Reviewerly/exHarmony huggingface dataset:
Application of Human Body Posture Recognition Technology in Robot Platform for Nursing Empty-Nesters

In order to solve the problem of missing the optimal treatment time for empty-nesters after falling, a mobile robot is designed that can follow the elderly autonomously and send GSM SMS to their children or community hospital when discovering their fall. The robot takes industrial PC as the main control module and STM32F1 as the bottom drive module. Kinect V1 depth camera was used to obtain the point cloud image and RGB image of the elderly, and the ROS system analyzed the image data to achieve target following. A simplified DeeperCut human posture estimation model and MPII data set was used to train deep neural network ResNet to detect the body posture coordinates through the changes of its data. Several tests have shown that the robot can follow the elderly in real time, accurately identify their falling posture and send alarm messages timely.
A System for Hierarchical Browsing of Mixed Reality Content in Smart Spaces

We present in this paper technical and engineering details of a wearable system for visualizing digital content. The digital content is overlaid onto the physical space by employing hierarchical structures for content organization. We implement analogies with the physical world adapting laws of physics, such as gravity, to digital content. To this end, we rely on concepts from the fields of Augmented Reality, Wearable Computing, Software Engineering, and Human-Computer Interaction to propose a new model for accessing digital content anchored in the physical reality. In our prototype, reality is augmented with containers of digital content represented graphically as semiopaque spheres that float in the physical space around the user. We describe the properties of the spherical containers of digital content, such as their size and relative location in the physical environment, and their relationships to the user and the physical world. We also present interaction techniques based on gesture input that enable users to access the digital content contained by the virtual spheres. We present our technical implementation using the Microsoft holographic computer HoloLens and exemplify use case scenarios for our wearable prototype.
A Flexible DNN Accelerator Design with Layer Pipeline for FPGAs

Deep Neural Networks (DNNs) are very effective for image recognition, object detection and tracking. To meet the need of deployment of DNNs, accelerator designs have been proposed, especially for implementation on FPGAs. Layer pipeline is a new technology to improve computation efficiency by reducing latency between layers. In this paper, we propose a group-based DNN pipeline accelerator design for FPGAs. An entire DNN is partitioned into layer groups with pipeline computation within every group. A configurable logic is used to switch between groups instantaneously. Thus, our design can handle any size DNNs on a single FPGA chip. We evaluate our accelerator for Alexnet and VGG16 on a Xilinx ZC706 board. Our experimental results show that we can increase the throughput by 19% and 42% when compared with layer-based accelerators in [1] and [16].
Scope of Managing Knowledge in Digital Forensics

Digital devices and services are becoming cheap and so availability has increased a lot. The extensive use of digital devices has made every individual to ride on rising wave of varied and stored digital information. Huge digital data is residing on standalone storage devices as well as on network and cloud based storage devices. All technologies are vulnerable to security. Risk has also increased because digital information is convenient to generate, economic to accumulate, and practically easy to reproduce. Rule-breakers are part of society and so they are also using the technology with the same pace for crime. Every crime now has necessarily digital dimension. Digital Forensics extracts evidence from digital sources to prove crime in court of law. Expert knowledge required in the all phase of forensic investigation and development of latest tools and benchmarking of tools. Use of existing and evolution of better frameworks and training of incumbents needs experience. Challenges are there in Digital Forensics due to continuous technological advancements, old training techniques, lack of standardization, varied frameworks and no formal mechanism to capture expert experiences. Knowledge Management has wide application in processes, training and formalization. Knowledge Management can be the solution for the issues in Digital Forensics. Knowledge Management techniques such as ontology, content management, knowledge maps, knowledge bases etc. can also be used in Digital Forensics. This paper throws light upon scope of knowledge management in Digital Forensics.
KCC QA Latent Semantic Representation using Deep Learning &amp; Hierarchical Semantic cluster Inferential Framework

Rapid digitization is in progress in even the most traditional domains like Agriculture. In line with this, the Government of India released call centre question-answer data called the Kisan Call Centre data ( KCC ). Using the KCC data available at Open Government Data platform this paper suggests a novel double-headed autoencoder architecture that outperforms ubiquitous deep learning architectures used as baselines. Experiments are carried out for the data from Tamil Nadu call centres. The various sentence embeddings generated are clustered and externally analyzed against carefully hand-annotated data using various measures like cluster entropy for various levels of semantic resolution. A framework is created to model and evaluate semantic embeddings to create scope for various downstream tasks such as predicting rare events like drought and low yield etc
Analysis sentiment in social media against election using the method naive Bayes

Abstract Election is the process of selecting people to fill a variety of political positions that are diverse. General elections in a country are usually held periodically where the country adheres to a democratic system. Indonesia is a country that adopts a democratic system. The development of the use of Social Media is very fast. Nowadays, there are many social media which have a big influence. As a result, society has experienced changes in culture, ethics, norms and a more critical mindset in responding to existing conditions as well. Social media is now increasingly easy to use by all groups, from the beginning only a small person to a successful person and famous for Social Media. Sentiment analysis is a science that is useful for analyzing someone’s opinions, sentiments, and emotions expressed in the text. In this study the Naïve Bayes method can be applied to the classification of positive, negative, and neutral opinions. In this study of 50 training data and 10 test data obtained an accuracy of 90%. And the results obtained positive sentiment percentage of 50%, negative 20%, neutral 10%. More positive sentiments gained.
Optimizing user interfaces in food production: gaze tracking is more sensitive for A-B-testing than behavioral data alone

Eye-tracking data often provide access to information about users’ strategies and preferences that extend beyond purely behavioral data. Thanks to modern eye-tracking technology, gaze can be tracked rather unobtrusively in real-world settings. Here we examine the usefulness of gaze tracking with a mobile eye-tracker for interface design in an industrial setting, specifically the operation of a food production line. We use a mock task that is similar in its interface usage to the actual production task in routine machine operation. We compare two interface designs to each other as well as two levels of user expertise. We do not find any effects of experience or interface type in the behavioral data - in particular, both user groups needed the same time to complete the task on average. However, gaze data reveals different strategies: users with high experience in using the interface spend significantly less time looking at the screen – that is, actually interacting with the interface – in absolute terms as well as expressed as fraction of the total time needed to complete the task. This exemplifies how gaze tracking can be utilized to uncover different user-dependent strategies that would not be accessible through behavioral data alone.
Deep Learning Based Stress Prediction From Offline Signatures

Soft-Biometric measurements are now increasingly adopted as a robust means of determining individual’s non-unique characteristics with the emerging models that are widely used in the deep learning domain. This approach is clearly valuable in a variety of scenarios, specially those relating to forensics. In this study, we specifically focus on stress emotion, and propose automatic stress prediction technique from offline signature biometrics using well-known deep learning architectures such as AlexNet, ResNet and DenseNet. Due to the limited number of research that study emotion prediction from offline handwritten signatures with deep learning methods, best to our knowledge this is the first experimental study that presents empirical achievable prediction accuracy around 77%.
Dempster-Shafer Theory: Ηow Constraint Programming Can Help

Dealing with uncertainty has always been a challenging topic in the area of knowledge representation. Nowadays, as the internet provides a vast platform for knowledge exploitation, the need becomes even more imminent. The kind of uncertainty encountered in most of these cases as well as its distributed nature make Dempster-Shafer (D-S) Theory to be an appropriate framework for its representation. However, we have to face the drawback of the computation burden of Dempster's rule of combination due to its combinatorial behavior. Constraint Programming (CP) has proved to be an efficient tool in cases where results have to satisfy some specified properties and pruning of the computation space can be achieved. As D-S theory measures' computation fulfills this requirement, CP seems a promising framework to employ for this purpose. In this paper, we present our approach to use CP to compute the belief and plausibility measures of D-S Theory and Dempster's rule of combination as well as the results of the effort. As it was expected, the results are quite promising and in many cases impressive.
The development of a trusted mechanism in building a web service to ensure the quality of data and information in the Universitas Jambi databorang application by using the ADDIE model

Abstract Developers or users of an application that utilizes various databases demand accurate data through a web service, and it is a challenge to provide a trusted web service. This study aims to develop a trusted mechanism in building the Universitas Jambi’s web service by applying the ADDIE model. Data from the web service will be queried by a Databorang Application. The trusted mechanisms produced in this study are: 1) Analysis of data and information requirements, 2) Web service design, 3) Development of web service which has procedures: query development, validation of queries by experts, validation of results from users through the Databorang application, 4) Implementation, and 5) Evaluation. In a restricted deployment it was revealed that the user strongly believes in the truth of the data / information displayed by the application of Databorang. In line with the increasing data retrieval functions on the webservice, it is hoped that the webservice can act as the data center of the University of Jambi in the future.
Towards an Efficient Log Data Protection in Software Systems through Data Minimization and Anonymization

IT infrastructures of companies generate large amounts of log data every day. These logs are typically analyzed by software engineers to gain insights about activities occurring within a company (e.g., to debug issues exhibited by the production systems). To facilitate this process, log data management is often outsourced to cloud providers. However, logs may contain information that is sensitive by nature and considered personal identifiable under most of the new privacy protection laws, such as the European General Data Protection Regulation (GDPR). To ensure that companies do not violate regulatory compliance, they must adopt, in their software systems, appropriate data protection measures. Such privacy protection laws also promote the use of anonymization techniques as possible mechanisms to operationalize data protection. However, companies struggle to put anonymization in practice due to the lack of integrated, intuitive, and easy-to-use tools that accommodate effectively with their log management systems. In this paper, we propose an automatic approach (SafeLog) to filter out information and anonymize log streams to safeguard the confidentiality of sensitive data and prevent its exposure and misuse from third parties. Our results show that atomic anonymization operations can be effectively applied to log streams to preserve the confidentiality of information, while still allowing to conduct different types of analysis tasks such as users behavior, and anomaly detection. Our approach also reduces the amount of data sent to cloud vendors, hence decreasing the financial costs and the risk of overexposing information.
A Digital Forensic Process Model for Cloud Computing

Existing digital forensic frameworks do not address issues relevant to cloud environment where multiple users could be generating events that may coalesce to or independently and in parallel result in a security incident. In this paper a forensic process model is proposed which considers possibility of multiple parallel event sequences that must be considered to achieve correctness in event reconstruction in digital forensic investigation. Towards this end, use of Event Sequence Graph (ESG) as a directed graph for outlining events and the sequences which they follow has been proposed by the authors. Two case studies of attack from multiple sources have been used as examples to explain the type of scenarios which could be easily understood using an ESG.
Semi-automatic Eye Movement-Controlled Wheelchair Using Low-Cost Embedded System

An intelligent wheelchair prototype is presented, reusing old damaged equipment and incorporating low cost elements for repowering it. The system can be operated in manual mode (total user control) and semi-automatic depending on tasks to be performed and whether other secondary tasks are carried out (answering questions or picking up objects) as simulated in this experiment. When manual mode is executed, a coherence algorithm has been incorporated that allows the wheelchair to be guided with the eye movement and in the semi-automatic mode, system takes control when user do not execute a control action when an obstacle is about to crash with the chair. For a greater interaction between system and user, a basic, friendly and easy-to-use interface has been developed that empowers the performance of activities throughout this experiment. Participants selection, as well as the experimental tests performed are described in this document. The qualitative and quantitative results obtained allow to validate the efficiency of this system, as well as the satisfaction of the users through the respective tests.
Testbed for Mobile Robot Platoon Control System Evaluation

This paper presents the development of a testbed for mobile robot platoons based on modern microcontroller units. The sensor/actuator interfacing capacity, the general-purpose communication module and the simple programming environment of the applied controller enable the rapid and flexible development of the robot platoon control system. In spite of the low implementation costs, the testbed allows the implementation and testing of advanced control strategies and communication protocols. The paper presents both the software and hardware development of the testbed. Real-time experimental measurements are also presented to show the applicability of the developed system.
Estimation of the Line of Sight from Eye Images with Eyelashes

There are two methods for tracking the line of sight: the corneal reflection method and the pupil method. The former allows highly accurate measurement because the focus point is detected, however, its weakness is that deviations can occur in the positional relations between the eye and the camera. The latter has a strong positional relation between the eye and the camera because the pupil is shot by the camera, but because elliptical approximation is used, its weakness is masking of the pupil by eyelashes and the eyelid. When using an HMD or headgear in sports such as Japanese fencing, in either method, trying to estimate the user's line of sight is easier if the small cameras are facing down at an angle from the forehead at the eyes because the user's field of vision is not obstructed. Therefore, in this study, the authors proposed a line of sight estimating method that uses eye images that include eyelashes. Specifically, we proposed using inexpensive small cameras to take images of the eyes from slightly above. We also proposed processing to separate the eyelashes and pupils in the images to allow estimating the line of sight, and an eyeglasses-type device. Additionally, in order to achieve a stable line of sight estimation, we also made proposals for adjusting the various parameters to suit the sizes of the heads and distances between the eyes of individual users, such as the positioning and facing of the small cameras and the optical axes and intensity of supplementary light sources.
Towards a Feature Selection for Multi-label Text Classification in Big Data

Feature selection is an important task in machine learning. It can improve classification accuracy and effectively reduce the dataset dimensionality by removing no discriminative features. Though a large body of researches were focused on feature selection for text classification, few works addressed the problem for multi-label data in big data context. Therefore, this paper proposes a distributed feature selection approach for multi-label textual big data based on the weighted chi-square method. First, a standard multi-label approach to transform the multi-label data into single-label data is applied. Then, the algorithm assigns different weights to the features based on the category term frequency and then calculates the chi-square based on the weight of each feature. The proposed method is implemented on Hadoop framework using MapReduce programming model. At last, a set of experiments were conducted on three benchmarking text datasets to evaluate the effectiveness of the proposed approach. A comparative analysis of the results with the state-of-the-art techniques proves that our method is efficient, robust and scalable.
Perceptive advertising using standardised facial features

Advertising has become the sole medium for businesses to propagate their products in the market, especially, using digital display hoardings and billboards. Today, the advertisement industry is more dynamic and widespread than ever before; thousands of products are launched every day, to cater to the changing needs and demands of its users the advertisement industry also needs to evolve. Heeding to the need for more effective advertising, this paper tries to establish the idea of perceptive advertising- a responsive advertising system based on facial recognition. This paper proposes a system that captures, identifies and analyses facial features of a person to estimate the gender (86% accuracy). The gender estimated will then be used to model another system which would display advertisements appropriate to that gender, as identified by the previous system strategy. The posited idea will make individuals the focal point of advertising, helping businesses reach the right audience with ease, eventually increasing their sales and profit. Consumers can avoid irrelevant ads and get quick access to the products and services they might need. In conclusion, the paper presents a new approach for advertising, focused on the market needs and on-the-rise smart cities with the use of Computer Vision.
Vehicle Data Aggregation from Highway Video of Madurai City Using Convolution Neural Network

Traffic is one of the key issues in highly populated countries like India. There are many research works going on in the areas of the travel and transportation sector. These sectors having challenges such as traffic monitoring, routing assistance, personalized driving. Nowadays, smart city is one of the Government mission which includes traffic management, crowd management etc. In the recent past, there are many cities received funds from the government of India under the smart city scheme. In this paper, the vehicle data aggregation process is proposed using deep Convolution Neural Network (CNN) particularly, in and around the areas of Madurai city, Tamilnadu, India. The purpose of the location chosen is Madurai city also received funds under a smart city scheme from Indian government. There are certain works are already going on in Madurai city under the smart city project. The real-time traffic video capturing used to raspberry pi B modules camera. To support further into the work, a data aggregation technique is proposed in this work to create a vehicle dataset for the Madurai city to help traffic management, driver assistance, and routing guidance through automation system. In this work, the CNN algorithm detected the different vehicles with 92.9% accuracy. This will be compared with region based CNN algorithm, which detected with 96.4% accurately. Hence it is proven that the region based CNN performs better in vehicle detection. The vehicle data are tabulated from some of the sample videos captured in and around Madurai highways using CNN through MATLAB.
Application of User Interface Transition Diagrams in the Construction of a Software System: A Case Study

We have successfully applied the User Interface Transition Diagrams to model the behavior of the system user interfaces and Detailed Sequence Diagrams to model the sequence of messages between the system modules in a project called QualiTeam. QualiTeam is a web application that aims to facilitate teaching some concepts of quality assurance in software projects. By using these two modeling tools, we were able to effectively communicate the detailed design to the novice programmers who implemented the system.
Face Recognition System for Complex Surveillance Scenarios

Abstract In recent years, with the continuous development of the Internet and artificial intelligence, face recognition technology has also been widely used in many application scenarios. Facing complex surveillance scenarios, face recognition technology still faces great challenges. This paper focuses on implementing real-time and efficient face recognition systems in complex surveillance scenarios, such as insufficient lighting, small faces, dense crowds, and sides at 45 ° environment. The system is mainly based on RetinaFace for face detection and face alignment, and uses lightweight mobilenet (0.25) as the backbone network of RetinaFace. Facial feature extraction is based on deep residual neural network combined with ArcFace loss, and feature matching is performed by Euclidean distance. The experimental results show that the face recognition system has good real-time performance, accuracy and robustness.
Comparison of Cryptography by Chaotic Neural Network and by AES

In this paper, the two methods for ciphering are presented and compared. The aim is to reveal the suitability of chaotic neural network approach to ciphering compared to AES cipher. The durations in seconds of both methods are presented and the two methods are compared. The results show, that the chaotic neural network is fast, suitable for ciphering of short plaintexts. AES ciphering is suitable for longer plaintexts or images and is also more reliable.