Miracle-dz/UniFAR-FTUs huggingface dataset:
Coordinated Cell Threshold user pairing criteria for uplink CoMP MU-MIMO

Multi-User Multiple-Input Multiple-Output (MU-MIMO) technique is introduced into 3rd Generation Partnership Project (3GPP) Long-Term Evolution (LTE) to enhance system throughput. Besides, Coordinated Multi-Point (CoMP) transmission/reception is considered by LTE-Advanced (LTE-A) standard to improve the cell-edge throughput. However, some new problems appear with the promotion of MU-MIMO technique in the CoMP scenario. The existing user pairing algorithms proposed for LTE do not take the state of multi-cells into account. It is necessary to enhance user pairing algorithm to fit the CoMP MU-MIMO scenario. Consequently this paper presents a Coordinated Cell Threshold Criteria (CCTC) algorithm for uplink CoMP MU-MIMO scenario. The CCTC algorithm considers the channel state information of entire CoMP cells and sets the threshold to avoid user pairing with bad channel orthogonal from paired users to coordinate cells. Simulation results show that CCTC can significantly enhance system throughput while it is utilized in uplink CoMP user pairing.
Comparing Ethernet & Soft RoCE over 1 Gigabit Ethernet

In recent years, we have witnessed a growing interest in optimizing the high performance computing (HPC) solutions using advanced CPU and Interconnect technologies. recent trends in HPC systems have shown that future increases in performance can only be achieved through increases in system scale using a larger number of components, such as multi-cores and faster interconnects. RDMA is an emerging technology, which is used for reducing system load & improves the performance. In this paper, we evaluate the heterogeneous Linux cluster, having multi nodes with fast interconnects i.e. gigabit Ethernet & Soft RoCE. This paper presents the heterogeneous Linux cluster configuration & evaluates its performance using MVAPICH platform & OSU Benchmarks. Our result shows that Soft RoCE is performing better or equal to Ethernet in various performance metrics like bandwidth, latency & throughput.
Negotiation and Management Strategies of Street Vendors in Developing Countries

The existing literature on the relationship between regulators and street vendors remain fragmented and limited to specific countries and contexts. This article presents a narrative review of the existing literature on the relationship between regulators and street vendors, and through that creates a unified picture of an otherwise fragmented literature and knowledge base. The concepts of negotiation, power, social network, and perception are used to analyze the various strategies street vendors have used in gaining access to urban public spaces in different parts of the developing world. It is revealed that even though city regulators have access to formal power, street vendors possess a variety of negotiating strategies that gives them access to both formal and informal power.
Optimization of Traffic Flow in a Road Network

The maximum entropy model was applied to obtain the optimal traffic assignment in a road network when incomplete information was available. Comparison with the user equilibrium method shows that unbiased guesses of the traffic assignment can be obtained from the maximum entropy model by adding logic constraints. The maximum entropy model can perform as a faster way than the traditional user equilibrium method.
1 Examining Microarray Experiments

Within the past decade, advances in biological technology have allowed researchers to penetrate the foundations of cellular biology, culminating with the publication of the draft sequence of the human genome. Researchers from both private and public institutions have succeeded in identifying thousands of coding regions of genetic sequences commonly referred to as genes. The genes of yeast and other simple organisms have been completely identified and an estimated two-thirds of the human genome has been identified. Despite this widely heralded effort, sequencing the genome and identifying its coding regions are only the first steps in understanding the wildly turbid and complicated functions of genes at a cellular level. Furthermore, gene array technologies, developed over the past six years, have begun to produce valuable information for understanding the functions of genes at a cellular level. The arrays simultaneously measure cellular concentrations of thousands of messenger RNAs or mRNAs since transcription from DNA to mRNA is the initial step in the creation of a protein from a gene. Measurement of mRNA provides a proxy for the activity level of a particular gene and is referred to as an expression level or gene expression. Prior to the advent of array technologies, researchers could only measure expression levels for individual genes through techniques such as Northern blots. Array technologies allow investigation of expression levels for all known genes in the cell and permit a more thorough characterization of the transcriptional state of the cell. So far array technologies have been used to characterize functions of newly found genes, to understand the machinery of cell maintenance and regulation and to classify certain types of cancer. Insofar as this new technology has become useful, it has required a good deal of statistical and computational methods aimed at understanding how the technology both succeeds and fails at representing the state of the cell accurately. There are two differnet array technologies currently used. Both measure the propensity of mRNA or cDNA to hybridize with single strands of complementary DNA sequestered and immobilized on a solid substrate. The technologies differ in the length of the sequesterd DNA strands and the number of spots used to detect the expression level of each gene. In this paper the focus is on the GeneChip, developed by Affymetrix Inc. It would not be an efficient use of space explaining how the GeneChip actually measures the expression levels of the genes. However, it is …
An Empirical Comparison of Techniques for Selecting and Combining Local Patterns into a Global Model

Local pattern discovery, pattern set discovery and global modeling build together as consecutive steps a specific case of global pattern discovery. As each of these three steps have gained an increased attention in recent years, a great variety of techniques for each step have been proposed. Though so far there has been no systematic comparison of the possible choices. In this paper, we will consider a special representative of local patterns, namely class association rules, and evaluate several options for pattern set discovery and for global modeling for this type of classification rules.
Understanding Group Interaction in Blogosphere : A Case Study

Social interactions are an essential ingredient of our lives. People convene groups and share views, opinions, thoughts, and perspectives. Similar tendencies for social behavior are observed in the World Wide Web. This inspires us to study and understand social interactions evolving in online communities especially in the blogosphere. In this paper, we study and analyze various interaction patterns in community blogs. This would lead to better understanding of the sociocultural ties between these communities to foster collaboration, better personalization, predictive modeling, and enable tracking and monitoring. Tapping community interactions via link analysis has its limitations due to exponentially large search space. We propose a model, circumventing the challenges with link analysis based approach, to observe interaction within community blogs via an observed event and community reaction to that by studying the opinion and sentiments of the members towards that event. We present a case study on ethnic community blogs exploiting the proposed model and report our findings and observations. During our study we encountered several challenges with the proposed model. We discuss these issues and present future directions to make the model more robust.
Fastbit-radix sort: Optimized version of radix sort

Sorting is applied in daily life from ordering simple lists to real world applications. Sorting presents the data in an ordered fashion which helps in analysis or allows computing data faster. Radix sort is a non-comparative integer sorting algorithm that sorts in a linear time complexity. Radix sort performs modulus operation on each data to extract the digits at a specific position and maintain count table, based on this table it sorts the data. It is observed that computing at a bit level is faster than performing arithmetic operation to the values. It is essential to optimize the computation time of this sorting algorithm as it has a linear time complexity. This paper aims to optimize radix sort by performing bitwise operations in contrast to the normal integer division.
A Testbed-based Analysis of the Incorrect Lookup Routing Attack on the Pastry DHT

Distributed Hashtables (DHT), with Pastry [2] as a prominent representative, are one of the most recent peer-to-peer (P2P) architectures. DHTs scale well to the network size due to a small routing table and a hierarchical routing scheme. In Pastry, the number of routing entries scales logarithmically with respect to the number of participating nodes, because each node maintains route entries to only a few nodes per hierarchical (sub) domain. Therefore, routing requests have to be be forwarded by intermediate nodes to the final destination. In order to route successfully, each node has to behave benignly according to the protocol specifications. The Incorrect Lookup Routing attack [4] misuses this routing scheme to launch a denial of service attack on the P2P network. While this kind of routing attack was extensively analyzed theoretically and by simulations, to the best of our knowledge, testbed based evaluations have not been performed to date. Such an evaluation may reveal unnoticed influences or (at least) verifies the impact of malicious nodes on a P2P network. Therefore, a testbed evaluation of the Incorrect Lookup Routing attack is presented in this paper.
A simple mathematical model for assessment of anti-toxin antibodies

The toxins associated with infectious diseases are potential targets for inhibitors which have the potential for prophylactic or therapeutic use. Many antibodies have been generated for this purpose, and the objective of this study was to develop a simple mathematical model that may be used to evaluate the potential protective effect of antibodies. This model was used to evaluate the contributions of antibody affinity and concentration to reducing antibody-receptor complex formation and internalization. The model also enables prediction of the antibody kinetic constants and concentration required to provide a specified degree of protection. We hope that this model, once validated experimentally, will be a useful tool for in vitro selection of potentially protective antibodies for progression to in vivo evaluation.
Analysis of Security Techniques Applied in Database Outsourcing

Database Outsourcing is a nascent data management paradigm in which the data owner stores the confidential data at the third party service provider’s site. The service provider is responsible for managing and administering the database and allows the data owner and clients to create, update, delete and access the database. There are chances of hampering the security of the data due to untrustworthiness of service provider. So, to secure the data which is outsourced to third party is a great challenge. The major requirements for achieving security in outsourced databases are confidentiality, privacy, integrity, freshness in case of dynamic updates, access control in multi-user environment, availability and query authentication and assurance. To achieve these requirements various security mechanisms like encryption based approach, fake tuple based approach, customized secret sharing approach, authenticated data structure approach, aggregate-able signature schemes have been put forth till date. In this paper, various mechanisms for implementing security in outsourced databases are analyzed along with their usefulness in a great detail. Keywords— Access Control, Confidentiality, Freshness, Integrity, Outsourced Databases, Query Authentication,
Two modified differential evolution algorithms and their applications to engineering design problems

Differential Evolution (DE) is a stochastic, population based search technique, which can be classified as an Evolutionary Algorithm (EA) using the concepts of selection crossover and reproduction to guide the search. It has emerged as a powerful tool for solving optimization problems in the past few years. However, the convergence rate of DE still does not meet all the requirements, and attempts to speed up differential evolution are considered necessary. In order to improve the performance of DE, We propose two versions of (DE) called Differential Evolution with Parent Centric Crossover (DEPCX) and Differential Evolution with probabilistic Parent Centric Crossover (Pro. DEPCX). The proposed algorithms are validated on a test bed of seven real life, nonlinear engineering design problems and numerical results are compared with original differential evolution (DE). Empirical analysis of the results indicates that the proposed schemes enhance the performance of basic DE in terms of convergence rate without compromising with the quality of solution.
Persistence of Labor Precariousness in Mexico: a PANIC Approach

Mexican labor conditions have deteriorated as a consequence of the advance of precariousness exhibited in the last years; the previous situation demands the development of an effective policy, seeking the strengthening of labor market. This research analyses specifically income precariousness throughout the Poverty Labor Trend Index (ITLP) within the application of the Panel Analysis of Non-Stationarity in Idiosyncratic and Common Components Method (PANIC) among the thirty-two states of Mexico. The results suggest the existence of a nonstationary behavior among the common components as well as for nine states of the country.
Win, Lose, or Get Out the Way - Eliminating Unnecessary Evaluation in Game Search

In this paper we present our approach of improving the traditional alpha-beta search process for strategic board games by modifying the method in two ways: 1) forgoing the evaluation of leaf nodes that are not terminal states and 2) employing a utility table that stores the utility for subsets of board configurations. In this paper we concentrate our efforts on the game of Connect Four. Our results have shown significant speed-up, as well as a framework that relaxes common agent assumptions in game search. In addition, it allows game designers to easily modify the agent’s strategy by changing the goal from dominance to interaction.
Iterative Approximate Linear Programming Decoding of LDPC Codes With Linear Complexity

The problem of low complexity linear programming (LP) decoding of low-density parity-check (LDPC) codes is considered. An iterative algorithm, similar to min-sum and belief propagation, for efficient approximate solution of this problem was proposed by Vontobel and Koetter. In this paper, the convergence rate and computational complexity of this algorithm are studied using a scheduling scheme that we propose. In particular, we are interested in obtaining a feasible vector in the LP decoding problem that is close to optimal in the following sense. The distance, normalized by the block length, between the minimum and the objective function value of this approximate solution can be made arbitrarily small. It is shown that such a feasible vector can be obtained with a computational complexity which scales linearly with the block length. Combined with previous results that have shown that the LP decoder can correct some fixed fraction of errors we conclude that this error correction can be achieved with linear computational complexity. This is achieved by first applying the iterative LP decoder that decodes the correct transmitted codeword up to an arbitrarily small fraction of erroneous bits, and then correcting the remaining errors using some standard method. These conclusions are also extended to generalized LDPC codes.
A Dynamic Model of Expropriation

Building on Schnitzer (1998), this paper develops a model of strategic interaction between a foreign direct investor, a host country, and the investor’s country. By including the investor country we are able to consider the impact of political retaliation on the likelihood of expropriation. The model confirms that expropriation is more likely for lower technology sectors and site-specific assets. It also predicts that more powerful host countries are more likely to expropriate, while economic integration between the investor and host country has no effect on expropriation. The model contradicts past arguments that interinvestor cooperation reduces the likelihood of expropriation, indicating a positive relationship instead. Author
A tapestry of identity-based encryption: practical frameworks compared

This paper surveys the practical benefits and drawbacks of several identity-based encryption schemes based on bilinear pairings. After providing some background on identity-based cryptography, we classify the known constructions into a handful of general approaches. We then describe efficient and fully secure IBE and IBKEM instantiations of each approach, with reducibility to practice as the main design parameter. Finally, we catalogue the strengths and weaknesses of each construction according to a few theoretical and many applied comparison criteria.
Ensemble learning with kernel mapping

Kernel learning is an important learning framework in machine learning, whose main idea is a mapping from input space to feature space induced by kernel function which yields a linear separation problem in the feature space. However, the generalization ability of kernel learning, which may lead to over-fitting of training data, has not been formally taken into consideration in previous literatures. We propose to tackle this problem by adopting ensemble learning in feature space. By bootstrapping training data set, several slightly different sets are obtained, with which we build up several slightly different kernels. The generated kernels are plugged into decision tree based learners to conduct similarity based learning and finally we combine all learners with a majority voting strategy. The proposed algorithm is tested in the famous UCI data repository with comparison to some previous baseline algorithms to show its effectiveness.
A Reexamination of Stock Valuation and Inflation : The Implications of Analysts ’ Earnings Forecasts

This paper examines the effect of inflation on stock valuations and expected long-run returns. Ex ante estimates of expected long-run returns are constructed by incorporating analysts’ earnings forecasts into a variant of the Campbell-Sh iller dividend-price ratio model. The negative relation between equity valuations and expected inflation is found to be the result of two effects: a rise in expected inflation coincides with both (i) lower expected real earnings growth and (ii) higher required real returns. The earnings channel mostly reflects a negative relation between expected long-term earnings growth and expected inflation. The effect of expected inflation on required (long-run) real stock returns is also substantial. A one percentage point increase in expected inflation is estimated to raise required real stock returns about one percentage point, which on average would imply a 20 percent decline in stock prices. But the inflation factor in expected real stock returns is also in long-term Treasury yields; consequently, expected inflation has little effect on the long-run equity premium. J.E. L. Classifications: E44, G12
The Effect of Task-Load Transitions on Team Postural Dynamics

This study examined order effects of task-load (TL) transitions on team task performance and interpersonal postural coordination. Twenty-three dyads played a cooperative video game (Air Force Spaceship Simulator-AFSS) under two task-load order conditions (high-low-high; low-high-low). Performance was affected by task-load, with dyads performing more efficiently under low task-load. For interpersonal postural coordination, we found an interaction between task-load and order of presentation. Specifically, pairs that transitioned from high-to-low TL exhibited less stable coordination in the high TL condition than pairs transitioning from low-to-high, indicating that measures of postural coordination may provide insight into team dynamics that is not captured by assessing team performance alone. We also confirmed that the postural coordination we observed was indeed driven by inherent demands of the team task, and was additionally indicative of individual team coordination patterns. This latter finding is potentially indicative of individual differences in team strategy.
On phase perception in speech

In this paper we define perceptual phase capacity as the size of a codebook of phase spectra necessary to represent all possible phase spectra in a perceptually accurate manner. We determine the perceptual phase capacity for voiced speech. To this purpose, we use an auditory model which indicates if phase spectrum changes are audible or not. The correct performance of the model was adjusted and verified by listening tests. The perceptual phase capacity in low pitched speech is found to be much higher than it is for high pitched speech. Our results are consistent with the well known fact that speech coding schemes which preserve the phase accurately work better for male voices, while coders which put more weight on the amplitude spectrum of the speech signal result in better quality for female speech.
Segway electric vehicle

This paper presents the design, fabrication and control of the Segway human transporter; a two-wheeled, self-balancing electric vehicle. We aim at producing a fully-operational Segway vehicle with a compact design which caters for shock absorption on rough terrains and includes an improved control system. The vehicle has a top speed of 13 km/h, a load capacity of over 2000N and a balancing time of less than 2 seconds.
Dimensions of Leader Behavior in Sports: Development of a Leadership Scale

Three different samples (total N = 485) participated in the development and refinement of the Leadership Scale for Sports (LSS). A five-factor solution with 40 items describing the most salient dimensions of coaching behavior was selected as the most meaningful. These factors were named Training and Instruction, Democratic Behavior, Autocratic Behavior, Social Support, and Positive Feedback. Internal consistency estimates ranged from . 45 to . 93 and the test-retest reliability coefficients ranged from . 71 to . 82. The relative stability of the factor structure across the different samples confirmed the factorial validity of the scale. The interpretation of the factors established the content validity of the scale. Finally, possible uses of the LSS were pointed out.
A Simple Algorithm for Adaptive Allpass-FIR Digital Filter Using Lattice Allpass Filter with Minimum Multipliers

Adaptive infinite impulse response (IIR) digital filter implemented using a cascade of second order direct form allpass filters and a finite impulse response (FIR) filter, has the property of its poles converging to those of the unknown system. In this paper we implement the adaptive allpass-FIR digital filter using a lattice allpass filter with minimum number of multipliers. We then derive a simple adaptive algorithm, which does not increase the overall number of multipliers of the proposed adaptive digital filter (ADF) in comparison to the ADF that uses the direct form allpass filter. The proposed structure and algorithm exhibit a kind of orthogonality, which ensures convergence of the poles of the ADF to those of the unknown system. Simulation results confirm this convergence. key words: adaptive filter, lattice IIR filter, allpass filter, orthogonality, residue theorem