Reviewerly/exHarmony huggingface dataset:
A Fine-Grained User-Divided Privacy-Preserving Access Control Protocol in Smart Watch

A smart watch is a kind of emerging wearable device in the Internet of Things. The security and privacy problems are the main obstacles that hinder the wide deployment of smart watches. Existing security mechanisms do not achieve a balance between the privacy-preserving and data access control. In this paper, we propose a fine-grained privacy-preserving access control architecture for smart watches (FPAS). In FPAS, we leverage the identity-based authentication scheme to protect the devices from malicious connection and policy-based access control for data privacy preservation. The core policy of FPAS is two-fold: (1) utilizing a homomorphic and re-encrypted scheme to ensure that the ciphertext information can be correctly calculated; (2) dividing the data requester by different attributes to avoid unauthorized access. We present a concrete scheme based on the above prototype and analyze the security of the FPAS. The performance and evaluation demonstrate that the FPAS scheme is efficient, practical, and extensible.
2016 Olympic Games on Twitter: Sentiment Analysis of Sports Fans Tweets using Big Data Framework

Big data analytics is one of the most important subjects in computer science. Today, due to the increasing expansion of Web technology, a large amount of data is available to researchers. Extracting information from these data is one of the requirements for many organizations and business centers. In recent years, the massive amount of Twitter's social networking data has become a platform for data mining research to discover facts, trends, events, and even predictions of some incidents. In this paper, a new framework for clustering and extraction of information is presented to analyze the sentiments from the big data. The proposed method is based on the keywords and the polarity determination which employs seven emotional signal groups. The dataset used is 2077610 tweets in both English and Persian. We utilize the Hive tool in the Hadoop environment to cluster the data, and the Wordnet and SentiWordnet 3.0 tools to analyze the sentiments of fans of Iranian athletes. The results of the 2016 Olympic and Paralympic events in a one-month period show a high degree of precision and recall of this approach compared to other keyword-based methods for sentiment analysis. Moreover, utilizing the big data processing tools such as Hive and Pig shows that these tools have a shorter response time than the traditional data processing methods for pre-processing, classifications and sentiment analysis of collected tweets.
Profilometry With Enhanced Accuracy Using Differential Structured Illumination Microscopy

The conventional structured illumination micro-scopy (SIM) reconstructs the surface through determining the focal position from the modulation depth response (MDR) using the Gaussian fitting. In this letter, an innovative differential structured illumination microscopy (DSIM) is developed. This technique exploits the position at the linear portion of the MDR to achieve the measurements. To extract this position, two detectors are used to generate the differential modulation depth response (DMDR). After that, the zero point of the DMDR for each pixel is extracted by the linear fitting technique. The experiments and theoretical analysis are elaborated to verify that the DSIM can greatly enhance the axial accuracy, providing that the potential to be applied in high-precision measurement.
A Simple Example for Linear Partial Differential Equations and Its Solution Using the Method of Separation of Variables

Summary In this article, we formalized in Mizar [4], [1] simple partial differential equations. In the first section, we formalized partial differentiability and partial derivative. The next section contains the method of separation of variables for one-dimensional wave equation. In the last section, we formalized the superposition principle.We referred to [6], [3], [5] and [9] in this formalization.
Protagonist vs Antagonist PROVANT: Narrative Generation as Counter Planning

Our motivation in this work is to develop a narrative generation mechanism for Interactive Storytelling that removes some of the authoring burden that is inherent to plan-based approaches. We focus on the class of narratives that dominate in Hollywood movies, television serial dramas and situation comedies. These narratives revolve around a central Protagonist in pursuit of a goal and who faces a series of obstructions placed in their way by an Antagonist and which they must overcome in order to reach their goal. We cast this problem as a non-cooperative multi-agent planning problem, in other words counter planning. We build on recent techniques in goal recognition and landmark identification to develop a novel plan-based narrative generation mechanism. A key opportunity that goal recognition provides is to reason explicitly with partially observed action sequences, reflecting the reasoning process of the antagonist. Thus the antagonist can only act to obstruct if it is reasonable (to the viewer) that they have guessed the protagonist's intentions. Starting from the believed goal, the narrative generator can reason about the protagonist's plan and what must be done to achieve it i.e., the plan landmarks and use these to automatically identify suitable points of obstruction. In the paper we detail the approach and illustrate it with a worked example. We report the results of an experimental evaluation and user study in a number of representative narrative domains. Results of the user study with system generated narratives confirm that viewers can clearly recognise agent roles and narrative structure.
Advice Replay Approach for Richer Knowledge Transfer in Teacher Student Framework

One of the major drawbacks of RL is the low sample efficiency of the learning algorithms. In many cases domain expertise can help to mitigate this effect. Teacher-Student framework is one such paradigm, where a more experienced agent (teacher) upon being queried helps to accelerate the student's learning by providing advice on the action to take in a given state. Real world teachers not only provide the action to take in a given state but also provide a more informative signal using the synthesis of knowledge they may have gained with experience. With this motivation, we propose a richer advising framework where the teacher augments the student's knowledge by also providing the expected long term reward of following that action. The student can then use this value to steadily guide its Q-Network in the correct direction which can lead to a quicker convergence. To help student relive the advices received throughout its learning, we introduce an additional memory called the Advice Replay Memory (ARM). Results show that a student following our approach (a) is able to exploit the environment better, and (b) has a steeper learning curve.
IoT Based Healthcare Infrastructure: A Case Study of Saudi Arabia

Internet of Things notion has benefitted many domains to improve the efficiency and effectiveness of the organizational processes. Likewise, the healthcare sector has adopted many Internet of Things based applications to raise healthcare standard. In this paper we carried out a systematic literature review of the Internet of Things adoption in the healthcare sector. Based on findings, we present a framework for the Internet of Things adoption in Saudi Arabia health sector. This framework is divided into three main sections which will help in shifting the healthcare sectors conventional working infrastructure in Saudi Arabia to improve healthcare provision for the patients.
Tracking Split Group with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mi>δ</mml:mi></mml:math>-Generalized Labeled Multi-Bernoulli Filter

As target splitting is not considered in the initial development of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mi>δ</mml:mi></mml:math>-generalized labeled multi-Bernoulli (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:mi>δ</mml:mi></mml:math>-GLMB) filter, the scenarios where the new targets appearing conditioned on the preexisting one are not readily addressed by this filter. In view of this, we model the group target as gamma Gaussian inverse Wishart (GGIW) distribution and derive a <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M4"><mml:mi>δ</mml:mi></mml:math>-GLMB filter based on the group splitting model, in which the target splitting event is investigated. Two simplifications of the approach are presented to improve the computing efficiency, where with splitting detection, we need not to predict the splitting events of all the GGIW components in every iteration. With component combination applied in adaptive birth, a redundant modeling for a newborn target or preexisting target could be avoided. Moreover, a method for labeling performance evaluation of the algorithm is provided. Simulations demonstrate the effectiveness of the proposed approach.
An Economical And Relatively Efficient Implementation of the Real–Time Solar Tracking System

The bi-facial solar system which is available in the commercial markets having a variety of advantages and efficiency but they are too much costly.Therefore there is a dire need to design a low price solar system that overcomes the increasing energy demand.In this research, we have designed a system which reflects the bi-facial model with an economical prize for the developing nations.However, the efficiency of the proposed solar system was checked on a sunny day and its observation was closely related to the real-time bi-facial solar system.The prototype has been designed by combining the two equal watts solar penal having anti-parallel alignment with each other.The rear penal of the design system is supported by concentrator for strengthening the efficiency of the scattered irradiation.The scattered irradiation generates extra energy due to the design structure of the proposed system.The voltage of the system is conjoint increases slightly as the timely increasing irradiation strength.The power of the designed system increases with the increasing voltage proportional relationship with the current.The design system verifies the voltage, current and power measurement from all location of the calculation.
An Efficient Digital Image Encryption Using Pixel Shuffling and Substitution for Wireless Networks

Most of the proposed security algorithms for wireless networks consider only securing scalar data, which are not suitable for other data types, e.g., digital images. In addition, many methods add noticeable overhead in securing such data types hence affecting the provided security level and network performance. In this paper, we propose an efficient security scheme based on chaotic algorithm to efficiently encrypt the digital images over wireless network applications while taking into consideration the processing capability and time constrains of most wireless networks. Our proposed security algorithm processes the digital images in three phases: security key generation, pixel permutation, and substitution. The 2D Logistic chaotic map is used in permutation and substitution stages for image pixel shuffling and transposition in order to enhance the required level of security and resist against various security attacks. Security performance analysis based on standard test images shows that our proposed scheme satisfies the needed security requirements and has better performance compared with other proposed techniques.
Removing the Target Network from Deep Q-Networks with the Mellowmax Operator

Deep Q-Network (DQN) is a learning algorithm that achieves human-level performance in high-dimensional domains like Atari games. We propose that using an softmax operator, Mellowmax, in DQN reduces its need for a separate target network, which is otherwise necessary to stabilize learning. We empirically show that, in the absence of a target network, the combination of Mellowmax and DQN outperforms DQN alone.
Neural Emotional Response Generation via Adversarial Transfer Learning

Emotional response generation is a key step to build an empathetic chatbot. However, previous emotional chatting models mainly focus on single-turn conversation, and multi-turn context emotional response generation has not been explored. In this paper, we propose an adversarial transfer emotional chatting (ATEC) model for multi-turn conversation which is based on conditional variational autoencoders (CVAE). ATEC has two alternate training phases: supervised training and transfer training. In the supervised training stage, we train the CVAE model, a content discriminator and an emotional classifier based on ground truth corpus. And in the transfer training stage, we change the target emotion and use the content discriminator to force the model to transfer the multi-turn context information, while the emotional classifier regularizes the emotions expressed in the generated responses. Experiments show that the proposed approach achieves state of the art performance with diverse responses and accurate emotional expression.
The unintended consequences of embedded values in socio-technical systems: A critical reflection using formal analysis of speedometers in customer vehicles.

(2019). The unintended consequences of embedded values in socio-technical systems: A critical reflection using formal analysis of speedometers in customer vehicles. The Design Journal: Vol. 22, Running with Scissors: 13th International Conference of the European Academy of Design, Dundee, 10-12th April, 2019, edited by Louise Valentine, Joanna Bletcher and Leon Cruickshank, pp. 1723-1734.
Indoor robot path planning assisted by wireless network

Indoor robot global path planning needs to complete the motion between the starting point and the target point according to robot position command transmitted by the wireless network. Behavior dynamics and rolling windows in global path planning methods have limitations in their applications because the path may not be optimal, there could be a pseudo attractor or blind search in an environment with a large state space, there could be an environment where offline learning is not applicable to real-time changes, or there could be a need to set the probability of selecting the robot action. To solve these problems, we propose a behavior dynamics and rolling windows approach to a path planning which is based on online reinforcement learning. It applies Q learning to optimize the behavior dynamics model parameters to improve the performance, behavior dynamics guides the learning process of Q learning and improves learning efficiency, and each round of intensive learning action selection knowledge is gradually corrected as the Q table is updated. The learning process is optimized. The simulation results show that this method has achieved remarkable improvement in path planning. And, in the actual experiment, the robot obtains the target location information by wireless network, and plans an optimized and smooth global path online.
Research on financial network big data processing technology based on fireworks algorithm

Big data is a hot issue in both theoretical and practical circles. Although many scholars have analyzed the risk of internal control of venture capital information system from different angles, there is still a lack of research on the risk of internal control of venture capital system under big data environment. Aiming at the concept and characteristics of large data, this paper proposes the research of internal control system of venture capital information system based on large data processing technology. The risk prediction model based on improved quantum support vector machine is used to verify the accuracy of the model. This paper divides the risk critical control process one by one for the hardware, software, personnel, information and operation rules of the venture capital object; probes into the main risks of different control objects in the process of information system construction under the big data environment; and puts forward the corresponding risk management methods of system internal control. Simulation experiments verify the reliability of the model and algorithm.
Automatic Scene Recognition Based on Constructed Knowledge Space Learning

An automatic visual scene recognition has attracted increasing attention for developing multimedia systems as it provides rich information beyond object recognition and action recognition. Each scene image often contains or is characterized by a certain of same essential objects and relations, for example, scene images of “wedding” usually have bridegroom and bride next to him. Theoretically, this kind of scene knowledge can be properly modeled by some essential objects in the scene image and with their relations for each scene class. Inspired by the observation, we proposed a novel approach to improve the accuracy of scene recognition by mining essential scene sub-graph and learning a bi-enhanced knowledge space. The essential scene sub-graph describes the essential objects and their relations for each scene class. The learned knowledge space is bi-enhanced by global representation on the entire image and local representation on the corresponding essential scene sub-graph. The experiment results in the widely used scene classification dataset Scene30 and Scene15 demonstrate the effectiveness of the proposed approach with improvements in scene recognition accuracy compared with the state-of-the-art techniques.
A gaze-based experimenter platform for designing and evaluating adaptive interventions in information visualizations

We present an experimenter platform for designing and evaluating user-adaptive support in information visualizations. Specifically, this platform leverages eye-tracking data in real time to deliver adaptive support in visualizations based on the user's attentional patterns and individual needs. We describe the main functionalities of this platform, and show an application to support processing of textual documents with embedded bar charts, by dynamically providing highlighting in the charts to guide a user's attention to the relevant information.
Optimal basis for the generalized Dicke model

A methodology is devised for building optimal bases for the generalized Dicke model based on the symmetry adapted variational solution to the problem. At order zero, the matter sector is constructed by distributing $N_a$ particles in all the possible two-level subsystems connected with electromagnetic radiation; the next order is obtained when the states of $N_a-1$ particles are added and distributed again into the two-level subsystems; and so on. In the electromagnetic sector, the order zero for each mode is the direct sum of the Fock spaces, truncated to a value of the corresponding constants of motion of each two-level subsystem; by including contributions of the other modes, the next orders are obtained. As an example of the procedure we consider $4$ atoms in the $\Xi$ configuration interacting dipolarly with two modes of electromagnetic radiation. The results may be applied to situations in quantum optics, quantum information, and quantum computing.
The Classroom as a Makerspace: Use of Tablets and Cutting Plotter to Create Pop-Up Cards in Educational Environments

In some classrooms the use of digital tablets for students is already standardized and is integrated into the teaching-learning process of the school. On the other hand, the use of cutting plotters is not common, although they are low-cost, easy to use and transportable devices. These machines are usually found in digital fab-rication spaces such as Makerspaces, Fab labs, etc. However, it is interesting to introduce these technologies in traditional classrooms. This article describes an experience carried out at Colegio San Isidro, Los Salesianos de la Orotava, Tene-rife in the 3rd year of secondary school in the subject of plastic, visual and audio-visual expression, in which an activity of design and creation of pop-up cards has been carried out. This activity is made in many subjects in order to develop crea-tivity or to understand three-dimensional concepts (mathematics, plastic, etc.). This activity involves cutting and folding paper that is usually done with scissors or cutter. It is proposed to digitalize this activity by means of digital tablets and portable cutting plotters. At the end of the activity, all the students were able to make their pop-up card using the proposed technologies and felt able to carry out the work autonomously.
Learning a Frequency-Matching Grammar together with Lexical Idiosyncrasy: MaxEnt versus Hierarchical Regression

Experimental research has uncovered language learners’ ability to frequency-match to statistical generalizations across the lexicon, while also acquiring the idiosyncratic behavior of individual attested words. How can we model the learning of a frequency-matching grammar together with lexical idiosyncrasy? A recent approach based in the single-level regression model Maximum Entropy Harmonic Grammar makes use of general constraints that putatively capture statistical generalizations across the lexicon, as well as lexical constraints governing the behavior of individual words. I argue on the basis of learning simulations that the approach fails to learn statistical generalizations across the lexicon, running into what I call the GRAMMAR-LEXICON BALANCING PROBLEM: lexical constraints are so powerful that the learner comes to acquire the behavior of each attested form using only these constraints, at which point the general constraint is rendered superfluous and ineffective. I argue that MaxEnt be replaced with the HIERARCHICAL REGRESSION MODEL: multiple layers of regression structure, corresponding to different levels of a hierarchy of generalizations. Hierarchical regression is shown to surmount the grammar-lexicon balancing problem—learning a frequency-matching grammar together with lexical idiosyncrasy—by encoding general constraints as fixed effects and lexical constraints as a random effect. The model is applied to variable Slovenian palatalization, with promising results.
MapReduce-based fuzzy very fast decision tree for constructing prediction intervals

We propose the fuzzy version of very fast decision tree (VFDT) to predict prediction intervals and compared them with those generated by traditional VFDT. The proposed fuzzy VFDT is able to capture intrinsic features of VFDT as well as uncertainties available in data. The VFDT and fuzzy VFDT were trained using the lower upper bound estimation (LUBE) method in order to generate prediction intervals. We also implemented VFDT; developed and implemented fuzzy VFDT using Apache Hadoop MapReduce framework, where multiple slave nodes build a VFDT and fuzzy VFDT model. The developed models were tested on six datasets taken from the web. We conducted sensitivity analysis by studying the influence of the window size of the data stream, number of bins in discretisation on the final results. Results demonstrated that the proposed MapReduce-based fuzzy VFDT and VFDT can construct high-quality prediction intervals precisely and quickly.