute to better communication, creativity, and self-realisation of a person. The authors discovered that modern educational standards are starting to focus on soft skills development, proposing requirements for educational programs that contain competencies covering soft skills. To fulfil such needs, there is the productive method of foreign language teaching that implies interactive technologies and masters foreign language communicative competence and soft skills at once. Therefore, the study aimed at developing a technology of soft skills development in engineering foreign language education using the productive method and checking its effectiveness. The authors designed a questionnaire and investigated engineering students’ opinions on a need to develop soft skills, which revealed high relevance of the topic, and the possibility of their development while studying a foreign language, which proved the productive method’s prospects. Based on findings received and literature analysed, we implemented case studies, problem-based learning, and essay writing in the technology and designed assessment criteria. Testing of the technology was performed by a pedagogical experiment, where qualitative and quantitative methods were applied. To critically analyse the results, we used Cronbach’s alpha, which revealed good reliability of the questionnaire, and t-test showed high efficiency of the technology that improved students’ soft skills.
Algorithmic support of the system of external observation and routing of autonomous mobile robots

This article presents the algorithmic support of the external monitoring and routing system of autonomous mobile robots. In some cases, the practical usage of mobile robots is related to the solution of navigation problems. In particular, the position of ground robots can be secured using unmanned aerial vehicles. In the proposed approach based on the video image obtained from an external video camera located above the working area of mobile robots, the location of both robots and nearby obstacles is recognized. The optimal route to the target point of the selected robot is built, and changes in its working area are monitored. Information about the allowed routes of the robot is transmitted to third-party applications via network communication channels. Primary image processing from the camera includes distortion correction, contouring and binarization, which allows to separate image fragments containing robots and obstacles from background surfaces and objects. Recognition of robots in a video frame is based on the use of a SURF detector. This technology extracts key points in the video frame and compares them with key points of reference images of robots. Trajectory planning is implemented using Dijkstra’s algorithm. The discreteness of the trajectories obtained using the algorithm for finding a path on the graph can be compensated for on board autonomous mobile robots by using spline approximation. Experimental studies have confirmed the efficiency of the proposed approach both in the problem of recognition and localization of mobile robots and in the problem of planning safe trajectories.
University Mission in Western-European Culture (Ethical and Sociological Aspects) P. ІІ

The paper by Sergiy Kurbatov and Mariya Rohozha “The Mission of University in the Western European Culture”(Part II) is devoted to the analyses of transformation of the university as social institution and cultural phenomenon in our time, which we started at the first part of this paper, that was published in “Philosophy of Education”, 2017, № 2 (21)). If the previous paper of these authors included a long chronological period from the origin of the university in late Medieval time up to the 20th century, the current paper is concentrated on analyses of radical challenges, that university faced at the end of the 20th century and at the beginning of the 21st century. For example, such popular in contemporary English language literature concept as the end (or the death) of university is observed. The authors tried to analyze sociological attempts to measure the main university activities in the form of international university rankings and the possibilities to develop in Ukraine the ideal models of university, which any system of university rankings have. The special stress was made on the influence of COVID 19 pandemic on transformative processes and institutional development of universities in the nearest future. The main challenges of the 21st century are crucial for the university, because this institution lost monopoly of producing and distribution of advanced knowledge for the first time in history. From the tactic viewpoint, university is less competitive than the different training programs and online courses, it is too conservative and bureaucratic one. But the authors think that in strategic perspective university has a chance for renovation, proving the old maxima that the values and spiritual dimensions of being and the relevant environment are crucial for human being. Almost the millennium of university history proves its ability to pass through the dramatic historical transformation and to continue to maintain its essence.
Task-Agnostic Exploration via Policy Gradient of a Non-Parametric State Entropy Estimate

In a reward-free environment, what is a suitable intrinsic objective for an agent to pursue so that it can learn an optimal task-agnostic exploration policy? In this paper, we argue that the entropy of the state distribution induced by finite-horizon trajectories is a sensible target. Especially, we present a novel and practical policy-search algorithm, Maximum Entropy POLicy optimization (MEPOL), to learn a policy that maximizes a non-parametric, $k$-nearest neighbors estimate of the state distribution entropy. In contrast to known methods, MEPOL is completely model-free as it requires neither to estimate the state distribution of any policy nor to model transition dynamics. Then, we empirically show that MEPOL allows learning a maximum-entropy exploration policy in high-dimensional, continuous-control domains, and how this policy facilitates learning meaningful reward-based tasks downstream.
V2X Wireless Technology Identification Using Time–Frequency Analysis and Random Forest Classifier

Signal identification is of great interest for various applications such as spectrum sharing and interference management. A typical signal identification system can be divided into two steps. A feature vector is first extracted from the received signal, then a decision is made by a classification algorithm according to its observed values. Some existing techniques show good performance but they are either sensitive to noise level or have high computational complexity. In this paper, a machine learning algorithm is proposed for the identification of vehicular communication signals. The feature vector is made up of Instantaneous Frequency (IF) resulting from time–frequency (TF) analysis. Its dimension is then reduced using the Singular Value Decomposition (SVD) technique, before being fed into a Random Forest classifier. Simulation results show the relevance and the low complexity of IF features compared to existing cyclostationarity-based ones. Furthermore, we found that the same accuracy can be maintained regardless of the noise level. The proposed framework thus provides a more accurate, robust and less complex V2X signal identification system.
Activity &amp; Emotion Detection of Recognized kids in CCTV Video for Day Care Using SlowFast &amp; CNN

For working parents a real-life challenge faced is to keep track of their child activities in playschool and creche. Despite having CCTV surveillance available to parents, monitoring 8-10 hours videos on a day-to-day basis is not possible, hence CCTV videos, which carry millions of information, get unnoticed by parents and day-cares. The aim of project was to process CCTV videos to identify the child, detect their respective expressions as well as planned/unplanned activities throughout the day. We have considered angry, disgust, scared, happy, sad, surprised, neutral expressions of kids to be monitored on daily basis. The various activities performed at daycare like playing, drawing, rhyming, dancing along with not so usual activities like slapping, falling, pushing would also be monitored on kids. It can be further extended to create a 8-10 minutes glimpse/summary in form of video/timeline of children's entire day activities and inform their respective parents. This can improvise the overall experience of the daycare for parents.
Experimental particle’s shapes reconstructions from their interferometric images using the Error-Reduction algorithm

The Error-Reduction algorithm is tested to reconstruct the exact 2D-shape of irregular rough particles from their experimental interferometric images. The particles tested are "programmable" particles generated with a digital micromirror device. The method is first applied to centrosymmetric particles, and then to non-centrosymmetric particles where the twin image problem brings additional difficulty.
The interplay between language similarity and script on a novel multi-layer Algerian dialect corpus

Recent years have seen a rise in interest for cross-lingual transfer between languages with similar typology, and between languages of various scripts.However, the interplay between language similarity and difference in script on cross-lingual transfer is a less studied problem.We explore this interplay on cross-lingual transfer for two supervised tasks, namely part-of-speech tagging and sentiment analysis.We introduce a newly annotated corpus of Algerian user-generated comments comprising parallel annotations of Algerian written in Latin, Arabic, and code-switched scripts, as well as annotations for sentiment and topic categories.We perform baseline experiments by fine-tuning multi-lingual language models.We further explore the effect of script vs. language similarity in cross-lingual transfer by fine-tuning multi-lingual models on languages which are a) typologically distinct, but use the same script, b) typologically similar, but use a distinct script, or c) are typologically similar and use the same script.We find there is a delicate relationship between script and typology for part-of-speech, while sentiment analysis is less sensitive.
Assessing Dialogue Systems with Distribution Distances

An important aspect of developing dialogue systems is how to evaluate and compare the performance of different systems.Existing automatic evaluation metrics are based on turnlevel quality evaluation and use average scores for system-level comparison.In this paper, we propose to measure the performance of a dialogue system by computing the distributionwise distance between its generated conversations and real-world conversations.Specifically, two distribution-wise metrics, FBD and PRD, are developed and evaluated.Experiments on several dialogue corpora show that our proposed metrics correlate better with human judgments than existing metrics.
Fast Montgomery Modular Multiplier Using FPGAs

This letter details a fast and efficient implementation of the Montgomery modular multiplication by taking advantage of parallel multipliers and adders. This implementation was programmed in high-level synthesis language and tested on a field-programmable gate array device. In order to test the performance of the proposal, a sequential version of the algorithm was also implemented in hardware. Moreover, we compared the parallel implementation with a software version and with five contributions from the literature. This way, we found that our proposal improves the performance of all other implementations.
Bounded Risk-Sensitive Markov Games: Forward Policy Design and Inverse Reward Learning with Iterative Reasoning and Cumulative Prospect Theory

Classical game-theoretic approaches for multi-agent systems in both the forward policy design problem and the inverse reward learning problem often make strong rationality assumptions: agents perfectly maximize expected utilities under uncertainties. Such assumptions, however, substantially mismatch with observed human behaviors such as satisficing with sub-optimal, risk-seeking, and loss-aversion decisions. Drawing on iterative reasoning models and cumulative prospect theory, we propose a new game-theoretic framework, bounded risk-sensitive Markov Game (BRSMG), that captures two aspects of realistic human behaviors: bounded intelligence and risk-sensitivity. General solutions to both the forward policy design problem and the inverse reward learning problem are provided with theoretical analysis and simulation verification. We validate the proposed forward policy design algorithm and the inverse reward learning algorithm in a two-player navigation scenario. The results show that agents demonstrate bounded-intelligence, risk-averse and risk-seeking behaviors in our framework. Moreover, in the inverse reward learning task, the proposed bounded risk-sensitive inverse learning algorithm outperforms a baseline risk-neutral inverse learning algorithm by effectively learning not only more accurate reward values but also the intelligence levels and the risk-measure parameters of agents from demonstrations.
StereoRel: Relational Triple Extraction from a Stereoscopic Perspective

Relational triple extraction is critical to understanding massive text corpora and constructing large-scale knowledge graph, which has attracted increasing research interest.However, existing studies still face some challenging issues, including information loss, error propagation and ignoring the interaction between entity and relation.To intuitively explore the above issues and address them, in this paper, we provide a revealing insight into relational triple extraction from a stereoscopic perspective, which rationalizes the occurrence of these issues and exposes the shortcomings of existing methods.Further, a novel model is proposed for relational triple extraction, which maps relational triples to a three-dimension (3-D) space and leverages three decoders to extract them, aimed at simultaneously handling the above issues.Extensive experiments are conducted on five public datasets, demonstrating that the proposed model outperforms the recent advanced baselines.
Dynamic Instance-wise Joint Feature Selection and Classification

In this article, a dynamic instance-wise joint feature selection and classification framework during testing is presented. Specifically, the proposed framework sequentially selects features one at a time for each data instance, given previously selected features, and stops this process to classify the instance once it determines that including additional features will not improve the final classification decision. In contrast to most of the existing work that utilizes a set of features, common for all data instances, the proposed framework utilizes different features to classify each data instance. An optimization problem is defined for each data instance in terms of the number of selected features and the associated classification accuracy. The optimum solution is derived, and its structure is analyzed. Based on the optimum solution and its properties, two new algorithms are designed. The expected number of features needed to achieve a given classification accuracy is also analytically derived. Finally, the performance of the proposed algorithms is illustrated on 11 public datasets, thus demonstrating their effectiveness and scalability across a broad range of application domains. Impact Statement-In many domains, including but not limited to medicine and criminal justice, experts need to reach an accurate decision in a timely manner using limited resources (e.g., costly tests and time-consuming evidence collection). At the same time, it is desirable to tailor decisions to each individual case (e.g., patient and defendant). Most of the existing machine learning algorithms, however, ignore resource constraints and/or acquire a general solution for all cases, while not scaling in big data settings. The algorithms proposed in this article address such challenges enabling tailored and timely decision making. With a reduction of up to 66% in the average number of features, while maintaining similar accuracy levels, the proposed algorithms can be used for dynamic instance-wise joint feature selection and classification in scenarios involving over one million variables.
N-Best ASR Transformer: Enhancing SLU Performance using Multiple ASR Hypotheses

Karthik Ganesan, Pakhi Bamdev, Jaivarsan B, Amresh Venugopal, Abhinav Tushar. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers). 2021.
Automatic Curriculum Learning With Over-repetition Penalty for Dialogue Policy Learning

Dialogue policy learning based on reinforcement learning is difficult to be applied to real users to train dialogue agents from scratch because of the high cost. User simulators, which choose random user goals for the dialogue agent to train on, have been considered as an affordable substitute for real users. However, this random sampling method ignores the law of human learning, making the learned dialogue policy inefficient and unstable. We propose a novel framework, Automatic Curriculum Learning-based Deep Q-Network (ACL-DQN), which replaces the traditional random sampling method with a teacher policy model to realize the dialogue policy for automatic curriculum learning. The teacher model arranges a meaningful ordered curriculum and automatically adjusts it by monitoring the learning progress of the dialogue agent and the over-repetition penalty without any requirement of prior knowledge. The learning progress of the dialogue agent reflects the relationship between the dialogue agent's ability and the sampled goals' difficulty for sample efficiency. The over-repetition penalty guarantees the sampled diversity. Experiments show that the ACL-DQN significantly improves the effectiveness and stability of dialogue tasks with a statistically significant margin. Furthermore, the framework can be further improved by equipping with different curriculum schedules, which demonstrates that the framework has strong generalizability.
Quantum Exploration Algorithms for Multi-Armed Bandits

Identifying the best arm of a multi-armed bandit is a central problem in bandit optimization. We study a quantum computational version of this problem with coherent oracle access to states encoding the reward probabilities of each arm as quantum amplitudes. Specifically, we show that we can find the best arm with fixed confidence using $\tilde{O}\bigl(\sqrt{\sum_{i=2}^n\Delta^{\smash{-2}}_i}\bigr)$ quantum queries, where $\Delta_{i}$ represents the difference between the mean reward of the best arm and the $i^\text{th}$-best arm. This algorithm, based on variable-time amplitude amplification and estimation, gives a quadratic speedup compared to the best possible classical result. We also prove a matching quantum lower bound (up to poly-logarithmic factors).
Introducing Orthogonal Constraint in Structural Probes

With the recent success of pre-trained models in NLP, a significant focus was put on interpreting their representations.One of the most prominent approaches is structural probing (Hewitt and Manning, 2019), where a linear projection of word embeddings is performed in order to approximate the topology of dependency structures.In this work, we introduce a new type of structural probing, where the linear projection is decomposed into 1.isomorphic space rotation; 2. linear scaling that identifies and scales the most relevant dimensions.In addition to syntactic dependency, we evaluate our method on novel tasks (lexical hypernymy and position in a sentence).We jointly train the probes for multiple tasks and experimentally show that lexical and syntactic information is separated in the representations.Moreover, the orthogonal constraint makes the Structural Probes less vulnerable to memorization.
Unsupervised Learning of Deterministic Dialogue Structure with Edge-Enhanced Graph Auto-Encoder

It is important for task-oriented dialogue systems to discover the dialogue structure (i.e. the general dialogue flow) from dialogue corpora automatically. Previous work models dialogue structure by extracting latent states for each utterance first and then calculating the transition probabilities among states. These two-stage methods ignore the contextual information when calculating the probabilities, which makes the transitions between the states ambiguous. This paper proposes a conversational graph (CG) to represent deterministic dialogue structure where nodes and edges represent the utterance and context information respectively. An unsupervised Edge-Enhanced Graph Auto-Encoder (EGAE) architecture is designed to model local-contextual and global-structural information for conversational graph learning. Furthermore, a self-supervised objective is introduced with the response selection task to guide the unsupervised learning of the dialogue structure. Experimental results on several public datasets demonstrate that the novel model outperforms several alternatives in aggregating utterances with similar semantics. The effectiveness of the learned dialogue structured is also verified by more than 5\% joint accuracy improvement in the downstream task of low resource dialogue state tracking.
Knowing the No-match: Entity Alignment with Dangling Cases

This paper studies a new problem setting of entity alignment for knowledge graphs (KGs).Since KGs possess different sets of entities, there could be entities that cannot find alignment across them, leading to the problem of dangling entities.As the first attempt to this problem, we construct a new dataset and design a multi-task learning framework for both entity alignment and dangling entity detection.The framework can opt to abstain from predicting alignment for the detected dangling entities.We propose three techniques for dangling entity detection that are based on the distribution of nearest-neighbor distances, i.e., nearest neighbor classification, marginal ranking and background ranking.After detecting and removing dangling entities, an incorporated entity alignment model in our framework can provide more robust alignment for remaining entities.Comprehensive experiments and analyses demonstrate the effectiveness of our framework.We further discover that the dangling entity detection module can, in turn, improve alignment learning and the final performance.The contributed resource is publicly available to foster further research.