Miracle-dz/UniFAR-FTUs huggingface dataset:
NOMAD : Non-locking , stOchastic Multi-machine algorithm for Asynchronous and Decentralized matrix completion

Hyokyun Yun Purdue University West Lafayette, IN United States yun3@purdue.edu Hsiang-Fu Yu University of Texas, Austin Austin, TX United States rofuyu@cs.utexas. edu Cho-Jui Hsieh University of Texas, Austin Austin, TX United States cjhsieh@cs.utexas. edu S V N Vishwanathan Purdue University West Lafayette, IN United States vishy@stat.purdue.edu Inderjit Dhillon University of Texas, Austin Austin, TX United States inderjit@cs.utexas. edu
Private Information Retrieval is Graph Based Replication Systems

Replication is prevalent in both theory and practice as a means for obtaining robustness in distributed storage systems. A system in which every data entry is stored on two separate servers gives rise to a graph structure in a natural way, and the combinatorial properties of this graph shed light on the possible features of the system. One possible feature of interest, that has recently gained renewed attention, is private information retrieval (PIR). A PIR protocol enables a user to obtain a data entry from a storage system without revealing the identity of the requested entry to sets of colluding servers. In this paper we suggest a simple PIR protocol for graph based replication systems, which guarantees perfect secrecy against any set of colluding servers that does not induce a cycle. Furthermore, it is shown that the secrecy deteriorates gracefully with the number of cycles in the colluding set, and that the upload complexity can be reduced for graphs of certain specialized structure.
Guessing Under Source Uncertainty

This paper considers the problem of guessing the realization of a finite alphabet source when some side information is provided. The only knowledge the guesser has about the source and the correlated side information is that the joint source is one among a family. A notion of redundancy is first defined and a new divergence quantity that measures this redundancy is identified. This divergence quantity shares the Pythagorean property with the Kullback-Leibler divergence. Good guessing strategies that minimize the supremum redundancy (over the family) are then identified. The min-sup value measures the richness of the uncertainty set. The min-sup redundancies for two examples - the families of discrete memoryless sources and finite-state arbitrarily varying sources - are then determined
Facial Emotions Recognition using Convolutional Neural Net

Facial expressions vary from person to person, and the brightness, contrast, and resolution of every random image are different. This is why recognizing facial expressions is very difficult. This article proposes an efficient system for facial emotion recognition for the seven basic human emotions (angry, disgust, fear, happy, sad, surprise, and neutral), using a convolution neural network (CNN), which predicts and assigns probabilities to each emotion. Since deep learning models learn from data, thus, our proposed system processes each image with various pre-processing steps for better prediction. Every image was first passed through the face detection algorithm to include in the training dataset. As CNN requires a large amount of data, we duplicated our data using various filters on each image. Pre-processed images of size 80*100 are passed as input to the first layer of CNN. Three convolutional layers were used, followed by a pooling layer and three dense layers. The dropout rate for the dense layer was 20%. The model was trained by combining two publicly available datasets, JAFFE and KDEF. 90% of the data was used for training, while 10% was used for testing. We achieved maximum accuracy of 78.1 % using the combined dataset. Moreover, we designed an application of the proposed system with a graphical user interface that classifies emotions in real-time.
Feature Selection Model based on EEG signals to Assess the Cognitive Workload in Drivers

In recent years, research has focused on generating mechanisms to assess the levels of subjects' cognitive workload when performing various activities that demand high concentration levels, such as driving a vehicle. These mechanisms have implemented several tools to analyze cognitive workload where the electroencephalographic (EEG) signals are the most used due to its high precision. However, one of the main challenges in the EEG signals implementing is finding the appropriate information to identify cognitive states. Here we show a new feature selection model for pattern recognition using information from EEG signals based on machine learning techniques called GALoRIS. GALoRIS combines Genetic Algorithms and Logistic Regression to create a new fitness function that identifies and selects the critical EEG features that contribute to recognizing high and low cognitive workload and structures a new dataset capable of optimizing the model's predictive process. We found that GALoRIS identifies data related to high and low cognitive workload of subjects while driving a vehicle using information extracted from multiple EEG signals, reducing the original dataset by more than 50%, maximizing the model's predictive capacity-achieving a precision rate greater than 90%.
Parametric Synthesis of Computational Circuits for Complex Quantum Algorithms

At the moment, quantum circuits are created mainly by manually placing logic elements on lines that symbolize quantum bits. The purpose of creating Quantum Circuit Synthesizer"Naginata"was due to the fact that even with a slight increase in the number of operations in a quantum algorithm, leads to the significant increase in size of the corresponding quantum circuit. This causes serious difficulties both in creating and debugging these quantum circuits. The purpose of our quantum synthesizer is enabling users an opportunity to implement quantum algorithms using higher-level commands. This is achieved by creating generic blocks for frequently used operations such as: the adder, multiplier, digital comparator (comparison operator), etc. Thus, the user could implement a quantum algorithm by using these generic blocks, and the quantum synthesizer would create a suitable circuit for this algorithm, in a format that is supported by the chosen quantum computation environment. This approach greatly simplifies the processes of development and debugging a quantum algorithm. The proposed approach for implementing quantum algorithms has a potential application in the field of machine learning, in this regard, we provided an example of creating a circuit for training a simple neural network. Neural networks have a significant impact on the technological development of the transport and road complex, and there is a potential for improving the reliability and efficiency of their learning process by utilizing quantum computation, through the introduction of quantum computing.
Standardisation of work: co-constructed practice

There is strong pressure to achieve greater uniformity, standardisation and application of best practices in the service professions, a sector which is growing in presence and importance. At the same time, there is a conflicting demand for the delivery of high-quality (or highly priced or 'knowledge intensive') specialised or localised services. Our paper analyses information systems’ embedded efforts of standardising service work through an in-depth interpretative study of an ongoing standardisation initiative within the field of nursing. Nursing provides a graphic illustration of the dilemmas involved in the standardisation of service work. In nursing, standardisation is commonly a feature of projects to improve both efficiency and quality in health care. In contrast to the dominant conception of standardisation as a largely top-down, imposed process, we offer a view of standardisation as incomplete, co-constructed with users and with significant unintended consequences. The paper contributes by i) developing a theoretical perspective for the standardisation of information-system-embedded service work and ii) operational and practical implications for system design and health care management.
Phase Transitions on Fixed Connected Graphs and Random Graphs in the Presence of Noise

In this paper, we study phase transition behavior emerging from the interactions between multiple agents in the presence of noise. We propose a simple discrete-time model in which a group of non-mobile agents form either a fixed connected graph or a random graph process, and each agent, taking bipolar value either +1 or -1, updates its value according to its previous value and the noisy measurements of the connected agents' values. We present proofs for the occurrence of the following phase transition behavior: At a noise level higher than some threshold, the system generates symmetric behavior; whereas at a noise level lower than the threshold, the system exhibits spontaneous symmetry breaking. We also verify the phase transition using simulations. This result may be found useful in the study of the collective behavior of complex systems under communication constraints.
Some restrictions on weight enumerators of singly even self-dual codes

In this correspondence, we give some restrictions on weight enumerators of singly even self-dual [n,n/2,d] codes whose shadows have minimum weight d/2. As a consequence, we determine the weight enumerators for which there is an extremal singly even self-dual [40,20,8] code and an optimal singly even self-dual [50,25,10] code
Sorting Networks

It is known that comparison-based sorting algorithms running on a singleprocessor computer have a lower bound on time complexity of ) log ( n n Ω [3]. This lower bound is due to the algorithm being able to perform only one comparison at a time. Specialized, non-comparison sorting algorithms, such as counting sort and radix sort, can achieve better performance, but are suitable only when the input meets certain requirements necessary in order for those algorithms to work properly [3].
Using permutations to detect, quantify and correct for confounding in machine learning predictions

Abstract: Clinical machine learning applications are often plagued with confounders that are clinically irrelevant, but can still artificially boost the predictive performance of the algorithms. Confounding is especially problematic in mobile health studies run “in the wild”, where it is challenging to balance the demographic characteristics of participants that self select to enter the study. An effective approach to remove the influence of confounders is to match samples in order to improve the balance in the data. The caveat is that we end-up with a smaller number of participants to train and evaluate the machine learning algorithm. Alternative confounding adjustment methods that make more efficient use of the data (e.g., inverse probability weighting) usually rely on modeling assumptions, and it is unclear how robust these methods are to violations of these assumptions. Here, rather than proposing a new approach to prevent/reduce the learning of confounding signals by a machine learning algorithm, we develop novel statistical tools to detect, quantify and correct for the influence of observed confounders. Our tools are based on restricted and standard permutation approaches and can be used to evaluate how well a confounding adjustment method is actually working. We use restricted permutations to test if an algorithm has learned disease signal in the presence of confounding signal, and to develop a novel statistical test to detect confounding learning per se. Furthermore, we prove that restricted permutations provide an alternative method to compute partial correlations, and use this result as a motivation to develop a novel approach to estimate the corrected predictive performance of a learner. We evaluate the statistical properties of our methods in simulation studies.
FlexiSec : A Configurable Link Layer Security Architecture for Wireless Sensor Networks

Ensuring communications security in Wireless Sensor Networks (WSNs) indeed is critical; due to the criticality of the resources in the sensor nodes as well as due to their ubiquitous and pervasive deployment, with varying attributes and degrees of security required. The proliferation of the next generation sensor nodes, has not solved this problem, because of the greater emphasis on low-cost deployment. In addition, the WSNs use data-centric multi-hop communication that in turn, necessitates the security support to be devised at the link layer (increasing the cost of security related operations), instead of being at the application layer, as in general networks. Therefore, an energy-efficient link layer security framework is necessitated. There do exists a number of link layer security architectures that offer some combinations of the security attributes desired by different WSN applications. However, as we show in this paper, none of them is responsive to the actual security demands of the applications. Therefore, we believe that there is a need for investigating the feasibility of a configurable software-based link layer security architecture wherein an application can be compiled flexibly, with respect to its actual security demands. In this paper, we analyze, propose and experiment with the basic design of such configurable link layer security architecture for WSNs. We also experimentally evaluate various aspects related to our scheme viz. configurable block ciphers, configurable block cipher modes of operations, configurable MAC sizes and configurable replay protection. The architecture proposed is aimed to offer the optimal level of security at the minimal overhead, thus saving the precious resources in the WSNs.
On the Reproducibility of Experiments of Indexing Repetitive Document

13 This work introduces a companion reproducible paper with the aim of allowing the exact replication of the 14 methods, experiments, and results discussed in a previous work [5]. In that parent paper, we proposed 15 many and varied techniques for compressing indexes which exploit that highly repetitive collections are 16 formed mostly of documents that are near-copies of others. More concretely, we describe a replication 17 framework, called uiHRDC (universal indexes for Highly Repetitive Document Collections), that allows our 18 original experimental setup to be easily replicated using various document collections. The corresponding 19 experimentation is carefully explained, providing precise details about the parameters that can be tuned for 20 each indexing solution. Finally, note that we also provide uiHRDC as reproducibility package. 21
State dependent attempt rate modeling of single cell IEEE 802.11 WLANs with homogeneous nodes and poisson arrivals

Analytical models of IEEE 802.11-based WLANs are invariably based on approximations, such as the well-known mean-field approximations proposed by Bianchi for saturated nodes. In this paper, we provide a new approach for modeling the situation when the nodes are not saturated. We study a State Dependent Attempt Rate (SDAR) approximation to model M queues (one queue per node) served by the CSMA/CA protocol as standardized in the IEEE 802.11 DCF. The approximation is that, when n of the M queues are non-empty, the attempt probability of the n non-empty nodes is given by the long-term attempt probability of n saturated nodes as provided by Bianchi's model. This yields a coupled queue system. When packets arrive to the M queues according to independent Poisson processes, we provide an exact model for the coupled queue system with SDAR service. The main contribution of this paper is to provide an analysis of the coupled queue process by studying a lower dimensional process and by introducing a certain conditional independence approximation. We show that the numerical results obtained from our finite buffer analysis are in excellent agreement with the corresponding results obtained from ns-2 simulations. We replace the CSMA/CA protocol as implemented in the ns-2 simulator with the SDAR service model to show that the SDAR approximation provides an accurate model for the CSMA/CA protocol. We also report the simulation speed-ups thus obtained by our model-based simulation.
Keyphrase Extraction using Linguistic knowledge and Machine Learning Techniques

In this paper, a supervised learning technique for extracting keyphrases of Arabic documents is presented. The extractor is supplied with linguistic knowledge to enhance its efficiency instead of relying only on statistical information such as term frequency and distance. During analysis, an annotated Arabic corpus is used to extract the required lexical features of the document words. The knowledge also includes syntactic rules based on part of speech tags and allowed word sequences to extract the candidate keyphrases. In this work, the abstract form of Arabic words is used instead of its stem form to represent the candidate terms. The Abstract form hides most of the inflections found in Arabic words. The paper introduces new features of keyphrases based on linguistic knowledge, to capture titles and subtitles of a document. A simple ANOVA test is used to evaluate the validity of selected features. Then, the learning model is built using the LDA Linear Discriminant Analysis – and training documents. Although, the presented system is trained using documents in the IT domain, experiments carried out show that it has a significantly better performance than the existing Arabic extractor systems, where precision and recall values reach double their corresponding values in the other systems especially for lengthy and non-scientific articles.
Dalitz plot analysis of the D +-> pi (-) pi ( + ) pi ( + ) decay

G. Bonvicini, D. Cinabro, M. Dubrovin, A. Lincoln, D. M. Asner, K. W. Edwards, P. Naik, R. A. Briere, T. Ferguson, G. Tatishvili, H. Vogel, M. E. Watkins, J. L. Rosner, N. E. Adam, J. P. Alexander, D. G. Cassel, J. E. Duboscq, R. Ehrlich, L. Fields, R. S. Galik, L. Gibbons, R. Gray, S. W. Gray, D. L. Hartill, B. K. Heltsley, D. Hertz, C. D. Jones, J. Kandaswamy, D. L. Kreinick, V. E. Kuznetsov, H. Mahlke-Kruger, D. Mohapatra, P. U. E. Onyisi, J. R. Patterson, D. Peterson, J. Pivarski, D. Riley, A. Ryd, A. J. Sadoff, H. Schwarthoff, X. Shi, S. Stroiney, W. M. Sun, T. Wilksen, S. B. Athar, R. Patel, J. Yelton, P. Rubin, C. Cawlfield, B. I. Eisenstein, I. Karliner, D. Kim, N. Lowrey, M. Selen, E. J. White, J. Wiss, R. E. Mitchell, M. R. Shepherd, D. Besson, T. K. Pedlar, D. Cronin-Hennessy, K. Y. Gao, J. Hietala, Y. Kubota, T. Klein, B. W. Lang, R. Poling, A. W. Scott, A. Smith, P. Zweber, S. Dobbs, Z. Metreveli, K. K. Seth, A. Tomaradze, J. Ernst, K. M. Ecklund, H. Severini, W. Love, V. Savinov, O. Aquines, A. Lopez, S. Mehrabyan, H. Mendez, J. Ramirez, G. S. Huang, D. H. Miller, V. Pavlunin, B. Sanghi, I. P. J. Shipsey, B. Xin, G. S. Adams, M. Anderson, J. P. Cummings, I. Danko, D. Hu, B. Moziak, J. Napolitano, Q. He, J. Insler, H. Muramatsu, C. S. Park, E. H. Thorndike, F. Yang, M. Artuso, S. Blusk, J. Butt, N. Horwitz, S. Khalil, J. Li, N. Menaa, R. Mountain, S. Nisar, K. Randrianarivony, R. Sia, T. Skwarnicki, S. Stone, and J. C. Wang
Nature Inspired Cooperative Strategies for Optimization (NICSO 2007) (Studies in Computational Intelligence) (Studies in Computational Intelligence) XXXX

Biological and natural processes have been a continuous source of inspiration for the sciences and engineering. For instance, the work of Wiener in cybernetics was influenced by feedback control processes observable in biological systems; McCulloch and Pitts description of the artificial neuron was instigated by biological observations of neural mechanisms; the idea of survival of the fittest inspired the field of evolutionary algorithms and similarly, artificial immune systems, ant colony optimisation, automated self-assembling programming, membrane computing, etc. also have their roots in natural phenomena. The second International Workshop on Nature Inspired Cooperative Strategies for Optimization (NICSO), was held in Acireale, Italy, during November 8-10, 2007. The aim for NICSO 2007 was to provide a forum were the latest ideas and state of the art research related to cooperative strategies for problem solving arising from Nature could be discussed. The contributions collected in this book were stric tly peer reviewed by at least three members of the international programme committee, to whom we are indebted for their support and assistance. The topics covered by the contributions include several well established nature inspired techniques like Genetic Algorithms, Ant Colonies, Artificial Immune Systems, Evolutionary Robotics, Evolvable Systems, Membrane Computing, Quantum Computing, Software Self Assembly, Swarm Intelligence, etc.
Improved LP-based Approximation Algorithms for Facility Location with Hard Capacities

The Capacitated Facility Location (CFL), a long-standing classic problem with intriguing approximability and literature dated back to the 90s, is considered. Following the open question posted in [Williamson and Shmoys, 2011] and the notable work due to [An et al., FOCS~2014], we present an LP-based approximation algorithm with a guarantee of $(10+\sqrt{67})/2 \approx 9.0927$, a significant improvement upon the previous LP-based ratio of $288$ due to An et al. in 2014. Our contribution for this part is a simple and elegant rounding algorithm that brings clear insights for the MFN relaxation and the CFL problem. For CFL with cardinality facility cost (CFL-CFC), we present an LP-based $4$-approximation algorithm, which improves upon the decades-old ratio of 5 due to Levi et al. that ages up since 2004. Prior to our work, it was not clear whether or not LP-based methods can be used to provide a guarantee better than 5 for the CFL problem, even for restricted versions of this problem, for which natural LPs are already known to have small integrality gaps. Our rounding algorithm provides the first affirmative answer on the case with cadinality facility cost.
Ju l 2 01 6 Spectral Sparse Representation for Clustering : Evolved from PCA , K-means , Laplacian Eigenmap , and Ratio Cut

Dimensionality reduction, cluster analysis, and sparse representation are among the cornerstones of machine learning. However, they seem unrelated to each other and are often applied independently in practice. In this paper, we discovered that the spectral graph theory underlies a series of these elementary methods and unifies them into a complete framework. The methods range from PCA, K-means, Laplacian eigenmap (LE), ratio cut (Rcut), and a new sparse representation method uncovered by us, called spectral sparse representation (SSR). Further, extended relations to conventional over-complete sparse representations, e.g., MOD, KSVD, manifold learning, e.g., kernel PCA, MDS, Isomap, LLE, and subspace clustering, e.g., SSC, LRR are incorporated. We will show that, under an ideal condition from the spectral graph theory, PCA, K-means, LE, and Rcut are unified together, and when the condition is relaxed, the unification evolves to SSR, which lies in the intermediate between PCA/LE and K-mean/Rcut, and combines merits of both sides: the sparse codes of it reduce dimensionality of data meanwhile revealing cluster structure. Plenty of properties and clear interpretations exhibit, and due to its inherent relation to cluster analysis, the codes of SSR can be directly used for clustering. The linear version of SSR is under-complete, complementing the conventional over-complete sparse representations. An efficient algorithm, NSCrt, is developed to solve the sparse codes of SSR. By virtue of its good performance, the application of SSR to clustering, called Scut, reaches state-of-the-art performance in spectral clustering family. Scut outperforms Kmeans based spectral clustering methods, which depend on initialization and easily get trapped in local minima. The one-shot solution obtained by Scut is comparable to the optimal result of K-means that are run many times. Experiments on data sets of diverse nature demonstrate the properties and strengths of SSR, NSCrt, and Scut.