Reviewerly/exHarmony huggingface dataset:
Answering (Unions of) Conjunctive Queries using Random Access and Random-Order Enumeration

As data analytics becomes more crucial to digital systems, so grows the importance of characterizing the database queries that admit a more efficient evaluation. We consider the tractability yardstick of answer enumeration with a polylogarithmic delay after a linear-time preprocessing phase. Such an evaluation is obtained by constructing, in the preprocessing phase, a data structure that supports polylogarithmic-delay enumeration. In this article, we seek a structure that supports the more demanding task of a “random permutation”: polylogarithmic-delay enumeration in truly random order. Enumeration of this kind is required if downstream applications assume that the intermediate results are representative of the whole result set in a statistically meaningful manner. An even more demanding task is that of “random access”: polylogarithmic-time retrieval of an answer whose position is given. We establish that the free-connex acyclic CQs are tractable in all three senses: enumeration, random-order enumeration, and random access; and in the absence of self-joins, it follows from past results that every other CQ is intractable by each of the three (under some fine-grained complexity assumptions). However, the three yardsticks are separated in the case of a union of CQs (UCQ ): while a union of free-connex acyclic CQs has a tractable enumeration, it may (provably) admit no random access. We identify a fragment of such UCQs where we can guarantee random access with polylogarithmic access time (and linear-time preprocessing) and a more general fragment where we can guarantee tractable random permutation. For general unions of free-connex acyclic CQs, we devise two algorithms with relaxed guarantees: one has logarithmic delay in expectation, and the other provides a permutation that is almost uniformly distributed. Finally, we present an implementation and an empirical study that show a considerable practical superiority of our random-order enumeration approach over state-of-the-art alternatives.
Characterization of Spoken English Vowels Using Tree Structures

Characterization of vowels in spoken English plays a significant role in designing speech processing systems. In this work, spoken English vowels are analysed to find features which can help in their characterization. The outcome of the analysis led to the proposal of a novel feature which uses tree structures for the representation of vowels. In this approach, the vowels are represented as trees with their structural properties being elements in the trees. These properties are extracted by understanding the geometrical shapes of acoustic events. To prove the features that they can distinguish between vowels, a comparison is shown by calculating the distances among the new features. The computation of distance is done by employing a tree matching algorithm. The performance of the proposed features are compared against the standard MFCC features. In the analysis, speech data of Indian native speakers was used. The analysis procedures and the results obtained are presented.
Support vector machine for imbalanced microarray dataset classification using ant colony optimization and genetic algorithm

The microarray dataset contains a series of samples with the number of variables that reach thousands of genes expression. DNA microarrays are used to determine the level of gene expression and gene sequence in the sample. In cancer research, microarrays are used to study variation of molecular between tumors in order to develop better diagnosis and treatment for this disease. Classification is one of the important methods in microarray research to classify gene expression. Some characteristics of microarray dataset are high dimensions and imbalanced. Those characteristics cause prediction of classification which is over fitting. The purpose of this study is to overcome that problem with selection variables and generate synthetic data. The method for variables selection is Ant Colony Optimization (ACO), this method will compare with Genetic Algorithm (GA). The ACO method was inspired by the behavior of ant colonies looking for the shortest distance between the nest and food sources. The Method to solve imbalanced data is Synthetic Minority Oversampling Technique (SMOTE). This method generates synthetic data in minor classes randomly. In this study, the Support Vector Machine (SVM) is used to classify microarray dataset. This study uses breast cancer and lymphoma dataset. These datasets have different imbalanced ratios and number of variables. The result is variable selection using ACO method has fewer variables selected and higher AUC than GA method, but GA method more efficient in running time. SVM with SMOTE has higher performance than SVM without SMOTE.
Content-aware Deep Perceptual Image Compression

Currently, multimedia data grow in a explosive manner, whereas the effective bandwidth is quite limited in the wireless communications, such as high definition image/video communication, satellite communication and so on. Generative adversarial network (GAN) based image compression goes beyond the traditional image encoding standards under a obviously low bitrate. However, the most existing methods do not consider the importance of different image contents, where the regions with complex and saliency structures generally are more essential to users' quality of experience (QoE). In this paper, we propose a content-aware deep perceptual image compression method (CDPIC). The proposed approach involves a saliency subnet to produce content saliency map for adaptive bit allocation. Moreover, using QoE as the ultimate goal, our approach designs different compression objectives for different image contents, where it tends to preserve more details for the regions with complex and saliency structures, and improves the perceptual quality of the background region. Our experiments show that the proposed approach outperforms the state-of-the-art methods, and produces advanced performance in preserving details and semantics.
Sparse Dynamic Binary Neural Networks for Storage and Switching of Binary Periodic Orbits

A sparse dynamic binary neural network is characterized by local binary connection and can exhibit various binary periodic orbits. First, we consider a condition of sparse connection parameters that guarantees storage and switching of binary periodic orbits. Second, we give two examples of the network that realize the storage and stitching of two periodic orbits related to insect gaits patterns. Presenting an FPGA-based hardware, typical phenomena are confirmed experimentally.
Address space partitioning for the parallel dictionary LZW data compression algorithm

The Lempel-Ziv-Welch (LZW) algorithm is an important dictionary-based data compression approach that is used in many communication and storage systems. The parallel dictionary LZW (PDLZW) algorithm speeds up the LZW encoding by using multiple dictionaries. The PDLZW algorithm applies different dictionaries to store strings of different lengths, where each dictionary stores only strings of the same length. This simplifies the parallel search in the dictionaries for hardware implementations. The compression gain of the PDLZW depends on the partitioning of the address space, i.e. on the sizes of the parallel dictionaries. However, there is no universal partitioning that is optimal for all data sources. This work proposes an address space partitioning technique that optimizes the compression rate of the PDLZW using a Markov model for the data. Numerical results for address spaces with 512,1024, and 2048 entries demonstrate that the proposed partitioning improves the performance of the PDLZW compared with the original proposal.
On the Mapping of Underlying Concepts of a Combined Use of Lean and User-Centered Design with Agile Development: The Case Study of the Transformation Process of an IT Company

The agile development of software requires new approaches to serve users and end customers. The combination of Lean and User-Centered Design with Agile gives software development a competitive advantage. Given the novelty and scarcity of studies on such combined use in software development, as part of our long-term research that aims to develop a maturity model to accelerate the transformation from agile to the use of the combined approaches, we posed as our first step to identify what are the underlying concepts involved on the use of agile, lean, and user-centered design. We first conducted multiple literature reviews to identify the concepts for each of the individual approaches to then conduct an empirical study in order to identify what is considered useful by two software teams of a multinational IT company that are going through such a transformation for about 6 months. Our study revealed that there are concepts from literature not yet considered in practice and the other way around, there are practiced concepts not found in literature. For now, we hypothesize that this is due to the early maturing process of the studied teams. We believe that this is an initial contribution that can be of help for other teams enduring this challenging transformation process. Our research will next investigate how the three approaches relate to one another in order to provide a unique and consolidated combined model of concepts that will further be used as the skeleton of our maturity model.
Automatic Generation of Neural Network Topology to Forecast the International Integration Processes

The article is devoted to the automatic generation of neural network topology based on genetic algorithm to solve the problem of forecasting indicators of international integration processes. The paper proposes and implements a modification of the classic algorithm NEAT, consisting in the automatic selection of activation functions, which allowed to expand the set of possible solutions. The proposed method of predicting targets using the window method based on pre-predicted factor variables. This approach significantly increases the accuracy of forecasting.
Exploring Topic Difficulty in Information Retrieval Systems Evaluation

Abstract Experimental or relevance assessment cost as well as reliability of an information retrieval (IR) evaluation is highly correlated to the number of topics used. The need of many assessors to produce equivalent large relevance judgments often incurs high cost and time. So, large number of topics in retrieval experiment is not practical and economical. This experiment proposes an approach to identify most effective topics in evaluating IR systems with regards to topic difficulty. The proposed approach is capable of identifying which topics and topic set size are reliable when evaluating system effectiveness. Easy topics appeared to be most suitable for effectively evaluating IR systems.
Improving Prediction Intervals Using Measured Solar Power with a Multi-Objective Approach

Prediction Intervals are pairs of lower and upper bounds on point forecasts and are useful to take into account the uncertainty on predictions. This article studies the influence of using measured solar power, available at prediction time, on the quality of prediction intervals. While previous studies have suggested that using measured variables can improve point forecasts, not much research has been done on the usefulness of that additional information, so that prediction intervals with less uncertainty can be obtained. With this aim, a multi-objective particle swarm optimization method was used to train neural networks whose outputs are the interval bounds. The inputs to the network used measured solar power in addition to hourly meteorological forecasts. This study was carried out on data from three different locations and for five forecast horizons, from 1 to 5 h. The results were compared with two benchmark methods (quantile regression and quantile regression forests). The Wilcoxon test was used to assess statistical significance. The results show that using measured power reduces the uncertainty associated to the prediction intervals, but mainly for the short forecasting horizons.
Initializing the Fuzzy C-Means Cluster Center With Particle Swarm Optimization for Sentiment Clustering

Abstract Fuzzy C-Means (FCM) is one of the best-known clustering algorithms, however, FCM is significantly sensitive to the initial cluster center values and easily trapped in a local optimum. To overcome this problem, this study proposes and improved FCM with Particle Swarm Optimization (PSO) algorithm for high dimensional and unstructured sentiment clustering. PSO is applied for the determination of better cluster center initials. The results showed that FCM-PSO can provide better performance compared to the conventional FCM in terms of Rand Index, F-measure and Objective Function Values (OFV). The better OFV value indicates that FCM-PSO requires faster convergence time and better noise handling.
Assessing the Rationale for the U.S. EPA’s Proposed “Strengthening Transparency In Regulatory Science” Rule

The U.S. Environmental Protection Agency (EPA) is considering a new policy that would prohibit the agency from issuing regulations that rely on studies whose underlying data are not publicly available. While the EPA claims it is pursuing this policy in the interest of transparency, we argue that such a prohibition would greatly hinder, rather than help, the rulemaking process and would likely result in undesirable regulatory outcomes that fail to maximize economic welfare. This policy brief argues that a good faith effort to encourage data availability should focus on forward-looking incentives for transparency rather than the exclusion of a whole class of studies, and that weighting older studies based on their evidentiary value is preferable to removing valuable information from agency consideration.
Simultaneous Compression and De-Speckling of Medical Ultrasound Images

Compression of medical ultrasound images is necessary in order to meet storage and transmission requirements. Moreover, de-speckling is essential for diagnostic applications. We propose an algorithm for simultaneous compression and de-speckling of ultrasound images, based on optimization of quantization parameters applied on the coefficients of the coding transform. The optimization is applied in the rate-fidelity space, such that for a given target compression ratio, an optimal fidelity is obtained with respect to an a-priori known de-speckled image. The algorithm was tested on a set of fetal images. Results show that the reconstructed images better resemble the de-speckled images, compared to those obtained using standard quantization.
The Construction of Set-Truncated Higher Inductive Types

We construct finitary set-truncated higher inductive types (HITs) from quotients and the propositional truncation. For that, we first define signatures as a modification of the schema by Basold et al., and we show they give rise to univalent categories of algebras in both sets and setoids. To interpret HITs, we use the well-known method of initial algebra semantics. The desired algebra is obtained by lifting the quotient adjunction to the level of algebras and adapting Dybjer's and Moeneclaey's interpretation of HITs in setoids. From this construction, we conclude that the equality types of HITs are freely generated and that HITs are unique. The results are formalized in the UniMath library.
A Systematic Mapping Study of Cloud-native Application Design and Engineering

Abstract Cloud computing is a desirable paradigm that is providing services to users in a convenient manner and ensuring that Cloud service providers have value for their infrastructure. Applications designed to run explicitly on the Cloud are usually referred to as Cloud-native applications. Determining a research focus in a particular field of study is sometimes challenging. A systematic mapping study gives an insight into the research level that is being conducted in any field of interest. The results generated from such study are presented using a map. The method used in this study was analyzing three facets categories namely, topics, research and contribution. Topics were retrieved from primary studies, while type of research such as evaluation and contribution such as tool, were used in the analysis. The objective of this study is to conduct a systematic mapping study of Cloud-native applications designs and engineering. This will provide an insight into the frequency of work that has been done in cloud-native applications area. The results showed that from publications relating to security in the field of metric (1.94%), more articles in the topic of application in terms of tool (13.59%), more work done on architecture in terms of model (15.53%), more papers published on Cloud migration in the area of method (10.68%). Furthermore, 11.82% publications were identified on applications in terms of evaluation research, more publication on implementation in the area of validation research (1.82%), more publications on implementation in solution research (6.36%), more publications on security and application with respect to philosophical research (1.82%) and more work done on applications in terms of experience research (6.36%). From the study, several gaps were identified which would be beneficial to researchers, practitioners and providers.
A Hybrid Data and Model Transfer Framework for Distributed Machine Learning

Centralized model transfer is widely used in distributed machine learning frameworks, however, such frameworks assign high computation load to the central parameter server and lead to heavy communication cost. To address this issue, a novel distributed machine learning framework with decentralized instance communication and centralized model ensemble is proposed in this paper. Capitalizing on fully self-controlled computation and communication on every data node, our framework exploits the instances in a decentralized way to conduct centralized model ensemble, thus alleviating the burden of parameter server. In addition, by making tradeoff between communication cost and learning performance, a reinforcement meta-learning based communication scheme is developed. It actively and adaptively determines the instance transfer process and thus considerably reduce the communication cost. Finally, numerical results validate the good performance of our framework in terms of learning accuracy and communication cost.
Reconstructor: a computer program that uses three-valued logics to represent lack of information in empirical scientific contexts

CONTACT Ariel Jonathan Roffé arielroffe@filo.uba.ar CEFHIC-UNQ-CONICET, Bernal, Argentina; UNTREF, Caseros, Argentina
A Model for Context-Driven Authentication in Physical Access Control Environments

Authentication is the first line of defense in the process of securing any system. For physical access, access points during peak hours tend to be bottlenecks due to the time it takes to authenticate through a contact-driven authentication barrier. Typically, these are two-factor authentication access points, which require a user to scan a token followed by a biometric scan. User error, coupled with hardware wear and tear, contribute to false rejections which increase the authentication time. The Model aims to reduce the amount of time it takes to typically authenticate past a physical access point with minimal interaction required from the user through the use of temporal routine profiles and other context-driven mechanisms. This approach preserves the privacy of users by leveraging existing information that is already collected at access points on a daily basis to automatically generate and maintain the profiles of user access patterns.
Learning Neural Circuit by AC Operation and Frequency Signal Output

In the machine learning field, many application models such as pattern recognition or event prediction have been proposed. Neural Network is a typically basic method of machine learning. In this study, we used analog electronic circuits using alternative current to realize the neural network learning model. These circuits are composed by a rectifier circuit, Voltage-Frequency converter, amplifier, subtract circuit, additional circuit and inverter. The connecting weights describe the frequency converted to direct current from alternating current by a rectifier circuit. This model’s architecture is on the analog elements. The learning time and working time are very short because this system is not depending on clock frequency. Moreover, we suggest the realization of the deep learning model regarding the proposed analog hardware neural circuit.
Finding Patterns in Signals Using Lossy Text Compression

Whether the source is autonomous car, robotic vacuum cleaner, or a quadcopter, signals from sensors tend to have some hidden patterns that repeat themselves. For example, typical GPS traces from a smartphone contain periodic trajectories such as “home, work, home, work, ⋯”. Our goal in this study was to automatically reverse engineer such signals, identify their periodicity, and then use it to compress and de-noise these signals. To do so, we present a novel method of using algorithms from the field of pattern matching and text compression to represent the “language” in such signals. Common text compression algorithms are less tailored to handle such strings. Moreover, they are lossless, and cannot be used to recover noisy signals. To this end, we define the recursive run-length encoding (RRLE) method, which is a generalization of the well known run-length encoding (RLE) method. Then, we suggest lossy and lossless algorithms to compress and de-noise such signals. Unlike previous results, running time and optimality guarantees are proved for each algorithm. Experimental results on synthetic and real data sets are provided. We demonstrate our system by showing how it can be used to turn commercial micro air-vehicles into autonomous robots. This is by reverse engineering their unpublished communication protocols and using a laptop or on-board micro-computer to control them. Our open source code may be useful for both the community of millions of toy robots users, as well as for researchers that may extend it for further protocols.
Self-Attention-Based Temporary Curiosity in Reinforcement Learning Exploration

In many real-world scenarios, extrinsic rewards provided by the environment are sparse. An agent trained with classic reinforcement learning algorithm fails to explore these environments in a sufficient and effective way. To address this problem, the exploration bonus which derives from environmental novelty serves as intrinsic motivation for the agent. In recent years, curiosity-driven exploration is a mainstream approach to describe environmental novelty through prediction errors of dynamics models. Due to the expressive ability limitations of curiosity-based environmental novelty and the difficulty of finding appropriate feature space, most curiosity-driven exploration methods have the problem of overprotection against repetition. This problem can reduce the efficiency of exploration and lead the agent into a trap with local optimality. In this article, we propose a combination of persisting curiosity and temporary curiosity framework to deal with the problem of overprotection against repetition. We introduce the self-attention mechanism from the field of computer vision and propose a sequence-based self-attention mechanism for temporary curiosity generation. We compare our framework with some previous exploration methods in hard-exploration environments, provide a series of comprehensive analysis of the proposed framework and investigate the effect of the individual components of our method. The experimental results indicate that the proposed framework delivers superior performance than existing methods.