Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of Improved Grammatical Evolution to Stock Price Prediction

Grammatical Evolution (GE), which is one of the evolutionary computations, aims to find function, program or program segment satisfying the design objective. This paper describes the improvement of the Grammatical Evolution according to Stochastic Schemata Exploiter (GE-SSE) and its application to symbolic regression problem. Firstly, GE-SSE is compared with original GE in symbolic regression problem. The results show that GE-SSE has faster convergence property than original GE. Secondly, GE-SSE is applied for the stock price prediction as the actual application of the GE-SSE. Keywords—Grammatical Evolution, Stochastic Schemata Exploiter, Symbolic Regression, Stock Price Prediction.
A 5.5mA 2.4-GHz two-point modulation Zigbee transmitter with modulation gain calibration

A 2.4-GHz two-point modulation IEEE 802.15. 4 (Zigbee) compliant transmitter is presented. This sigma-delta fractional-N PLL based transmitter is optimized for both lowpower and low-cost purposes. A novel closed-loop calibration scheme is proposed to minimize the gain mismatch between two modulation points, which is the main source of error in two-point modulation. Fabricated in a 0.15-μm CMOS process, the proposed transmitter achieves EVM less than 8% for 2-Mchips/s MSK modulated signal and consumes 5.5mA under a 1.55-V regulated power supply. The core area is 0.8×1.1mm2.
Redes neuronales adversarias para el reconocimiento de malezas

Resumen Se aborda el problema de reconocimiento de malezas en video para poder realizar una fumigación selectiva de la maleza sobre campo con cultivo. El sistema de reconocimiento propuesto es compatible además con la implementación de técnicas de robótica para remover la maleza con actuadores mecánicos sin el uso de agroqúımicos. El problema es abordado con técnicas de Deep Learning, donde los datos de entrenamiento son filmaciones del campo con la presencia de cultivo y maleza. El sistema de visión propuesto está basado en Convolutional Neural Networks (CNN). Se utilizó la técnica de Generative Adversarial Networks (GAN) para hacer un pre-entrenamiento no supervisado del modelo de modo de explotar la gran cantidad de imágenes que se obtienen a partir de secuencias de video. Luego se entrena en forma supervisada con una mı́nima cantidad de datos etiquetados para especializar el modelo. Se analizan y comparan resultados de distintos métodos utilizados y su aporte en el reconocimiento. Se combinan dos redes discriminantes de DCGAN y se utiliza una SVM en la última capa de la red entrenada sobre datos etiquetados utilizando Data Augmentation para lograr mayor robustez.
Gesture Analysis and Synthesis for Intelligent Tutoring Systems

This work describes a new interaction technique for intelligent tutoring systems based on the integration of results from computer graphics and computer vision. In the last few years this integration has shown important results and applications [Eisert, 98; Pentland, 96]. For example, Richard Szeliski described the use of image mosaics for virtual environments in 1996 and, in the following year for combining multiple images into a single panoramic image. H. Ohzu et al. described hyper-realistic communications for computer supported cooperative work.
Saliency map for visual attention region prediction based on fuzzy neural network

Visual attention region prediction has been paid much attention by researchers in intelligent systems recent years because it can make the interaction between human and intelligent agents to be more convenient. In this paper, the prediction method of the visual attention region inferred by using fuzzy neural network (FNN) after extracting and computing of images feature maps and saliency maps was proposed. A method for training FNN is also proposed. A user experiment was conducted to evaluate the prediction effect of proposed method by making surveys for the prediction results. The results indicated that prediction method proposed by us has a better performance in the level of attention regions' position prediction according to different images.
Remote Sensing Exploration of Nb-Ta-LREE-Enriched Carbonatite (Epembe/Namibia)

On the example of the Epembe carbonatite-hosted Nb-Ta-LREE deposit, we demonstrate the use of hyperspectral reflectance data and geomorphic indicators for improving the accuracy of remote sensing exploration data of structurally-controlled critical raw material deposits. The results further show how exploration can benefit from a combination of expert knowledge and remotely-sensed relief, as well as imaging data. In the first stage, multi-source remote sensing data were used in lithological mapping based on Kohonen Self-Organizing Maps (SOM). We exemplify that morphological indices, such as Topographic Position Index (TPI), and spatial coordinates are crucial parameters to improve the accuracy of carbonate classification as much as 10%. The resulting lithological map shows the spatial distribution of the ridge forming carbonatite dyke, the fenitization zone, syenite plugs and mafic intrusions. In a second step, the internal zones of the carbonatite complex were identified using the Multi-Range Spectral Feature Fitting (MRSFF) algorithm and a specific decision tree. This approach allowed detecting potential enrichment zones characterized by an abundance of fluorapatite and pyroxene, as well as dolomite-carbonatite (beforsite). Cross-validation of the mineral map with field observations and radiometric data confirms the accuracy of the proposed method.
Depth Mapping Using the Hierarchical Reconstruction of Multiple Sequence

One method was proposed for recovering structure model of the object by using monocular vision equipment. The fundamental idea can be summed up as sequential reconstruction under hierarchical method. First of all, by dividing the single observation path into multiple, more details of structure can be observed. After outliers removing, these sequences can be aligned with the referenced one which can be specified by users. Finally, after surface fitting, depth map of the object can be densely retrieved. The experiment shows that the depth map can be retrieved much densely than traditional method from the same dataset.
Average-case ray shooting and minimum weight triangulations

Consider an environment filled with polyhedral obstacles. The answer to aray-shoottng queyis the first obstacle encountered by the ray. A simple way to answer ray-shooting queries is to triangulate space in a manner compatible with theobstades,and then walk through thetriangulation along therayuntil the first obstacle is encountered. Weshow that the average walk length can be reduced by choosing a triangulation with weight (i.e., length in two dimensions or area in three dimensions) as small as possible. In two dimensions, we observe that the length of the minimum-length triangulation can be estimated by the total length of the obstacles plus the total length of a minimum spanuing tree of the obstacles, up to a logarithmic factor; this gives an a prior-i estimate of the average-cese walk length. Our main result is in three dimensions. We give a polynomial-time algorithm that computes a triangulation compatible with a set of polyhedral obstacles; the area of the triangulation is within a multiplicative constant of the smallest possible.
A review of energy efficiency technology in computer servers and cluster systems

Energy efficient computing has become a critical concern in the IT industry. For both hardware manufacturers and software developers, energy management work spans all levels from chips, operating systems, servers, to applications. In this paper, we overview energy-saving techniques developed at hardware, system software, and application levels, with an emphasis on energy-efficient computer servers and cluster systems.
On the advantages of information sharing

During the evolution of life, there have been several transitions in which individuals began to cooperate, forming higher levels of organization, and sometimes losing their independent reproductive identity. For example, multicellularity and insect societies evolved independently multiple times. Several factors that confer evolutionary advantages on higher levels of organization have been proposed. In this paper we highlight one additional factor: the sharing of information between individuals. Information sharing is not subject to the intrinsic conservation laws that characterize the sharing of physical resources. A simple model will illustrate how information sharing can result in aggregates in which the individuals both receive more information about their environment and pay less for it. This may have played a role in the evolution of higher levels of organization.
Module size distribution and defect density

Data from several projects show a significant relationship between the size of a module and its defect density. We address implications of this observation. Does the overall defect density of a software project vary with its module size distribution? Even more interesting is the question can we exploit this dependence to reduce the total number of defects? We examine the available data sets and propose a model relating module size and defect density. It takes into account defects that arise due to the interconnections among the modules as well as defects that occur due to the complexity of individual modules. Model parameters are estimated using actual data. We then present a key observation that allows use of this model for not just estimation of the defect density, but also potentially optimizing a design to minimize defects. This observation, supported by several data sets examined, is that the module sizes often follow exponential distribution. We show how the two models used together provide a way of projecting defect density variation. We also consider the possibility of minimizing the defect density by controlling module size distribution.
Accuracy Error Span Evaluations : Implications for User Interface Design of Speech Dictation and Handwriting Recognition Applications

This paper describes a number of experiments and analyses conducted to gain an understanding of the typical properties of error spans – the number of consecutive incorrect words generated when producing text with recognition technologies. The data were drawn from three studies of recognition applications: one study of an IBM dictation system, one of a non-IBM dictation system, and one of an IBM handwriting recognition application. Across the three recognition studies, the appropriate width for a text field designed to display text selected for correction appeared to be the width that would accommodate the presentation of three words (or 17 characters). An analysis of the position of correct text in the alternates list for the IBM dictation system indicated that a correction control should ideally present four alternates (covering 97% of the cases for error spans of one word and 90% of cases for error spans of two words). There appears to be little reason to provide a control that displays more than four alternates for any type of recognition user interface.
Location-Based Influence Maximization in Social Networks

In this paper, we aim at the product promotion in O2O model and carry out the research of location-based influence maximization on the platform of LBSN. As offline consuming behavior exists under the O2O environment, the traditional online influence diffusion model could not describe the product acceptance accurately. Moreover, the existing researches of influence maximization tend to only concern on the online network of relationships but rarely take the offline part into consideration. This paper introduces the location property into the influence maximization to accord with the characteristic of O2O model. Firstly, we propose an improved influence diffusion model called TP Model which could accurately describe the process of accepting products under the O2O environment. Meanwhile, the definition of location-based influence maximization is presented. Then the user mobility pattern is analyzed and the calculation method of offline probability is designed. Considering the influence ability, a location-based influence maximization algorithm named TPH is proposed. Experiments prove TPH algorithm has general advantage. Finally, focusing on the performance of TPH algorithm under special circumstances, MR algorithm is designed as complement and experiments also verify its high effectiveness.
Slingshot: A modular framework for designing data processing systems

Traditional relational database engines have been losing ground to specialized data processing engines in virtually every market segment, from data warehousing, OLTP, and stream processing, to scientific applications. Although relational database engines are evolving to leverage new technologies and more efficient processing paradigms, the generality of a large monolithic engine often makes this a significant effort. Our aim is to delimit and decouple database engine components to design a more lightweight and flexible data processing engine that can support any application domain efficiently and without the effort of a complete redesign. We introduce Slingshot, a new data processing engine, where modularity and implementation flexibility are the top priority. Its core database engine is minimal and mainly handles inter-operation of the database components. Each component, abstracted by an interface, can be externally implemented and plugged into the framework as a module that handles the component's functionality. As a result, this allows designers the liberty to choose suitable features for their target applications, to drop excess functionality, and to optimize code independent of the rest of the engine. We compare Slingshot to a traditional RDBMS and to custom solutions on queries that are representative of three application types (spatial, OLAP, and OLTP). We show that Slingshot outperforms the RDBMS in most cases, while performing comparably in others. Furthermore, Slingshot performs better or comparable to custom solutions on most tests. Finally, Slingshot's flexibility allows us to efficiently leverage computer architectures such as GPUs for speeding up complex computational tasks.
Bringing the territories closer together at higher speeds Design and Improvement of the Thrust Force Characteristic of a LSM for a propulsion / levitation of the 700 km / h-speed tube train

In case of high-speed maglev trains, the propulsion force of their Linear Synchronous Motor (LSM) is an essential performance element since the LSM is responsible for both the propulsion and levitation of the train. A large thrust force ripple causes vibration, noise and severe levitation disturbance during the train operation. Because of these reasons, efforts must be made to reduce the thrust ripple of the LSM for propulsion and levitation. In this study, a method to decrease the thrust force ripple that causes the noise, vibration and levitation disturbance generated during the highspeed operation of the 700km/h speed tube train’s LSM was investigated. In this method, the Module Phase Set Shift (MPSS) was applied. If the MPSS method is applied, the position of each field magnet module of the LSM changes as much as MPSS value relative to the reference load angle. To precisely calculate the total thrust force that occurs due to the position changes, Thrust Force Mapping (TFM) method was applied to each field magnet module, and the generated total thrust force of the LSM were accurately obtained. Through this study, a method of reducing the thrust force ripple without considerable reduction of the LSM’s average thrust force was presented. Keywords—Linear Synchronous Motor, Module Phase Set Shift, Thrust Ripple, Thrust Force Mapping, Tube train.
LDPC Options for Next Generation Wireless Systems

Low-Density Parity-Check (LDPC) codes have recently drawn much attention due to their nearcapacity error correction performance, and are currently in the focus of many standardization activities, e.g., IEEE 802.11n, IEEE 802.16e, and ETSI DVB-S. In this contribution, we discuss several aspects related to the practical application of such codes to wireless communications systems. We consider flexibility, memory requirements, enand decoding complexity and different variants of decoding algorithms for LDPC codes that enable to effectively trade-off error correction performance for implementation simplicity. We conclude that many of what have been considered significant disadvantages of LDPC codes (inflexibility, high encoding complexity, etc.) can be overcome by appropriate use of different algorithms and strategies that have been recently developed – making LDPC codes a highly attractive option for forward error correction for B3G/4G systems.
Automatic Inference in BLOG

BLOG is a powerful language to express models with an unknown number of objects and identity uncertainty. Current inference engines for BLOG are either too slow or require users to write a model-specific proposal distribution. We describe here, ongoing work to design a new, fast, generic inference engine for BLOG called blogc. The new implementation uses Gibbs sampling for finite-valued variables and performs an analysis of the model to generate customized sampling code in C. We describe our algorithms and methods in the context of various commonly used models and demonstrate significant performance improvement.
Oops!...I think I scanned a malware

This article presents a proof-of-concept illustrating the feasibility of creating a covert channel between a C\&C server and a malware installed in an organization by exploiting an organization's scanner and using it as a means of interaction. We take advantage of the light sensitivity of a flatbed scanner, using a light source to infiltrate data to an organization. We present an implementation of the method for different purposes (even to trigger a ransomware attack) in various experimental setups using: (1) a laser connected to a stand (2) a laser carried by a drone, and (3) a hijacked smart bulb within the targeted organization from a passing car. In our experiments we were able to infiltrate data using different types of light sources (including infrared light), from a distance of up to 900 meters away from the scanner. We discuss potential counter measures to prevent the attack.
Clustering Context-Specific Gene Regulatory Networks

Gene regulatory networks (GRNs) learned from high throughput genomic data are often hard to visualize due to the large number of nodes and edges involved, rendering them difficult to appreciate. This becomes an important issue when modular structures are inherent in the inferred networks, such as in the recently proposed context-specific GRNs. (12) In this study, we investigate the application of graph clustering techniques to discern modularity in such highly complex graphs, focusing on context-specific GRNs. Identified modules are then associated with a subset of samples and the key pathways enriched in the module. Specifically, we study the use of Markov clustering and spectral clustering on cancer datasets to yield evidence on the possible association amongst different tumor types. Two sets of gene expression profiling data were analyzed to reveal context-specificity as well as modularity in genomic regulations.
Massively parallel phase-field simulations for ternary eutectic directional solidification

Microstructures forming during ternary eutectic directional solidification processes have significant influence on the macroscopic mechanical properties of metal alloys. For a realistic simulation, we use the well established thermodynamically consistent phase-field method and improve it with a new grand potential formulation to couple the concentration evolution. This extension is very compute intensive due to a temperature dependent diffusive concentration. We significantly extend previous simulations that have used simpler phase-field models or were performed on smaller domain sizes. The new method has been implemented within the massively parallel HPC framework waLBerla that is designed to exploit current supercomputers efficiently. We apply various optimization techniques, including buffering techniques, explicit SIMD kernel vectorization, and communication hiding. Simulations utilizing up to 262,144 cores have been run on three different supercomputing architectures and weak scalability results are shown. Additionally, a hierarchical, mesh-based data reduction strategy is developed to keep the I/O problem manageable at scale.
Impact of Organization Cynicism on Work Outcomes: Mediating Role of Work Alienation

A limited study has been conducted on the relationship between organizational cynicism and work outcomes in the area of management and organizational behavior. The purpose of this study was to investigate the main effects of organizational cynicism on job stress and employee performance. This study also examined the mediating role of work alienation a mechanism through which organizational cynicism affects different employee negative as well as positive outcomes. This study is based on the well-known theory of Cognitive appraisal theory of stress (Lazarus & Folkman, 1984) to justify the relationship of the proposed research model. The study collected data from the employee (n=350) working in the manufacturing and service sector of Pakistan. A Cross-sectional time lag research design was used for data collection in this study. The finding revealed that organizational cynicism is positively related to job stress and negatively related to job performance. Complete support was found for all hypotheses. Moreover, the findings further demonstrated that work alienation mediated the relationship between organizational cynicism and work outcomes. Support was found for all direct and indirect mediation effects.
GSNs : Generative Stochastic Networks

We introduce a novel training principle for generative probabilistic models that is an alternative to maximum likelihood. The proposed Generative Stochastic Networks (GSN) framework generalizes Denoising Auto-Encoders (DAE) and is based on learning the transition operator of a Markov chain whose stationary distribution estimates the data distribution. The transition distribution is a conditional distribution that generally involves a small move, so it has fewer dominant modes and is unimodal in the limit of small moves. This simplies the learning problem, making it less like density estimation and more akin to supervised function approximation, with gradients that can be obtained by backprop. The theorems provided here provide a probabilistic interpretation for denoising autoencoders and generalize them; seen in the context of this framework, auto-encoders that learn with injected noise are a special case of GSNs and can be interpreted as generative models. The theorems also provide an interesting justication for dependency networks and generalized pseudolikelihood and dene an appropriate joint distribution and sampling mechanism, even when the conditionals are not consistent. GSNs can be used with missing inputs and can be used to sample subsets of variables given the rest. Experiments validating these theoretical results are conducted on both synthetic datasets and image datasets. The experiments employ a particular architecture that mimics the Deep Boltzmann Machine Gibbs sampler but that allows training to proceed with backprop through a recurrent neural network with noise injected inside and without the need for layerwise pretraining.