Reviewerly/exHarmony huggingface dataset:
Nighttime periocular recognition at long standoffs with deep learned features

The periocular region is considered as a relatively new modality of biometrics and serves as a substitute solution for face recognition with occlusion. Moreover, many application scenarios occur at nighttime, such as nighttime surveillance. To address this problem, we study the topic of periocular recognition at nighttime using the infrared spectrum. Utilizing a simplified version of DeepFace, a convolutional neural networks designed for face recognition, we investigate nighttime periocular recognition at both short and long standoffs, namely 1.5 m, 50 m and 106 m. A subband of the active infrared spectrum { near-infrared (NIR) { is involved. During generation of the periocular dataset, preprocessing is conducted on the original face images, including alignment, cropping and intensity conversion. The verification results of the periocular region using DeepFace are compared with the results of two conventional methods { LBP and PCA. Experiments have shown that the DeepFace algorithm performs fairly well (with GAR over 90% at FAR=0.1%) using the periocular region as a modality even at nighttime. The framework also shows superiority to both LBP and PCA in all cases of different light wavelengths and standoffs.
Theme Evolution Analysis of Public Security Events Based on Hierarchical Dirichlet Process

In order to analyze the topic evolution in the field of public safety events, this paper proposed a model named Dynamic Micro-blog Hierarchical Dirichlet Process (MD-HDP). First, taking the specific security incidents as the key words, the related texts in micro-blog are extracted precisely by using time features and topic labels. Then, the event texts in each time window are modeled separately to get the corresponding topics for each incident. Finally, the evolution of the subject matter is analyzed of the security incident. The comparison experiments show that the MD-HDP model has a good performance on mining the evolution of the security incident topic and provides more accurate results on the evolution of the security incident topic over time.
Data Imputation with an Improved Robust and Sparse Fuzzy K-Means Algorithm

Missing data may be one of the biggest problems hindering modern research science. It occurs frequently, for various reasons, and slows down crucial data analytics required to answer important questions related to global issues like climate change and water management. The modern answer to this problem of missing data is data imputation. Specifically, data imputation with advanced machine learning techniques. Unfortunately, an approach with demonstrable success for accurate imputation, Fuzzy K-Means Clustering, is famously slow compared to other algorithms. This paper aims to remedy this foible of such a promising method by proposing a Robust and Sparse Fuzzy K-Means algorithm that operates on multiple GPUs. We demonstrate the effectiveness of our implementation with multiple experiments, clustering real environmental sensor data. These experiments show that the our improved multi-GPU implementation is significantly faster than sequential implementations with 185 times speedup over 8 GPUs. Experiments also indicated greater than 300x increase in throughput with 8 GPUs and 95% efficiency with two GPUs compared to one.
Fuzzy transfer learning of human activities in heterogeneous feature spaces

Data-driven machine learning methods usually require large amounts of annotated data to be able to develop high performance learning systems. In practical situations, such large amounts of data are not easily obtainable. Transfer Learning evolved as one of the solutions to this challenge. It aims to make use of knowledge acquired in one domain to facilitate prediction in a target domain. Transfer learning can be a daunting task when feature spaces which require transfer differ in their distribution of information. A case of this is in the application of assisted robotics, where a robot is required to learn a task by mere observation of a human perform the task. The differences in the feature spaces poses a challenge in the effective transfer of such tasks. In this paper, we propose a method of effective transfer learning across heterogeneous feature spaces for the purpose of learning in assisted living environments. A fuzzy latent space exploration is used to obtain mappings of feature spaces. This approach is used in simplifying the learning for an assistive robot seeking to execute human actions.
How Easy Is It to Surf the Semantic Web?

In this paper, we report our ongoing project about the issue of the navigability of the Semantic Web which refers to whether users can find desired information while they surf the Semantic Web. To this end, we model the Semantic Web as a space that is a sequence of words over which hyperlinks are added so that relevant words can be connected by hyperlinks according to an ontology. Users can follow hyperlinks in order to find a pattern which is a sequence of words. In order to show the navigability of the Semantic Web compared to the World Wide Web (WWW), we define a space for the WWW similarly and calculate the maximum number of steps taken to find a pattern in both spaces. It turns out that it takes fewer number of steps to find a pattern in the space for the Semantic Web than in the space for the WWW.
Comprehensive cooperative deep deterministic policy gradients for multi-agent systems in unstable environment

Nowadays, intelligent unmanned vehicles, such as unmanned aircraft and tanks, are involved in many complex tasks in the modern battlefield. They compose the networked intelligent systems with varying degrees of operational autonomy, which will continue to be used increasingly on the future battlefield. To deal with such a highly unstable environment, intelligent agents need to collaborate to explore the information and achieve the entire goal. In this paper, we will establish a novel comprehensive cooperative deep deterministic policy gradients (C<sub>2</sub>DDPG) algorithm by designing a special reward function for each agent to help collaboration and exploration. The agents will receive states information from their neighboring teammates to achieve better teamwork. The method is demonstrated in a real-time strategy game, StarCraft micromanagement, which is similar to a battlefield with two groups of units.
An Adaptive Pointwise P-norm Regularization for Image Deconvolution

We propose a pointwise P-Norm based nonparametric image deconvolution method. In our algorithm, a pointwise p-norm based regular term is used to deal with the different types of regions in a blurry image such as edges, texture and smooth areas. A new hyper-parameter updating method is also adopted to improve the effectiveness and robustness of the proposed algorithm. The experimental results show that the quality of our restored images can outperform the results derived by other algorithms. In addition, our algorithm has a wide adaptation to different blur kernels.
Dress Identification for Camp Security

With the development of artificial intelligence and internet of things, the intelligent embedded devices are becoming more and more popular. So it is urgent to achieve the lightweight application of artificial intelligence. In this paper, a dress identification framework is developed for camp security. The framework has both hardware and software. The hardware is composed of LattePanda board, Arduino chip, USB camera, and buzzer. To achieve the identification, we extract the color histogram feature of moving person from the images captured by USB camera, and identify the military dress by using support vector machine algorithm. The equipment can output sound or light signals by Arduino chip, when it identifies a non-military dress. We implement the identification function based on the OpenCV library. The framework can run in real-time, with a reliable precision.
On the overhead of using zero-knowledge proofs for electric vehicle authentication

As Electric Vehicles (EVs) are becoming widely available, their secure management is crucial to fully enable their potential. For instance, for convenient charging, they may require quick authentication with the charging stations while they are on the go. As charging is frequently needed, exposing one's charging frequency to the stations may risk the exposure of privacy for the EV driver. Therefore, a mechanism is needed to hide EV information. In this paper, we propose using zero-knowledge proofs to achieve this goal. While zero-knowledge proofs can provide anonymous authentication, they require computation for generation of witnesses. Therefore, we assess the overhead of generating a witness and proof computation at the resource constrained on-board units (OBUs) which are deployed on EVs that utilize wireless communications for scheduling. The results indicate that computation overhead is minimal and can be delployed on resource contrained devices.
Finite semiotics: Cognitive sets, semiotic vectors, and semiosic oscillation

Abstract The grounding of semiotics in the finiteness of cognition is extended into constructs and methods for analysis by incorporating the assumption that cognition can be similar within and between agents. After examining and formalizing cognitive similarity as an ontological commitment, the recurrence of cognitive states is examined in terms of a “cognitive set.” In the individual, the cognitive set is seen as evolving under the bidirectional, cyclical determination of thought by the historical environment. At the population level, the distributed “global” cognitive set is argued to be constrained to a manifold in which the cognition of individuals is determined only when their cognitive sets meet certain conditions in the world: a result seen as consistent with Lotman’s semiosphere. With these foundations in place, dimensional modelling of the semiosic field is inaugurated. Firstly, measures of cognitive similarity are formalized as cognitive “distance” and on this basis the concept of a semiotic vector is defined. Secondly, semiotic vectors are seen to shape a general pattern of oscillation in semiosis, and thus to imply zero points in semiosic potential. Thirdly, semiosic oscillation in individual agents is shown to be consistent with a novel diachronic or longitudinal interpretation of Greimas’ semiotic square expanded into a “semiotic pipe” in which cognition traverses an n-dimensional space structured by axes of oscillation. Finally, the expanded theory of finite semiotics is advanced as a useful basis for two new complementary disciplines: (1) a computational, mathematical science of “natural semiotic processing” (NSP) to trace and model semiotic vectors and oscillation; and (2) an ethical, rhetorical art of “technological influencing” (TI) to guide its inputs and applications.
Layout Optimization of Book Sorting Machine Based on Sequential Control

Book sorting is one of the most important technical means of contemporary library management institutions, and book sorting equipment is the carrier of this technology means. Based on an existing small book sorting machine, this paper proposes an improved scheme. The feasibility of machine improvement is analyzed, and the improved machine model is built through MATLAB programming language, then the simulation test is carried out. Ten sets of data were obtained at the end of the test. The ten sets of parameters obtained from the program test are used to characterize the relationship between machine efficiency and time. However, in fact, the sequence of books waiting for transportation is random. Therefore, through the theory of probability, we get the relationship between them. Through theoretical analysis and software testing, the machine improvement scheme and control strategy proposed in this paper have high accuracy and can improve the automation level of the equipment. Finally, we get the optimal layout of the book sorting machine.
Telecom Network Monitoring and Fault Isolation with Visual Analytics

The telecommunication sector is one of the major industries where the amount of data is enormous. Yet, the problem in this sector is multi-fold: fierce competition, high market penetration rate, poor customer experience and inability to handle large amounts of data. The main objective of this research is to obtain insights by developing a data analytic model to monitor network performance. Visual analytics approach provided deeper insights into precise problems by applying diagnostic analysis on the network performance. Network issues can be identified at rapid-fire speed and it could be applied in real-time in order to improve competitiveness.
Neural network classification of degraded imagery using soft labels: towards human-level performance with “accurate” likelihoods?

The vast majority of recent progress in deep learning for computer vision has been demonstrated on problems with low irreducible error rates. While it is natural to use hard “one-hot” encoded training labels for such problems, this may not be the case in applications with large irreducible error. This includes classification problems on severely degraded or classambiguous imagery. Furthermore, databases primarily consisting of degraded examples are difficult to diagnose in terms of assuring that non-causal statistical correlations across the training and test sets do not exist for certain classes. Expert image analysts however, are typically well-regularized and will not overfit to such correlations. In this work, soft labels are applied to a surrogate problem with large irreducible error, where the labels are generated by an ensemble of networks serving as a proxy for human expert labelers. Results of networks trained on these soft targets, versus their one-hot counterparts are compared. A concept for an “imagination mechanism” in neural networks training on soft labels is also introduced.
Linear Processor Array Architectures for Similarity Distance Computation

Processor array architectures have been employed, as an accelerator, to compute similarity distance found in a variety of data mining algorithms. However, most of the proposed architectures in existing literature are designed in an ad hoc manner. Furthermore, data dependencies have not been analyzed and often only one design choice is considered for the scheduling and mapping of computational tasks. In this work, we present a systematic technique to design linear processor arrays for the computation of similarity distance matrices. The technique employed is used to define the computation domain of the algorithm, with time restrictions on input and output variables. Six scheduling vectors and their associated projection matrices are generated to illustrate our systematic technique. The six possible design options obtained are analyzed in terms of area and time complexities. We are also able to derive a previously existing processor array in the literature by modifying the scheduling vector for one of the proposed architectures. Field Programmable Gate Array (FPGA) Implementations show that our proposed architecture achieves better performance in both speed and area.
An Improved Face Synthesis Model for Two-Pathway Generative Adversarial Network

Synthesizing photorealistic frontal face images from multiple-view profile face images has a wide range of applications in the field of face recognition. However, existing models still have some disadvantages such as high cost and high computational complexity. At present, the Two-Pathway Generative Adversarial Network (TP-GAN) is the state-of-the-art face synthesis model, which can perceive the global structure and local details at the same time. It solves the prier problems but has disadvantages such as training difficulty and lack of diversity of generated samples. Based on Wasserstein GAN with Gradient Penalty (WGAN-GP), this paper proposes a novel Two-Pathway Wasserstein GAN with Gradient Penalty (TPWGAN-GP) model to tackle these defects. TPWGAN-GP uses a gradient penalty method to satisfy the Lipschitz continuity condition, which solves the problems of difficulty in hyper-parameter adjustment and gradient explosion in the TP-GAN, making the convergence speed faster and the model more stable in training process. The generated samples are of higher quality, resulting in more photorealistic faces for recognition tasks.
A Design of Automated Surgical Illumination System

Surgical lights which consist of a single- or multiple-light head assembly attached to a suspension arm are designed to illuminate surgical site. However, it was found out that the need of nurse or surgeon to move the surgical light manually might obstruct the surgical flow and cause contamination of instruments. Thus, this project aims to design an illumination system that will automatically track the movement of the hand of surgeon with specific color of glove. Yet, the project will mainly focus on the automation movement of light with the tracking of hand glove, not related to the specifications of light, operating table, operating room condition and design of suspension arm. An algorithm that could track the surgeon’s hand movement is designed using image processing. The project will also include designing a surgical light which consists of a single light head attached with camera to illuminate surgical site. Raspberry Pi is used with the Pi camera to track the movement of the hand of surgeon through the program called OpenCV and the programming language, Python. At the end of this project, the algorithm of color object tracking and the prototype of automation surgical illumination system will be produced. The significant finding of this study is in terms of the capturing process of camera at each of the eight positions of the prototype after each movement of the light.
The Writing Teacher is the (Real) Writer Herself: From Passionate Beliefs to a Professional Journey

This research portrays the beliefs and practices of a teacher writer which shaping the journey of her professional development as a teacher writer. The teacher writer being investigated in this study teaching English at a one of junior high school in Indonesia. Being a teacher is one profession, so is the case being a writer is another different profession. Thus, become a teacher as well as a writer is a tenuous experience. In addition, such beliefs, experiences, and practices from a teacher writer are thought-provoking to explore, as there were only few to mention the teacher who concurrent as a writer. The data were collected through an in- depth interviews, questionnaires, and documentary studies. The research showed that the teacher’s beliefs on the practice of writing are consistent with her practice as reflected on her works as a professional writer. Her writing activities are also published along with her profession as a fulltime English teacher. The findings also revealed that she was actively involved in academic activities as her genuine passion to improve her professional development. Ultimately, this is the pondering that never goes out from full dedication and enthusiasm as a professional teacher writer.
An Architectural Approach to Increase Adoption of the MDBCI Tool

This paper describes improvement of MariaDB continuous integration infrastructure (MDBCI) and the issues that were encountered on a way towards making it an easy to pick and use tool for everyday use by the end users. The ease of distribution and installation is achieved by providing MDBCI in the form of single executable file with minimum external dependencies. Contribution towards everyday use by developers is done by reducing the total execution time via introduction of multi-threaded approach.
A Study on ELM(Election pledge management for Local governors Model) Based on Machine Learning -Focused on On-Nara Document System-

The background of this paper is new social trend of more public's interest in the implementation of the pledge of the local governors who were elected by citizens. In these days the election pledge for enhanced local governmental policies became more important. The objective of this paper is to suggest the model of election pledge management for local government heads based on machine learning focused on On-Nara document system. The system is currently used by Korean governmental organizations for document processes. The methods to prove a comparative advantage of the proposed model are the comparison tests between As-Is system and To-Be system based on a few criteria such as time, efficiency and extraction rate. Through this model, local governors could present systematic goals and road map of pledges in order to get closer to citizens and local residents. In other words, this study proposes a model, so called ELM(Election pledge management for Local governors Model), for efficiently extracting necessary data from planned and implemented details of pledge projects that are prepared in the form of unstructured documents. We carried out research to prove empirically our machine learning-based model is more efficient than current semi-manual system with some automated processes in order to manage efficiently the pledge project implementation of local governors to get the results. In conclusion, this research proved that the proposed model is more competitive than the existing models. In the 4th industrial revolution era the new approach using machine learning and big data will become more popular.
Moving space-matter as the basis of the intelligence in the Physical World

The concept of space-matter motion in the new Cartesian physics, based on the identity of space and matter, creates the basis for the study of consciousness as the action of the brain in space inside and outside itself and off ers a way of materialistic explanation of life on Earth.She claims that consciousness in living matter arises when the brain begins to create the surrounding space the image of themselves and the world.And since space according to Descartes is identical to matter, the images created by the brain of itself and the external world in the surrounding space have a material basis and therefore the displayed organs interact with each other and the external world.
Distributed Secure Data Mining with Updating Database Using Fully Homomorphic Encryption

Uploading commercial data to third-party cloud services is popular in general. To further promote the active utilization of big data, outsourcing data mining systems that can execute statistical calculations using the uploaded data have been proposed. In this case, personal and sensitive data are required to be encrypted for privacy protection. In previous research, data protection using fully homomorphic encryption (FHE) was proposed for a client/server secret data mining system using the Apriori algorithm. However, this system requires much time because of the computational complexity of FHE calculations. Additionally, although frequent database updates occurred in the practical use of the system, the Apriori algorithm needs recalculation of the whole database at each update. In this study, to solve these two problems, we proposed the implementation of a master/worker distributed system using the FUP algorithm, which generates candidate item sets efficiently while updating the database. We improved execution time of the secure data mining system and made it suitable for practical use.
Cardiac biometrics for continuous and non-contact mobile authentication

Continuous authentication is superior to conventional one-pass authentication by maintaining the security level of a system throughout the entire login session. Leveraging the unique geometric and non-volitional credentials in the cardiac motion, we present a trustworthy, continuous, and non-contact user authentication system. Based on a pilot study with 78 subjects, we evaluate Cardiac Scan in terms of accuracy, authentication time, permanence, and vulnerability. The results show that Cardiac Scan is a robust and usable continuous authentication system.