Reviewerly/exHarmony huggingface dataset:
SoMem: A Self-optimizing Memory Network for Distributed Person Re-identification

Person Re-Identification (Re-ID) aims to match the persons contained in surveillance videos, and is usually run on powerful servers in a supervised mode. However, centralized processing of massive video from thousands of cameras in a city is very costly and causes serious problems of privacy protection. Moreover, the labeling of numerous data for supervised training is also infeasible in this scenario. To address this problem, we propose a novel Self-optimizing Memory Network model, namely SoMem, which runs person Re-ID on edge devices in a totally unsupervised and distributed way. Specifically, SoMem adopts a random walk based collaborative training procedure to optimize the visual model on each camera based on locally collected images, and builds a distributed memory network to memorize and match the observed persons by using a distributed mutual ranking algorithm. Based on the cross-camera person matching results learned by the memory network, the visual models on edge devices are further optimized in a self-organized manner. Comprehensive experiments are conducted on several real person Re-ID datasets and deployed on edge devices to show the effectiveness and efficiency of this novel distributed Re-ID model.
Improving Pseudo-random Number Generators in a Floating-point Implementation

Chaotic systems are prospective for secure data transfer and processing. Over the past decades, many chaos-based encryption schemes have been proposed. It was shown that such cryptosystems are faster than traditional approaches. In this study, we consider a technique for improving pseudo-random sequence generators based on a binary representation of chaotic sequences calculated using a floating-point data type. We show that we can extract more than 40 bits from the binary representation of each real number and use them as the output of the generator in stream ciphers. Moreover, we show that the features of floating-point calculations lead to pseudo-random properties of the least significant bits of the mantissa of numbers obtained by solving the simple systems of linear and nonlinear differential equations without chaotic behavior as well. The obtained results can be used to reconsider and improve chaos-based cryptographic algorithms.
Multi-source Meteorological Observation Data Quality Control Algorithm Based on Data Mining

Because of the development of the social economy, people's living standards are also constantly improving recent years. The effect of weather forecast on social economy is more and more important, which has a great influence on agricultural production and personal life. With the advancement of the observation automati-on business, small disturbances may cause systematic errors in observation data. The meteorological observation data's quality is an important factor that directly affects the accuracy of weather forecast and climate forecast. The traditional quality control algorithm uses the climatological limit value of historical data and the allowable value of elements to check, lacks sensitivity to elements fancy changes, and not suitable for demend of quality control. This paper introduces a quality control project of multi-source weather observation based on data mining. Starting from the correlation between the observations of the alike observation element which is not at the same time (time correlation), and the correlation between unlike observation elements at the same time (element correlation), combined with the relevant algorithms in data mining, the paper proposes Two different quality control methods for multi-source meteorological observation data, combined with the complementarity and correlation of the two methods, a synthetic quality control programme is established.
BirthMark

BirthMark proposes an artificial intelligence model of an audience to evaluate and anticipate the audience reaction to media art. In BirthMark, human cognitive process of appreciating artwork is defined in three stages: "camouflage," "solution" and "insight." In other words, understanding the intention (solution) from hidden images (camouflage) and realizing its meaning (insight). Watching the archive video clips featuring different works by 16 artists, the A.I. in BirthMark tries to appreciate works of art in a similar way to humans. YOLO-9000, an object detection system, tracks objects in the images of the works, while ACT-R, a cognitive architecture designed to mimic the structure of the brain, reads and perceives them. The A.I.'s process of recognizing works is shown in the video and the keywords of the works found in this process appear on a small screen. At the same time, an old slide projector shows what the A.I. understands semantically about the artists' interpretations of their own work.
Distributing Active Learning Algorithms

Active Learning is a machine learning strategy that aims at finding out an optimal labeling sequence for a huge pool of unlabeled data. We observe that sometimes there are not enough labeled data in contrast to unlabeled samples. Moreover, in some scenarios labeling requires a lot of time and expert supervision. In those cases, we need to chalk out an optimal labeling order so that with a relatively small amount of labeled samples the model gives fairly good accuracy levels. This problem becomes more serious when dealing with distributed data that needs a distributed processing framework. In this work, we propose distributed implementations of state of the art active learning algorithms and perform various analyses on them. The algorithms are tested with real datasets on multinode spark clusters with data distributed on a distributed file system (HDFS). We show that our algorithms perform better than randomly labeling data i.e. non-active learning scenarios and show their mutual performance comparisons. The code is publicly available at https://github.com/dv66/Distributed-Active-Learning
What do Teachers Seek?

It is of importance that the children with visual impairments have equal educational and occupational opportunities as enjoyed by their sighted peers in the field of Science and Math. This paper follows a small qualitative research done with five teachers who teach children with visual impairments. These teachers were from three different schools for the blind, present across the state of Karnataka, India. Through this research, we understand a teacher's process when preparing for a classroom, thus identify their tasks, needs, and challenges. We collected the data via telephonic interviews, and it was analyzed using thematic analysis. The paper eventually discusses the implications of these findings in the design of instructional content intended to aid the teacher in achieving an efficient transference of concepts in Math and Science to children with visual impairments.
Data Expansion Method of Transformer Oil Chromatogram Based on Generative Adversarial Net Model

The lack and imbalance of oil chromatographic data lead to problems including over-fitting, unsatisfactory effect and poor representativeness, making it difficult to accurately evaluate the state of power transformers. In this paper, a method of transformer fault case expansion based on policy gradient and generative adversarial networks is proposed to expand the number and diversity of transformer oil chromatographic cases, and the method is verified by case study. The quality of synthesized data is tested by applying a transformer fault classification model based on BP neural network. The dataset is divided into 9 classes of 700 cases. Compared with the classification model obtained by training only the real data as train set, the classification accuracy of the model obtained by mixing the synthesized data with the real data as the train set is improved. And the improvement is higher than other traditional expansion algorithms. These show the effectiveness of the proposed method.
Extracting information objects from handwriting laboratory notes: an interaction design approach

In this work, the design of an interactive electronic notebook application for extracting and classifying information elements such as diagrams, equations, tables, from smartpen handwritten notes is presented. The software development process is based on the IEEE ISO 9241-210: 2010 standard for interactive human-centered design and the ISO/IEC 25040 for efficient prototype evaluation. The information elements are manually outlined on the handwriting notes using geometrical figures, such as rectangles and ellipses, and then extracted. As a result, a prototype was developed following two stages: 1) analysis and design of a prototype to extract rectangles and ellipses enclosed regions from images, 2) design of an experiment to investigate which geometric figures to use and how to associate them with the information elements.
Tracking With CNN Based Correlation Filters on Spherical Manifolds

The correlation filters tracker allows features from multiple channels. The fusion of features by simply summing over them in an Euclidean space would destroy the inherent geometry among multiple features, resulting in the lose of their phase information which is crucial to tracking task. To provide a better fusion of features from multiple layers of a convolutional neural network (CNN) in the classical CNNs based correlation filters algorithm, we introduce spherical manifolds and computing intrinsic mean on spherical manifolds in the article, so that fusion of features and online update of filter kernels can be implemented over a spherical manifolds. In addition, we introduce a random projection method, imposed on CNN features before feature fusion to compress features for the sake of reducing computational complexity and modeling complexity. Extensive experiments on OTB-50 dataset demonstrate that the proposed algorithm outperforms state-of-the-art methods with respect to both precision and success rate.
Improved MDNET Tracker in Better Localization Accuracy

Object tracking is one of the most important issues in the field of computer vision, which has many applications in the automotive, defense, robotics, medicine, industries, etc. In recent years, various researches have been done in this field and due to its many applications, research in this field continues. In this paper, a deep end-to-end, multi-domain method, or MDNET was examined. The MDNET method works well in tracking video sequences, but has the following problems: The first problem is finding the target position, where the candidate is selected as the target with the highest score. To solve this problem, a new way to find the target position is provided. In this way, the average of the top five candidates is selected as the target position, which was able to increase the IOU standard of the basic method on the OTB100 dataset from 69 to 70.3 and also the results based on the OTB50 and the various challenges are investigated. The second problem is the loss of target in some frames, In this case, instead of drawing the candidates around the target, the candidates are drawn in the whole image and the candidate with the highest score is selected as the target. This method was able to increase the IOU standard on the OTB100 dataset from 70.3 to 72.2 and the results based on the OTB50 and the various challenges are investigated.
Automated Brain Extraction and Separation in Triphenyltetrazolium Chloride-Stained Rat Images

Ischemic stroke is one of the leading causes of death among aged population worldwide. To understand the mechanism and damage of cerebral ischemia, the middle cerebral artery occlusion (MCAO) model in rodents has been generally adopted. For its celerity and veracity, the 2,3,5-triphenyltetrazolium chloride (TTC) staining has been widely utilized to visualize the infarct lesion. An important precursor is to segment the brain regions and compute the midline that separates the brain for subsequent processing. This paper develops an automated brain extraction and hemisphere separation framework in TTC-stained rat images captured by a smartphone. A saliency feature detection scheme associated with superpixels is exploited to extract the brain region into individual slices from the compound image. A chain of edge detection, morphological operation, and polynomial regression methods are introduced to compute the midline. Massive experiments were conducted to quantitatively evaluate the proposed framework. Experimental results indicated that our brain extraction algorithm outperformed competitive methods and our hemisphere separation scheme provided high accuracy.
Single depth map super-resolution via joint non-local and local modeling

Depth maps are widely used in 3D imaging techniques because of the appearance of the consumer depth cameras. However, the practical application of the depth map is limited by the poor image quality. In this paper, we propose a novel framework for the single depth map super-resolution via joint the local and non-local constraints simultaneously in the depth map. For the non-local constraint, we use the group-based sparse representation to explore the non-local self-similarity of the depth map. For the local constraint, we first estimate gradient images in different directions of the desired high-resolution (HR) depth map, and then build a multi-directional gradient guided regularizer using these estimated gradient images to describe depth gradients with different orientations. Finally, the two complementary regularizers are cast into a unified optimization framework to obtain the desired HR image. The experimental results show that the proposed method can achieve better depth super-resolution performance than state-of-the-art methods.
Term-Space Semantics of Typed Lambda Calculus

Barendregt gave a sound semantics of the simple type assignment system λ → by generalizing Tait’s proof of the strong normalization theorem. In this paper, we aim to extend the semantics so that the completeness theorem holds.
Shielding of a Seismic Sensor from Nearby Area Interferences

Seismic monitoring is often required in the construction and operation of large industrial sites and their associated infrastructure. For this purpose, seismic sensors are most often used for the detection of horizontal seismic oscillations caused by relatively distant sources. At the same time, sources located in close proximity to the sensor can create parasitic vertical seismic waves which considerably exceed the sensitivity of the sensor. Thus, they cause an increase in false alarms and reduce the probability of correctly detecting horizontal seismic oscillations by the sensors.The purpose of this article is to create and explore the effectiveness of a device which shields the sensor from vertical seismic mechanical disturbances occurring in the nearby area. The device is based on the attenuation and dispersion of mechanical waves passing through a sequence of media with different mechanical impedances.
Framework for Protecting the Confidentiality of Outsourced Data on Cloud

Cloud computing is the practice of utilizing remote servers over the internet, to perform online activities such as storing, managing and processing data. However, outsourcing data to the cloud in the encrypted form and searching over this data introduces several challenges. This paper discusses and compares four possible schemas of the search process, including their points of strength and weakness. The core of the discussion lies in the possibility of cutting down the cost of communication between entities involved in the search process or reducing the size of the intermediate data. Moreover, protecting the privacy of the data, privacy of the issued queries and privacy of the search access pattern is the main goal of the proposed schemas.
Twizzle - A Multi-purpose Benchmarking Framework for Semantic Comparisons of Multimedia Object Pairs

This paper describes Twizzle Benchmarking, a framework originally developed for evaluating and comparing the performance of perceptual image hashing algorithms. There are numerous perceptual hashing approaches with different characteristics in terms of robustness and sensitivity, which also use different techniques for feature extraction and distance measurements, making comparison difficult. For this reason, we have developed Twizzle Benchmarking, which enables comparison and evaluation regardless of the algorithm, distance calculation, data set or type of data. Furthermore, Twizzle is not limited to perceptual hashing approaches, but can be used for a variety of purposes and classification problems, such as multimedia forensics, face recognition or biometric authentication.
Novel Imputation Method Using Average Code from Autoencoders in Clinical Data

It is possible to improve the reconstruction of clinical data combining codes from autoencoders (AE). The extracted in-formation can be used for enhancing existing imputation methods in this type of data. In the proposed approach, initially, encoder and decoder functions from trained autoencoder are extracted. Then, imputers equally spaced from normalized distribution of the variables generate codes that are combined in the average one that is finally used to reconstruct the original information. The proposed method is compared imputing by mean values of variables and using a single AE for reconstruction. The proposed approach has an outstanding performance recovering original information. It is even better with missing values in more than one variable. The error is at least 70% less than the other methods imputing one variable, and also the proposed approach is highly recommended with missing values in more than one variable.
Analysis of the Joint Functioning of the Timing and Phase Synchronization Algorithms of the GMSK-Signal

The paper provides an analysis of phase and timing synchronization algorithms for a GMSK signal with a changing frequency shift without using known data in the signal, based on raising to a power. To eliminate the changing frequency shift, a feedback loop with first-order astatism is used. The possibility of the simultaneous operation of these algorithms is shown, a joint block diagram for their implementation is presented. The numerical dependences of the variances of the phase and timing estimates versus the signal-to-noise ratio are calculated with various values of the coefficient in the feedback loop and the filter length as parameters.
Learning Systems Under Attack—Adversarial Attacks, Defenses and Beyond

Deep learning has brought many advances to various fields and enabled applications such as speech and visual recognition to flourish. However, recent findings show that Deep Neural NetworksDeep neural network (DNN) still have many problems of their own. The many vulnerabilities present in DNNs unable their application to critical problems. Here, some of these vulnerabilities will be reviewed and many of their possible solutions will be discussed. Regarding legislation, a series of practices will be discussed that could allow for legislation to deal with the increasingly different algorithms available. A small overhead for a safer society. Lastly, as artificial intelligenceArtificial intelligence advances, algorithms should get closer to human beings and legislation itself should face deep philosophical questions in an age in which we will be challenged to reinvent ourselves, as a society and beyond.
A Light-weight stereo matching network for an embedded vision system

In the embedded computer vision field, the stereo vision matching using two cameras can identify the distance of the surrounding environment at low prices. Recently, CNN based artificial intelligence algorithms have been showing good results with high accuracy. However, huge network size and slow computation prevent them used in embedded edge systems. In this paper, a CNN algorithm to use reduced parameters to find a accurate disparity map of stereoscopic images based on Siamese network is proposed. The proposed method performs network training through GPU-based parallel operation and produces better results than the existing one using fewer parameters.
Emotion Dependent Facial Animation from Affective Speech

In human-to-computer interaction, facial animation in synchrony with affective speech can deliver more naturalistic conversational agents. In this paper, we present a two-stage deep learning approach for affective speech driven facial shape animation. In the first stage, we classify affective speech into seven emotion categories. In the second stage, we train separate deep estimators within each emotion category to synthesize facial shape from the affective speech. Objective and subjective evaluations are performed over the SAVEE dataset. The proposed emotion dependent facial shape model performs better in terms of the Mean Squared Error (MSE) loss and in generating the landmark animations, as compared to training a universal model regardless of the emotion.
Extending the CST: The Distributed Cognitive Toolkit

This work presents the first steps towards the development of a toolkit for aiding in the construction of Distributed Cognitive Systems, designed within the spirit of the System of Systems (SoS) paradigm. The Toolkit is language agnostic and general enough to be used in building problem-specific Cognitive Systems that can spread across several physical or virtual devices such as low-power computers, microcontrollers, and virtual containers. The Toolkit is conceived targeting to be suitable for IoT and Smart City applications.
Real Time Detection and Identification of UAV Abnormal Trajectory

Abnormal behavior detection based on video sequence is a hot field. At the same time, monitoring and tracking the UAV (Unmanned Aerial Vehicle) and identifying its abnormal behavior are great significance for the UAV defense. This paper focuses on the detection and recognition of the UAV abnormal trajectory based on real-time video sequence. By tracking and analyzing the characteristics of the UAV, the detection and recognition of abnormal trajectory are divided into two stages. First, by analyzing the UAV's abnormal trajectory satisfying the change conditions is extracted by the quantitative analysis of the UAV's directional angle change features. Second, the normalized polar path fourier spectrum feature of abnormal trajectory is established, and the feature is combined with window search length to accelerate the classification and identification of the UAV trajectory types. Through the contrast experiment, it shows that the method in this paper has good real-time performance and accuracy for trajectory recognition with scale and translation changes.