Reviewerly/exHarmony huggingface dataset:
Benchmarking Multimodal Regex Synthesis with Complex Structures

Existing datasets for regular expression (regex) generation from natural language are limited in complexity; compared to regex tasks that users post on StackOverflow, the regexes in these datasets are simple, and the language used to describe them is not diverse.We introduce STRUCTUREDREGEX, a new regex synthesis dataset differing from prior ones in three aspects.First, to obtain structurally complex and realistic regexes, we generate the regexes using a probabilistic grammar with pre-defined macros observed from real-world StackOverflow posts.Second, to obtain linguistically diverse natural language descriptions, we show crowdworkers abstract depictions of the underlying regex and ask them to describe the pattern they see, rather than having them paraphrase synthetic language.Third, we augment each regex example with a collection of strings that are and are not matched by the ground truth regex, similar to how real users give examples.Our quantitative and qualitative analysis demonstrates the advantages of STRUCTUREDREGEX over prior datasets.Further experimental results using various multimodal synthesis techniques highlight the challenge presented by our dataset, including non-local constraints and multi-modal inputs. 1
Pose-Guided Knowledge Transfer for Object Part Segmentation

Object part segmentation is an important problem for many applications, but generating the annotations to train a part segmentation model is typically quite labor-intensive. Recently, Fang et al. augmented object part segmentation datasets by using keypoint locations as weak supervision to transfer a source object instance's part annotations to an unlabeled target object. We show that while their approach works well when the source and target objects have clearly visible keypoints, it often fails for severely articulated poses. Also, their model does not generalize well across multiple object classes, even if they are very similar. In this paper, we propose and evaluate a new model for transferring part segmentations using keypoints, even for complex object poses and across different object classes.
Exploring Parameter Space with Structured Noise for Meta-Reinforcement Learning

Efficient exploration is a major challenge in Reinforcement Learning (RL) and has been studied extensively. However, for a new task existing methods explore either by taking actions that maximize task agnostic objectives (such as information gain) or applying a simple dithering strategy (such as noise injection), which might not be effective enough. In this paper, we investigate whether previous learning experiences can be leveraged to guide exploration of current new task. To this end, we propose a novel Exploration with Structured Noise in Parameter Space (ESNPS) approach. ESNPS utilizes meta-learning and directly uses meta-policy parameters, which contain prior knowledge, as structured noises to perturb the base model for effective exploration in new tasks. Experimental results on four groups of tasks: cheetah velocity, cheetah direction, ant velocity and ant direction demonstrate the superiority of ESNPS against a number of competitive baselines.
Learned Low Bit-rate Image Compression with Adversarial Mechanism

Adversarial mechanism is introduced to learned image compression system in this paper. Our motivation is that the number of quantization levels is limited with the constraint of low bit-rate, resulting in severe distortion in details after reconstruction. The adversarial training manner enhances the ability of Decoder/Generator to enrich textures and details in the reconstructed image. Channel-spatial attention mechanism is used to refine the intermediate features implicitly to boost the representation power of CNNs. As for entropy model, we jointly take hyperpriors and autoregressive priors for accurate probability estimation. Moreover, an EDSR-like post-processing subnetwork is concatenated after Decoder for further quality enhancement. The proposed approach demonstrates competitive performance when evaluated with multi-scale structural similarity (MSSSIM) and favorably visual quality at low bit-rate.
Representations of Generalized Self-Shrunken Sequences

Output sequences of the cryptographic pseudo-random number generator, known as the generalized self-shrinking generator, are obtained self-decimating Pseudo-Noise (PN)-sequences with shifted versions of themselves. In this paper, we present three different representations of this family of sequences. Two of them, the p and G-representations, are based on the parameters p and G corresponding to shifts and binary vectors, respectively, used to compute the shifted versions of the original PN-sequence. In addition, such sequences can be also computed as the binary sum of diagonals of the Sierpinski’s triangle. This is called the B-representation. Characteristics and generalities of the three representations are analyzed in detail. Under such representations, we determine some properties of these cryptographic sequences. Furthermore, these sequences form a family that has a group structure with the bit-wise XOR operation.
Philosophy of Liberal Education: The Contexts

The first article devoted to philosophy of liberal education (Voprosy obrazovaniya / Educational Studies Moscow no 1, 2020) laid out systematically the principles of that philosophy, such as (1) lifelong learning, (2) academic freedom, (3) importance of practice and experience, (4) critical thinking and civil competency, (5) competency development instead of knowledge accumulation, (6) priority of general education over specialized education, (7) the concept of learning to learn, (8) self-directed learning effort, (9) political neutrality, and (10) interaction and Socratic dialogue. In this second part of the article, the liberal model of education is contextualized under two main perspectives, historical and socio-theoretical. The historical perspective is used to discuss the ancient origins of the liberal model, German classical philosophy as a direct origin of its principles, and the trajectories of liberal education discourse elements penetrating Russia’s educational and cultural policy. The socio-theoretical perspective is applied to the context in which the liberal model was conceived (the nascent stage of modernity), the social conditions that led to its crisis (stabilized industrial societies of the modern age), and its relevance in the era of late modernity.
Classifying design‐level requirements using machine learning for a recommender of interaction design patterns

Software Engineering is a discipline that encompasses processes associated with the development of interactive systems. The perceived quality of an interactive system is heavily influenced by the user interface design, which may result in many challenges. One such challenge is design-level requirements analysis. The success of the software system is mostly dependent on how well users’ requirements have been understood and translated into appropriate functionalities. During the interactive system design process, it is common to find recurring problems in human–computer interactions, for which reusing solutions is highly feasible. Interaction design patterns seek to support designers in decision making during the design of interactive systems. Due to the design task tends to be subjective and prone to errors. This work aims at presenting and evaluating an interaction design patterns recommendation model based on design-level requirements classification, through the application of supervised machine learning algorithms. To compare the performance of four classification algorithms, a study was carried out, in which the linear support vector machine was the most suitable to this problem. The results of this work can be used for implementing frameworks that can better support designers’ decision making when designing user interfaces.
EFP 2.0: A Multi-Agent Epistemic Solver with Multiple E-State Representations

Multi-agent systems have been employed to model, simulate and explore a variety of real-world scenarios. It is becoming more and more important to investigate formalisms and tools that would allow us to exploit automated reasoning in these domains. An area that has received increasing attention is the use of multi-agent systems which allow an agent to reason about the knowledge and beliefs of other agents. This type of reasoning, i.e., about agents' perception of the world and also about agents' knowledge of her and others' knowledge, is referred to as epistemic reasoning. This paper presents an updated formalization and implementation of a multi-agent epistemic planner, called EFP. In particular, the paper explores the advantages of using alternative state representations that deviate from the commonly used Kripke structures. The paper explores such alternatives in the context of an action language for multi-agent epistemic planning. The paper presents also an actual implementation of a planner that uses the novel ideas, demonstrating concrete performance improvements on benchmarks collected from the literature.
A Quantum Annealing Algorithm for Finding Pure Nash Equilibria in Graphical Games

We introduce Q-Nash, a quantum annealing algorithm for the NP-complete problem of finding pure Nash equilibria in graphical games. The algorithm consists of two phases. The first phase determines all combinations of best response strategies for each player using classical computation. The second phase finds pure Nash equilibria using a quantum annealing device by mapping the computed combinations to a quadratic unconstrained binary optimization formulation based on the Set Cover problem. We empirically evaluate Q-Nash on D-Wave’s Quantum Annealer 2000Q using different graphical game topologies. The results with respect to solution quality and computing time are compared to a Brute Force algorithm and the Iterated Best Response heuristic.
Adaptive Informative Path Planning with Multimodal Sensing

Adaptive Informative Path Planning (AIPP) problems model an agent tasked with obtaining information subject to resource constraints in unknown, partially observable environments. Existing work on AIPP has focused on representing observations about the world as a result of agent movement. We formulate the more general setting where the agent may choose between different sensors at the cost of some energy, in addition to traversing the environment to gather information. We call this problem AIPPMS (MS for Multimodal Sensing). AIPPMS requires reasoning jointly about the effects of sensing and movement in terms of both energy expended and information gained. We frame AIPPMS as a Partially Observable Markov Decision Process (POMDP) and solve it with online planning. Our approach is based on the Partially Observable Monte Carlo Planning framework with modifications to ensure constraint feasibility and a heuristic rollout policy tailored for AIPPMS. We evaluate our method on two domains: a simulated search-and-rescue scenario and a challenging extension to the classic RockSample problem. We find that our approach outperforms a classic AIPP algorithm that is modified for AIPPMS, as well as online planning using a random rollout policy.
In search of isoglosses: continuous and discrete language embeddings in Slavic historical phonology

This paper investigates the ability of neural network architectures to effectively learn diachronic phonological generalizations in a multilingual setting.We employ models using three different types of language embedding (dense, sigmoid, and straight-through).We find that the Straight-Through model outperforms the other two in terms of accuracy, but the Sigmoid model's language embeddings show the strongest agreement with the traditional subgrouping of the Slavic languages.We find that the Straight-Through model has learned coherent, semi-interpretable information about sound change, and outline directions for future research.
Context Mover's Distance & Barycenters: Optimal transport of contexts for building representations.

We present a framework for building unsupervised representations of entities and their compositions, where each entity is viewed as a probability distribution rather than a vector embedding. In particular, this distribution is supported over the contexts which co-occur with the entity and are embedded in a suitable low-dimensional space. This enables us to consider representation learning from the perspective of Optimal Transport and take advantage of its tools such as Wasserstein distance and barycenters. We elaborate how the method can be applied for obtaining unsupervised representations of text and illustrate the performance (quantitatively as well as qualitatively) on tasks such as measuring sentence similarity, word entailment and similarity, where we empirically observe significant gains (e.g., 4.1% relative improvement over Sent2vec, GenSen). The key benefits of the proposed approach include: (a) capturing uncertainty and polysemy via modeling the entities as distributions, (b) utilizing the underlying geometry of the particular task (with the ground cost), (c) simultaneously providing interpretability with the notion of optimal transport between contexts and (d) easy applicability on top of existing point embedding methods. The code, as well as prebuilt histograms, are available under this https URL.
Choosing the Sample with Lowest Loss makes SGD Robust

The presence of outliers can potentially significantly skew the parameters of machine learning models trained via stochastic gradient descent (SGD). In this paper we propose a simple variant of the simple SGD method: in each step, first choose a set of k samples, then from these choose the one with the smallest current loss, and do an SGD-like update with this chosen sample. Vanilla SGD corresponds to k = 1, i.e. no choice; k >= 2 represents a new algorithm that is however effectively minimizing a non-convex surrogate loss. Our main contribution is a theoretical analysis of the robustness properties of this idea for ML problems which are sums of convex losses; these are backed up with linear regression and small-scale neural network experiments
Linguist vs. Machine: Rapid Development of Finite-State Morphological Grammars

Sequence-to-sequence models have proven to be highly successful in learning morphological inflection from examples as the series of SIG-MORPHON/CoNLL shared tasks have shown.It is usually assumed, however, that a linguist working with inflectional examples could in principle develop a gold standard-level morphological analyzer and generator that would surpass a trained neural network model in accuracy of predictions, but that it may require significant amounts of human labor.In this paper, we discuss an experiment where a group of people with some linguistic training develop 25+ grammars as part of the shared task and weigh the cost/benefit ratio of developing grammars by hand.We also present tools that can help linguists triage difficult complex morphophonological phenomena within a language and hypothesize inflectional class membership.We conclude that a significant development effort by trained linguists to analyze and model morphophonological patterns are required in order to surpass the accuracy of neural models.
Modeling the Relationships Between Basic and Achievement Emotions in Computer-Based Learning Environments

Commercial facial affect detection software is typically trained on large databases and achieves high accuracy in detecting basic emotions, but their use in educational settings is unclear. The goal of this research is to determine how basic emotions relate to the achievement emotion states that are more relevant in academic settings. Such relations, if accurate and consistent, may be leveraged to make more effective use of the commercial affect-detection software. For this study, we collected affect data over four days from a classroom study with 65 students using Betty’s Brain. Basic emotions obtained from commercial software were aligned to achievement emotions obtained using sensor-free models. Interpretable classifiers enabled the study of relationships between the two types of emotions. Our findings show that certain basic emotions can help infer complex achievement emotions such as confusion, frustration and engaged concentration. This suggests the possibility of using commercial software as a less context-sensitive and more development-friendly alternative to the affect detector models currently used in learning environments.
An efficient priority-driven congestion control algorithm for data center networks

With the emerging diverse applications in data centers, the demands on quality of service in data centers also become diverse, such as high throughput of elephant flows and low latency of deadline-sensitive flows. However, traditional TCPs are ill-suited to such situations and always result in the inefficiency (e.g. missing the flow deadline, inevitable throughput collapse) of data transfers. This further degrades the user-perceived quality of service (QoS) in data centers. To reduce the flow completion time of mice and deadline-sensitive flows along with promoting the throughput of elephant flows, an efficient and deadline-aware priority-driven congestion control (PCC) protocol, which grants mice and deadline-sensitive flows the highest priority, is proposed in this paper. Specifically, PCC computes the priority of different flows according to the size of transmitted data, the remaining data volume, and the flows' deadline. Then PCC adjusts the congestion window according to the flow priority and the degree of network congestion. Furthermore, switches in data centers control the input/output of packets based on the flow priority and the queue length. Different from existing TCPs, to speed up the data transfers of mice and deadline-sensitive flows, PCC provides an effective method to compute and encode the flow priority explicitly. According to the flow priority, switches can manage packets efficiently and ensure the data transfers of high priority flows through a weighted priority scheduling with minor modification. The experimental results prove that PCC can improve the data transfer performance of mice and deadline-sensitive flows while guaranting the throughput of elephant flows.
Solar Thermal Collector Output Temperature Prediction by Hybrid Intelligent Model for Smartgrid and Smartbuildings Applications and Optimization

Currently, there is great interest in reducing the consumption of fossil fuels (and other non-renewable energy sources) in order to preserve the environment; smart buildings are commonly proposed for this purpose as they are capable of producing their own energy and using it optimally. However, at times, solar energy is not able to supply the energy demand fully; it is mandatory to know the quantity of energy needed to optimize the system. This research focuses on the prediction of output temperature from a solar thermal collector. The aim is to measure solar thermal energy and optimize the energy system of a house (or building). The dataset used in this research has been taken from a real installation in a bio-climate house located on the Sotavento Experimental Wind Farm, in north-west Spain. A hybrid intelligent model has been developed by combining clustering and regression methods such as neural networks, polynomial regression, and support vector machines. The main findings show that, by dividing the dataset into small clusters on the basis of similarity in behavior, it is possible to create more accurate models. Moreover, combining different regression methods for each cluster provides better results than when a global model of the whole dataset is used. In temperature prediction, mean absolute error was lower than 4 ∘ C.
MonkeyKing: Adaptive Parameter Tuning on Big Data Platforms with Deep Reinforcement Learning

Choosing the right parameter configurations for recurring jobs running on big data analytics platforms is difficult because there can be hundreds of possible parameter configurations to pick from. Even the selection of parameter configurations is based on different types of applications and user requirements. The difference between the best configuration and the worst configuration can have a performance impact of more than 10 times. However, parameters of big data platforms are not independent, which makes it a challenge to automatically identify the optimal configuration for a broad spectrum of applications. To alleviate these problems, we proposed MonkeyKing, a system that leverages past experience and collects new information to adjust parameter configurations of big data platforms. It can recommend key parameters, which have strong impact on performance according to job types, and then combine deep reinforcement learning (DRL) to optimize key parameters to improve job performance. We choose the current popular deep Q-network (DQN) structure and its four improved algorithms, including DQN, Double DQN, Dueling DQN, and the combined Double DQN and Dueling DQN, and finally found that the combined Double DQN and Dueling DQN has a better effect. Our experiments and evaluations on Spark show that performance can be improved by ∼25% under best conditions.
Co-Generation of Game Levels and Game-Playing Agents

Open-endedness, a longstanding cornerstone of artificial life research, is the ability of systems to generate potentially unbounded ontologies of increasing novelty and complexity. Engineering generative systems displaying at least some degree of this ability is a goal with clear applications to procedural content generation in games. The Paired Open-Ended Trailblazer (POET) algorithm, heretofore explored only in a biped walking domain, is a coevolutionary system that simultaneously generates environments and agents that can solve them. This paper introduces a POET-Inspired Neuroevolutionary System for KreativitY (PINSKY) in games, which co-generates levels for multiple video games and agents that play them. This system leverages the General Video Game Artificial Intelligence (GVGAI) framework to enable co-generation of levels and agents for the 2D Atari-style games Zelda and Solar Fox. Results demonstrate the ability of PINSKY to generate curricula of game levels, opening up a promising new avenue for research at the intersection of procedural content generation and artificial life. At the same time, results in these challenging game domains highlight the limitations of the current algorithm and opportunities for improvement.
A System Model of Post-Migration Risk Factors Affecting the Mental Health of Unaccompanied Minor Refugees in Austria—A Multi-Step Modeling Process Involving Expert Knowledge from Science and Practice

Various studies have indicated that unaccompanied minor refugees (UMRs) have a higher risk of suffering from mental health problems than do accompanied minor refugees and general population norm. However, only a few studies provide data on UMRs regarding post-migration risk factors, their interrelations, and their influence on mental health. In this study, system models of post-migration risk factors for mental health and their interactions were developed in the case of Austria. In three consecutive interactive workshops with scientists and practitioners, fuzzy-logic cognitive mapping techniques were used to integrate the experts’ knowledge. The resulting final system model consists of 11 risk factors (e.g., social contacts in the host country, housing situation, or professional health care services). The model provides a deeper insight into the complexity of interrelated direct, indirect, and reciprocal relations, as well as self-reinforcing triads. This systemic approach provides a sound basis for further investigations, taking into account the inherent complex multifactorial dependencies in this topic.