Miracle-dz/UniFAR-FTUs huggingface dataset:
Tilde’s Machine Translation Systems for WMT 2019

The paper describes Tilde’s EnglishLatvian and Latvian-English machine translation systems for the WMT 2017 shared task in news translation. Both constrained and unconstrained systems are described. Our constrained systems were ranked as the best performing systems according to the automatic evaluation results. The paper gives details to how we pre-processed training data, the NMT system architecture that we used for training the NMT models, the SMT systems and their usage in NMT-SMT hybrid system configurations.
Service Flow Management with deadline and budget Constraints using Genetic Algorithm in Heterogeneous Computing

The service flow management is one from the most challenges especially in heterogeneous environments which has several and various processors for computing. Service flow is used to explain services configuration process when service’s formation according to the precedence relations of configuration should be considered. Its management should take into account multi-objective constraints. The total execution time should not be completed after the specified time that leading to consider the deadline constraint into account. Also the cost minimization that is a critical issue shouldn’t be ignored. Obtaining the optimal management in a sensible time is so hard because there are many candidate with different processing power and price, constraints from the user and the precedence of heterogeneous services. In this paper, the service flow management problem is solved by a genetic algorithm that considers deadline and cost constraints. It focuses on the improvement of execution time to meet the deadline constraint and minimizes the execution cost according to the budget in heterogeneous computing. The results from the applied experiments proves that the proposed algorithm can be able to minimize total cost, and consolidate the execution time with the deadline constraint. It reach to a near-optimal solution in reasonable time. It outperforms the compared algorithms in the metric of Schedule Length Ratio (SLR), cost, risk ratio, speed up and completion time measurements.
1.4 GHz Low-Cost PIN Diode Phase Shifter for ${L}$ -Band Radiometer Antenna

A 1.4 GHz 4-bit positive intrinsic negative (PIN) diode phase shifter with compact reflective loads for an ${L}$ -band soil moisture measuring radiometer phased array antenna has been successfully designed. Unlike conventional PIN diode phase shifters using PIN diodes as switches, the designed PIN diode phase shifter adopting the low-cost PIN diodes with poor isolation as equivalent lumped components corresponding to the forward and reverse bias states. First, the PIN diode is fully characterized for its equivalent circuit models at on- and off-states, and, then, the reflective load is designed with only one inductance in parallel with the diode models to obtain the desired phase shift. The phase shifter consists of four individual bits in cascade, providing 16 states of phase shifts. Each bit is constructed with a meander branch-line 90° coupler and two identical PIN diode reflective loads. Thus, the novel design yields a highly compact phase shifter at L-band. The phase shifter is theoretically analyzed, fabricated, and measured. The measured results show good agreement with analysis and calculations. The insertion loss of the 4-bit phase shifter is less than 2 dB within 25 MHz radiometer bandwidth and less than 2.3 dB from 1.37 to 1.43 GHz. The input and output return losses are larger than 16 dB within the 1.37 to 1.43 GHz frequency band. The root mean square (RMS) phase error is just less than 1° at the center frequency of 1.4 GHz and less than 3° within the radiometer bandwidth. The phase shifter is controlled with switching electronics and a microcontroller and integrated within the beamforming network of an L-band ${4\times 4}$ -element radiometer phased array antenna for soil moisture measurement.
Tsinghua Science and Technology Tsinghua

: Indoor navigation has received much attention by both industry and academia in recent years. To locate users, a number of existing methods use various localization algorithms in combination with an indoor map, which require expensive infrastructures deployed in advance. In this study, we propose the use of existing indoor objects with attached RFID tags and a reader to navigate users to their destinations, without the need for any additional hardware. The key insight upon which our proposal is based is that a person’s movement has an impact on the frequency shift values collected from indoor objects when they near a tag. We leverage this local human-item spatial relation to infer the user’s position and then navigate the user to the desired destination step by step. We implement a prototype navigation system, called RollCaller, and conduct a comprehensive range of experiments to examine its performance.
Transformation-Based Approach to Security Verification for Cyber-Physical Systems

The increasing complexity of cyber-physical systems motivates new modeling approaches to ensure system security right from the design process. In this paper, we present a model-based approach to formally validate communicating systems against cyber-attacks. Security requirements are modeled by using the unified modeling language (UML) extended attack tree profile with temporal logic operators. Moreover, to identify attack propagation, another UML profile, i.e., the connectivity profile, has been integrated to model interactions between system components. In order to carry out a formal verification of the system, a transformation platform that automatically generates a new symbolic model verifier code from systems modeling language (SysML) models for both static and dynamic aspects has been developed. The modeling and validation process is illustrated via two case studies on connected cars: 2014 Jeep Cherokee attack and 2016 Tesla Model S attack.
Weakly-Supervised Learning of a Deep Convolutional Neural Networks for Semantic Segmentation

Deep convolutional neural networks (DCNNs) trained on the pixel-wise annotated images have dramatically improved the state-of-the-art in semantic segmentation. However, due to the high cost of labeling training data, its application has great limitation. In this paper, we propose a DCNNs model for generating the pixel-level labels using the image-level annotation. The model consists of an encoder-decoder, a feature decomposer, and a multi-label classifier. The encoder extracts the deep convolutional feature maps of the input image. The feature decomposer can decompose the convolutional feature extracted by the encoder into feature components of different semantics. The decomposer is based on the orthogonal non-negative matrix factorization (NMF) technology. The function of the decoder is to map the feature components of different semantics to the input resolution images. The decoder uses the position index of maximum pooling provided by the corresponding encoder to perform non-linear up-sampling, which eliminates the learning requirement of up-sampling. Since the image reconstruction is conducted according to the semantic categories, image regions of different semantics are restored to different images. Then, the regions of different semantics can be segmented through the posted-processing algorithms. The experimental results on open data set show that the proposed model outperforms some recently developed methods.
SqueezeFlow: A Sparse CNN Accelerator Exploiting Concise Convolution Rules

Convolutional Neural Networks (CNNs) have been widely used in machine learning tasks. While delivering state-of-the-art accuracy, CNNs are known as both compute- and memory-intensive. This paper presents the SqueezeFlow accelerator architecture that exploits sparsity of CNN models for increased efficiency. Unlike prior accelerators that trade complexity for flexibility, SqueezeFlow exploits concise convolution rules to benefit from the reduction of computation and memory accesses as well as the acceleration of existing dense architectures without intrusive PE modifications. Specifically, SqueezeFlow employs a PT-OS-sparse dataflow that removes the ineffective computations while maintaining the regularity of CNN computations. We present a full design down to the layout at 65 $nm$nm, with an area of 4.80 $\mathrm{mm^2}$ mm 2 and power of 536.09 $\mathrm{mW}$ mW . The experiments show that SqueezeFlow achieves a speedup of $2.9\times$2.9× on VGG16 compared to the dense architectures, with an area and power overhead of only 8.8 and 15.3 percent, respectively. On three representative sparse CNNs, SqueezeFlow improves the performance and energy efficiency by $1.8\times$1.8× and $1.5\times$1.5× over the state-of-the-art sparse accelerators.
HYBRID IMPROVED BACTERIAL SWARM OPTIMIZATION ALGORITHM FOR HAND-BASED MULTIMODAL BIOMETRIC AUTHENTICATION SYSTEM

This paper proposes a Hybrid Improved Bacterial Swarm (HIBS) optimization algorithm for the minimization of Equal Error Rate (EER) as a performance measure in a hand-based multimodal biometric authentication system. The hybridization of the algorithm was conducted by incorporating Bacterial Foraging Optimization (BFO) and Particle Swarm Optimization (PSO) algorithm to mitigate weaknesses in slow and premature convergence. In the proposed HIBS algorithm, the slow convergence of BFO algorithm was mitigated by using the random walk procedure of Firefly algorithm as an adaptive varying step size instead of using fixed step size. Concurrently, the local optima trap (i.e. premature convergence) of PSO algorithm was averted by using mutation operator. The HIBS algorithm was tested using benchmark functions and compared against classical BFO, PSO and other hybrid algorithms like Genetic Algorithm-Bacterial Foraging Optimization (GA-BFO), Genetic Algorithm-Particle Swarm Optimization (GA-PSO) and other BFO-PSO algorithms to prove its exploration and exploitation ability. It was observed from the experimental results that the EER values, after the influence of the proposed HIBS algorithm, dropped to 0.0070% and 0.0049% from 1.56% and 0.86% for the right and left hand images of the Bosphorus database, respectively. The results indicated the ability of the proposed HIBS in optimization problem where it optimized relevant weights in an authentication system.
Big data analytics for manufacturing internet of things: opportunities, challenges and enabling technologies

ABSTRACT Data analytics in massive manufacturing data can extract huge business values while can also result in research challenges due to the heterogeneous data types, enormous volume and real-time velocity of manufacturing data. This paper provides an overview on big data analytics in manufacturing Internet of Things (MIoT). This paper first starts with a discussion on necessities and challenges of big data analytics in manufacturing data of MIoT. Then, the enabling technologies of big data analytics of manufacturing data are surveyed and discussed. Moreover, this paper also outlines the future directions in this promising area.
A cladistic revision of Tortopus Needham & Murphy with description of the new genus Tortopsis (Ephemeroptera: Polymitarcyidae)

The 12 species previously placed in Tortopus together with 3 species newly described here, are revised and included in a phylogenetic analysis. Based on synapomorphic characters on the nymphs and adults of both sexes, Tortopus is restricted to T. igaranus Needham & Murphy, T. circumfluus Ulmer, T. harrisi Traver, T. zottai (Navás), T. bellus Lugo-Ortiz & McCafferty, and T. arenales sp. nov., and the genus is defined by: female parastyli receptors with long furrows anterior to sockets; penes entirely flattened; male ninth abdominal sternum almost separated in two portions by a median notch; mesosternum with furcasternal protuberances contiguous only on basal corner; and nymphs with two subapical tubercles on mandibular tusks. Tortopsis is newly described for T. bruchianus (Navás), T. limoncocha sp. nov., T. obscuripennis (Domínguez), T. parishi (Banks), T. primus (McDunnough), T. puella (Pictet), T. sarae (Domínguez), T. spatula sp. nov., and T. unguiculatus (Ulmer). Tortopsis is characterized by: R sector of female fore wing without additional veins between R 2 and IR; female parastyli receptors C or V-shaped, with sockets opening towards median line; male gonopore associated with a claw-like structure; penes separated from the base; parastyli more than 5 times length of pedestals; parastyli curved in lateral view; nymphs with a single subapical tubercle on mandibular tusks. The study of available type material permitted inclusion of comparative diagnoses, with figures and redescriptions as needed. The male imago of the type species of Tortopus (T. igaranus Needham & Murphy) is described for the first time, as are the female adults of Tortopus bellus Lugo-Ortiz & McCafferty and T. harrisi Traver. Three new Neotropical species based on male and female adults are described: Tortopus arenales and Tortopsis limoncocha from Ecuador, and Tortopsis spatula from Colombia. Keys to separate the adults and nymphs of the genera of Polymitarcyidae, and for male and female adults of all the species of Tortopus and Tortopsis are presented, as well as line drawings, pictures and SEM photographs of important structures.
DIAGNOSIS OF THE OBSERVATION PROCESS AS A TOOL TO DESIGN PRODUCTS. CASE STUDY OF THE UNIVERSIDAD DEL BIO-BIO SCHOOL OF DESIGN

Observing is an ability which some of us have developed more than others. However, for designers, observation is a cognitive tool which appears as a key stage in the creation process, focusing on the study and understanding of the environment/context through a phenomenological approach, covered from the personal vision of the observer. After 5 years of analysing results of evaluations associated to the application of observation in teaching design workshops for the concept creation process in students of Universidad del Bio-Bio's Industrial Design School, it was possible to detect differences in methodological approaches when teaching this process. This research, whose intention is to implement improvements in these methodologies, has a goal of diagnosing and gathering the perception of the role of observation within the education of industrial designers, making a qualitative investigation using a case study approach, interviewing 145 designers chosen by sampling done by convenience and accessibility, on-line surveys and two focus groups. As a main result, 117 meaningful units associated to the observation process are gathered, grouped into 16 conceptual codes and 4 categories of study. As a main conclusion, a categorisation of the observation stages and identification of the key stages to consider while teaching product design are highlighted.
Can we enhance domain-general learning abilities to improve language function?

One of the most exciting recent developments in the cognitive neurosciences is the growing realization that the mind and brain are highly plastic. Whereas traditionally the brain was thought to be relatively immutable past a certain age, there is a growing body of evidence suggesting that neural connections remain modifiable even late into adulthood (Kleim & Jones, 2008; van Praag, Kempermann, & Gage, 2000). In addition, recent research has demonstrated that even relatively short-term training can lead to improvements to neurocognitive abilities such as working memory capacity (Klingberg, 2010), with improvements transferring to a host of non-trained tasks of memory and cognition. In addition to working memory (WM), it may be fruitful to explore whether it is possible to enhance statistical learning abilities. Here we use the term statistical learning in a fairly broad sense, to refer to incidental learning that results in sensitivity to structured patterns in the environment. Under this definition, statistical learning is related to other forms of nondeclarative pattern learning abilities such as implicit learning (Perruchet & Pacton, 2006) and sequential learning (Conway, in press). These kinds of domain-general learning abilities appear to be important for language acquisition and processing (Conway & Pisoni, 2008; Gervain & Mehler, 2010; Gogate & Hollich, 2010; Gupta & Dell, 1999; Kuhl, 2004; Reber, 1967; Sa¤ran, 2003; Ullman, 2004). The question we address is whether it is possible to improve statistical learning abilities by capitalizing on the highly plastic nature of the mind and brain, in a similar vein to the demonstrations of improvements to WM capacity. Given that statistical learning is important for language acquisition and processing, then we should expect that if statistical learning can be enhanced through some type of training regimen, that this would result in a better facility for acquiring and processing language. Providing a demonstration of the causal e¤ects of enhanced statistical learning on language acquisition would be important theoretically as well 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40
Profit Maximization Incentive Mechanism for Resource Providers in Mobile Edge Computing

Mobile edge computing (MEC) has become a promising technique to accommodate demands of resource-constrained mobile devices by offloading the task onto edge clouds nearby. However, most existing works only focus on whether to offload or where to offload the task but ignore the motivations of edge clouds to offer service. To stimulate service provisioning by edge clouds, it is essential to design an incentive mechanism that charges mobile devices and rewards edge clouds. In this paper, we first propose an incentive mechanism in a non-competitive environment. We utilize market-based profit maximization pricing model to establish the relationship between the resources provided by edge clouds and the price charged to mobile devices. By solving the optimization problem, we provide a reasonable pricing strategy to not only ensure the profit of resource providers but guarantee the quality of experience (QoE) of mobile devices. Furthermore, we design an online profit maximization multi-round auction (PMMRA) mechanism for the resource trading between edge clouds as sellers and mobile devices as buyers in a competitive environment. The mechanism can effectively determine the price paid by buyers to use the resources provided by sellers and make the corresponding match between edge clouds and mobile devices. Finally, numerical results show that proposed mechanism outperforms other existing algorithms in maximizing the profit of edge clouds.
Semantic region proposals for adaptive license plate detection in open environment

Abstract. With the development of intelligent transportation and parking, license plate detection in open environments is in great demand. However, due to the clutter of background and variation of license plates, the existing methods could not make a good balance between accuracy and efficiency. A method based on semantic region proposals is presented. By thinking from the pixel level, this method first adopts a semantic segmentation convolutional network for license plate candidate region extraction. To improve accuracy of segmentation, an enhanced loss function is designed. Afterward, a classification and regression network based on the oriented bounding box regression algorithm is used for region verification and refinement. Experiments on three public datasets show that the proposed method can be adapted to license plate images captured under different scenarios and can achieve better performance than the state-of-the-art methods.
Special issue on evolutionary computation

In recent years, many researchers have been paid attention to machine learning, and at the same time attention has been focused on optimization algorithms. In the case where the objective function of the optimization problems is not given explicitly, such optimization problem is called “black-box problem”. For example, the problem of finding optimal hyperparameters in machine learning is classified into black-box problems. In recent years, evolutionary computation has attracted attention as a method for solving such black-box problems. Evolutionary computation is an algorithm inspired by the evolution of organisms. This special issue focuses on recent progress in evolutionary computation from scientific viewpoints.
Spatiotemporal saliency detection using border connectivity

This paper proposes a border connectivity-based spatiotemporal saliency model for videos with complicated motion and complex scenes. Based on the superpixel segmentation results of video frames, feature extraction is performed to obtain the three features, including motion orientation histogram, motion amplitude histogram and color histogram. Then the border connectivity is exploited to evaluate the importance of three features for distance fusion. Finally the background weighted contrast and saliency optimization are utilized to generate superpixel-level spatiotemporal saliency maps. Experimental results on a public benchmark dataset demonstrate that the proposed model outperforms the state-of-the- art saliency models on saliency detection performance.
Classification of Motor Imagery Using Combination of Feature Extraction and Reduction Methods for Brain-Computer Interface

The motor imagery (MI) based brain-computer interface systems (BCIs) can help with new communication ways. A typical electroencephalography (EEG)-based BCI system consists of several components including signal acquisition, signal pre-processing, feature extraction and feature classification. This paper focuses on the feature extraction step and proposes to use a combination of different feature extraction and feature reduction methods. The research presented in the paper explores the methods of band power, time domain parameters, fast Fourier transform and channel variance for feature extraction. These methods are investigated by combining them in pairs. The application of two feature extraction methods increases the number of selected features that can be redundant or irrelevant. The utilization of too many features can lead to wrong classification results. Therefore, the methods of feature reduction have to be applied. The following feature reduction methods are investigated: principal component analysis, sequential forward selection, sequential backward selection, locality preserving projections and local Fisher discriminant analysis. The combination of the methods of fast Fourier transform, channel variance and principal component analysis performed the best among the combinations of methods. The obtained classification accuracy of the above-mentioned combination of the methods is much higher than that of the individual feature extraction method. The novelty of the approach is based on consolidated sequence of methods for feature extraction and feature reduction.
An Origami-Based Soft Actuator and the Application as A Soft Gripper

This study aims to investigate characteristics of origami-based elastomeric actuator and its application to soft gripper. The employed lightweight paper-elastomer structure demonstrates features of efficient actuation in three ways: (1) It experiences nearly 20% less pressure for equal bending amplitude than pneumatic network actuator (Pneu-Net) of the same weight does; and even less pressure than other actuators with non-linear bending behavior; (2) It bends fully in 50 milliseconds under pressurization at the rate of 2.4 L/min; (3) less energy is dissipated in the inflation-recovery cycle as ballooning effect is minimized in the design. Additionally, controllability is discussed by validating associations between pressure and bending angle, and between interaction force and pressure at a fixed bending angle. A soft robotic gripper comprised of three actuators is designed. Enveloping and pinch gripping experiments that performed on various shapes imply the potential of grasping a wide range of objects for many applications.