Miracle-dz/UniFAR-FTUs huggingface dataset:
Attention-based Recurrent Generator with Gaussian Tolerance for Statistical Parametric Speech Synthesis

Conventional statistical parametric speech synthesis (SPSS) generates frame-level acoustic features in two separately optimized steps—namely, duration prediction and acoustic feature generation. It also incorporates a conditional independence assumption to generate independent output frames given textual inputs. Both factors constrain the quality of the generated speech output. This work proposes to apply the attention-based recurrent generator (ARG) with Gaussian Tolerance (GT) for SPSS, where duration prediction and acoustic feature generation are jointly optimized with attention mechanism, and the dependency across output frames is modeled by acoustic feature generation conditioned on preceding frames. GT is introduced to train ARG to acquire robustness based on previous output frames with errors. Perceptual experiments comparing the naturalness between ARG and the conventional hidden Markov model show a gain in MOS score and the effectiveness of GT.
Techniques for Cyber Attack Attribution

Abstract : This paper summarizes various techniques to perform attribution of computer attackers who are exploiting data networks. Attribution can be defined as determining the identity or location of an attacker or an attacker's intermediary. In the public literature "traceback" or "source tracking" are often used as terms instead of "attribution. " This paper is intended for use by the U.S. Department of Defense (DoD) as it considers if it should improve its attribution capability, and if so, how to do so. However, since the focus of this paper is on technology, it may also be of use to many others such as law enforcement personnel. This is a technical report, and assumes that the reader understands the basics of network technology, especially the Transmission Control Protocol/Internet Protocol (TCP/IP) suite of protocols.
Spatial analysis of crime displacement and diffusion of crime control benefits in central London ’ s Lower Super Output Areas

This paper investigates the impact of a crime deterring initiative called MetTrace rolled out by the Metropolitan Police Service and SmartWater Technology Ltd, and if this has led to spatial displacement or diffusion of crime control benefits. A combination of spatial analytical methods, including differential local Moran’s I and Weighted Displacement Quotient, are employed to assess the tendency of displacement of burglaries around areas SmartWater was rolled out, covering the period before and after the implementation of the scheme. The results support the recent literature in that applying situational crime prevention measures does not necessarily trigger a significant crime displacement.
A Mass Storage System for Bare PC Applications Using USBs

Bare machine applications eliminate the overhead and the security vulnerabilities that are due to operating systems. This paper describes a mass storage system for bare PC applications that uses USBs. It is implemented by extending a scalable FAT32 USB file system for a bare PC. First, details of the bare PC file system including the file API, file system internals, and file operations are given. Then the architecture of the mass storage system and its design and implementation are presented. A mass storage system based on this architecture is built by using four USBs on a desktop PC. Capabilities of the mass storage system are demonstrated by storing conventional files and SQLite database files on multiple USBs. Experiments to measure raw versus conventional file system performance show a 12% improvement for writes and a 33% improvement for reads with 30 MB files. This work is a first step towards building mass storage systems to support future bare machine big data and mobile applications with improved security and performance. Keywords-bare machine computing; bare PC; FAT32 file system; mass storage; USB.
Golf Analytics : A Random Putting Model and its Applications to Optimal Targeting Strategy and Attribution Analysis

Golf analytics is the study of data to discover patterns and opportunities that enable the creation of decision support systems in golf, incorporating analytically based simulation and optimization technologies. In this paper we use the PGA Tour data from the ShotLink system to infer putting performance statistics as a function of putt length, angle, and slope. Contrast to the existing studies that rely on the relationship between the putting performance statistics and putt length only, we infer interesting features in the data for different putt angles and slopes. Motivated by the patterns observed in the data, we develop and validate a random trajectory model and calibrate it for use in simulation. Based on the calibrated model we numerically find optimal target distance beyond hole as a function of putt length, angle, and slope, and suggest how much one can expect to improve putting performance by adopting the optimal targeting strategy, without improving intrinsic putting skills. Lastly, we perform an attribution analysis to evaluate what putting skill is most critical to the performance.
OpenFOAM User Conference 2016 , Cologne-Germany Large strain solid dynamics in OpenFOAM

An industry-driven computational framework for the numerical simulation of extremely large strain solid dynamics is presented. This work focuses on the tailor-made implementation, from scratch, of the TOtal Lagrangian Upwind Cell Centred Finite Volume Method for Hyperbolic conservation laws (TOUCH [1]) into the CFD-based open source platform OpenFOAM. Crucially, the proposed framework bridges the gap between CFD and large strain solid dynamics. A series of challenging numerical examples are examined in order to assess the robustness and accuracy of the proposed algorithm, benchmarking it against an ample spectrum of alternative numerical strategies [2, 3, 4, 5, 6, 7, 8]. The TOUCH scheme shows excellent behaviour in highly nonlinear (bending dominated) nearly incompressible scenarios and overcomes the current drawbacks of existing industry computer codes.
On the measurement of intraday overreaction of stock prices

We propose a concept of intraday overreaction characterized by intraday price movements which are corrected within the same trading day. It is a concept of relative overreaction in the sense that the extremal intraday price fluctuation is compared with the open-close price change. As a one-sided concept it allows to distinguish between upward and downward overreaction. A test for overreaction is proposed and applied to daily high, low, and open-close returns of the components of the S&P500 and to the German XETRA-DAX stock shares, providing strong support for intraday overreactions to bad news. JEL-classifications: C22, C52, G10
Energy-efficient beamforming for two-tier massive MIMO downlink

Heterogeneous networks (HetNets) consisting of macro cells with very large antenna arrays and a secondary tier of small cells with a few antennas each can well tackle the contradiction of large coverage of the network and high data rate at the hot spots. However, it is not permissible to assign orthogonal pilot sequences for all the supported users due to the large number. Hence, we propose a pilot reduction scheme based on the heterogeneous system configurations and the unique topology of this HetNet. The reusing of pilot sequences causes the presence of the contaminated channel state information (CSI) and results in receivers' Quality of Service (QoS) outage. With the contaminated CSI, we provide an energy-efficient beamforming based on minimizing the total power consumption while keeping the QoS constraints satisfied and restricting the QoS outage probability below a given specification. By applying the approach of Bernstein approximation and semi-definite relaxation, we transform the original intractable chance constrained program to a convex problem conservatively. Numerical results show that the average power consumption of the proposed beamforming for our pilot reduction scheme is close to that of the perfect CSI case. Since our scheme will greatly compress the length of pilot sequence especially for those highly densified network with large number of small cells, it will be crucially helpful to put such two-tier massive multiple-input and multiple-output (MIMO) systems into practice.
Application of a Global Mesoscale Model for Predicting the Formation of Twin Tropical Cyclones associated with a Madden-Julian Oscillation

Over the past several decades, tropical cyclone (TC) track forecasts have been steadily improving, but intensity and genesis forecasts have lagged behind. One of the major challenges in TC genesis prediction is the accurate simulation of complex interactions across a wide range of scales, from the large-scale environment (deterministic), to mesoscale flows, down to convective-scale motions (stochastic). General circulation models (GCMs) have been used to study TC genesis statistics and inter-annual variability, but their insufficient grid spacing and physics parameterizations are known limiting factors. Recent advances in high-resolution global modeling and supercomputing have made it possible to mitigate some of the aforementioned issues. One of the important questions to be answered is: if and how the lead time for predicted TC formation can be extended? In this study, genesis of two pairs of twin TCs associated with a Madden-Julian Oscillation (MJO) event in May 2002 is investigated by performing 10-day numerical simulations with a global mesoscale model (GMM). Sensitivity experiments are also conducted with different moist convection schemes to understand the aggregate effects of precipitation processes on TC activity. It is found that the model is capable of predicting the genesis of these TCs about two to three days in advance as well as their subsequent movements. Real-data simulations show the transition from larger-scale convectively driven systems to smaller-scale TCs and, therefore, suggest that the lead time for predicting TC formation could be extended with realistic representations and improved simulations of an MJO.
SUPER MARIO : A COLLABORATIVE GAME FOR THE LEARNING OF VARIABLES IN PROGRAMMING

This paper presents the design and implementation of a collaborative game for learning about the various uses of variables in programming, by high school students. This is a 4level game that was designed and implemented using the programming environment Scratch integrating the well known collaborative method Jigsaw (1). The basic learning goals of this game are to provide students with opportunities: a) to conceptualize some different aspects of variables in programming, by playing, interacting, and modifying this game and b) to become equipped with appropriate knowledge -at the end of the proposed activitiesto collaborate and construct their own games themselves. Students are divided into 4 expert groups, by applying the aforementioned collaborative method, where each team explores one specific level of the game, which focuses on learning about a specific category of variables in programming. Afterwards, the students from the expert groups try to design their own games and then return back to the original Jigsaw groups in order to teach their colleagues -through their involvement in a sequence of game based activitiesabout each of the different categories of variables. In this article the aforementioned levels of this game and the specific learning activities to be assigned to these groups are presented.
Gradient calculation for anisotropic FWI

The 3-D TTI medium can be characterized as eight parameters (5 independent elastic moduli in constitutive coordinate system, density, tilt as well as azimuth angle) at each spatial point. One of the key issues in implementing FWI for parameter characterizing TTI media is efficient gradient calculation of objective function with respect to each model parameter. To calculate the gradient of each independent parameter involves the synthetic and adjoint data, as well as the derivatives of elastic moduli with respect to the independent parameters of the model. In this paper, the synthetic and adjoint wavefields are simulated with a staggered-grid finite-difference algorithm in anisotropic media. The derivatives of the elastic modulus tensor for TTI media are also analyzed in this paper. Numerical examples of the gradients calculation are thus illustrated in a three-layer TTI model. One of the issues addressed in the discussion is that the synthetic data at each time step should be stored on the disk so as to perform cross-correlation with the adjoint wavefields to generate the gradient for FWI in time domian. The huge dataset storage during synthetic wavefield simulation and loading when calculating the gradient is highly memory cost and time consuming. The use of random boundary layer allows us to compute both the adjoint and synthetic wavefield simultaneously without the necessity of storing total synthetic data. In this paper, cubic grains are implemented as random boundary layers. The randomized elastic moduli instead of velocities are thus added in elastic wave equations in TTI medium. The synthetic waveforms with random boundary layers are finally illustrated.
Critical Performance Analysis of Object Tracking Algorithm for Indoor Surveillance using modified GMM and Kalman Filtering

Our objective is to ensure high level of security in public places using static PTZ camera and robust detection and tracking algorithm for video sequences and also to generate the multi model background subtraction approach that can handle dynamic scenes. In this paper we are focussing to design the robust foreground and background detection technique using the statistical approach and implement it on the different indoor environments. Among all familiar background subtraction approach our proposed method used Gaussian mixture model and predictive filters to detect and track objection successive frames. To test the performance of our algorithm we will take the standard test sequences and own datasets. We will analyze our algorithm with the qualitative and quantitative approaches. so, our aim is to develop such an smart surveillance system that can not only analyze but also to interpret and act with reference to the object behaviour against illumination changes, clutter background, moving background, occlusions and complex silhouette.
An Ontology for Question-Answering on Minecraft

The ability to answer complicated questions by advanced logical inference is one of the ultimate goal of NLP. In this paper, we present a new corpus for QA on an open world video game called Minecraft. The game has a specific logic, which is more restricted than the real world and supposedly easier to be handled by an artificial intelligence. Yet, the openness of the game guarantees a large number of possible questions to be asked, and the popularity has attracted players to creating plenty of resource on the web. We have (i) created a QA corpus with 1684 questions, (ii) extracted and organized a knowledge base of 1222 documents about entities in the game, and (iii) manually constructed an ontology scheme for the QA task. We believe the data can provide a testbed for combining NLP with advanced logical inference techniques.
Clustering Crude Oil Samples Using Swarm Intelligence

The identification patterns in the crude oil intrinsic qualities provides useful information for the refinery operation and logistics. The a priori information concerning the characteristics expected by a given crude oil improves the logistic concerning which refineries should process this crude, together with pricing and marketing. This article presents the results of data mining models applied to a generic database of crude oil samples. Only information available in the crude oil intrinsic qualities is used. Clustering techniques based on bio-inspired algorithms are applied to the data samples in order to extract structured patterns from data. Three algorithms were used: PSO, FSS and ABC. Particles and fishes represent the possible clustering solutions. ABC represents solutions as food sources to be evaluated by bees. The silhouette index was used as the fitness function to be optimized. The results were later evaluated using other clustering quality index. The algorithms were able to find patterns beyond the standard oil classification, which considers only the oil density measurement.
Targeting the MDM 2 – p 53 Protein – Protein Interaction for New Cancer Therapy : Progress and Challenges

MDM2 is a primary cellular inhibitor of p53. It inhibits p53 function by multiple mechanisms, each of which, however, is mediated by their direct interaction. It has been proposed that small-molecule inhibitors designed to block the MDM2–p53 interaction may be effective in the treatment of human cancer retaining wild-type p53 by reactivating the p53 tumor suppressor function. Through nearly two decades of intense efforts, a number of structurally distinct, highly potent, nonpeptide, small-molecule inhibitors of the MDM2–p53 interaction (MDM2 inhibitors) have been successfully designed and developed, and at least seven such compounds have now been advanced into human clinical trials as new anticancer drugs. This review offers a perspective on the design and development of MDM2 smallmolecule inhibitors and discusses early clinical data for some of the MDM2 small-molecule inhibitors and future challenges for the successful clinical development of MDM2 inhibitors for cancer treatment.
Manipulating Neural Policies with Adversarial Observations

This paper deals with adversarial attacks on neural policy perceptions in a reinforcement learning (RL) context. Classic approaches perform untargeted attacks on the state of the agent. Here, we argue that it is more realistic to attack the observations provided by the environment rather than the internal state computed by the agent. We propose an untargeted attack over observations and show that its effectiveness even holds when reduced to a constant attack over observations. We also propose an approach to perform targeted attacks on the observations of the agent, so that it acts as told by an opponent policy. We illustrate this on deep RL agents playing Space Invaders.
Clad: Accurate Capture Of Transmission In Multi-Hop Wireless Networks

In wireless networks so far, multiple active flows and cooperative transmissions has not been yet considered. The case of VMISO links is considered here. To achieve this and to improve performance, cooperative transmission is used in multi-hop wireless networks. In the end, VMISO in physical layer benefits and improves network layer performance. After an investigation on this end result, we show that the improvements are nontrivial. A study is made on routing decisions and finally a Cluster Adaptive (ClAd) routing protocol is introduced. This protocol also strengthens Virtual Multiple Input Single Output (VMISO) links to achieve performance improvements. It also gives solutions to decision problems like the number of cooperating transmitters for each link and cooperation strategy used by the transmitters. Keywords— cooperative communication, VMISO, multi-hop wireless networks, routing protocol.
Predictive modeling of microcystin concentrations in a hypertrophic lake by means of Adaptive Neuro Fuzzy Inference System ( ANFIS )

Cyanobacterial blooms are becoming a major water quality problem in Mediterranean lakes. The threat from the occurrence and persistence of cyanobacteria is considered high, and there is a risk that this might be increased by climate change. Approximately, 75% of water samples containing cyanobacteria also contain toxic cyanobacterial metabolites, known as cyanotoxins. The most widespread cyanobacterial toxins are microcystins, which are responsible for significant health hazards to livestock, wildlife, birds, fishes, and even humans via various exposure routes. The knowledge of occurrence and distribution of microcystins in water bodies used for recreational, agricultural and fishing activities is important. Lake Karla—one of the most important wetlands in Greece and a Ramsar site— is already characterized by the presence of cyanobacteria and cyanotoxins, even though it is used for irrigation and recreational purposes. In the present study, we focused on the development of a reliable method to predict microcystin concentrations in Lake Karla, through the Adaptive Neuro-Fuzzy Inference System (ANFIS), using as input, water quality parameters. A range of water quality parameters were tested in order to find out which ones show stronger correlation with microcystins and thus, phycocyanin, chlorophyll-a, total phosphorus, nitrogen to phosphorus ratio and dissolved oxygen were selected as inputs to the model. According to the results, the model showed an adequate efficiency on the prediction of microcystin concentrations. ANFIS modeling analysis showed that the aforementioned water quality parameters could act as indicators of the presence of microcystins and could produce risk levels. ANFIS could also be used as a tool for risk management associated with microcystin concentrations in shallow Mediterranean lakes.
OPTIMIZATION OF THE MANAGEMENT CONDITIONS OF MALOLACTIC FERMENTATION IN RED WINES OF THE NEMEA REGION

Malolactic fermentation (MLF) and its impact on wine quality are often not taken into consideration in the modern Greek winemaking reality. Relatively low initial L-malic acid concentration, rarely exceeding 2.0 g/l, in conjunction with rather high pH values and warm temperatures during the winemaking period result, in most cases, in spontaneously triggered malolactic fermentations. Malic acid will be depleted by indigenous LAB and winemakers will consider “the job completed” neglecting the fact that quality and health issues have been raised by this “wild” non-controlled process. Quality issues would be mainly undesired products such as volatile acidity, diacetyl, acetoin, volatile phenols and others, where health issues would be related with biogenic amine production. Inoculation with selected Lactic Acid Bacteria (LAB) strains provides a good tool to overcome these issues provided that it is conducted in optimum conditions. Time of inoculation of LAB seems to be the most crucial factor. We have demonstrated that inoculation after the completion of the alcoholic fermentation as well as early inoculation at the beginning of fermentation improve performances but not erase problems. LAB inoculation when 50% of reducing sugars are fermented seems to be the most adapted MLF modality in the AOC Nemea region, leading to better MLF kinetic that yields higher quality end products.
The Monte Carlo Event Generator AcerMC version 2 . 0 with interfaces to PYTHIA 6 . 2 and HERWIG 6 . 5

TheAcerMC Monte Carlo Event Generator is dedicated for the generation of Standard Model background processes at pp LHC collisions. The program itself provides a library of the massive matrix elements and phase space modules for generation of selected processes: gg, qq̄ → tt̄bb̄; qq̄W (→ lν)bb̄; qq̄W (→ lν)tt̄; gg, qq̄ → Z/γ(→ ll)bb̄; gg, qq̄ → Z/γ(→ ll, νν, bb̄)tt̄; complete electroweak gg, qq̄ → (Z/W/γ →)bb̄tt̄; gg, qq̄ → tt̄tt̄; gg, qq̄ → (tt̄ →)ff̄bff̄ b̄; gg, qq̄ → (WWbb →)ff̄f f̄bb̄. The hard process event, generated with one of these modules , can be completed by the initial and final state radiation, hadronisation and decays, simula ted with eitherPYTHIA or HERWIG Monte Carlo event generator and (optionally) withTAUOLA andPHOTOS. Interfaces to all these packages are provided in the distri bu ion version. The matrix element codes have been derived with the help of the MADGRAPH package. The phase-space generation is based on the multi-channel self-optimising approach using the modi fie Kajantie-Byckling formalism for phase space construct ion and further smoothing of the phase space was obtained by using a m odifiedac-VEGAS algorithm.