Reviewerly/exHarmony huggingface dataset:
All-optical diffractive neural networked terahertz hologram

Holography has garnered an explosion of interest in tremendous applications, owing to its capability of storing amplitude and phase of light and reconstructing the full-wave information of targets. Spatial light modulators, metalenses, metasurfaces, and other devices have been explored to achieve holographic images. However, the required phase distributions for conventional holograms are generally calculated using the Gerchberg-Saxton algorithm, and the iteration is time-consuming without Fourier transform or other acceleration techniques. Few studies on designing holograms using artificial intelligence methods have been conducted. In this Letter, a three-dimensional (3D)-printed hologram for terahertz (THz) imaging based on a diffractive neural network (DNN) is proposed. Target imaging letters "THZ" with uniform field amplitude are assigned to a predefined imaging surface. Quantified phase profiles are primarily obtained by training the DNN with the target image and input field pattern. The entire training process takes only 60 s. Consequently, the hologram, that is, a two-dimensional array of dielectric posts with variational heights that store phase information, is fabricated using a 3D printer. The full-wave simulation and experimental results demonstrate the capability of the proposed hologram to achieve high-quality imaging in the THz regime. The proposed lens and design strategy may open new possibilities in display, optical-data storage, and optical encryption.
Robot path planning optimization method based on heuristic multi-directional rapidly-exploring tree

Aiming at the problems of low exploration efficiency and high optimal solution cost for existing robot path planning methods, a robot path planning optimization method based on heuristic multi-directional rapidly-exploring tree is implemented. In high-dimensional configuration space, drawing on the Rapid-exploration Random Tree (RRT) related algorithm idea, directional sampling control module works under the guidance of the robot goal course as a heuristic exploration. A flexible multi-directional rapidly-exploring tree construction method is used due to a degree of directional instability. In accordance with the principle of the centripetal growth of the tree, new multi-directional trees will be built on demand to arrive at specific coverage of the space. Then based on the previous path exploration vertices, through the merging method of the trees, a closed loop path is formed and optimized to finally generate a relative optimal path. Simulation experiment results show that this method could effectively improve the exploring efficiency with low computational cost.
Hidden Fault Analysis of FPGA Projects for Critical Applications

This paper focuses on the problem of hidden faults, which is seen like a growth one inherent in modern safety-related systems. The special feature of these is the designing for operation in two modes: normal and emergency. Digital components can accumulate hidden failures over a long-term normal mode. This reduces their fault tolerance and functional safety of the system in the most responsible emergency mode. Two conditions for occurrence of the hidden fault problem as a growth one are considered in view of a resource approach, which in the development of models, methods and means highlights levels of replication and diversification. Safety-related systems are analyzed as computer systems that have increased to the level of diversification to address the security challenge. Their components are still stamped at a level of resource replication using matrix structures to process data in parallel codes. Fault-tolerant solutions become fault-safe with a sufficient level of circuit checkability, which is commonly known as testability, i.e. structural checkability, depending only on the structure of the circuit. In the operating mode, the checkability becomes structurally-functional, and in critical applications it is converted into a dual-mode, the shortage of which causes the hidden fault problem. A method of analyzing circuits for the possibility of hidden faults is suggested. The method is illustrated on example of an iterative array multiplier implemented in an FPGA project with a LUT-oriented architecture. A program model for the resulting scheme has been developed and potentially dangerous points have been found in it in which the hidden fault problem of may manifest itself.
Self-organizing neighborhood-based differential evolution for global optimization

Combining neighborhood utilization technique (NUT) has shown a tremendous benefit to differential evolution (DE) due to that the acquired neighborhood information of population is of great help in guiding the search. However, in most NUT-based DE algorithms, on the one hand, the neighborhood relationships between individuals cannot be effectively and properly learned, and on the other hand, the search roles of different individuals have not yet been fully considered in the design of the NUT. Therefore, this study develops a novel NUT, termed self-organizing neighborhood (SON), with three features: 1) the neighborhood relationships between individuals are incrementally learned and extracted by self-organizing map with the cosine similarity; 2) the neighborhood sizes for different individuals are adaptively adjusted according to their distinct roles in the search; 3) the evolution direction constructed with the neighborhood of each individual is incorporated into the mutation process to guide the search. By combining SON with DE, a SON-based DE (SON-DE) framework is proposed for global optimization. Experimental results on 58 real-parameter functions and 17 real-world problems have demonstrated the superiority of SON-DE in comparison with several state-of-the-art DE algorithms and evolutionary algorithms (EAs).
Redundancy and Complexity Metrics for Big Data Classification: Towards Smart Data

It is recognized the importance of knowing the descriptive properties of a dataset when tackling a data science problem.Having information about the redundancy, complexity and density of a problem allows us to make decisions as to which data preprocessing and machine learning techniques are most suitable.In classification problems, there are multiple metrics to describe the overlapping of the features between classes, class imbalances or separability, among others.However, these metrics may not scale up well when dealing with big datasets, or may not simply be sufficiently informative in this context.In this paper, we provide a package of metrics for big data classification problems.In particular, we propose two new big data metrics: Neighborhood Density and Decision Tree Progression, which study density and accuracy progression by discarding half of the samples.In addition, we enable a number of basic metrics to handle big data.The experimental study carried out in standard big data classification problems shows that our metrics can quickly characterize big datasets.We identified a clear redundancy of information in most datasets, so that, discarding randomly 75% of the samples does not drastically affect the accuracy of the classifiers used.Thus, the proposed big data metrics, which are available as a Spark-Package, provide a fast assessment of the shape of a classification dataset prior to applying big data preprocessing, toward smart data.
A differential evolutionary adaptive Harris hawks optimization for two dimensional practical Masi entropy-based multilevel image thresholding

The prey gets completely exhausted in Harris hawks optimization (HHO), when the escape energy is zero. Its exploration ability is limited. The random operator selected in HHO is a waste of search agents (Harris hawks). To solve the problem, we propose a new differential evolutionary adaptive Harris Hawks optimization (DEAHHO). In this effort, the exploration phase is suitably modified to limit the escape energy within the range 2,0. Also, it is made adaptive to decide when the Harris hawk would do perch along with the other family members or move to a random tall tree. Further, the differential evolutionary concept is incorporated to enhance the exploration ability. Recently, 1-D histogram-based multilevel image thresholding methods, using Masi entropic function, are used for multilevel image thresholding. However, the contextual information is missing in 1-D formulation. To solve this problem, a new 2-D practical Masi entropy function is suggested in this work. Widely used 23 benchmark test functions are considered to validate our DEAHHO method. In this experiment, 500 images from the Berkeley Segmentation Dataset BSDS 500 are used. It is compared with the state-of-the-art methods and found better than other methods. The method could be used in the segmentation of biomedical images.
Validating an Instrument to Measure Teachers’Preparedness to Use Digital Technology in their Teaching

In order to effectively integrate digital technology into education, it is necessary to examine and understand teachers' preparedness to use digital technology in education. The objective of this p ...
Mechatronics Design of High-Altitude Balloon Paulet-1 for Peruvian Aerospace Monitoring

This research has been implemented with a focus on the optimal mechatronics resources for Aerospace Monitoring in Peruvian Sky, specifically on the Troposphere. The device is called Paulet-1, which is a prototype developed in the Mechatronics Laboratory at Ricardo Palma University, Peru from 2017 to 2018. The design consisted of the analysis of the size, weight and price in order to create a sustainable embedded system to measure variables of humidity, pressure, temperature and localization, besides, the development was based on the technological risk caused by the earth’s atmosphere conditions. The Tests were performed in 3 locations, the First was in Santiago de Surco District, the Second was in Lurín District and the Third was in Asia District, all were in Lima Province. In conclusion, favorable results were achieved; therefore, the next version of the prototype Paultet-2 is confirmed to be ready for developing in 2020.
How Online Content Providers Moderate User‐Generated Content to Prevent Harmful Online Communication: An Analysis of Policies and Their Implementation

This article reports two studies conducted in the United States, Germany, South Korea, and China to examine how online content providers (OCPs) exercise their responsibility in dealing with harmful online communication (HOC) by moderating user‐generated content. The first study employed content analysis of 547 HOC policy documents. In the second study, 41 representatives of OCPs were interviewed regarding the implementation of these policies. We show that HOC policies are most often communicated through user‐unfriendly terms of service. Only Korean OCPs present their policies very vividly. Few organizations, mainly United States and German, encourage counter‐speech. The most common organizational actions against HOC mentioned in the policies are deleting posts or blocking accounts. The interviews reveal, however, that organizations—apart from those from China—are cautious in implementing such reactive actions. They fear accusations of censorship and acknowledge the tension between free speech and their content moderation practice. What emerged as the “gold standard” for identifying HOC was manual inspection. However, organizations operating large platforms widely apply machine‐learning technology or artificial intelligence. In sum, our research suggests that OCPs are not proactive enough in their communication for HOC prevention and often focus more on avoiding legal ramifications than on educating users when handling HOC.
EmoDet2: Emotion Detection in English Textual Dialogue using BERT and BiLSTM Models

Emotion detection is one of the most challenging problems in the automated understand of language. Understanding human emotions using text without facial expression is considered a complicated task. Therefore, building a machine that understands the context of the sentences and differentiates between emotions has motivated the machine learning community recently. We propose a system to detect emotions using deep learning approaches. The main input to the system is a combination of GloVe word embeddings, BERT Embeddings and a set of psycholinguistic features (e.g. from AffectiveTweets Weka-package). The proposed system (EmoDet2) is combining a fully connected neural network architecture and BiLSTM neural network to obtain performance results that show substantial improvements (F1-Score 0.748) over the baseline model provided by Semeval-2019 / Task-3 organizers (F1-score 0.58).
Converse for Multi-Server Single-Message PIR with Side Information

Multi-server single-message private information retrieval is studied in the presence of side information. In this problem, K independent messages are replicatively stored at N non-colluding servers. The user wants to privately download one message from the servers without revealing the index of the message to any of the servers, leveraging its M side information messages. We assume that the servers only know the number of the side information messages available at the user but not their indices. We prove a converse bound on the maximum download rates, which coincides with the known achievability scheme proposed by Kadhe et. al.. Hence, we characterize the capacity for this problem, which is${\left( {1 + \frac{1}{N} + \frac{1}{{{N^2}}} + \cdots + \frac{1}{{N\left\lceil {\frac{K}{{M + 1}}} \right\rceil - 1}}} \right)^{ - 1}}$. The proof leverages a novel concept that we call virtual side information, which, for a fixed query and any message, identifies the side information that would be needed in order to recover that message.
Query Reconstruction Network for Referring Expression Image Segmentation

Referring expression image segmentation aims at segmenting out the object described by a natural language query. Due to the diversity of visual content and language descriptions, it is very challenging to accurately model the correspondence between the vision and language, which inevitably produces some undesired segmentation objects from the queries. In this paper, we propose a query reconstruction network (QRN) to build more consistent corresponding relations between the language queries and object segmentation results. QRN not only generates segmentations from the queries and images but also reversely reconstructs the queries from the segmentations and the images. Through query reconstruction, QRN can confirm the vision-language consistency between the segmentations and queries. In the inference stage, for inconsistent segmentations and queries, we propose an iterative segmentation correction (ISC) method to correct them. ISC takes the difference between the reconstructed and input queries as a loss to optimize the proposed QRN. Then, the proposed QRN can generate new segmentations and queries. By iterative optimization, the segmentations can be gradually corrected. Extensive experiments on four referring expression image segmentation databases demonstrate the effectiveness of the proposed method.
Co-Saliency Detection With Co-Attention Fully Convolutional Network

Co-saliency detection aims to detect common salient objects from a group of relevant images. Some attempts have been made with the Fully Convolutional Network (FCN) framework and achieve satisfactory detection results. However, due to stacking convolution layers and pooling operation, the boundary details tend to be lost. In addition, existing models often utilize the extracted features without discrimination, leading to redundancy in representation since actually not all features are helpful to the final prediction and some even bring distraction. In this paper, we propose a co-attention module embedded FCN framework, called as Co-Attention FCN (CA-FCN). Specifically, the co-attention module is plugged into the high-level convolution layers of FCN, which can assign larger attention weights on the common salient objects and smaller ones on the background and uncommon distractors to boost final detection performance. Extensive experiments on three popular co-saliency benchmark datasets demonstrate the superiority of the proposed CA-FCN, which outperforms state-of-the-arts in most cases. Besides, the effectiveness of our new co-attention module is also validated with ablation studies.
A New Feature Selection Method for Text Classification Based on Independent Feature Space Search

Feature selection method is designed to select the representative feature subsets from the original feature set by different evaluation of feature relevance, which focuses on reducing the dimension of the features while maintaining the predictive accuracy of a classifier. In this study, we propose a feature selection method for text classification based on independent feature space search. Firstly, a relative document-term frequency difference (RDTFD) method is proposed to divide the features in all text documents into two independent feature sets according to the features’ ability to discriminate the positive and negative samples, which has two important functions: one is to improve the high class correlation of the features and reduce the correlation between the features and the other is to reduce the search range of feature space and maintain appropriate feature redundancy. Secondly, the feature search strategy is used to search the optimal feature subset in independent feature space, which can improve the performance of text classification. Finally, we evaluate several experiments conduced on six benchmark corpora, the experimental results show the RDTFD method based on independent feature space search is more robust than the other feature selection methods.
How Much Deep Learning does Neural Style Transfer Really Need? An Ablation Study

Neural style transfer has been a "killer app" for deep learning, drawing attention from and advertising the effectiveness to both the academic and the general public. However, we have found by ablative experiments that optimizing an image in the way neural style transfer does, while the objective functions (or more precisely, the functions to transform raw images to corresponding feature maps being compared) are constructed without pretrained weights or biases, worked almost as well. We can even factor out the deepness (multiple layers of alternating linear and nonlinear transformations) alltogether and have neural style transfer working to a certain extent. This raises the question how much of the the current success of deep learning in computer vision should be attributed to training, structure or simply spatially aggregating the image.
Guide Me in Analysis: A Framework for Guidance Designers

Guidance is an emerging topic in the field of visual analytics. Guidance can support users in pursuing their analytical goals more efficiently and help in making the analysis successful. However, it is not clear how guidance approaches should be designed and what specific factors should be considered for effective support. In this paper, we approach this problem from the perspective of guidance designers. We present a framework comprising requirements and a set of specific phases designers should go through when designing guidance for visual analytics. We relate this process with a set of quality criteria we aim to support with our framework, that are necessary for obtaining a suitable and effective guidance solution. To demonstrate the practical usability of our methodology, we apply our framework to the design of guidance in three analysis scenarios and a design walk-through session. Moreover, we list the emerging challenges and report how the framework can be used to design guidance solutions that mitigate these issues.
PE-MIU: A Training-Free Privacy-Enhancing Face Recognition Approach Based on Minimum Information Units

Research on soft-biometrics showed that privacy-sensitive information can be deduced from biometric data. Utilizing biometric templates only, information about a persons gender, age, ethnicity, sexual orientation, and health state can be deduced. For many applications, these templates are expected to be used for recognition purposes only. Thus, extracting this information raises major privacy issues. Previous work proposed two kinds of learning-based solutions for this problem. The first ones provide strong privacy-enhancements, but limited to pre-defined attributes. The second ones achieve more comprehensive but weaker privacy-improvements. In this work, we propose a Privacy-Enhancing face recognition approach based on Minimum Information Units (PE-MIU). PE-MIU, as we demonstrate in this work, is a privacy-enhancement approach for face recognition templates that achieves strong privacy-improvements and is not limited to pre-defined attributes. We exploit the structural differences between face recognition and facial attribute estimation by creating templates in a mixed representation of minimal information units. These representations contain pattern of privacy-sensitive attributes in a highly randomized form. Therefore, the estimation of these attributes becomes hard for function creep attacks. During verification, these units of a probe template are assigned to the units of a reference template by solving an optimal best-matching problem. This allows our approach to maintain a high recognition ability. The experiments are conducted on three publicly available datasets and with five state-of-the-art approaches. Moreover, we conduct the experiments simulating an attacker that knows and adapts to the systems privacy mechanism. The experiments demonstrate that PE-MIU is able to suppress privacy-sensitive information to a significantly higher degree than previous work in all investigated scenarios. At the same time, our solution is able to achieve a verification performance close to that of the unmodified recognition system. Unlike previous works, our approach offers a strong and comprehensive privacy-enhancement without the need of training.
Online Index Selection Using Deep Reinforcement Learning for a Cluster Database

Index selection plays a substantial role in database performance by reducing the I/O cost. Existing index advisors apply different heuristic methods to search the large search space of possible attributes for indexing. These heuristic approaches do not have a mechanism to learn about the goodness of the recommended index set. Thus, they might choose the same index set with a low impact on I/O cost reduction. Learning from their decisions can improve the quality of the recommended index set. We believe that Deep Reinforcement Learning (DRL) is a solution to tackle this issue. Using DRL, an index advisor can improve its decision using the feedbacks of its decisions. In this paper, we propose a DRL-index advisor for a cluster database. We describe the major components such as agent, environment, set of actions, the reward function, and other modules. We conclude the paper with open challenges and possible future work.