Reviewerly/exHarmony huggingface dataset:
Fair Dimensionality Reduction and Iterative Rounding for SDPs

We model dimensionality reduction as an optimization problem. A central example is the fair PCA problem: the input data is divided into $k$ groups, and the goal is to find a single $d$-dimensional representation for all groups for which the maximum variance (or minimum reconstruction error) is optimized for all groups in a fair (or balanced) manner, e.g., by maximizing the minimum variance over the $k$ groups of the projection to a $d$-dimensional subspace. This problem was introduced by Samadi et al. (2018) who gave a polynomial-time algorithm which, for $k=2$ groups, returns a $(d+1)$-dimensional solution of value at least the best $d$-dimensional solution. We give an exact polynomial-time algorithm for $k=2$ groups. The result relies on extending results of Pataki (1998) regarding rank of extreme point solutions to semi-definite programs. This approach applies more generally to any monotone concave function of the individual group objectives. For $k>2$ groups, our results generalize to give a $(d+\sqrt{2k+0.25}-1.5)$-dimensional solution with objective value as good as the optimal $d$-dimensional solution for arbitrary $k,d$ in polynomial time. Using our extreme point characterization result for SDPs, we give an iterative rounding framework for general SDPs which generalizes the well-known iterative rounding approach for LPs. It returns low-rank solutions with bounded violation of constraints. We obtain a $d$-dimensional projection where the violation in the objective can be bounded additively in terms of the top $O(\sqrt{k})$-singular values of the data matrices. We also give an exact polynomial-time algorithm for any fixed number of groups and target dimension via the algorithm of Grigoriev and Pasechnik (2005). In contrast, when the number of groups is part of the input, even for target dimension $d=1$, we show this problem is NP-hard.
Perceptual Quality-preserving Black-Box Attack against Deep Learning Image Classifiers

Deep neural networks provide unprecedented performance in all image classification problems, taking advantage of huge amounts of data available for training. Recent studies, however, have shown their vulnerability to adversarial attacks, spawning an intense research effort in this field. With the aim of building better systems, new countermeasures and stronger attacks are proposed by the day. On the attacker's side, there is growing interest for the realistic black-box scenario, in which the user has no access to the neural network parameters. The problem is to design efficient attacks which mislead the neural network without compromising image quality. In this work, we propose to perform the black-box attack along a low-distortion path, so as to improve both the attack efficiency and the perceptual quality of the adversarial image. Numerical experiments on real-world systems prove the effectiveness of the proposed approach, both in benchmark classification tasks and in key applications in biometrics and forensics.
LMOnto: An Ontology-Based Learner Model for Technology Enhanced Learning Systems

Several initiatives concerning the learner model has been proposed in the context of technology-enhanced learning systems. In fact, there are two main challenges encountered in this regard: Firstly, which information will serve to represent and reflect better the learner? Secondly, which formalism chooses for representing and managing the learner model? To overcome those challenges, we propose a new ontological approach for learner modeling enriched from the existing models.
Teacher’s Strategies in Teaching Speaking for Cadets

This study aims at finding the strategies used in teaching speaking and the problems faced by the teacher during a teaching process at BP2IP Malahayati Aceh . It also investigated the students’ responses toward the teachers’ strategies by involving two English teachers and two classes of 58 students. To gain the needed data, the writers used classroom observation, interview and questionnaire as research instruments. Classroom observation and interview were used to identify the teachers’ challenges and also strategies in teaching speaking, and questionnaire was utilized to collect the data about students’ responses towards the strategies. The results showed that the teachers used five strategies in teaching speaking for cadets in BP2IP Malahayati Aceh , namely: role play, drilling, games, describing picture, and also discussion group. Moreover, three common obstacles were found during teaching process; limited vocabulary, improper pronunciation, and less confident of the students. This study also showed that students’ responses towards the strategies were positive.
Additively Homomorphic IBE from Higher Residuosity

We present an identity-Based encryption (IBE) scheme that is group homomorphic for addition modulo a "large" (i.e. superpolynomial) integer, the first such group homomorphic IBE. Our first result is the construction of an IBE scheme supporting homomorphic addition modulo a poly-sized prime e. Our construction builds upon the IBE scheme of Boneh, LaVigne and Sabin (BLS). BLS relies on a hash function that maps identities to $$e^{\text {th}}$$ residues. However there is no known way to securely instantiate such a function. Our construction extends BLS so that it can use a hash function that can be securely instantiated. We prove our scheme secure under the (slightly modified) $$e^{\text {th}}$$ residuosity assumption in the random oracle model and show that it supports a (modular) additive homomorphism. By using multiple instances of the scheme with distinct primes and leveraging the Chinese Remainder Theorem, we can support homomorphic addition modulo a "large" (i.e. superpolynomial) integer. We also show that our scheme for $$e > 2$$ is anonymous by additionally assuming the hardness of deciding solvability of a special system of multivariate polynomial equations. We provide a justification for this assumption by considering known attacks.
Query Expansion in Information Retrieval using Frequent Pattern (FP) Growth Algorithm for Frequent Itemset Search and Association Rules Mining

Documents on the Internet have increased in number exponentially; this has resulted in users having difficulty finding documents or information needed.Special techniques are needed to retrieve documents that are relevant to user queries.One technique that can be used is Information Retrieval (IR).IR is the process of finding data (generally documents) in the form of text that matches the information needed from a collection of documents stored on a computer.Problems that often appear on IRs are incorrect user queries; this is caused by user limitations in representing their needs in the query.Researchers have proposed various solutions to overcome these limitations, one of which is to use the Expansion Query (QE).Various methods that have been applied to QE include Ontology, Latent Semantic Indexing (LSI), Local Co-Occurrence, Relevance Feedback, Concept Based, WordNet / Synonym Mapping.However, these methods still have limitations, one of them in terms of displaying the connection or relevance of the appearance of words or phrases in the document collection.To overcome this limitation, in this study we have proposed an approach to QE using the FP-Growth algorithm for the search for frequent itemset and Association Rules (AR) on QE.In this study, we applied the use of AR to QE to display the relevance of the appearance of a word or term with another word or term in the collection of documents, where the term produced is used to perform QE on user queries.The main contribution in this study is the use of Association rules with FP-Growth in the collection of documents to look for the connection of the emergence of words, which is then used to expand the original query of users on IR.For the evaluation of QE performance, we use recall, precision, and f-measure.Based on the research that has been done, it can be concluded that the use of AR on QE can improve the relevance of the documents produced.This is indicated by the average recall, precision, and f-measure values produced at 94.44%, 89.98%, and 92.07%.After comparing the IR process without QE with IR using QE, an increase in recall value was 25.65%, precision was 1.93%, and F-Measure was 15.78%.
Gradient Coding with Clustering and Multi-message Communication

Gradient descent (GD) methods are commonly employed in machine learning problems to optimize the parameters of the model in an iterative fashion. For problems with massive datasets, computations are distributed to many parallel computing servers (i.e., workers) to speed up GD iterations. While distributed computing can increase the computation speed significantly, the per-iteration completion time is limited by the slowest straggling workers. Coded distributed computing can mitigate straggling workers by introducing redundant computations; however, existing coded computing schemes are mainly designed against persistent stragglers, and partial computations at straggling workers are discarded, leading to wasted computational capacity. In this paper, we propose a novel gradient coding (GC) scheme which allows multiple coded computations to be conveyed from each worker to the master per iteration. We numerically show that the proposed GC with multi-message communication (MMC) together with clustering provides significant improvements in the average completion time (of each iteration), with minimal or no increase in the communication load.
Topic Hierarchies for Knowledge Capitalization using Hierarchical Dirichlet Processes in Big Data Context

Intelligent Technologies and research results from the field of knowledge management, have steadily and progressively improved knowledge quality, over the course of the last decades, especially in the current industrial context. The companies consider the knowledge as an important strategic resource for innovation. This paper focus on the problem of learning topic hierarchies from knowledge. The aim targeted is to respond to knowledge capitalization issues in big data context, by proposing a Knowledge capitalization system as an adaptive intelligent technique. This system acts on top of a big data platform, and runs on large scale globally to constitute a robust intelligent knowledge capitalization paradigm, with a clear separation of concerns. The architecture considers a batch processing as a preparation stage which starts by extracting hidden topics, by means of the HLDA in order to handle the complexity of multi knowledge domains, and to keep the semantic relations between knowledge entities. The hierarchical mechanism gives an effective and flexible way to store and analyses the knowledge. As a result, the time responding is obtained of high quality, and with the best precision, in comparison with the systems which uses LSA and LDA approach as a preparation stage.
AttoNets: Compact and Efficient Deep Neural Networks for the Edge via Human-Machine Collaborative Design

While deep neural networks have achieved state-of-the-art performance across a large number of complex tasks, it remains a big challenge to deploy such networks for practical, on-device edge scenarios such as on mobile devices, consumer devices, drones, and vehicles. In this study, we take a deeper exploration into a human-machine collaborative design approach for creating highly efficient deep neural networks through a synergy between principled network design prototyping and machine-driven design exploration. The efficacy of human-machine collaborative design is demonstrated through the creation of AttoNets, a family of highly efficient deep neural networks for on-device edge deep learning. Each AttoNet possesses a human-specified network-level macro-architecture comprising of custom modules with unique machine-designed module-level macro-architecture and micro-architecture designs, all driven by human-specified design requirements. Experimental results for the task of object recognition showed that the AttoNets created via human-machine collaborative design has significantly fewer parameters and computational costs than state-of-the-art networks designed for efficiency while achieving noticeably higher accuracy (with the smallest AttoNet achieving ~1.8% higher accuracy while requiring ~10x fewer multiply-add operations and parameters than MobileNet-V1). Furthermore, the efficacy of the AttoNets is demonstrated for the task of instance-level object segmentation and object detection, where an AttoNet-based Mask R-CNN network was constructed with significantly fewer parameters and computational costs (~5x fewer multiply-add operations and ~2x fewer parameters) than a ResNet-50 based Mask R-CNN network.
Facial Recognition Technology on ELcore Semantic Processors for Smart Cameras

This article introduces a solution to the problem of improving security in public places by analyzing the behaviour of people using smart cameras. The article describes the technologies for creating smart cameras for semantic image analysis based on the ELcore cores. The stages of semantic image analysis to detect and recognize faces are considered. The most resource-intensive algorithms are identified and implemented on DSP-cores of ELcore. The general path of image processing on DSP-cores of ELcore for face detection and recognition is no longer than 32 ms. This parameter meets the requirements for processing video frames in real-time mode and can be used in smart cameras for security systems.
Pseudo-Random Numbers and Congruential Methods

The pseudo numbers are the essential basis of the simulation. Usually, all randomness involved in the model is obtained from a random number generator that produces a succession of values that are supposed to be realizations of a sequence of independent random variables and identically distributed uniforms U (0, 1). To be more explicit about the use of pseudo numbers, we will analyze concepts such as mixed or linear congruence method, multiplicative congruence method, additive congruence method.
Removal of Historical Document Degradations using Conditional GANs

One of the most crucial problem in document analysis and OCR pipeline is document binarization. Many traditional algorithms over the past few decades like Sauvola, Niblack, Otsu etc,. were used for binarization which gave insufficient results for historical texts with degradations. Recently many attempts have been made to solve binarization using deep learning approaches like Autoencoders, FCNs. However, these models do not generalize well to real world historical document images qualitatively. In this paper, we propose a model based on conditional GAN, well known for its high-resolution image synthesis. Here, the proposed model is used for image manipulation task which can remove different degradations in historical documents like stains, bleed-through and non-uniform shadings. The performance of the proposed model outperforms recent state-of-the-art models for document image binarization. We support our claims by benchmarking the proposed model on publicly available PHIBC 2012, DIBCO (2009-2017) and Palm Leaf datasets. The main objective of this paper is to illuminate the advantages of generative modeling and adversarial training for document image binarization in supervised setting which shows good generalization capabilities on different inter/intra class domain document images.
Low-Power Current-Mode Interval Type-2 Fuzzy Inference Engine Circuit

A novel strategy for implementation of an analog current-mode interval type-2 fuzzy inference engine circuit with low-power consumption is presented. Previous current-mode architectures rely on minimum/maximum circuits, using current mirrors to perform bounded-difference operations or winner-take-all circuits to mirror the appropriate current among the inputs. It means that currents representing the membership degrees of input fuzzy sets may need to be copied several times to obtain the firing levels of each rule. To avoid this drawback, the proposed method uses high-gain differential amplifiers, operating with low bias currents, as comparators, creating digital signals that indicate the largest/smallest current. It allows digital gates to determine directly which of the currents from the membership degree generator circuits represent the correct solution to the overall inference process. The methodology for developing the system and obtaining the logical expressions is presented. Simulations show that a traditional implementation using min/max circuits presented in the literature consumes 4.4 times more power than the proposed architecture. The circuit was prototyped in TSMC 0.18-μm technology and its functionality demonstrated by the correct generation of the consequents firing levels. The measured power consumption was 413.42 μW , including the power from the fuzzifier circuits, with a power supply of 1.2 V.
Improvement of UAV Track Trajectory Algorithm Based on Ant Colony Algorithm

The track planning of the drone is to find the optimal flight route from the starting point to the target point to meet the maneuvering performance of the UAV and the requirements of the operational environment under certain constraints. In this paper, the corresponding threat constraint model is established for the constraints of the UAV flight path planning problem, including radar, missile, anti-aircraft and other threat models. The basic idea is to increase the target node pheromone guiding factor in the state transition of the basic ant colony algorithm, thereby reducing the blindness of the ant search and making the search toward the target node. At the same time, introducing the re-excitation learning mechanism, reasonable on the path The pheromone is renewed. At the end of each iteration, the individual behavior of ants is evaluated, and then the evaluation is fed back to the ant colony to promote the behavior of high quality ants and to punish the behavior of inferior ants, so as to realize the self-learning of ant colony on their own search behavior. After these improvements, the convergence speed and convergence ability of the algorithm are significantly improved.
Decay-Function-Free Time-Aware Attention to Context and Speaker Indicator for Spoken Language Understanding

Jonggu Kim, Jong-Hyeok Lee. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Model of the Data Analysis Process to Determine the Person’s Professional Inclinations and Abilities

In this paper, the authors analyzed the data of vocational guidance tests. Accordingly, a model of data analysis was proposed to determine the person’s professional inclinations and abilities, in particular, a methodological approach is described that uses intelligent data analysis to find hidden dependencies in the results of vocational guidance testing (the professional orientation questionnaire by Holland, the questionnaire about professional inclinations by L. Yovashi, the questionnaire about the profession type by E. Klimov, the questionnaire of interests by A. Holomshtok), on the basis of which decisions can be made on the choice of profession. The model of the data analysis process for identifying the professional inclinations and abilities of a person was implemented in the software and algorithmic complex of information and technological support of the profession choice, which allows to optimize the process for choosing the professional direction of a person.
Under the Hood of Membership Inference Attacks on Aggregate Location Time-Series.

Aggregate location statistics are used in a number of mobility analytics to express how many people are in a certain location at a given time (but not who). However, prior work has shown that an adversary with some prior knowledge of a victim's mobility patterns can mount membership inference attacks to determine whether or not that user contributed to the aggregates. In this paper, we set to understand why such inferences are successful and what can be done to mitigate them. We conduct an in-depth feature analysis, finding that the volume of data contributed and the regularity and particularity of mobility patterns play a crucial role in the attack. We then use these insights to adapt defenses proposed in the location privacy literature to the aggregate setting, and evaluate their privacy-utility trade-offs for common mobility analytics. We show that, while there is no silver bullet that enables arbitrary analysis, there are defenses that provide reasonable utility for particular tasks while reducing the extent of the inference.
Site-Specific and Coherent Manipulation of Individual Qubits in a 1D Optical Lattice with a 532-nm Site Separation

We demonstrate gate operations on a single qubit at a specific site without perturbing the coherence of an adjacent qubit in a 1D optical lattice when the site separation is only 532 nm. Three types of spin rotations are performed on the target qubit with fidelities between 0.88±0.05 and 0.99±0.01, whereas the superposition state of the adjacent one is preserved with fidelities between 0.93±0.04 and 0.97±0.04. The qubit is realized by a pair of Zeeman-sensitive ground hyperfine states of a ^{7}Li atom, and each site is identified by its resonance frequency in a magnetic field gradient of 1.6 G/cm. We achieve the site-specific resolving power in the frequency domain by using magic polarization for the lattice beam that allows a Fourier-limited transition linewidth as well as by highly stabilizing the lattice parameters and the ambient conditions. We also discuss a two-atom entanglement scheme using a blockade by cold collisional shifts in a 1D superlattice, for which a coherent manipulation of individual qubits is a prerequisite.
Fast Incremental Learning With Swarm Decision Table and Stochastic Feature Selection in an IoT Extreme Automation Environment

Fog computing and a collection of incremental learning algorithms are introduced to overcome the challenge of enabling smart home applications. Specifically, there are two cases of Internet-of-things (IoT) sensing extreme automation (EA) scenarios, i.e., one being autorecognizing the gas type from the air composition and the other predicting the next unusual energy consumption, are looked into. Our experimentation over a collection of the popular incremental learning algorithm, such as HT and our proposed novel SDT, as well as heuristics and metaheuristics feature selection methods, are presented. The simulation results show that compared with Hoeffding tree (HT), SDT is more stable in performance. HT is fast because the model representation is in a tree format; tree traversal is fast by design. SDT learns and modifies the modeling rules in the style of k-nearest neighbor, checking on many data points within the decision table in each update. The paper demonstrates a simulation experiment combining a feature selection method coupled with a swarm intelligence and decision Table classifier as SDT to find the most suitable SDT models in the Fog computing environment. According to the results, we find three appropriate swarm feature selection algorithms: BestFirst, Elephant, and Harmony. In addition, each SDT model demonstrates its unique ability in terms of recovery ability, fluctuation e degree, and successive time.
Preservation Mechanism of Network Electronic Records Based on Broadcast-Storage Network in Urban Construction

With the wide application of information technology in urban infrastructure, urban construction has entered the stage of smart city, forming a large number of network electronic records. These electronic records play a vital ... | Find, read and cite all the research you need on Tech Science Press