Reviewerly/exHarmony huggingface dataset:
Model-Agnostic Explanations for Decisions Using Minimal Patterns

Recently, numerous high-performance machine learning models have been proposed. Unfortunately, such models often produce black-box decisions derived using opaque reasons and logic. Therefore, it is important to develop a tool that automatically gives the reasons underlying the black-box model’s decision. Ideally, the tool should be model-agnostic: applicable to any machine-learning model without knowing model details. A well-known previous work, LIME, is based on the linear decision. Although LIME provides important features for the decision, the result is still difficult to understand for users because the result might not contain the features required for the decision. We propose a novel model-agnostic explanation method named MP-LIME. The explanation consists of feature sets, each of which can reconstruct the decision correctly. Thereby, users can easily understand each feature set. By comparing our method to LIME, we demonstrate that our method often improves precision drastically. We also provide practical examples in which our method provides reasons for the decisions.
INTERPRETATION OF LITERATURE ESSENCE BY STRUCTURALISM AND POSTSTRUCTURALISM

The article attempts to study the interpretation of literature essence on the basis of the conceptions of structuralism and poststructuralism. The focus is on literature as a result of creativity with special requirements. Basing on the analysis of the attitude of the two schools (structuralism and poststructuralism) to the text, sign, meaning, it is shown that structuralists and poststructuralists promoted the emergence of new views on the essence of literature in terms of understanding the sign and symbol as the basis of language, writing and literature.
ParRefCom

Transmission, storage, and archival of high-throughput sequencing (HTS) short-read datasets pose significant challenges due to the large size of such datasets. Constant improvements to HTS technology, in the form of increasing throughput and decreasing cost, and its increasing adoption amplify the problem. General-purpose compression algorithms have been widely adopted for representing read datasets in a compact form. However, they are unable to fully leverage the domain-specific properties of read datasets. In response, researchers proposed special-purpose compression algorithms which improve upon the compression efficiency of general-purpose compression algorithms. In this paper, we present ParRefCom, a parallel reference-based algorithm for compressing HTS genomics short-read datasets. HTS instruments are typically used to generate paired-end reads as they hold significance for biological analysis. In contrast to existing special-purpose compression algorithms, ParRefCom treats paired-end reads as first-class citizens. Owing to this treatment of paired-end reads, our algorithm is able to significantly improve compression efficiency over the state-of-the-art. More specifically, for a benchmark human dataset, the size of the compressed output is 21% smaller than that produced by the current best algorithm. Further, ParRefCom is scalable and its compression and decompression speeds are better than those of reference-free methods.
Research on Cross-Language Retrieval Using Bilingual Word Vectors in Different Languages

Bilingual word vectors have been exploited a lot in cross-language information retrieval research. However, most of the research is currently focused on similar language pairs. There are very few studies exploring the impact of using bilingual word vectors for cross-language information retrieval in long-distance language pairs. In this paper, it systematically analyzes the retrieval performance of various European languages (English, German, Italian, French, Finnish, Dutch) as well as Asian languages (Chinese, Japanese) in the adhoc task of CLEF 2002–2003 campaign. Genetic proximity was used to visually represent the relationships between languages and compare their cross-lingual retrieval performance in various settings. The results show that the differences in language vocabulary would dramatically affect the retrieval performance. At the same time, the term by term translation retrieval method performs slightly better than the simple vector addition retrieval methods. It proves that the translation-based retrieval model can still maintain its advantage under the new semantic scheme.
A Sparse Filtering-Based Approach for Non-blind Deep Image Denoising

During the image acquisition process, some level of noise is usually added to the data mainly due to physical limitations of the sensor, and also regarding imprecisions during the data transmission and manipulation. Therefore, the resultant image needs to be further processed for noise attenuation without losing details. In this work, we attempt to denoise images using the advantage of sparse-based encoding and deep networks. Experiments on public images corrupted by different levels of Gaussian noise support the effectiveness of the proposed approach concerning some state-of-the-art image denoising approaches.
On Practical Approach to Uniform Quantization of Non-redundant Neural Networks

The neural network quantization is highly desired procedure to perform before running the neural networks on mobile devices. Quantization without fine-tuning leads to accuracy drop of the model, whereas commonly used training with quantization is done on the full set of the labeled data and therefore is both time- and resource-consuming. Real life applications require simplification and acceleration of the quantization procedure that will maintain the accuracy of full-precision neural network, especially for modern mobile neural network architectures like Mobilenet-v1, MobileNet-v2 and MNAS. Here we present two methods to significantly optimize the training with the quantization procedure. The first one is introducing the trained scale factors for discretization thresholds that are separate for each filter. The second one is based on mutual rescaling of consequent depth-wise separable convolution and convolution layers. Using the proposed techniques, we quantize the modern mobile architectures of neural networks with the set of train data of only $$\sim $$ 10% of the total ImageNet 2012 sample. Such reduction of the train dataset size and a small number of trainable parameters allow to fine-tune the network for several hours while maintaining the high accuracy of the quantized model (the accuracy drop was less than 0.5%). The ready-for-use models and code are available at: https://github.com/agoncharenko1992/FAT-fast-adjustable-threshold .
A Human-Centered Design Process for an Augmented Reality based Training System

The purpose of this paper is to present a research proposal for an AR-based assembly training system. Findings from previous user studies on AR-based assembly training and their limitations are analyzed. Research questions are drafted and a research proposal for future research of an AR-based assembly training within the frame of a user study is presented. An iterative human-centered design process is proposed that integrates both production employees as well as training content creators (e.g. trainers or foremen) as end users in a co-creation process of an industrial assembly training system, which has not been done in previous studies. Including user groups in the early stages of the process will help to understand different user needs and requirements for an AR-based assembly training system that will be evaluated in an industrial training setting. A co-creation workshop according to the Design Sprint process is proposed to integrate different key users in the development process of the AR-based assembly training system. The final aim of this research project is to gain insights of how an assembly training system should be designed in order to create real value for the different users and the organization.
Approximate Pattern Matching using Hierarchical Graph Construction and Sparse Distributed Representation

With recent developments in deep networks, there have been significant advances in visual object detection and recognition. However, some of these networks are still easily fooled/hacked and have shown "bag of features" failures. Some of this is due to the fact that even deep networks make only marginal use of the complex structure that exists in real-world images, even after training on huge numbers of images. Biology appears to take advantage of such a structure, but how?
On the Information Ratio of Graphs with Many Leaves

Abstract We investigate the information ratio of graph-based secret sharing schemes. This ratio characterizes the efficiency of a scheme measured by the amount of information the participants must remember for each bits in the secret. We examine the information ratio of several systems based on graphs with many leaves, by proving non-trivial lower and upper bounds for the ratio. On one hand, we apply the so-called entropy method for proving that the lower bound for the information ratio of n -sunlet graphs composed of a 1-factor between the vertices of a cycle C n and n independent vertices is 2. On the other hand, some symmetric and recursive constructions are given that yield the upper bounds. In particular, we show that the information ratio of every graph composed of a 1-factor between a complete graph K n and at most 4 independent vertices is smaller than 2.
Overall Loss for Deep Neural Networks

Convolutional Neural Network (CNN) have been widely used for image classification and computer vision tasks such as face recognition, target detection. Softmax loss is one of the most commonly used components to train CNN, which only penalizes the classification loss. So we consider how to train intra-class compactness and inter-class separability better. In this paper, we proposed an Overall Loss to make inter-class having a better separability, which means that Overall loss penalizes the difference between each center of classes. With Overall loss, we trained a robust CNN to achieve a better performance. Extensive experiments on MNIST, CIFAR10, LFW (face datasets for face recognition) demonstrate the effectiveness of the Overall loss. We have tried different models, visualized the experimental results and showed the effectiveness of our proposed Overall loss.
Software Requirements Elicitation Based on Ontology Learning

User demand is the key to software development. The domain ontology established by artificial intelligence can be used to describe the relationship between concepts and concepts in a specific domain, which can enable users to agree on conceptual understanding with developers. This paper uses the ontology learning method to extract the concept, and the ontology is constructed semi-automatically or automatically. Because the traditional weight calculation method ignores the distribution of feature items, the concept of information entropy is introduced and the CCM method is further integrated. This method can improve the automation degree of ontology construction, and also make the user requirements of software more accurate and complete.
OpenHAR: A Matlab Toolbox for Easy Access to Publicly Open Human Activity Data Sets—Introduction and Experimental Results

OpenHAR is a toolbox for Matlab to combine and unify 3D accelerometer data of ten publicly open data sets. This chapter introduces OpenHAR and provides initial experimental results based on it. Moreover, OpenHAR provides an easy access to these data sets by providing them in the same format, and in addition, units, measurement range, sampling rates, labels, and body position IDs are unified. Moreover, data sets have been visually inspected to fix visible errors, such as sensor in wrong orientation. For Matlab users OpenHAR provides code which user can use to easily select only desired parts of this data. This chapter also introduces OpenHAR to users without Matlab. For them, the whole OpenHAR data is provided as a one .txt-file. Altogether, OpenHAR contains over 280 h of accelerometer data from 211 study subjects performing 17 daily human activities and wearing sensors in 14 different body positions. This chapter shown the first experimental results based on OpenHAR data. The experiment was done using three classifiers: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and classification and regression tree (CART). The experiment showed that using LDA and QDA classifiers and OpenHAR data, as high recognition rates can be achieved in a previously unseen test data than by using a data set specially collected for this purpose. With CART the results obtained using OpenHAR data were slightly lower.
Innovations Using Blockchain–Part 2

The articles in this special section focus on new technologies and applications supported by Blockchain.
Research and Implementation of Globalize JS in Improving the Level of Machine Translation

At present, there are three basic solutions for localization in industrial production environment: traditional source code modification, micro-service architecture model and browser plug-in model. Now, this paper propose a new solution NSX, which uses machine learning and machine translation technology to implement L2 and L3 internationally and reduce the coding burden of developers. Reduce the possibility of localization leading to functional problems; Eliminate QE work in localization functional testing.
A Novel Method for Text Detection in Arbitrary Scenes Based on Multi-scale Segmentation Networks

In recent years, most of the scene text detection algorithms are benefited from the development of deep learning methods which depend on bounding box regression. These methods usually perform two kinds of predictions: text/non-text classification and location regression. Regression may be an important part when predicting bounding boxes, but it is not necessary because the text/non-text prediction can be used as a semantic segmentation which contains location information. However, text instances usually distribute closely in scene images, which is difficult to separate. Therefore, instance segmentation is proposed to fix the problem. In this paper, we propose a pixel-based method based on an efficient segmentation network with multi-scale features. Firstly, the high-level and low-level features are extracted from the modified Xception network. The positive links between the pixels are then calculated to complete the instance segmentation. After we obtain the two predictions of both features, the standard Non-Maximum Suppression (NMS) is used to remove the redundant frames and keep the best output of the bounding boxes. The results show that our method performs well on several public benchmarks, and this method requires less training time and eliminates the trouble of setting anchors.
FMNet: Multi-agent Cooperation by Communicating with Featured Message Network

Multi-agent systems are taking great part in nowadays industries. Cooperative environments may help agents perform better, but the training process is usually time consuming and costly. Although the training process can be speeded up by communicating with important information (such as observations, action sequence, model data and other potential data) between agents, redundant information is still a disturbance term. In this paper, we present a method, called Featured Message Network, which uses a fixed random network to extract features of information from agents as featured message. By communicating with featured messages, agents can cooperate more efficiently while not suffering from useful information loss or redundant information disturbing. We optimized a traditional Deep Q-learning system with our method. We tested our method on Meeting in a Grid problem and experimental results show that training process becomes faster and more stable with our method.
How to Sign with White-Boxed AES

We investigate the possibility to use obfuscated implementations of the Advanced Encryption Standard AES ("white-boxed AES") to devise secure signature schemes. We show that the intuitive idea to use AES-based message authentication codes to sign, and the white-boxed implementation to verify, fails in general. This underlines that providing a secure white-box implementation is only the first step and that using it securely as a component in cryptographic protocols may be harder than originally thought. We therefore provide secure signature schemes based on white-boxed AES and on random oracles, as well as stateful and stateless constructions without random oracles. All our solutions are shown to be secure for reasonable parameters.
Face Recognition Based on the Improved Sparse Representation

In recent years, face recognition is a hot research topic in the field of biometrics, and sparse representation is a hot topic in this field. In order to solve the problem that the sparse representation algorithm does not have good recognition effect in the face image training samples and test samples including both expression changes and illumination condition changes, a face recognition algorithm based on sparse coefficient and residual fusion is proposed.. The algorithm combines the sparse coefficient into the residual classification decision, considering that the sparse coefficient is the fidelity condition that discriminates the correlation between the feature correlation and the class in the training sample class, and the feature correlation between the training sample and the test sample class. The new residual is defined as the classification standard, and the improved residual fully exploits the correlation information of the sparse coefficient. At the same time, the algorithm in this article is combined with the PCA feature extraction method to effectively reduce the data dimension while ensuring the recognition rate of face images. Several experiments were carried out in the standard face database to verify the effectiveness of the proposed method, and it is proved that the algorithm is robust.
The Automatic Extraction Method of Webpage News Keywords Based on the Improved CART Algorithm

This paper presents a model of keyword extraction by using CART decision tree, which is mainly applied in the extraction of key words. This paper applies the CART decision tree method to keyword extraction for the first time. When used the CART decision tree method, the word position, the relative frequency, the number of words, the title and so on are selected as the eigenvector to find the text rules. On the basis of the CART decision tree method, the algorithm is improved by combining the improved attribute elements with high similarity to improve the accuracy of classification and reduce the complexity of the algorithm. This paper chooses a large number of archival texts as a training set, establishes a model by training and learning through the CART decision tree method. The establishment of a good model can greatly reduce the workload of keyword extraction and improve the accuracy of keyword extraction.
Towards Agile Architecting: Proposing an Architectural Pathway Within an Industry 4.0 Project

Software architecture design, when performed in context of agile software development (ASD), sometimes referred as “agile architecting”, promotes the emerging and incremental design of the architectural artifact, in a sense of avoiding “big design upfront” (BDUF). Performing “agile architecting” is not always straightforward, mainly because the architecture has a required life-cycle and each stage responds to different needs. There is a lack of a pathway that guides agile architecting in an end-to-end approach (from business requirements to deployment). This paper proposes a pathway that includes architecture design from software development life-cycle (SDLC) stages of software development that use ASD approaches, where two main artifacts are considered: a candidate logical architecture and a refined logical architecture. These artifacts are included in a pathway where they receive input from business processes perspective and guide software development during ASD iterations (Sprints).
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Facial Expression Recognition by Considering Nonuniform Local Binary Patterns

Recognizing a face with an expression has paying attention due to its well-known applications in a broad range of fields like data-driven animation, human–machine interaction, robotics, and driver fatigue detection. People can vary significantly their facial expression; hence, facial expression recognition is not an easy problem. This paper presents a significant contribution for facial expression recognition by deriving a new set of stable transitions of local binary pattern by selecting the significant nonuniform local binary patterns. The proposed patterns are stable, because of the transitions from two or more consecutive ones to two or more consecutive zeros. For better recognition rate, the new set of patterns are combined with uniform patterns of local binary pattern. A distance function is used on proposed texture features for effective facial expression recognition. Preprocessing method is also used to get rid of the effects of illumination changes in facial expression by preserving the significant appearance details that are needed for facial expression recognition. The investigational analysis was done on the popular JAFFE facial expression database and has shown good performance.
Study on the Theory and Practice of Mechanical Design

Mechanical design is an important course combined with theory and practice. Usually, the course design is arranged after the theory course to train the ability of the machine design. The practice content of course design can be reasonably separated according to the theory knowledge of the relevant chapter. In the reform process, the course specific chapter and the content of the design example are combined and studied. By means of detailing and early studying the design practicing content, student can know the purpose and significance of studying after the part of course studying. In the paper, the measures are also put forward in response to the problems during the reform implementation.
A Novel Pedestrian Positioning System Using Monocular in-Vehicle Cameras

Monocular depth estimation and pedestrian positioning have become promising in recent years. However, there are few systems specifically designed for pedestrian positioning and most of the current depth estimators deal with the whole images which is redundant. We propose a pedestrian positioning system implemented on monocular in-vehicle cameras. This is an end-to-end system integrating pedestrian detection algorithm, distance estimation algorithm and mapping algorithm. The input of the system is monocular images, and the output which can be visualized is the position information of pedestrians. In order to improve efficiency of the system, we propose a depth estimation algorithm using bounding boxes to obtain distances of pedestrians. Besides, a new pedestrian occlusion (NPO) dataset is created and an anti-occlusion detection algorithm is proposed based on the NPO dataset to eliminate the effects of pedestrian occlusion. In contrast to the current depth estimators, our new system can obtain the whole GNSS information of pedestrians rather than the depth information only. Our method produces competitive results for monocular depth estimation on the KITTI driving dataset. Furthermore, on the dataset collected by ourselves, the system outperforms the current depth estimators especially for estimating long distances.