Reviewerly/exHarmony huggingface dataset:
Shell Game Optimization: A Novel Game-Based Algorithm

This article presents a new game-based optimization method entitled Shell Game Optimization (SGO).The novelty of this article is simulating the rules of a game known as shell game to design an algorithm for solving optimization problems in different fields of science.The key idea of the SGO is to find the ball hidden under one of the three shells, which should be guessed by players.The main feature and advantage of SGO is that it does not have any control parameters and hence, there is no need to set parameters.SGO is mathematically modeled and implemented on 23 well-known benchmark test functions as well as on a real life-engineering problem entitled pressure vessel design problem.Moreover, SGO is compared with eight optimization algorithms: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Teaching Learning Based Optimization (TLBO), Grey Wolf Optimizer (GWO), Grasshopper Optimization Algorithm (GOA), Spotted Hyena Optimizer (SHO), and Emperor Penguin Optimizer (EPO).The results and data obtained from applying SGO and other mentioned algorithms on unimodal test functions, multimodal test functions, and pressure vessel design problem show that SGO is able to provide better results in comparison with other well-known optimization algorithms.Moreover, results of Wilcoxon signed rank test confirm that SGO achieves more accuracy in comparison with the mentioned algorithms.
Human Activity Discovery and Recognition Using Probabilistic Finite-State Automata

Ambient assisted living and smart home technologies are a good way to take care of dependent people whose number will increase in the future. They allow the discovery and the recognition of human's activities of daily living (ADLs) in order to take care of people by keeping them in their home. In order to consider the human behavior nondeterminism, probabilistic approaches are used despite difficulties encountered in model generation and probabilistic indicators computing. In this article, a global method based on probabilistic finite-state automata and the definition of the normalized likelihood and perplexity is proposed to manage ADLs discovery and recognition. In order to reduce the computational complexity, some results about a simplified normalized likelihood computation are proved. A real case study showing the efficiency of the proposed method is discussed. Note to Practitioners-This article is motivated by the problem of the automatic recognition of activities that are daily performed by elderly or disabled people in a smart dwelling. The set of activities to be recognized is defined by a medical staff (e.g., to prepare meal, to do housework, to take leisure, etc.) and correspond to pathologies that have to be monitored by doctors (e.g., loss of memory, loss of mobility, etc.). The proposed method is based on a systematic procedure of offline construction of a model for each activity to be monitored (the activity discovering step). The online recognition of activities actually performed (the activity recognition step) is afterward based on these models of activities. Since the human behavior is nondeterministic, and may even be irrational, probabilistic activity models are built from a learning database. In the same way, probabilistic indicators are used for determining online the most probable activities actually performed. The efficiency of the proposed approach is illustrated through a case study performed in a smart living lab.
Computing-in-Memory for Performance and Energy-Efficient Homomorphic Encryption

Homomorphic encryption (HE) allows direct computations on encrypted data. Despite numerous research efforts, the practicality of HE schemes remains to be demonstrated. In this regard, the enormous size of ciphertexts involved in HE computations degrades computational efficiency. Near-memory Processing (NMP) and Computing-in-memory (CiM) - paradigms where computation is done within the memory boundaries - represent architectural solutions for reducing latency and energy associated with data transfers in data-intensive applications such as HE. This paper introduces CiM-HE, a Computing-in-memory (CiM) architecture that can support operations for the B/FV scheme, a somewhat homomorphic encryption scheme for general computation. CiM-HE hardware consists of customized peripherals such as sense amplifiers, adders, bit-shifters, and sequencing circuits. The peripherals are based on CMOS technology, and could support computations with memory cells of different technologies. Circuit-level simulations are used to evaluate our CiM-HE framework assuming a 6T-SRAM memory. We compare our CiM-HE implementation against (i) two optimized CPU HE implementations, and (ii) an FPGA-based HE accelerator implementation. When compared to a CPU solution, CiM-HE obtains speedups between 4.6x and 9.1x, and energy savings between 266.4x and 532.8x for homomorphic multiplications (the most expensive HE operation). Also, a set of four end-to-end tasks, i.e., mean, variance, linear regression, and inference are up to 1.1x, 7.7x, 7.1x, and 7.5x faster (and 301.1x, 404.6x, 532.3x, and 532.8x more energy efficient). Compared to CPU-based HE in a previous work, CiM-HE obtain 14.3x speed-up and >2600x energy savings. Finally, our design offers 2.2x speed-up with 88.1x energy savings compared to a state-of-the-art FPGA-based accelerator.
Classical communication with indefinite causal order for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>N</mml:mi></mml:math> completely depolarizing channels

If two identical copies of a completely depolarizing channel are put into a superposition of their possible causal orders, they can transmit nonzero classical information. Here we study how well we can transmit classical information with $N$ depolarizing channels put in superposition of $M$ causal orders via a quantum switch. We calculate the Holevo quantity if the superposition uses only cyclic permutations of channels and find that it increases with $M$ and it is independent of $N$. For a qubit it never reaches 1 if we are increasing $M$. On the other hand, the classical capacity decreases with the dimension $d$ of the message system. Further, for $N=3$ and 4 we studied the superposition of all causal orders and uniformly superposed causal orders belonging to different cosets created by a cyclic permutation subgroup.
struc2gauss: Structural role preserving network embedding via Gaussian embedding

Abstract Network embedding (NE) is playing a principal role in network mining, due to its ability to map nodes into efficient low-dimensional embedding vectors. However, two major limitations exist in state-of-the-art NE methods: role preservation and uncertainty modeling . Almost all previous methods represent a node into a point in space and focus on local structural information, i.e., neighborhood information. However, neighborhood information does not capture global structural information and point vector representation fails in modeling the uncertainty of node representations. In this paper, we propose a new NE framework, struc2gauss , which learns node representations in the space of Gaussian distributions and performs network embedding based on global structural information. struc2gauss first employs a given node similarity metric to measure the global structural information, then generates structural context for nodes and finally learns node representations via Gaussian embedding. Different structural similarity measures of networks and energy functions of Gaussian embedding are investigated. Experiments conducted on real-world networks demonstrate that struc2gauss effectively captures global structural information while state-of-the-art network embedding methods fail to, outperforms other methods on the structure-based clustering and classification task and provides more information on uncertainties of node representations.
Statistical learning theory of structured data

The traditional approach of statistical physics to supervised learning routinely assumes unrealistic generative models for the data: usually inputs are independent random variables, uncorrelated with their labels. Only recently, statistical physicists started to explore more complex forms of data, such as equally-labelled points lying on (possibly low dimensional) object manifolds. Here we provide a bridge between this recently-established research area and the framework of statistical learning theory, a branch of mathematics devoted to inference in machine learning. The overarching motivation is the inadequacy of the classic rigorous results in explaining the remarkable generalization properties of deep learning. We propose a way to integrate physical models of data into statistical learning theory, and address, with both combinatorial and statistical mechanics methods, the computation of the Vapnik-Chervonenkis entropy, which counts the number of different binary classifications compatible with the loss class. As a proof of concept, we focus on kernel machines and on two simple realizations of data structure introduced in recent physics literature: $k$-dimensional simplexes with prescribed geometric relations and spherical manifolds (equivalent to margin classification). Entropy, contrary to what happens for unstructured data, is nonmonotonic in the sample size, in contrast with the rigorous bounds. Moreover, data structure induces a novel transition beyond the storage capacity, which we advocate as a proxy of the nonmonotonicity, and ultimately a cue of low generalization error. The identification of a synaptic volume vanishing at the transition allows a quantification of the impact of data structure within replica theory, applicable in cases where combinatorial methods are not available, as we demonstrate for margin learning.
CUI@CHI: Mapping Grand Challenges for the Conversational User Interface Community

The aim of this workshop is twofold. First, it aims to grow critical mass in Conversational User Interfaces (CUI) research by mapping the grand challenges in designing and researching these interactions. Second, this workshop is intended to further build the CUI community with these challenges in mind, whilst also growing CUI research presence at CHI. In particular, the workshop will survey and map topics such as: interaction design for text and voice-based CUI; the interplay between engineering efforts such as in Natural language Processing (NLP) and the design of CUI; practical CUI applications (e.g. human-robot interaction, public spaces, hands-free and wearables); and social, contextual, and cultural aspects of CUI design (e.g. ethics, privacy, trust, information exploration, persuasion, well-being, or decision-making, marginalized users). By drawing from the diverse interdisciplinary expertise that defines CHI, we are proposing this workshop as a platform on which to build a community that is better equipped to tackle an emerging field that is rapidly-evolving, yet is under-studied --- especially as the commercial advances seem to outpace the scholarly research in this space.
Graph‐Partitioning Based Convolutional Neural Network for Earthquake Detection Using a Seismic Array

Abstract We present a deep‐learning approach for earthquake detection using waveforms from a seismic array consisting of multiple seismographs. Although automated, deep‐learning earthquake detection techniques have recently been developed at the single‐station level, they have potential difficulty in reducing false detections owing to the presence of local noise inherent to each station. Here, we propose a deep‐learning‐based approach to efficiently analyze the waveforms observed by a seismic array, whereby we employ convolutional neural networks in conjunction with graph partitioning to group the waveforms from seismic stations within the array. We then apply the proposed method to waveform data recorded by a dense, local seismic array in the regional seismograph network around the Tokyo metropolitan area, Japan. Our method detects more than 97% of the local seismicity catalog, with less than 4% false positive rate, based on an optimal threshold value of the output earthquake probability of 0.61. A comparison with conventional deep‐learning‐based detectors demonstrates that our method yields fewer false detections for a given true earthquake detection rate. Furthermore, the current method exhibits the robustness to poor‐quality data and/or data that are missing at several stations within the array. Numerical experiments using subsampled data demonstrate that the present method has the potential to detect earthquakes even when half of the normally available seismic data are missing. We apply the proposed method to analyze 1‐h‐long continuous waveforms and identify new seismic events with extremely low signal‐to‐noise ratios that are not listed in existing catalogs. We also show the potential portability of the proposed method by applying it to seismic array data not used for the training.
Comparison-Level Mitigation of Ethnic Bias in Face Recognition

Current face recognition systems achieve high performance on several benchmark tests. Despite this progress, recent works showed that these systems are strongly biased against demographic sub-groups. Previous works introduced approaches that aim at learning less biased representations. However, applying these approaches in real applications requires a complete replacement of the templates in the database. This replacement procedure further requires that a face image of each enrolled individual is stored as well. In this work, we propose the first bias-mitigating solution that works on the comparison-level of a biometric system. We propose a fairness- driven neural network classifier for the comparison of two biometric templates to replace the systems similarity function. This fair classifier is trained with a novel penalization term in the loss function to introduce the criteria of group and individual fairness to the decision process. This penalization term forces the score distributions of different ethnicities to be similar, leading to a reduction of the intra-ethnic performance differences. Experiments were conducted on two publicly available datasets and evaluated the performance of four different ethnicities. The results showed that for both fairness criteria, our proposed approach is able to significantly reduce the ethnic bias, while it preserves a high recognition ability. Our model, build on individual fairness, achieves bias reduction rate between 15.35% and 52.67%. In contrast to previous work, our solution is easy to integrate into existing systems by simply replacing the systems similarity functions with our fair template comparison approach.
Towards Interpretable Clinical Diagnosis with Bayesian Network Ensembles Stacked on Entity-Aware CNNs

The automatic text-based diagnosis remains a challenging task for clinical use because it requires appropriate balance between accuracy and interpretability.In this paper, we attempt to propose a solution by introducing a novel framework that stacks Bayesian Network Ensembles on top of Entity-Aware Convolutional Neural Networks (CNN) towards building an accurate yet interpretable diagnosis system.The proposed framework takes advantage of the high accuracy and generality of deep neural networks as well as the interpretability of Bayesian Networks, which is critical for AI-empowered healthcare.The evaluation conducted on the real Electronic Medical Record (EMR) documents from hospitals and annotated by professional doctors proves that, the proposed framework outperforms the previous automatic diagnosis methods in accuracy performance and the diagnosis explanation of the framework is reasonable.
Learning Low-Resource End-To-End Goal-Oriented Dialog for Fast and Reliable System Deployment

Existing end-to-end dialog systems perform less effectively when data is scarce. To obtain an acceptable success in real-life online services with only a handful of training examples, both fast adaptability and reliable performance are highly desirable for dialog systems. In this paper, we propose the Meta-Dialog System (MDS), which combines the advantages of both meta-learning approaches and human-machine collaboration. We evaluate our methods on a new extended-bAbI dataset and a transformed MultiWOZ dataset for low-resource goal-oriented dialog learning. Experimental results show that MDS significantly outperforms non-meta-learning baselines and can achieve more than 90% per-turn accuracies with only 10 dialogs on the extended-bAbI dataset.
Fusion Learning: A One Shot Federated Learning

Federated Learning is an emerging distributed machine learning technique which does not require the transmission of data to a central server to build a global model. Instead, individual devices build their own models, and the model parameters are transmitted. The server constructs a global model using these parameters, which is then re-transmitted back to the devices. The major bottleneck of this approach is the communication overhead as all the devices need to transmit their model parameters at regular intervals. Here we present an interesting and novel alternative to federated learning known as Fusion Learning, where the distribution parameters of the client’s data along with its local model parameters are sent to the server. The server regenerates the data from these distribution parameters and fuses all the data from multiple devices. This combined dataset is now used to build a global model that is transmitted back to the individual devices. Our experiments show that the accuracy achieved through this approach is in par with both a federated setup and a centralized framework needing only one round of communication to the central server.
Sentence Retrieval using Stemming and Lemmatization with Different Length of the Queries

In this paper we focus on Sentence retrieval which is similar to Document retrieval but with a smaller unit of retrieval.Using data pre-processing in document retrieval is generally considered useful.When it comes to sentence retrieval the situation is not that clear.In this paper we use - (term frequency -inverse sentence frequency) method for sentence retrieval.As pre-processing steps, we use stop word removal and language modeling techniques: stemming and lemmatization.We also experiment with different query lengths.The results show that data pre-processing with stemming and lemmatization is useful with sentences retrieval as it is with document retrieval.Lemmatization produces better results with longer queries, while stemming shows worse results with longer queries.For the experiment we used data of the Text Retrieval Conference (TREC) novelty tracks.
Implementing Superposition in iProver (System Description)

iProver is a saturation theorem prover for first-order logic with equality, which is originally based on an instantiation calculus Inst-Gen. In this paper we describe an extension of iProver with the superposition calculus. We have developed a flexible simplification setup that subsumes and generalises common architectures such as DISCOUNT or Otter. This also includes the concept of “immediate simplification”, wherein newly derived clauses are more aggressively simplified among themselves, and the concept of “light normalisation”, wherein ground unit equations are stored in an interreduced TRS which is then used to simplify new clauses. We have also added support for associative-commutative theories (AC), namely by deletion of AC-joinable clauses, semantic detection of AC axioms, and preprocessing normalisation.
Two Families of Optimal Linear Codes and Their Subfield Codes

In this paper, a family of [q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> - 1, 4, q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> - q - 2] cyclic codes over F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> meeting the Griesmer bound is presented. Their duals are [q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> - 1, q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> - 5,4] almost MDS codes and are optimal with respect to the sphere-packing bound. The q <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> -ary subfield codes of this family of cyclic codes are also investigated, where q <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> is any prime power such that q is power of q <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> . Some of the subfield codes are optimal and some have the best known parameters. It is shown that the subfield codes are equivalent to a family of primitive BCH codes and thus the parameters of the BCH codes are solved. The duals of the subfield codes are also optimal with respect to the sphere-packing bound. A family of [q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , 4, q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> - q - 1] linear codes over F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> meeting the Griesmer bound is presented. Their duals are [q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> - 4, 4] almost MDS codes and are optimal with respect to the sphere-packing bound. The q <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> -ary subfield codes of this family of linear codes are also investigated, where q <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> is any prime power such that q is power of q <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> . Five infinite families of 2-designs are also constructed with three families of linear codes of this paper.
Multi-emitter MAP-elites

Quality-Diversity (QD) optimisation is a new family of learning algorithms that aims at generating collections of diverse and high-performing solutions. Among those algorithms, the recently introduced Covariance Matrix Adaptation MAP-Elites (CMA-ME) algorithm proposes the concept of emitters, which uses a predefined heuristic to drive the algorithm's exploration. This algorithm was shown to outperform MAP-Elites, a popular QD algorithm that has demonstrated promising results in numerous applications. In this paper, we introduce Multi-Emitter MAP-Elites (ME-MAP-Elites), an algorithm that directly extends CMA-ME and improves its quality, diversity and data efficiency. It leverages the diversity of a heterogeneous set of emitters, in which each emitter type improves the optimisation process in different ways. A bandit algorithm dynamically finds the best selection of emitters depending on the current situation. We evaluate the performance of ME-MAP-Elites on six tasks, ranging from standard optimisation problems (in 100 dimensions) to complex locomotion tasks in robotics. Our comparisons against CMA-ME and MAP-Elites show that ME-MAP-Elites is faster at providing collections of solutions that are significantly more diverse and higher performing. Moreover, in cases where no fruitful synergy can be found between the different emitters, ME-MAP-Elites is equivalent to the best of the compared algorithms.