Reviewerly/exHarmony huggingface dataset:
Comprehensive feature fusion mechanism for video-based person re-identification via significance-aware attention

Video-based person re-identification (Re-ID) is of important capability for artificial intelligence and human–computer interaction. The spatial and temporal features play indispensable roles to comprehensively represent the person sequences. In this paper, we propose a comprehensive feature fusion mechanism (CFFM) for video-based Re-ID. We use multiple significance-aware attention to learn attention-based spatial–temporal feature fusion to better represent the person sequences. Specifically, CFFM consists of spatial attention, periodic attention, significance attention and residual learning. The spatial attention and periodic attention aim to respectively make the system focus on more useful spatial feature extracted by CNN and temporal feature extracted by the recurrent networks. The significance attention is to measure the two features that contribute to the sequence representation. Then the residual learning plays between the spatial and temporal features based on the significance scores for final significance-aware feature fusion. We apply our approach to different representative state-of-the-art networks, proposing several improved networks for improving the video-based Re-ID task. We conduct extensive experimental results on the widely utilized datasets, PRID-2011, i-LIDS-VID and MARS, for the video-based Re-ID task. Results show that the improved networks perform favorably against existing approaches, demonstrating the effectiveness of our proposed CFFM for comprehensive feature fusion. Furthermore, we compare the performance of different modules in CFFM, investigating the varied significance of the different networks, features and sequential feature aggregation modes.
3D Navigational Insight using AR Technology

Navigation, the ability to relocate from one place to another, is a critical skill for any individual or group. Navigating safely across unknown environments is a critical factor in determining the success of a mission. While there is an existing body of applications in the field of land navigation, they primarily rely on GPS-enabled technologies. Moreover, there is limited research on Augmented Reality (AR) as a tool for navigation in unknown environments. This research proposes to develop an AR system to provide 3-dimensional (3D) navigational insights in unfamiliar environments. This can be accomplished by generating 3D terrestrial maps leveraging Synthetic Aperture Radar (SAR) data, Google earth imagery and sparse knowledge of GPS coordinates of the region. Furthermore, the 3D terrestrial images are converted to navigational meshes to make it feasible for path-finding algorithms to work. The proposed method can be used to create an iteratively refined 3D landscape knowledge-database that can assist personnel in navigating novel environments or assist in mission planning for other operations. It can also be used to help plan/access the best strategic vantage points in the landscape.
A Survey on Text Question Responsive Systems in English and Indian Languages

It is the character of mortals to raise inquiries to refresh our information on any obscure issues. Question answering systems (QAS) as they are popularly known is employed to bring out answers asked in linguistic communication. On the get-go, the system was designed to answer queries on a closed domain, then again open-domain question respondent evolved to require up and answer queries from any domain. Building a QAS turns into a great deal of crucial once confining one for our Indian territorial dialects. Thus, so as to comprehend the comprehensiveness, a survey would be helpful on English and within the regional languages for researchers and furthermore intrigued understudies. Areas which involve frequent question answering such as the email systems can have an open arm to function like a QAS, so by trying to automatically answer an incoming email by fetching the content from previously answered, similar kind of mail and generating the reply in the user’s style. This survey covers all the main works done pertaining to QAS till 2017.
Semi-Seasonally Optimized Tilt Angles for Design of Utility-Scale Photovoltaic Generation Station

The performance of a photovoltaic (PV) generation station relies on several factors which can be classified into three main types of parameters: electrical, mechanical, and environmental. Low-cost parameters should be prioritized for the efficient design optimization of an installed PV station. In this paper, the solar panel tilt angles are selected as the main factor to optimize the design of an installed PV generation design. In particular, the effect of semi-seasonally optimal tilt angles at which maximum global solar irradiation can be collected is examined, considering the case of a PV generation system located in Eaton County, Michigan, USA. These angles are chosen as 6 sets changing every two months. The optimal tilt angles are found using Oracle Crystal Ball software. The proposed semi-seasonal approach is compared against yearly, seasonally, and monthly optimized tilt angles in terms of solar irradiation collection.
Authorship Attribution by Functional Discriminant Analysis

Recognizing the author of a given text is a very difficult task that relies on several complicated and correlated criterias. For this purpose, several classification methods are used (neuronal network, discriminant analysis, SVM...). But a good representation of the text that keeps the maximum of the stylistic information is very important and has a considerable influence on the result. In this paper, we will tackle the problem of the authorship attribution for very long texts using the discriminant analysis extended to the functional case after presenting the texts as elements of a separable Hilbert space.
Multi-Stage Distributed Computing for Big Data: Evaluating Connective Topologies

With the increase in computation and data intensive needs along with the real-time requirements in the big data era, a distributed framework that can handle parallel processing at various levels becomes a necessity. In this paper, we propose a multi-stage framework that leverages distributed computational agents to handle large-scaled data in parallel. The computational agents are distributed both in breadth (parallelism) and depth (various stages) to pursue the scalability, parallelism, and heterogeneity of modern computational big data workflows. Additionally, the proposed framework allows the interleaving of computation among the stages to speed up the processing. This high throughput computing (HTC) framework is resource efficient, depth and breadth scalable, and supports both distributed agents and centralized analytics. Herein, three computational agent sequential-stage connection topologies are investigated: fully connected, partition, and mesh connectivity. To evaluate the performance of the proposed framework, a large-scale, remote sensing imagery data deep feature extraction and clustering application has been adapted as a use case. Various experiments show that the internal performances of the framework based on the queue size varies according to the type of the topology. While the total time of the experiments are affected mostly by the number of agents in each stage as expected. Additionally, it has been observed that the setup of the partition topology affects the time as well. The system is shown to maintain its resource efficient HTC performance under all the connection topologies, wherein measurement metrics, such as the average CPU usage and average memory usage, reveal nearly the same performance for all the topologies except the partitioning topology.
An Immersive Virtual Environment for Visualization of Complex and/or Infinitely Distant Territory

With the advent of the high-performance graphics and networking technologies that enable us to create virtual worlds networked via the Internet, various virtual environments have been developed to support mathematics education at around the beginning of the 21st century. In the environments that have been inherently two- or three-dimensional Euclidean, students have discovered and experienced mathematical concepts and processes in almost the same ways that they can do in real life. Although elementary mathematics, for instance, calculus and linear algebra, plays an essential role in areas of understanding and knowledge to solve real-world problems, there are traditionally three general areas in pure mathematics for advanced problem-solving techniques: algebra, analysis, and geometry. So, using Virtual Reality (VR) as a general and advanced tool for mathematics education to support students not only in the primary and secondary, also in higher education, the virtual environment ideally provides a wide variety of mathematical domains as possible. We present an immersive virtual environment that allows the user to set environmental limits beyond three-dimensional Euclidean space. More specifically, by setting the limits to n-dimensional complex projective space, an element of both complex and infinitely distant domain can be naturally visualized as a recognizable form in the Euclidean 3-space. The problem here is that the higher the level of mathematics, the more the visualization method tends to become abstract that only experts with advanced degrees can fathom. We also show how our figurative approach is essential for bridging the gap between elementary and more advanced mathematical visualizations.
User Classification by Keystroke Dynamics using Text Retrieval Methods

To verify the identity of users, the majority of computer systems employ conventional authentication schemes such as Personal Identification Number (PIN), password and token. Over the years, these schemes have become less robust as they can be guessed, cracked, stolen or shared. Biometric-based authentication methods have positioned themselves as better options as they do not suffer from the same limitations as conventional methods. The reason being that biometrics exploit the uniqueness of a subject with regards to what they are or how they behave. In this paper, we investigate the use of a behavioural biometric, Keystroke Dynamics (KSD) as a means of identity verification for online persons. We present the proof-of-concept of a novel keystroke dynamics authentication method that is based on text retrieval concepts and methods. We test our algorithm on a classification task and achieve promising results. Furthermore, we show experimentally that a person's typing behaviour is susceptible to the environment in which they are typing.
Continuous Wavelet Transform Calculator for Operation in Ultra-Low Temperatures of the Arctic and Antarctic

The continuous wavelet transform calculator is the basis of the algorithms for automatic analysis of biomedical signals. The use of such systems is justified in conditions of isolation, also from large medical institutions. The device developed in this project is designed to operate in ultra-low temperatures and is designed to operate in the Arctic and Antarctic regions. The continuous wavelet transform calculator is implemented on an elemental base with ultra-low power consumption and can operate for 16 hours 40 minutes 12 seconds from an autonomous power source (excluding losses).
DSSPS

Data sharing plays an important role in the development of the Internet, and it is widely used in various fields. This paper focuses on the security issues in the process of data sharing, combined with blockchain and access control technology, a data sharing security protection scheme (DSSPS) based on consortium chain and Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is proposed, and a resource sharing platform (RSPL) is designed. The scheme first classifies and stores data by setting up a double-chain architecture (record chain, and log chain) based on the consortium chain and three databases (attribute database, file database and index database); secondly, CP-ABE is used to control access to shared files. Achieve fine-grained access control and secure sharing. Finally, the security of the scheme is analyzed.
Differential evolution based global best algorithm: an efficient optimizer for solving constrained and unconstrained optimization problems

This study proposes an optimization method called Global Best Algorithm for successful solution of constrained and unconstrained optimization problems. This propounded method uses the manipulation equations of Differential Evolution, dexterously combines them with some of the perturbation schemes of Differential Search algorithm, and takes advantages of the global best solution obtained on the course of the iterations to benefit the productive and feasible in the search span through which the optimum solution can be easily achieved. A set of 16 optimization benchmark functions is then applied on the proposed algorithm as well as some of the cutting edge optimizers. Comparative study between these methods reveals that GBEST has the ability to achieve more competitive results when compared to other algorithms. Effects of algorithm parameters on optimization accuracy have been benchmarked with some high-dimensional unimodal and multimodal optimization test functions. Five real world design problems accompanied with three challenging test functions have been solved and verified against the literature approaches. Optimal solution obtained for economic dispatch problem also proves the applicability of the proposed method on multidimensional constrained problems with having large solution spaces.
Fine-grained Human Action Recognition Based on Zero-Shot Learning

In recent years, the number of categories of human action recognition is increasing rapidly. On the one hand, the traditional supervised learning model has become increasingly difficult to collect enough training data to identify all categories. On the other hand, for some well-trained traditional supervised learning models, it is a waste of time to collect enough samples of new categories and retrain them together in order to identify new categories. We proposes a mapping between visual features of video and semantic description of fine-grained human action recognition. Unlike most current zero-shot learning methods, which use manual features as visual features, we uses features learnt from I3D network model as visual features, which are more general than manual features.
Cybersecurity Associate Degree Program Curriculum

The spotlight is on cybersecurity education programs to develop a qualified cybersecurity workforce to meet the demand of the professional field. The ACM CCECC (Committee for Computing Education in Community Colleges) is leading the creation of a set of guidelines for associate degree cybersecurity programs called Cyber2yr, formerly known as CSEC2Y. A task force of community college educators have created a student competency focused curriculum that will serve as a global cybersecurity guide for applied (AAS) and transfer (AS) degree programs to develop a knowledgeable and capable associate level cybersecurity workforce. Based on the importance of the Cyber2yr work; ABET a nonprofit, non-governmental agency that accredits computing programs has created accreditation criteria for two-year cybersecurity programs.
Pedagogical Innovative Research Endeavor: Visualization of Streamed Big Data through Augmented Reality

The speed of technical and scientific innovation is accelerating much faster than humans can learn. Thus, there is a need for innovative pedagogical research to decrease this gap. Toward that goal, the approach of pedagogical component experiment was to design, develop, and measure the effectiveness of an augmented reality (AR) application for the visualization of streamed network traffic data to create an innovative pedagogical research environment and opportunities for learners. Specifically, this experiment was developed for the Microsoft HoloLens, with several AR models of data visualization. The effectiveness of this application was measured. Preliminary results showed that using augmented reality to visualize network data enhances comprehension of the data, though, results using statistical analysis were inconclusive, and further research is needed to determine whether using AR to visualize streaming data is more efficient than other methods of visualization. However, in spite of the outcome of this research experiment, a pedagogical environment was positively created for learners that most likely increased their involvement in research and learning.
Phrase-Based Tibetan-Chinese Statistical Machine Translation

Statistical machine translation has made great progress in recent years, and Tibetan-Chinese machine translation has many needs. A phrase-based translation model is suitable for machine translation between Tibetan and Chinese, which have similar morphological changes. This paper studies the key technologies of phrase-based Tibetan-Chinese statistical machine translation, including phrase-translation models and reordering models, and proposes a phrase-based Tibetan-Chinese statistical machine translation prototype system. The method proposed in this paper has better accuracy than Moses, the current mainstream model, in the CWMT 2013 development set, and shows great performance improvement.
An Interactive Approach to Bias Identification in a Machine Teaching Task

Supervised machine learning requires labelled data examples to train models, and those examples often come from humans who may not be experts in artificial intelligence (i.e., "AI"). Currently, many resources are devoted to these labelling tasks; a majority of which are outsourced by companies to reduce costs, and oversight on such tasks can be cumbersome. Concurrently, biases in machine learning models and human cognition are a growing concern in applications of AI.
A System for Operating Home Appliances with Hand Positioning in a User-definable Command Space

In this paper, a novel system that operates home appliances at arbitrary positions in a room is proposed based on a command space associated with the operation of the home appliance. In the proposed system, a hand positioning gesture is used to operate home appliances. By installing a user-definable command space, it is possible to operate home appliances freely in an arbitrary place chosen by the user. Experiments were conducted to verify the operational accuracy and operation time for commands using the proposed system for detailed operations such as TV channel switching.
Radar seeker performance evaluation based on information fusion method

Radar seeker is responsible for transmitting the dynamic position information of the target back to the weapon control center in the guidance phase. It is of great engineering significance to evaluate the anti-jamming ability of radar seeker in the anti-jamming environment Aimed at obtaining more reasonable evaluation result, an evaluation method based on information fusion is proposed against the background of angle measurement performance in this paper. Radar seeker performance test have many stages with different characteristics. Information fusion has advantages in radar performance evaluation which takes advantage of measured data obtained in different stages rather than process them respectively. Furthermore, we studied two specific issues in the information-fusion stage. The first problem is about consistency testing between different information using K-L information distance measurement, which is the premise of information fusion. The second problem is about the information weights calculation by PLT criterion, which can take sample size into account. Information fusion can make full use of the multi-source test information to obtain more reliable evaluation conclusion. Experimental results prove the effectiveness of the proposed method.
The Resume Corpus: A Large Dataset for Research in Information Extraction Systems

We publish a Chinese Resume Corpus for researches of information extraction. The corpus contains 178 thousand resume documents and over 33 million words. The resume documents with unstructured form contain many types of common information like name, gender, birthday, education, work experience, and so forth. We evaluate this corpus by using four types of mainstream neural network models for demonstrating the potential of our corpus to promote the development of information extraction research.
Recognizing Seatbelt-Fastening Behavior with Wearable Technology and Machine Learning

Nearly 1.35 million people are killed in automobile accidents every year, and nearly half of all individuals involved in these accidents were not wearing their seatbelt at the time of the crash. This lack of safety precaution occurs in spite of the numerous safety sensors and warning indicators embedded within modern vehicles. This presents a clear need for more effective methods of encouraging consistent seatbelt use. To that end, this work leverages wearable technology and activity recognition techniques to detect when individuals have buckled their seatbelt. To develop such a system, we collected smartwatch data from 26 different users. From this data, we identified trends which inspired the development of novel features. Using these features, we trained models to identify the motion of fastening a seatbelt in real-time. This model serves as the basis for future work in which systems can provide personalized and effective interventions to ensure seatbelt use.
Can We Simulate Generative Process of Acoustic Modeling Data? Towards Data Restoration for Acoustic Modeling

In this paper, we present an initial study on data restoration for acoustic modeling in automatic speech recognition (ASR). In the ASR field, the speech log data collected during practical services include customers' personal information, so the log data must often be preserved in segregated storage areas. Our motivation is to permanently and flexibly utilize the log data for acoustic modeling even though the log data cannot be moved from the segregated storage areas. Our key idea is to construct portable models that can simulate the generative process of acoustic modeling data so as to artificially restore the acoustic modeling data. Therefore, this paper proposes novel generative models called acoustic modeling data restorers (AMDRs), that can randomly sample triplets of a phonetic state sequence, an acoustic feature sequence, and utterance attribute information, even if original data is not directly accessible. In order to precisely model the generative process of the acoustic modeling data, we introduce neural language modeling to generate the phonetic state sequences and neural speech synthesis to generate the acoustic feature sequences. Experiments using Japanese speech data sets reveal how close the restored acoustic data is to the original data in terms of ASR performance.
Intra and Inter Spatial Color Descriptor for Content Based Image Retrieval

Color is one of the most important and widely used low-level features in content analysis and retrieval. However, most proposed color descriptors lack the spatial information about color distribution. The majorities of spatial color descriptor characterize the spatial color distribution either by the intra or the inter spatial color information. Thus, we provide a complete description by proposing an Intra and Inter Spatial Color Descriptor (IISCD). In fact, we describe the dispersion of each color in the image. In addition, we propose an improvement of the spatial reasoning to describe the inter-color relationships. In addition, a new similarity approach is proposed to image retrieval. The experimental results prove the high effectiveness and feasibility of the proposed descriptor, through Corel database.