Reviewerly/exHarmony huggingface dataset:
Language and Robotics: Complex Sentence Understanding

Existing robotic systems can take actions based on natural language commands but they tend to be only simple commands. On the other hand, in the domain of Natural Language Processing (NLP), complex sentences are processed, but this NLP domain does not make close contact with robotics. The beginning of computer processing of natural language, when traced back to a system such as Winograd’s SHRUDLU, conceived in 1973, actually aimed to address the issues of Natural Language Understanding (NLU) of relatively complex sentences by a robotic system which in turn takes actions accordingly based on the natural language input. NLU, in the robotic context, thus constitutes taking the correct actions from language instructions. This paper explores the use of cognitive linguistic constructs as well as other constructs such as spatial relationship constructs to configure an NLU system for translating complex natural language instructions into actions to be taken by a robot. This research work illustrates that two important steps are necessary: the first step is to translate a language-dependent surface sentential structure into a language independent deep-level predicate representation, and then the next step is to translate the predicate representation into grounded real-world references and constructs that enable a robot to carry out the language instructions accordingly.
A Comparative Study and Performance Analysis of ATM Card Fraud Detection Techniques

ATM card fraud is increasing gradually with the expansion of modern technology and global communication.In the whole world, it is resulting in the loss of billions of dollars each year.Fraud detection systems have become essential for all ATM card issuing banks to minimize their losses.The main goals are, firstly, to review alternative techniques that have been used in fraud detection and secondly compare and analyze these techniques that are already used in ATM card fraud detection.Recently different card security systems used different fraud detection techniques; these techniques are based on neural network, genetic algorithm, hidden Markov model, Bayesian network, decision tree, clustering method, support vector machine, etc.According to our survey, the most important parameters used for comparing these fraud detection systems are accuracy, speed and cost of fraud detection.This study is very useful for any ATM card provider to choose an appropriate solution for fraud detection problem and also enable us to build a hybrid approach for developing some effective algorithms which can perform properly on fraud detection mechanism.
Meta-Relation Networks for Few Shot Learning

We propose a meta-relation network to solve the few shot learning problem, where the classifier must learn to recognize new classes given only few examples from each. Meta-relation networks is based on relation networks and Model-Agnostic Meta-Learning (MAML) training methods, which can be trained end-to-end. After training with MAML algorithm, the meta-relation networks can adapt to learning quickly for a small number of samples from a new task with only a small amount of gradient step. It can also classify new classes of images by calculating the scores between query images and few examples of each new class. In experiments, we will demonstrate that the proposed approach leads to good performance on two few-shot image classification benchmarks.
On the Reader Service of the Intelligent Library in Universities

With the development of society and science and technology, the ideas of intelligent city, intelligent community, intelligent life, intelligent campus and intelligent enterprise have been explored and implemented in various industries. Intelligent libraries have also become a hot topic in the library industry. The research on the new model of library in China is developing rapidly, and the construction of intelligent library has become the goal of the library construction. Therefore, this paper discusses the intelligent reader service in the increasingly intelligent environment of university libraries. This research has broad application prospects and important practical significance.
Exploration Strategies for Homeostatic Agents

This paper evaluates two new strategies for investigating artificial animals called animats. Animats are homeostatic agents with the objective of keeping their internal variables as close to optimal as possible. Steps towards the optimal are rewarded and steps away punished. Using reinforcement learning for exploration and decision making, the animats can consider predetermined optimal/acceptable levels in light of current levels, giving them greater flexibility for exploration and better survival chances. This paper considers the resulting strategies as evaluated in a range of environments, showing them to outperform common reinforcement learning, where internal variables are not taken into consideration.
Event Recognition Oriented to Emergency Events and Its Application

Event recognition is a sub-task of event extraction. Its main task is to find event triggers in news texts such as earthquakes and fires. In the field of event recognition, there is a problem of insufficient utilization of single feature. Aiming at this problem, a new event recognition method based on positive and negative weighting is proposed by constructing trigger table. Firstly, according to whether the word to which the feature belongs is a trigger, the feature is divided into positive feature or negative feature. The divided positive and negative features are combined to perform trigger recognition, and the effect of single feature on event recognition is enhanced. Further, the related word features, part-of-speech features and dependency features are combined. The combination of these three features with positive and negative weighting is used to identify triggers. And it further improves the effect of event recognition. Finally, the application research about the text classification is carried out on the basis of event recognition. The experimental results show that event recognition method based on the positive and negative weighting and its application have achieved better results.
Occupant-Building Interaction (OBI) Model for University Buildings

Buildings are becoming ever smarter through being equipped with emerging technologies including variety of sensors.With the advances in building automation, the data that can be captured and shared between the building and its occupants has increased in amount and become more complex.However, the effective use of sensors to capture building-generated data and occupant-generated data and the subsequent use of this data to increase the efficacy of interactions between building and their occupants remains a challenge.This research aims at identifying the important data types that are essential to enable efficient two-way communication between buildings and their occupants.Among these, we focus on occupancy data as one of the major types of data that measures the position and movement of occupants inside the building.We also propose a conceptual hybrid framework to collect and use occupancy-data for university buildings and visualize it on a Building Information Modelling (BIM)-based model in real-time.The importance of indoor real-time location sensing (RTLS) in Occupant-Building Interaction (OBI) is highlighted.Proposed conceptual framework will be used to identify occupant location by analysing both coordinate data and contextual data.RFID tags will be used to collect coordinate data and sound sensors will be used to collect contextual data to identify location and support the RTLS system.The analysed occupancy-data can be communicated to the Building Management System (BMS) to help in monitoring occupant movement.It can assist the occupants in navigating inside the building through a dedicated smartphone application (APP).
Arbiter

In a high-performance computing (HPC) environment, shared resources are often the most capricious and unreliable. The performance of each process is affected by the behavior of all connected users. This applies particularly to the login nodes of HPC clusters, which are used by multiple people at a time. Often, policies govern their use, typically limiting users to small, lightweight tasks such as scripting, compiling, submitting batch jobs, and staging data. If a user is behaving poorly, such as using a significant portion of CPU time or consuming a large fraction of the total memory, processes using the same resources can be slowed or run out of memory. Policies on login nodes that prohibit such behavior, however, may not be enforced automatically because of technical limitations.
Dynamic Cascaded Regression Network with Reinforcement Learning for Robust Face Alignment

Regression-based methods for facial landmark detection usually learn a series of regressors to update the landmark positions from an initial shape with a fixed number of iterations. Their accuracy is sensitive to the initial shape, and the fixed number of iterations always leads to massive unnecessary computation. In this paper, we propose a Dynamic Cascaded Regression Network (DCRN) with a two-stage architecture to address these issues. In the first stage, we introduce a Global Estimation Network (GEN) to provide a coarse landmark estimation. In the second stage, we propose a Local Regression Network (LRN) to iteratively refine the coarse estimation in a reinforcement learning (RL) paradigm. Our DCRN takes the face image as input, and adaptively learns facial landmarks. Extensive experiments on 300W, COFW, and AFLW datasets show the effectiveness of our proposed method and demonstrate that DCRN consistently achieves the state-of-the-art performance.
Smart TV Terminal Interface Design for the Elderly Based on User Experience

Abstract With the advent of aging society and intelligent era, designing smart product interfaces for the elderly is positively significant to improve their experience and life. Combined with the ideas and methods of user experience, an in-depth study on the elderly using smart TV terminal is conducted through interview, observation, questionnaire, etc. It constructs the personas and user experience map to excavate the pains and opportunities of the products. The current products and interfaces in the market are taken as the case to construct the relationship between the product opportunities and the five elements of user experience to analyze the terminal interface of smart TV and propose the optimal design scheme. It meets the needs of users, improves their experience and offers reference for the design of homogeneous interface.
Integrated Management in Disaster: A Discussion of Competences in a Real Simulation

The frequent changes in the cultural, technological, social and environmental aspects of our society demonstrate the need for better crisis management and process safety. This improvement is not limited to industrial facilities, but should be extended to all organizations involved in crisis management. Knowing and understanding what actions to take during a disaster facilitates integrated management, which is crucial for successful operations. Prioritizing actions becomes challenging when multiple events occur. The time for action is short, requiring efficient communication between the parties involved and nimble decision making. This paper discusses a case study of the identification and analysis of human error during a disaster simulation and understanding the risks related to human decisions and actions in crisis management. Thus, the object of study is to evaluate the perception, decision, memory, competence, communication, emotional balance of the team and leader, in the treatment of emergency actions and contingency in disasters.
Towards QoS-Aware Cloud Live Transcoding: A Deep Reinforcement Learning Approach

Video transcoding is widely adopted in live streaming services to bridge the format and resolution gap between content producers and consumers (i.e., broadcasters and viewers). Meanwhile, the cloud has been recognized as one of the most reliable and cost-effective ways for video transcoding. However, due to the dynamic and uncertainty of the transcoding workloads in live streaming, it is very challenging for cloud service providers to provision computing resources and schedule transcoding tasks while guaranteeing the Service Level Agreement (SLA). To this end, we propose a joint resource provisioning and task scheduling approach for transcoding live streams in the cloud. We adopt Deep Reinforcement Learning (DRL) to train a neural network model for resource provisioning under dynamic workloads. Moreover, we design a QoS-aware task scheduling algorithm that maps transcoding tasks to Virtual Machines (VMs) by considering the real-time QoS requirement. We evaluate our approach with trace-driven experiments and the results demonstrate that our approach outperforms heuristic baselines by up to 89% improvements on average QoS with 4% extra resource overhead at most.
Multisensory Nudging: A Design Intervention for Sustainable Hand-Washing Behavior in Public Space

Abstract The scarce availability of water in highly populated cities is about to become a social problem. While the water service companies work on improving the distribution network in order to reduce losses, it is evident that one of the main problems is due to an excess of use of this resource by users. This consumption is relatively controlled when excessive consumption is clearly associated, in the consumer mind, with high costs. However, when users are in public places they tend to consume water because of a loss of correlation with costs. In this paper, we describe the design of a device to be installed in public environments, which aims to reduce the consumption of water. The device measures in real time the flow of water and sends the user visual and sound information trying to create a link between consumption and costs. The device has been installed in a university campus bathroom and has been tested. Test results show a reduction in water consumption, especially in the interactive prototype approach compared to the conventional treatment. Further modifications for future development of the interactive device is also discussed.
d(Tree)-by-dx: Automatic and Exact Differentiation of Genetic Programming Trees

Genetic programming (GP) has developed to the point where it is a credible candidate for the 'black box' modeling of real systems. Wider application, however, could greatly benefit from its seamless embedding in conventional optimization schemes, which are most efficiently carried out using gradient-based methods. This paper describes the development of a method to automatically differentiate GP trees using a series of tree transformation rules; the resulting method can be applied an unlimited number of times to obtain higher derivatives of the function approximated by the original, trained GP tree. We demonstrate the utility of our method using a number of illustrative gradient-based optimizations that embed GP models.
Community Participation, Mitigation Flood Disaster in Indonesia

Flooding is a natural phenomenon that often occurs in various countries including in Indonesia. Flooding is the process of rainwater overflow that occurs in large quantities with uncontrolled flow and crashing into an area or settlement due to not being able to accommodate large enough water discharge and causing overflowing of rivers, lakes, or ditches, so that the water inundates the surrounding land . In flood disasters in Indonesia there are major factors in the occurrence of flood disasters namely natural factors, and human factors. In improving flood mitigation must involve the participation of the community, especially the local community, without supporting community participation, the disaster mitigation program will not be realized, where community participation includes environmental cleanliness and illegal logging ban which is one of the flood mitigation efforts, where this is very important in carrying out activities prior to the flood disaster.
A new watermarking method to protect blockchain records comprising small graphic files

We propose a new type of watermarks for graphic black and white documents. Insertion of the watermark in a document minimizes distortion of the latter. The watermarking allows extra verification of blockchain database records containing scans of documents.
No-reference quality metric for HEVC compression distortion estimation in depth maps

Multiview video plus depth (MVD) is the most popular 3D video format due to its efficient compression and provision for novel view generation enabling the free-viewpoint applications. In addition to color images, MVD format provides depth maps which are exploited to generate intermediate virtual views using the depth image-based rendering (DIBR) techniques. Compression affects the quality of the depth maps which in turn may introduce various structural and textural distortions in the DIBR-synthesized images. Estimation of the compression-related distortion in depth maps is very important for a high-quality 3D experience. The task becomes challenging when the corresponding reference depth maps are unavailable, e.g., when evaluating the quality on the decoder side. In this paper, we present a no-reference quality assessment algorithm to estimate the distortion in the depth maps induced by compression. The proposed algorithm exploits the depth saliency and local statistical characteristics of the depth maps to predict the compression distortion. The proposed ‘depth distortion evaluator’ (DDE) is evaluated on depth videos from standard MVD database compressed with the state-of-the-art high-efficiency video coding at various quality levels. The results demonstrate that DDE can be used to effectively estimate the compression distortion in depth videos.
Analyzing the Effectiveness of Machine Learning Algorithms for Determining Faulty Classes: A Comparative Analysis

The quality of the software can be improved by determining its faulty portions in the initial phases of the lifecycle of a software product. There are various machine learning algorithms proposed in literature studies that can be used to predict faulty classes. The machine learning algorithms determine faulty classes by using object oriented metrics as predictors. These models will allow the developers to predict faulty classes and concentrate the constraint resources in testing these weaker portions of the software. This study evaluates and compares the predictive capability of six machine learning algorithms amongst themselves and with logistic regression, a statistical algorithm for determining faulty portions of a software. The results are validated using seven open source software.
Review on Security Problems of Bitcoin

Bitcoin is a cryptocurrency which means transactions are carried by means of electronic cash. It is a digital currency that has no central bank or an administrator. Bitcoins are transferred from user to user on the bitcoin network. The market capitalization of bitcoin is 170 million and over 375,000 transactions take place every day. There are a lot of security issues in bitcoin. Some of them are double spending, Finney attack, brute force attack, vector 76, Goldfinger. These attacks have some solutions which are known as Conjoin, Coinshuffle, xim, value shuffle, dandelion etc. In this paper, I review these security problems and the solutions to them.
Online Multi-Object Tracking with Three-Stage Data Association

Tracking-by-detection is the main paradigm in the field of multi-object tracking. Tracking may fail easily when objects are occluded. In this paper, we propose a method to solve this problem. We adopt a novel three-stage data association. In the first stage, the matching is directly performed according to optimal overlapping if targets and trajectories of overlapping area are very large; in the second stage, Siamese Network is used for the similarity calculation for targets and trajectories with smaller overlapping area and then the Hungarian algorithm is used for matching; the third stage, the remaining trajectories and targets are matched by using the Hungarian algorithm based on the overlapping area. Experiments show that our method effectively improves the tracking success rate and speed.
Secure Analysis of Social Media Data

Confidentiality of the social media data during analysis is a major concern. Several real evidences show how the privacy and security of the data is compromised. One of the essential processes with social media data is to find the shortest paths between selected pair of nodes. This paper proposes a technique to modify the original data before analysis. The algorithm calculates shortest paths (data utility) between target nodes and then classifies edges into partially visited, all-visited and unvisited edges. Each category of edges is then perturbed using a dynamic variable value that is bound to satisfy specific constraints such that the shortest path as well as the shortest paths lengths, between the target node pairs remains the same. This paper proposes an approach to preserve the privacy of the weights and also generates an accurate length of the shortest path. It is also observed that the shortest path lengths between any target pairs of nodes are retained. The output is in the form of graphs, that shows that the proposed perturbation strategy perturbs the sensitive edge weights up to a maximum 72%, while keeping the difference in shortest path lengths minimum (up to 3%). It is hence demonstrated that along with preserving the sensitive information by perturbing the edge weights, the data utility is preserved i.e. the shortest path lengths are kept as near as potential to the original ones.
Deep-Facial Feature-Based Person Reidentification for Authentication in Surveillance Applications

Person reidentification (Re-ID) has been a problem recently faced in computer vision. Most of the existing methods focus on body features which are captured in the scene with high-end surveillance system. However, it is unhelpful for authentication. The technology came up empty in surveillance scenario such as in London's subway bomb blast, and Bangalore ATM brutal attack cases, even though the suspected images exist in official databases. Hence, the prime objective of this chapter is to develop an efficient facial feature-based person reidentification framework for controlled scenario to authenticate a person. Initially, faces are detected by faster region-based convolutional neural network (Faster R-CNN). Subsequently, landmark points are obtained using supervised descent method (SDM) algorithm, and the face is recognized, by the joint Bayesian model. Each image is given an ID in the training database. Based on their similarity with the query image, it is ranked with the Re-ID index. The proposed framework overcomes the challenges such as pose variations, low resolution, and partial occlusions (mask and goggles). The experimental results (accuracy) on benchmark dataset demonstrate the effectiveness of the proposed method which is inferred from the observation of receiver operating characteristic (ROC) curve and cumulative matching characteristics (CMC) curve.
HSVM-Based Human Activity Recognition Using Smartphones

Human Activity Recognition (HAR) based on smartphones has a lot of applications to our daily life. Through the years, researchers reached different goals in this topic. Different issues were presented at each approach like accuracy, computational cost, device placement, battery life and limited hardware and software resources. In this paper we developed an HSVM that is capable of classifying 5 types of human activities walking, running, jumping, standing and sitting. 5-fold cross validation was used for the offline training achieving very high accuracy, but in online validation the result was no as accurate as in offline validation. A device placement (orientation) in pocket was considered too, then we present a variety of confusion matrices with four, possible device placement, and their respective accuracy. Wavelet Transform was used to minimize noise into the feature vector.