Reviewerly/exHarmony huggingface dataset:
Person Re-identification by Two-stream Feature-fusion Architecture Utilizing a Partial Body Image

Because of the proliferation of surveillance camera and the wide range of its utilization, “Person Re-identification” technology has been drawing attention. However, the issues such as differences in person's appearances depending on their wearing items, clothes and behaviors still remain. Therefore, in this paper, we propose a two-stream feature-fusion architecture to improve the re-identification accuracy, where spatio-temporal features of partial body images, that we conceive to represent person's individuality robust to such differences, and the corresponding entire images, by applying convolutional LSTM and 3D CNN. The evaluation using the MARS dataset shows that the feet features are most effective among the four horizontally-split partial body images. And the CMS (Cumulative Match Score) by convolutional LSTM applied to the feet features in the proposed architecture is higher than the existing method which applies CNN and temporal pooling only to the entire images. The results show that it is effective to additionally use spatio-temporal features of feet in the MARS dataset.
HealthMine: A Tool for Social Media Text Mining in Health

Social media has become a major source of information in recent years, with millions of posts every minute if not seconds. Containing information on various topics like health, politics and sports, one cannot deny that social media has become a good leverage for the field of data analytics. The objective of this work was to apply techniques of text mining, data analytics, and machine learning to Implement a web application, HealthMine, which extracts and classifies relevant data collected from social media platforms, namely Twitter and MedHelp, following a health-related user query. This would spare the user the burden of filtering out irrelevant information and focusing more on what is relevant. However, since content on social media is user-generated, the reliability is dubitable and the advice of a certified medical practitioner is always recommended. The main purpose of the tool is to allow users to share experiences. The tool was evaluated using 1400 tweets and 1800 MedHelp posts and it was found that Naive Bayes Classifier yielded the best accuracy among other classifiers, with an accuracy of 86.3 and 76.6 for Twitter and Medhelp respectively.
Implementation smart contract with finite state machines

Smart contract is an event-driven code contract with state attributes, which has been widely concerned and studied with the deep development of blockchain technology. This article proposes a new smart contract architecture, and discusses the main functions implemented at each layer and some of the key technologies and their operation mechanism in the blockchain. On this basis, it is attempted to establish a core description of commitment, and use a finite state machine to implement a formal description of smart contracts with the support of actions and finite periods. Finally, a commercial contract is implemented on the blockchain as an example to verify the feasibility of this standardized definition of smart contracts.
Top-down, Spatio-Temporal Attentional Guidance for On-road Object Detection

On-road object detection is a crucial component for environmental perception in intelligent vehicles. Anchor generation is an intermediate step in object detection to derive a set of reference boxes for possible objects in different scales and aspect ratios. State-of-the-art object detectors rely on a massive number of pre-determined anchors over the whole scene. The required operational cost is a drawback in resource-constrained, mobile environments. In contrast, humans rapidly attend to relevant regions in the scene in detail based on the prior knowledge on the current goal and task in hand. Inspired by this observation, we aim to computationally model this top-down visual attention mechanism for the driving task to guide the anchoring process of on-road object detection. In particular, we use the knowledge about the environmental risk level and the underlying risk factors specifically for the driving task to derive an attention region to remain vigilant upon. Then, we perform anchor generation and subsequent operations for object detection only in the extracted attention region. Experimental results demonstrate that the proposed method significantly reduces the operational cost while preserving a competitive accuracy.
Performance Analysis of Fuzzy based Restoration Technique for Ink Bleed-through Degraded Documents

Historical manuscripts are written several hundreds of years ago and are available in the various forms like palm leaves, parchments, wood, stone inscriptions etc. These manuscripts are now undergoing quality degradation due to the factors like physical, weather conditions etc. Digitization is the only way to preserve these manuscripts. Perhaps, in the process of digitalization due to the background colour as well as the other noisy disturbances, the digital image suffers from degradation. Many research works have already been done on the palm leaf manuscripts earlier till now. Here, the proposed research work aims at the restoration of degraded papers in various manuscripts types. In the medieval manuscripts typically written on historical paper, most of them are composed by both sides, as the front and back. Though many specific problems were arising due to the above, bleed-through of the ink from the backside to the front end of the historic document is the one considered for this proposed research. This bleed-through typically performs the possible reading or deciphering the valuable information on those specific pages with considerable difficulty. In existing systems, Independent Component Analysis, Image Enhancement techniques and thresholding methods have been used and typically the results obtained are poor. Hence, the proposed work aims at developing the Ink bleed-through removal from degraded documents employing the fuzzy based technique. The input parameters are preprocessed and then applied to the Fuzzy inference system. At this juncture, the parameters are fuzzified and given to the inference engine with the rule base. The fuzzy output is then defuzzified to obtain the desired output.
Optimizing Interpretation Generation in Natural Language Query Answering for Real Time End Users

Natural Language Querying over Database is gaining popularity across different use cases. Most common of them is to democratize the process of data analysis and querying of backend data to naive end users especially business users, obviating the need of knowing back end query language. Natural Language Query answering systems have thus seen widespread usage in industry too where business users want to search their own data to make business decisions. However, a common challenge faced by any natural language query answering system is generation of precise interpretations. The research community although tries to handle the problem via asking clarification questions back to the user, in industry setup this remains an ineffective solution due to various practical usage limitations. For example, it is not fair to assume any end user will be aware of the correct option to answer these clarification questions. Moreover, involving clarification questions and user feedbacks makes the system unusable by one shot API calls, which is the most intuitive usage among common use cases in industry like automated report generation. In this paper, we investigate practical ways to address the problem of precise interpretation generation. We propose novel algorithms to make use of existing technologies like Functional Partitioning of Ontology and Lazy Inclusion to solve this problem. We take our previous state-of-the-art paper ATHENA and further extend it to include our proposed methods. We test with 3 benchmark ontologies to empirically demonstrate the huge improvement over state-of-the-art results by factors of at least 400% in number of interpretation generation and also in the computation time.
Optimizing Rate-Distortion Performance of Motion Compensated Wavelet Lifting with Denoised Prediction and Update

Efficient lossless coding of medical volume data with temporal axis can be achieved by motion compensated wavelet lifting. As side benefit, a scalable bit stream is generated, which allows for displaying the data at different resolution layers, highly demanded for telemedicine applications. Additionally, the similarity of the temporal base layer to the input sequence is preserved by the use of motion compensated temporal filtering. However, for medical sequences the overall rate is increased due to the specific noise characteristics of the data. The use of denoising filters inside the lifting structure can improve the compression efficiency significantly without endangering the property of perfect reconstruction. However, the design of an optimum filter is a crucial task. In this paper, we present a new method for selecting the optimal filter strength for a certain denoising filter in a rate-distortion sense. This allows to minimize the required rate based on a single input parameter for the encoder to control the requested distortion of the temporal base layer.
Gaussian Process Latent Variable Models Applied to Study Maritime Traffic Patterns from VIIRS Data

Gaussian process latent variable models are used as a data dimensionality reduction technique and applied to analyze long spatio-temporal series of ship traffic patterns measured from data acquired by the Visible Infrared Imaging Radiometer Suite nighttime sensor on board the NOAA-Suomi National Polar-Orbiting Partnership spacecraft. The results show that these techniques are able to model traffic pattern with a number of variables much lower than the number of cells of the time-spatial grid supporting the input data. The use of a Bayesian formulation allows the introduction of spatio-temporal prior constraints that clearly improve the visualization of the time series in the reduced dimensionality space with respect to the classical principal component analysis.
Continuous Authentication Using Creative Writing

Traditional keyboards remain the input device of choice for typing-heavy environments. When attached to sensitive data, security is a major concern. To continuously authenticate users in these environments, use of keystroke dynamics can be a preferred choice. An integral part of user enrollment in a keystroke based continuous authentication system is the writing instruction (prompt) given to the users, to use as a basis for their improvised writing. There are many prompts possible, and they directly impact the performance of authentication systems. Hence, prompts should be designed carefully, and with purpose. In this paper, we bridge the gap between cognitive psychology and computer science and attempt to influence the mental state of the users to acquire a better authentication performance. We compare two kinds of writing prompts, creative and factual, for generating reference samples. In addition, we perform two robustness tests: robustness to dissimilar writing style (e.g., creative reference and factual test) and robustness to surface (e.g., hard surface reference and soft surface test). We collect data from thirty participants in four weekly sessions. We experiment with three features: key interval, key press, and key hold latencies. We use Relative (R) measure to generate the match score between the reference and test samples. Results show that creative writing consistently performs better than the factual one. Both writing prompts perform well with dissimilar style in testing, i.e., continuous authentication is found robust to writing style. Also, we find that the surface (hard or soft) used in testing need not match that used for the reference, thus continuous authentication is also surface robust.
Similarity Calculation Algorithm for Intelligent Electronic Customer Service Problems

Customer service question similarity calculation is a key technology in the development of intelligent electronic customer service system, and its main challenge comes from how to find the correlation between words. In response to this challenge, the paper proposes a weighted text similarity calculation method based self-attention. The paper uses deep neural network BiLSTM and self-attention vectors to carry out text similarity research, and explores the problem of deep neural network and self-attention vectors describing the semantic relationship between words. The research of this paper is of great significance to improve the service quality of the intelligent electronic customer service system, and provides new ideas for the accurate comparison of text semantics. The paper takes the semantically vague and complex Chinese text as the research object, and uses the deep neural network structure design, self-attention vector calculation, dynamic weighting, and synchronization comparison as processing methods. Combined with the development of the Weizhong bank intelligent electronic customer service system, an intelligent electronic Customer service question matching experiment platform is constructed. Experimental results show that the dynamic weighted self-attention text similarity calculation model is superior to the existing models in terms of calculation accuracy and running time. It has certain reference value for the development of similar intelligent electronic customer service systems.
Spatial-Time MIMO Autoencoder for Physical Layer

Autoencoder-based communication systems show great potential due to the fast development of Deep Learning. In this letter, we propose an end-to-end communication structure for transmitting spatial-time symbol blocks. For simulation, we build up three schemes of multi-input multi-output(MIMO) systems respectively. The results show that the proposed autoencoder structures are able to learn a regular constellation alphabet and attain a lower bit error rate than conventional communication systems in low signal-to-noise(SNR) situation.
Implementation of unmanned aerial system for surveillance mode

A drone is an inexpensive, low-risk aircraft without a human pilot on-board. Recently, a drone is an emerging technology for some applications because it can access the zone where human difficult to enter. In this situation, specific human detection who want to find by drones can be useful tools. Since the drone has a limited payload, it has low communication performance. This performance degradation makes monitoring remote areas difficult. In this paper, we design an unmanned aerial system in a surveillance environment that can send monitoring video from Surveillance drones to ground control system. In particular, we add a relay mechanism to prevent degrading communication performance. As a result of the experiment, we compare performance between relay system and a single link system. Also, we evaluate the accuracy of human detection to use the OpenFace module by changing the distance from the Surveillance drone to the target.
Generation of Missing Words in Assamese text using N-gram based Model

Abstract It is very common to miss certain words when writing while listening to others. A similar problem can arise when typing on the computer. The automatic generation of missed words shall very much helpful for users by suggesting the required words. In this research work, missed words of the Assamese sentences are generated, at present, there is no such tool/method exists which can provide or generate the missed words in an Assamese sentence. N-gram based models like bigram and trigram are used to generate missed words. Using the bigram and trigram models a rank is calculated for each possible suggested words and the suggested word list is sorted according to this rank value in decreasing order. Finally, these suggested words can be used to fill the place of the missed Assamese words. Different levels of experiments are carried out and the present proposed system can correct the missed words at an accuracy ranging from 58% to 66%. The proposed model can precisely generate accurate five relevant suggestions for a sentence containing an average of six words with two missed words separated by three words.
A comparative analysis of usability evaluation methods of academic mobile application

Usability evaluation is an important element which has used to add on insights related to the usability problem of an application. This study aims to identify application's usability problems and to compare the effectiveness of four evaluation methods which has used on an academic portal mobile application: usability testing, interviews, surveys, and heuristic evaluation. The data has collected from users who are in the student category and expert evaluator. The number detail of respondents used are usability testing (N = 10), interviews (N = 10), surveys (N = 110), and heuristic evaluation (N = 3). The four methods identified a total of 44 usability problems: 45% using heuristic evaluation, 24% using surveys, 17% using interviews and 14% using usability testing, resulting into a few similar findings. Then, The problems are categorized using Usability Taxonomy Problem (UPT) which has divided into 5 categories with details of 17 categories of visualness, 6 categories of language, 3 categories of manipulation, 11 categories of task-mapping and 7 others including the category of task-facilitation. The results of this study are capable to prove that the four methods are complementary, each method provides a unique insight to improve the usability of the application user interface. Both researchers recommend using a multi-method approach when evaluating the usability of an application due to it could provide a more comprehensive representation of usability issues.
Application-Specific Instruction Set Architecture for an Ultralight Hardware Security Module

Due to the rapid growth of using Internet of Things (IoT) devices in the daily life, the need to achieve an acceptable level of security and privacy according to the real security risks for these devices is rising. Security risks may include privacy threats like gaining sensitive information from a device, and authentication problems from counterfeit or cloned devices. It becomes more challenging to add strong security features to extremely constrained devices compared to battery operated devices that have more computational and storage capabilities. We propose a novel application specific instruction-set architecture that allows flexibility on many design levels and achieves the required security level for the Electronic Product Code (EPC) passive Radio Frequency Identification (RFID) tag device. Our solution moves a major design effort from hardware to software, which largely reduces the final unit cost. The proposed architecture can be implemented with 4,662 gate equivalent units (GEs) for 65 nm CMOS technology excluding the memory and the cryptographic units. The synthesis results fulfill the requirements of extremely constrained devices and allow the inclusion of cryptographic units into the datapath of the proposed application-specific instruction set processor (ASIP).
Software Construction Oriented Multi-agent Collaborative Modeling and Simulation

Improper collaboration frequency causes low efficiency in software development. To improve collaboration efficiency in software construction, we propose a method of multi-agent collaborative modeling and simulation. First, we propose collective action model and individual action model to describe the whole construction environment. Then, we design agents in our simulation, establish a mapping relationship between artificial bee colonies and developers group, and propose buzz factor as a parameter to describe the collaboration frequency. In particular, we introduce Q-learning algorithm to obtain the dynamic buzz factor that maximizes construction efficiency. Finally, we perform experiments on groups of agents based on our model to observe the effect of buzz factor. Comparing to the classic feature-oriented stigmergy-based model, our experiment results show a better construction efficiency and provide a reference for controlling the collaboration. Through our work, we can find a suitable collaboration frequency through the simulation, to guide the practice of software development.
Verification, Validation and Accreditation and Credibility Evaluation of Aerospace Product Performance Prototype

In this paper, we give a new definition to the aerospace product performance prototype on the foundation of lately studies and emphasize on the importance of the credibility of the performance prototype according to the definition and characteristics of it. Then we design a VV&A scheme for the aerospace product performance prototype according to the characteristics and development process of it and emphasize that the VV&A activities should run through the whole lifecycle of it. The VV&A scheme is designed based on the general VV&A process of M&S and corresponding to the characteristics and development process of the performance prototype. Then we introduce the VV&A scheme in details step by step and analyze appropriate V&V techniques for each V&V activity. This research gives a macroscopic description for the VV&A of the aerospace product performance prototype and can be the foundation for the further researches on the implementation of the VV&A of aerospace product performance prototype and choosing and optimizing the appropriate V&V techniques for different V&V activities.
Face recognition of students based on ArcFace

With the development of AI technology, the field of face recognition has also entered a period of rapid development. Since 2012, deep learning has shined in ILSVRC-2012, the application of Convolutional Neural Network in image recognition is more and more extensive. Recognition research also relies on the deep learning algorithm and the superior learning ability of neural networks to achieve the unprecedented height of face recognition accuracy and recognition speed. This article is based on deep learning and neural network, using MXNet framework and ArcFace to design a more efficient face access control system.
Nonlinear optical tomography of q-deformed entangled pair coherent states

We introduce entangled pair coherent states of a noncommutative harmonic oscillator operators associated with a q-deformed oscillator algebra. The definition of two-mode q-deformed operator and the decomposition of its eigenstates in terms of the q-deformed Fock states are obtained. The general solution of the recurrence relation of the first nondeformed quadrature is obtained. The asymptotic behavior of the general solution is studied, a complete expansion is derived, and also representations of the general solution in terms of the Gauss hypergeometric function are given. Also, we prove that the general solution is related to Meixner-Pollaczek orthogonal polynomial and calculate the weight function. The photons number distribution, the second order correlation function and the optical tomogram of the entangled pair coherent states are discussed. The recurrence relation for the first q-deformed quadrature is inferred. We evaluate the eigenstate of the q-deformed quadrature operator and give a numerical study for the optical tomogram of the q-deformed of the entangled pair coherent states. Finally, we show the change of the parameters in the entangled pair coherent states that connected with physical quantities and explain how they affect the format of optimal tomography.
Monitoring cumulus clouds by using global horizontal irradiance data

Abstract A previously proposed method for the detection of cumulus clouds by using time series of global horizontal irradiance from a pyranometer is developed. A characteristic feature of this method is the acceptance of short 21-minute moving analysis windows as stationary. This approach makes it possible not to construct a clear sky model for transforming a non-stationary daily time series of global horizontal irradiance to stationary time series.