Reviewerly/exHarmony huggingface dataset:
When Lagged Fibonacci Generators jump

Jansen introduced a primitive called jumped Linear Feedback Shift Register (LFSR) for building LFSRs that can be clocked a large number of times with a single simple operation. This is useful in the construction of stream ciphers based on clock-controlled LFSRs. A concept of Lagged Fibonacci Generator (LFG) is also used as an important building block of key-stream generators in stream cipher cryptography. In this paper, we use the jumping concept of Jansen in case of LFG. We show that unlike LFSRs, LFGs need not jump always in the state space itself, even though the characteristic polynomial is primitive. Instead, it may have a hyper space jump depending on the characteristic primitive polynomial. We give a necessary and sufficient condition for an LFG to jump within the state space itself and when it exists, it is same as the degree of the characteristic polynomial.
Incorporating Prior Knowledge by Selective Context Features to Enhance Topic Coherence

Latent variable model has been widely used to extract topics from document collections, but this unsupervised mode makes it difficult to interpret the topics covered by the constructed cluster due to no artificial tags. Considering the influence of preprocessing on subsequent data mining, this study extracts news feature words from CSCP word segmentation method and named entity identification (NER). For NER, names of people, places, organizations are extracted by syntax rules and verified by Wikipedia. In terms of CSCP, the probability of continuing characters, the distribution probabilities and numbers of links before and after the character are used to constantly merge the unit words, extract the important narrative words effectively, and then conduct Unigram, Compounds and Mixture word processing. The corpus of words obtained after word processing by CSCP and NER were used as LDA prior knowledge. Finally, the topic coherence of NER and CSCP is evaluated by UMass Topic Coherence Measurement. The experimental results show that Compounds are of specific meaning; Mixtures represents its diverse scope; Unigrams and NER are relatively short, while NER can accurately represent the important features of news content, the topic is more cohesive. In terms of efficiency, NER-LDA takes the longest, while it had the highest degree of topic coherence.
Towards implementing defect prediction in the software development process

Defect prediction is a method of identifying possible locations of software defects without testing. Software tests can be laborious and costly thus one may expect defect prediction to be a first class citizen in software engineering. Nonetheless, the industry apparently does not see it that way as the level of practical usages is limited. The study describes the possible reasons of the low adoption and suggests a number of improvements for defect prediction, including a confusion matrix-based model for assessing the costs and gains. The improvements are designed to increase the level of practitioners acceptance of defect prediction by removing the recognized by authors implementation obstacles. The obtained predictors showed acceptable performance. The results were processed through the suggested model for assessing the costs and gains and showed the potential of significant benefits, i.e. up to 90% of the overall cost of the considered test activities.
Evolutionary extreme learning machine for the behavioral modeling of RF power amplifiers

Abstract In this paper, evolutionary extreme learning machine (E‐ELM) is first introduced for RF power amplifiers (PAs) behavioral modeling. This approach combined differential evolution (DE) and extreme learning machine (ELM) to effectively solve the problem that more neurons of hidden layer are required, and repeated trials are necessary in behavioral modeling PAs by conventional ELM. As revealed in the modeling practices on Class‐AB and Class‐E PAs, fewer hidden layer neurons are used than the condition of conventional ELM. Meanwhile, it is found that ELM's unstable generalization ability in modeling PAs is also significantly improved, thanks to the internal DE method in the E‐ELM.
Effective Online Evaluation for Web Search

We present you a program of a balanced mix between an overview of academic achievements in the field of online evaluation and a portion of unique industrial practical experience shared by both the leading researchers and engineers from global Internet companies. First, we give basic knowledge from mathematical statistics. This is followed by foundations of main evaluation methods such as A/B testing, interleaving, and observational studies. Then, we share rich industrial experiences on constructing of an experimentation pipeline and evaluation metrics (emphasizing best practices and common pitfalls). A large part of our tutorial is devoted to modern and state-of-the-art techniques (including the ones based on machine learning) that allow to conduct online experimentation efficiently. We invite software engineers, designers, analysts, and managers of web services and software products, as well as beginners, advanced specialists, and researchers to learn how to make web service development effectively data-driven.
Location-aware discovery of services in the IoT: a Swarm approach

The vast number of devices connected to the IoT leverages cyber-physical systems, where the device location is used to properly take advantage of the device environment. Service-oriented architectures for the IoT converge with the need for locating physical devices, and more enabling technologies are required to leverage the development of real world use cases.In this work we propose a location-aware discovery mechanism for the IoT, based on a decentralized service-oriented platform called Swarm. The Swarm uses Semantic Web technologies for device communication and we extended that model to describe the geolocation information of services in a network of IoT devices. We describe in detail our architecture and implementation. As a result, we created a reference implementation of the complete mechanism, and tested it in a personal assistant scenario that recruits services based on their location.
Locality-constrained discrete graph hashing

Hashing techniques have been widely used for large-scale image retrieval because of its low storage cost and high query speed. Hashing maps similar data onto binary codes with a smaller Hamming distance, which is essentially a discrete optimization problem with constraints. Recently, several discrete hashing methods have begun focusing on minimizing the discrete loss between hash codes and continuous values but ignoring the hidden structure among data points in the original space. Following these efforts, we propose a locality-constrained discrete graph hashing (LCH) method that attempts to minimize the similarity distance of the data points in the original space by softening the hash codes’ hard constraint into two spaces: the discrete constraint space and the bit-balanced, bit-uncorrected constraint space. Furthermore, both spaces maintain the neighbourhood structure of the data points in the original space, and the best hash codes are found by minimizing the similarity distance between the two spaces. This is consistent with the nature of hashing for similarity preservation. Experiments on four real image datasets show that LCH obtains superior search accuracy over state-of-the-art hashing methods.
Multi-objective Particle Swarm Optimization

Swarm Intelligence (SI) refers to the collective behaviour of a group of creatures without a centralized unit control. This field was first established in 1989 in a robotic project [1]. Systems built based on SI typically have independent intelligent agents that interact locally to achieve a goal as a team [2]. Most of the algorithms in this field mimic swarm intelligence in nature. For instance, Ant Colony Optimization (ACO) [3] mimics swarm intelligence of ants in an ant colony using stigmergy, which is the communication between individuals in a swarm by modifying environment. It has been proved that ants can find the shortest path between multiple path to a food course from their nest by a depositing and marking the ground using pheromone
Sums with the Möbius function twisted by characters with powerful moduli

In a recent work, the authors (2016) have combined classical ideas of A. G. Postnikov (1956) and N. M. Korobov (1974) to derive improved bounds on short character sums for certain nonprincipal characters with powerful moduli. In the present paper, these results are used to bound sums with the Möbius function twisted by characters of the same type, which complements and improves some earlier work of B. Green (2012). To achieve this, we obtain a series of results about the size and zero-free region of <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="upper L"> <mml:semantics> <mml:mi>L</mml:mi> <mml:annotation encoding="application/x-tex">L</mml:annotation> </mml:semantics> </mml:math> </inline-formula>-functions with the same class of moduli.
A General Construction of Ordered Orthogonal Arrays Using LFSRs

The q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sup> x(q+1) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sup> ordered orthogonal arrays (OOAs) of strength t over the alphabet Fq were constructed using linear feedback shift register sequences (LFSRs) defined by primitive polynomials in F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> [x]. In this paper, we extend this result to all polynomials in F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> [x] which satisfy some fairly simple restrictions, i.e., the restrictions that are automatically satisfied by primitive polynomials. While these restrictions sometimes reduce the number of columns produced from (q + 1)t to a smaller multiple oft, in many cases, we still obtain the maximum number of columns in the constructed OOA when using non-primitive polynomials. For 2 ≤ q ≤ 9 and small t, we generate OOAs in this manner for all permissible polynomials of degree t in Fq[x] and compare the results to the ones produced in [2], [16], and [17] showing how close the arrays are to being “full” orthogonal arrays. Unusually for the finite fields, our arrays based on the non-primitive irreducible and even reducible polynomials are closer to the orthogonal arrays than those built from the primitive polynomials.
Neural Networks in an Adversarial Setting and Ill-Conditioned Weight Space

Recently, neural networks have seen a surge in their adoption due to their ability to provide high accuracy on various tasks. On the other hand, the existence of adversarial examples has raised suspicions regarding the generalization capabilities of neural networks. In this work, we focus on the weight matrix learned by the neural networks and hypothesize that an ill-conditioned weight matrix is one of the contributing factors in the neural network’s susceptibility towards adversarial examples. For ensuring that the learned weight matrix’s condition number remains sufficiently low, we suggest using an orthogonal regularizer. We show that this indeed helps in increasing the adversarial accuracy on MNIST and F-MNIST datasets.
A Spatiotemporal Semantic Search Engine For Cultural Events

In the field of geographic information science spatiotemporal information extraction from Web pages, especially unstructured documents, is one of the growing areas of the research. Abundant news is publishing every hour on the Web, which contains valuable spatiotemporal information for its users. It is cumbersome and time-consuming to search among unstructured texts and find events of interest. In this work, we will show you how to extract spatiotemporal and semantic entities and relationships representing in cultural event news reports and search within the information. Natural Language Processing (NLP) and automatic ontology population are tightly coupled, and together they make it possible to have Web documents semantically so that not only can machines comprehend the Web documents, but also as a result, users are able to find the ideal information with ease. A spatiotemporal semantic search engine enables us to answer, where and when an event will take place.
An Unified View on the Feedforward Neural Network Architecture

In this paper, an unified view on feedforward neural networks (FNNs) is provided from the free perception of the architecture design, learning algorithm, cost function, regularization, activation functions, etc. Furthermore, we consider the ensemble networks and swarm intelligence as the same sytems which consists of multiple learning algorithms or collective behavior of decentralized, self-organized systems with similar unified view of FNNs based architecture. This work is a small step on the road toward to the general theory of neural networks.
Tractability Frontier of Data Complexity in Team Semantics

We study the data complexity of model checking for logics with team semantics. We focus on dependence, inclusion, and independence logic formulas under both strict and lax team semantics. Our results delineate a clear tractability/intractability frontiers in data complexity of both quantifier-free and quantified formulas for each of the logics. For inclusion logic under the lax semantics, we reduce the model-checking problem to the satisfiability problem of so-called dual-Horn Boolean formulas. Via this reduction, we give an alternative proof for the known result that the data complexity of inclusion logic is in PTIME.
Augmented Reality for Presenting Local Wisdom: Sumatera Utara Traditional House

Abstract Augmented Reality is a technology that allows users to see the real world, with virtual objects placed on it or combined with the real world. Augmented Reality itself can be applied to various things, including as an educational media. In the field of education, technology can be used a culture introduction to society. One part of Indonesian culture is traditional houses. Traditional house is a building that symbolizes the culture of the local community. In North Sumatra itself, there are several traditional houses that have different forms. The use of Vuforia SDK in the construction of this application can display augmented objects in the form of 3-dimentional objects and also provide information on the house. This application will run on the Android mobile platform to make it easier for users to access it. In addition, users can choose their own objects to be used as markers to make the application use much easier.
An Entropy Lower Bound for Non-Malleable Extractors

A (k, ε)-non-malleable extractor is a function nmExt : {0, 1} <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> × {0, 1} <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sup> → {0, 1} that takes two inputs, a weak source X ~ {0, 1} <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> of min-entropy k and an independent uniform seed s E {0, 1} <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sup> , and outputs a bit nmExt(X, s) that is ε-close to uniform, even given the seed s and the value nmExt(X, s') for an adversarially chosen seed s' ≠ s. Dodis and Wichs (STOC 2009) showed the existence of (k, ε)-non-malleable extractors with seed length d = log(n - k - 1) + 2 log(1/ε) + 6 that support sources of min-entropy k > log(d) + 2 log(1/ε) + 8. We show that the foregoing bound is essentially tight, by proving that any (k, ε)-non-malleable extractor must satisfy the min-entropy bound k > log(d) + 2 log(1/ε) - log log(1/ε) - C for an absolute constant C. In particular, this implies that non-malleable extractors require min-entropy at least Ω(loglog(n)). This is in stark contrast to the existence of strong seeded extractors that support sources of min-entropy k = O(log(1/ε)). Our techniques strongly rely on coding theory. In particular, we reveal an inherent connection between non-malleable extractors and error correcting codes, by proving a new lemma which shows that any (k, ε)-non-malleable extractor with seed length d induces a code C ⊆ {0,1} <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2k</sup> with relative distance 1/2 - 2ε and rate d-1/2k .
Efficient methods for generating multi-mode NOON states

Two experimental setups to generate multi-mode NOON states are proposed. The first setup is a universal model, which can create a d-mode N-photon NOON state with an arbitrary mode number d and photon number N in principle in a lossless system with perfect optical devices, using states with non-deterministic distributions of photons as the input sources and post-selection of total N photons for detection. The second approach, on the other hand, can generate a 4-mode 4-photon NOON state using single photon sources. The core unit of both setups is the Fock state filter, which is used to filter out an arbitrary Fock state component from an incident state using a beam splitter with proper transmissivity. The intrinsic generation efficiencies of the NOON states produced by both methods, with the first setup particularly focused on using squeezed vacuum states as the inputs, are calculated. They are found to be at least two orders of magnitude larger than and scale more favorably to the previously proposed approach using coherent state inputs. We also show that the second setup has the best performance for the case with d=N=4, while the first method scales better for a larger N or d. These results manifest that multi-mode NOON states with 3 or 4 modes and 4 or 6 photons could feasibly be generated in experiments with current photonic technologies.
On the Unreported-Profile-is-Negative Assumption for Predictive Cheminformatics

In cheminformatics, compound-target binding profiles has been a main source of data for research. For data repositories that only provide positive profiles, a popular assumption is that unreported profiles are all negative. In this paper, we caution the audience not to take this assumption for granted, and present empirical evidence of its ineffectiveness from a machine learning perspective. Our examination is based on a setting where binding profiles are used as features to train predictive models; we show (1) prediction performance degrades when the assumption fails and (2) explicit recovery of unreported profiles improves prediction performance. In particular, we propose a framework that jointly recovers profiles and learns predictive model, and show it achieves further performance improvement. The presented study not only suggests applying matrix recovery methods to recover unreported profiles, but also initiates a new missing feature problem which we called Learning with Positive and Unknown Features.
Attention-Based GAN for Single Image Super-Resolution

Single Image Super-Resolution task based on GANs has shown a great improvement in all methods, but still has the optimization problems of texture details and distortion of local regions in super-resolved images. In this paper, we proposed an attention-based GAN architecture to solve preceding problems. Specifically, we first implemented attention mechanism in both Generator and Discriminator. Secondly, we adopted a three-step training for all architecture models and adjusted the adoption frequency of attention implement to make pre-trained model perform better. Extensive experiments on Set5, Set14 and BSD100 showed that the better pre-trained model of ours not only remedied the distortion of local regions, but also achieved the better perceptual quality than the original architecture.
Optimization-Based Data Generation for Photo Enhancement

The preparation of large amounts of high-quality training data has always been the bottleneck for the performance of supervised learning methods. It is especially time-consuming for complicated tasks such as photo enhancement. A recent approach to ease data annotation creates realistic training data automatically with optimization. In this paper, we improve upon this approach by learning image-similarity which, in combination with a Covariance Matrix Adaptation optimization method, allows us to create higher quality training data for enhancing photos. We evaluate our approach on challenging real world photo-enhancement images by conducting a perceptual user study, which shows that its performance compares favorably with existing approaches.
An adaptive prefix-assignment technique for symmetry reduction

This paper presents a technique for symmetry reduction that adaptively assigns a prefix of variables in a system of constraints so that the generated prefix-assignments are pairwise nonisomorphic under the action of the symmetry group of the system. The technique is based on McKay's canonical extension framework (McKay, 1998). Among key features of the technique are (i) adaptability—the prefix sequence can be user-prescribed and truncated for compatibility with the group of symmetries; (ii) parallelizability—prefix-assignments can be processed in parallel independently of each other; (iii) versatility—the method is applicable whenever the group of symmetries can be concisely represented as the automorphism group of a vertex-colored graph; and (iv) implementability—the method can be implemented relying on a canonical labeling map for vertex-colored graphs as the only nontrivial subroutine. To demonstrate the practical applicability of our technique, we have prepared an experimental open-source implementation of the technique and carry out a set of experiments that demonstrate ability to reduce symmetry on hard instances. Furthermore, we demonstrate that the implementation effectively parallelizes to compute clusters with multiple nodes via a message-passing interface.
Bidirectional Attention for SQL Generation

Generating structural query language (SQL) queries from natural language is a long-standing open problem. Answering a natural language question about a database table requires modeling complex interactions between the columns of the table and the question. It has been attracting considerable interest recently and driven by the explosive development of deep learning techniques. In this paper, we apply the sketch-based approach or synthesizing way to solve this problem. Based on the structure of SQL queries, we break down the model to three sub-modules and design specific deep neural networks for each of them. We employ the bidirectional attention mechanisms and character-level embedding with convolutional neural networks (CNNs) to improve the result. Experimental evaluations show that our model achieves the state-of-the-art results in WikiSQL dataset.