Reviewerly/exHarmony huggingface dataset:
Constructing a Convolutional Neural Network with a Suitable Capacity for a Semantic Segmentation Task

Although the state-of-the-art performance has been achieved in many computer vision tasks such as image classification, object detection, saliency prediction and depth estimation, Convolutional Neural Networks (CNNs) still perform unsatisfactorily in some difficult tasks such as human parsing which is the focus of our research. The inappropriate capacity of a CNN model and insufficient training data both contribute to the failure in perceiving the semantic information of detailed regions. The feature representations learned by a high-capacity model cannot generalize to the variations in viewpoints, human poses and occlusions in real-world scenarios due to overfitting. On the other hand, the under-fitting problem prevents a low-capacity model from developing the representations which are sufficiently expressive. In this chapter, we propose an approach to estimate the complexity of a task and match the capacity of a CNN model to the complexity of a task while avoiding under-fitting and overfitting. Firstly, a novel training scheme is proposed to fully explore the potential of low-capacity CNN models. The scheme outperforms existing end-to-end training schemes and enables low-capacity models to outperform models with higher capacity. Secondly, three methods are proposed to optimize the capacity of a CNN model on a task. The first method is based on improving the orthogonality among kernels which contributes to higher computational efficiency and better performance. In the second method, the convolutional kernels within each layer are evaluated according to their semantic functions and contributions to the training and test accuracy. The kernels which only contribute to the training accuracy but has no effect on the testing accuracy are removed to avoid overfitting. In the third method, the capacity of a CNN model is optimized by adjusting the dependency among convolutional kernels. A novel structure of convolutional layers is proposed to reduce the number of parameters while maintaining the similar performance. Besides capacity optimization, we further propose a method to evaluate the complexity of a human parsing task. An independent CNN model is trained for this purpose using the labels for pose estimation. The evaluation on complexity is achieved based on estimated pose information in images. The proposed scheme for complexity evaluation was conducted on the Pascal Person Part dataset and the Look into Person dataset which are for human parsing. The schemes for capacity optimization were conducted on our models for human parsing which were trained on the two data sets. Both quantitative and qualitative results demonstrate that our proposed algorithms can match the capacity of a CNN model well to the complexity of a task.
A Dynamic Map-based Framework for Real-Time Mapping of Vehicles and their Surroundings

The great attraction that unmanned vehicles have gained in the past years has marked this century as the era of automation. In particular, automated vehicles have recently become the center of attention of many research activities. All of which have had one goal in common: achieving a complete or partial automation of driving functions, all while ensuring safety and security of other traffic agents. It is with this purpose in mind that the concept of dynamic maps has been introduced to allow vehicles to be aware of their surroundings and have precise knowledge of their environment and all of its components. Aiming for a mapping system that sets out a layered view of a vehicle vicinity ranging from highly-static to highly-dynamic data layers, researchers have taken different approaches. In this paper, we present a system, inspired from the concept of dynamic maps, that collects data from user vehicles and maps them out by location and time, keeping a history of all recorded information. We then evaluate the performance of the system module in charge of localization and tracking of users' devices, and present and discuss the obtained results.
A second-order screening method for Preventive regions

The screening and ranking of expected accidents is often the first step in the safety and stability prevention and control strategy. The analysis time of the screening process is long and the calculation is large. In this paper, a second-order screening method for anticipatory accidents is proposed. in essence, all the expected events are screened quickly and the target set is obtained. then, according to the characteristics of each event in the target set, some events are selected for margin analysis. The proposed method can quickly obtain the prevention area. The simulation results show that the method is correct.
Accuracy-Aware Service Recommendation with Privacy

With the advent of IoT(Internet of Things) age, the variety and volume of web services have been increasing at an fast speed. This often leads to a heavy burden on users' service selections. Under this circumstances, a variety of methods such as Collaborative Filtering are adopted to deal with this challenging situation. While traditional Collaborative Filtering method has some shortcomings, one of which is that only centralized user-service data are considered while distributed quality data from multiple platform are ignored. Generally, service recommendation across different platforms often involves the data communication among the multiple platforms, during which user privacy may be disclosed and much computational time is required. Considering these challenges, a novel amplified LSH(Locality-Snsitive)-based service recommendation method, i.e., SR Amplified-LSH, is proposed in this paper. SR <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Amplified-LSH</sub> can achieve a good balance among recommendation accuracy, efficiency and user privacy. Finally, extensive experiments deployed on MovieLens dataset validate the feasibility of our proposed method.
Simulations for Multi-Agent Systems with Imperfect Information

Equivalence-checking and simulations are well-known methods used to reduce the size of a system in order to verify it more efficiently. While Alur et al. proposed a notion of simulation sound and complete for ATL as early as 1998, there have been very few works on equivalence-checking performed on extensions of ATL* with probabilities, imperfect information, counters etc. In the case of multi-agent systems (MASs) with imperfect information, the lack of sound and complete algorithm mostly follows from the undecidability of ATL model-checking. However, while ATL is undecidable overall, there exist sub-classes of MASs for which ATL becomes decidable. In this paper, we propose a notion of simulation sound for ATL/ATL* on any MASs and complete on naive MASs. Using our simulations we design an equivalence-checking algorithm sound and complete for MASs with public actions.
Quality evaluation of solar magnetic field images at EUV wavelengths in digital image correlation method

Application in the actual using, obviously different intensity distribution characteristics are shown in the images of solar magnetic field at extreme ultraviolet (EUV) wavelength. When it comes to displacement measurements by digital image correlation (DIC), it has a great impact on ultimate accur acy. It is an interesting and confusing issue that how to evaluate the quality of different regional solar magnetic field images at EUV wavelengths with a simple but effective parameter. This article presents a new valid part parameter for assessment of the quality of different part solar magnetic field images at EUV wavelengths used in DIC which is called comprehensive consideration of mean subset intensity and gradients (CCMSIG). Three regional images (active region, flare region, and coronal hole) are numerically translated in order to confirm that the validity of the novel concept and DIC method is used to compare the displacement measurement results with the accurate results. This paper also discusses the impact on accuracy of deformation measurements when it comes to the subset size associated with regional image quality and subset displacement functions. The results show that mean bias error and standard deviation (STD) for the sub-pixel displacement measurement are in close proximity to the CCMSIG of the calculated regional images and the high-quality regional images are supposed to be with small CCMSIG which is coincident with the theoretical predictions.
Robustness of Piece-Wise Linear Neural Network with Feasible Region Approaches

A Piece-wise Linear Neural Network (PLNN) is a deep neural network composed of only Rectified Linear Units (ReLU) activation function. Interestingly, even though PLNNs are a nonlinear system in general, we show that PLNNs can be expressed in terms of linear constraints because ReLU function is a piece-wise linear function. We suggested that the robustness of Neural Networks (NNs) can be verified by investigating the feasible region of these constraints. Intuitively, suggested robustness represents the minimum Euclidean distance from the input needed to change its predicted class. Moreover, the run-time of calculating robustness is as fast as a feed forward neural network.
Probabilistic Weighted Extreme Learning Machine for Robust Modeling

Radar performance prediction is becoming more and more important in Product-Life-Cycle-Management of radar. However, modeling the performance is difficult because of the strong nonlinearity from radar’s complex system as well as immeasurable effects like electromagnetic interference, instrument accuracy and so on. This paper proposes a probability weighted extreme learning machine to model the performance of radar under noise. First, a distributed extreme learning machine modeling is developed, upon which the probability distribution function (PDF) of multiple local models is estimated by the Parzen window method. This distribution function is further used as weights to integrate all local models to construct a global robust ELM model. The successful application of this robust probabilistic weighted ELM method to both artificial case and real life case demonstrates its great advantages in the modeling of an unknown system with various kinds of random noise.
Autonomous Robotic Rearrangement of Nuclear Fuel Pellets for Formation of Fixed Length Stacks

This paper present novel algorithms for automatic preparation of stacks of prescribed length of sintered UO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> pellets. They are used for fabrication of fuel pins for use in Nuclear Reactors. This system was developed to cut down on manual operations during fabrication of nuclear fuel. The solution integrates three technologies: precise vision based measurement of pellets and stacks, intelligent pellet rearrangement algorithm, and robotic transfer of pellets. In this paper, we mainly talk about the intelligent and efficient algorithms that rearrange stacks to prescribed length (within a tolerance of ± 1mm) through exchange, addition or removal of pellets. These algorithms have been tested systematically in simulation using pellet length distribution of a batch of pellets used for assembling fuel stacks. Simulation results were verified on a real setup comprising a KUKA KR-6 robot and a conveyor carrying a fuel tray under a pair of line-scan cameras. This development is expected to reduce worker exposure to radiation and radioactive contamination, apart from making the process fast, accurate and reliable.
Comparison of Two Data Compression Approaches for Surveillance and Detection Displays in Acoustic Processing Systems

Surveillance and detection displays in underwater acoustic processing systems provide information about acoustic frequency signatures radiated by the surface and submerged targets. Data compression is one of the most important tasks in surveillance and detection displays as there is always limited amount of space for data presentation due to limited sizes of displays and number of pixels. Hence there is always a need for an efficient and accurate algorithm that can reduce the number of data points without disturbing or losing the information present in the data. The data compression algorithm needs to be fast enough to cope up with the real time processing requirements required by the detection displays. In this paper we have proposed an improved data compression algorithm that is compared with the well-known “cubic spline interpolation”. The proposed algorithm has successfully reduced the data points and showed almost no loss in the frequency contents of data and its mapping on the surveillance and detection displays.
A Novel Scheme of Single-Line-Grounded-Fault Detection and Its Practical Implementation for Non-Effective Grounded System

Manual isolation of transmission line so far has been a common way for single-line-grounded-fault detection in non-effective grounded system. However, in practice, due to factors such as insufficient selection criterion and poor function, the line selection accuracy is not high and the application effect is not good as expected. This paper proposes a novel scheme of grounded-fault line detection which can selectively and sequentially perform line trip by dynamic weighed line selection and automatic sequential isolation. The proposed method can reduce manual participation, alleviate operating personnel work pressure and consequently improve the efficiency of fault isolation. Besides, the proposed scheme can shorten the duration of single-phase grounded fault and effectively reduce the hidden human safety risk.
Cascaded Scheme for Sport Event Classification based on Object Level Motion Trajectory

In this paper, a sport training events classification is proposed by a cascaded scheme based on object level motion trajectory. Firstly, a new dataset was introduced that separates and marks the ball-stopping events of sport training videos from 133 players cover 5 different schools. Secondly, object detection neural network is used to localize soccer and players in a video and a new method is proposed to separate the different ball-stopping events. Thirdly, a cascaded scheme is proposed by concatenating an object detection network with a Long Short-Term Memory (LSTM) network. The LSTM network is employed to classify discrete ball-stopping events based on the bounding boxes detected by the object detection network. Experiments tested on more than 1800 ball-stopping events show that the proposed method can achieve an average accuracy of 93.12%.
Graph Optimized Locality Preserving Projection Via Heuristic Optimization Algorithms

Dimensionality reduction has been an active research topic in hyperspectral image analysis due to complexity and non-linearity of the hundreds of the spectral bands. Locality preserving projection (LPP) is a linear extension of the manifold learning and has been very effective in dimensionality reduction compared to linear methods. However, its performance heavily depends on construction of the graph affinity matrix, which has two parameters need to be optimized: k-nearest neighbor parameter and heat kernel parameter. These two parameters might be optimally chosen simply based on a grid search when using only one representative kernel parameter for all the features, but this solution is not feasible when considering a generalized heat kernel in construction the affinity matrix. In this paper, we propose to use heuristic methods, including harmony search (HS) and particle swarm optimization (PSO), in exploring the effects of the heat kernel parameters on embedding quality in terms of classification accuracy. The preliminary results obtained with the experiments on the hyperspectral images showed that HS performs better than PSO, and the heat kernel with multiple parameters achieves better performance than the isotropic kernel with single parameter.
Compression Method in JPEG Standard

Data compression is to minimize the communication and storage. The bandwidth and storage required for a given communication system. Data compression is obviated by wideband technologies such as optical fiber communication networks, packet radio and optical storage disk. Images can generate data in such high volumes that any transmission media can be quickly overwhelmed. Personal computers and workstations has led to an increasing demand to store images in compressed from on magnetic or optical disks. The massive efforts taking place today to adopt national and international standards for signal compression. Example is the JPEG (Joint Photographic Experts Group) standard for image compression. Compression in the image from RGB in JPEG standard converted to YCbCr. Each Y, Cb, Cr divided in block 8×8 pixel. The image 8×8 pixel then transformed use DCT. Image from DCT quantized yield DC and AC component. DC component processed use DPCM (Differential Pulse Code Modulation) use Huffman table yield AC bit stream. Merge DC component and AC component yield JPEG bit stream. To get image from JPEG bit stream first use Huffman decoding. From Huffman decoding dequantized. Then transform use IDCT. Image from IDCT merge block 8×8 pixel. From block 8×8 pixel transformed YCbCr to RGB. Then get the image. Process compression is consist of lossy compression and lossless compression. Lossy compression is compression with change of data but not change meaning of information in there. Lossless compression is compression not to change data.
Rancang Bangun Detektor Standart Preform Botol Minuman Menggunakan Metode Jaringan Saraf Tiruan (Design Of Standart Detector Standart Drink Bottle Using Artificial Neural Network Method)

Preform is a semi-finished material from a bottle before cooking in the blowing process. Standards form most, same shapes and colors in one production. However, it does not have to close in one production which requires several preforms that have different colors and weights than other preforms so that they are not included in the standard and must be rejected. In this case a standard detector and color of the preform drink bottle were made using backpropagation neural network method where hardware that loaded arduino uno, photodiode sensor, load cell and HX 711 module and LCD i2c 16 x 2. Photodiode sensors can be used in blue preform together with load cell which is translated directly preform which is directly converted by the HX711 module. Two input data is then processed in the Arduino UNO module. Data output from Arduino UNO is approved on the LCD and processed in the Artificial Neural Network in Matlab on the laptop. The final output of the research results will be displayed in the command window matlab column containing rich "YES" or "NO". In this study backpropagation artificial neural networks as a method to provide accurate assessment by displaying the test results with 19 grams, color density 8 with a voltage of 0.038 Volts and output data is 1 with error data -4.75E13.
Electronic dictionaries of Ingrian and Votic – minor Finnic languages on the verge of extinction

This paper presents electronic dictionaries of Ingrian and Votic, two minor Finnic languages on the verge of extinction. The dictionaries are designed to facilitate the language learning when no native speakers can work as teachers. They include a morphological synthesizer and provide audio illustrations for different forms. (session 3.3.3)
Sms Encryption Application Using 3Des (Triple Data Encryption Standard) Algorithm Based on Android

Abstract Security is an important aspect in communicating using information technology. SMS is a service provided by mobile phones to communicate by sending short messages quickly and cheaply. Short message delivery is done by SMS network, so the messages are not sent directly to the destination mobile phone. Therefore the messages that are sending by the SMS service need to be protected, so the security of the messages sent to the SMS network remains protected by its confidentiality. In this study the software will be built to improve the security of messages on SMS communication. The software that is built serves to encrypt messages that will be sent and decrypt messages with entered through the SMS service. Software built using the 3DES (Triple Data Encryption Standard) algorithm. The 3DES algorithm is a flow algorithm (block cipher) which belongs to the type of symmetry key cryptography system. The implementation of the 3DES algorithm on software that is built using the Java-based programming language Java. So only software that uses the Android operating system can use this encryption and decryption application.
August Meineke, Ugo Grozio e le traduzioni dei frammenti dei poeti comici dell’<i>archaia</i>

The paper deals with an aspect of the history of the modern translation of the fragments of ancient comedy, namely the reception of Hugo Grotius’ first translations in Latin (1626) in Meineke’s Fragmenta Comicorum Graecorum (1839-1841); the main focus is on eight cases (including the peculiar one of Pherecr. 283 K.-A., dubium) in which, as an integral part of his exegetical process, Meineke discusses, comments, and where necessary emends Grotius’ translations.
Real-time 3D Traffic Cone Detection for Autonomous Driving.

Considerable progress has been made in semantic scene understanding of road scenes with monocular cameras. It is, however, mainly focused on certain specific classes such as cars, bicyclists and pedestrians. This work investigates traffic cones, an object category crucial for traffic control in the context of autonomous vehicles. 3D object detection using images from a monocular camera is intrinsically an ill-posed problem. In this work, we exploit the unique structure of traffic cones and propose a pipelined approach to solve this problem. Specifically, we first detect cones in images by a modified 2D object detector. Following which the keypoints on a traffic cone are recognized with the help of our deep structural regression network, here, the fact that the cross-ratio is projection invariant is leveraged for network regularization. Finally, the 3D position of cones is recovered via the classical Perspective n-Point algorithm using correspondences obtained from the keypoint regression. Extensive experiments show that our approach can accurately detect traffic cones and estimate their position in the 3D world in real time. The proposed method is also deployed on a real-time, autonomous system. It runs efficiently on the low-power Jetson TX2, providing accurate 3D position estimates, allowing a race-car to map and drive autonomously on an unseen track indicated by traffic cones. With the help of robust and accurate perception, our race-car won both Formula Student Competitions held in Italy and Germany in 2018, cruising at a top speed of 54 km/h on our driverless platform “gotthard driverless” https://youtu. be/HegmIXASKow?t==11694. Visualization of the complete pipeline, mapping and navigation can be found on our project page http://people.ee.ethz.ch/=tracczucrich/Trnfflcf.lonc/.