Reviewerly/exHarmony huggingface dataset:
A Knowledge Driven Dialogue Model With Reinforcement Learning

In recent decades, many researchers pay a lot of attention on generating informative responses in end-to-end neural dialogue systems. In order to output the responses with knowledge and fact, many works leverage external knowledge to guide the process of response generation. However, human dialogue is not a simple sequence to sequence task but a process heavily relying on their background knowledge about the topic. Thus, the key of generating informative responses is leveraging the appropriate knowledge associated with current topic. This paper focus on addressing incorporating the appropriate knowledge in response generation. We adopt the reinforcement learning to select the most proper knowledge as the input information of the response generation part. Then we design an end-to-end dialogue model consisting of the knowledge decision part and the response generation part. The proposed model is able to effectively complete the knowledge driven dialogue task with specific topic. Our experiments clearly demonstrate the superior performance of our model over other baselines.
Pain Experience in Social VR: The Competing Effect on Objective Pain Tolerance and Subjective Pain Perception

The distractive qualities of virtual reality are known to decrease pain. As technology develops, new opportunities to augment VR's distractive capacities are arising. For example, social interaction in virtual reality can increase distraction and possibly also provide social support. Below, we describe a pilot study designed to investigate whether interacting with an attachment Figure versus interacting with a stranger in the virtual world would yield any difference on participants' pain ratings and pain threshold in an experimental pain task. We designed a within-group single-factor experiment with three conditions; being alone in VR, interacting with an attachment Figure in VR, and interacting with a stranger in VR. We aimed to investigate the effect on participants of distinct attachment styles in close relationships. In preliminary findings, we observed trends for the highest tolerance for mild induced pain when participants interacted with their loved ones. Besides, participants reported the most severe perceived pain when interacting with strangers in VR. We discuss implications for future work.
A Secure Model to Execute Queries Over Encrypted Databases in the Cloud

Recently, database users have begun to use cloud database services to outsource their databases. This is due to the high computation speed and the huge storage capacity the cloud owners provide at low prices. Despite cloud computing being an attractive environment for database users, privacy issues are cause for concern for database owners since the data access will be out of their control. Encryption is the only way of assuaging users' fears surrounding data privacy. However, executing Structured Query Language (SQL) queries over encrypted data is a challenging task, especially if the data are encrypted by a randomized encryption algorithm. Many researchers have addressed the privacy issues by using deterministic encryption, onion layers encryption, or homomorphic encryption to encrypt data. But, even with these systems, the encrypted data can still be subject to attack. In this research, we propose a model to execute SQL queries over encrypted data, where the data are encrypted by a single randomized encryption algorithm-namely, Advanced Encryption Standard AES-CBC. We move most of the computations to the cloud and leave users with no crypto computation. Our model intends to narrow the range of retrieved encrypted records from the cloud to a small set of records that are candidates for the user's query. We implement and evaluate our model and find that it is both practical and efficient. Our experiments show that our model succeeds in minimizing the decryption processes to less than 30% of decryption the whole set of encrypted records in a table.
Syntonets: toward a harmony-inspired general model of complex networks

We report an approach to obtaining complex networks with diverse topology, here called syntonets, taking into account the consonances and dissonances between notes as defined by scale temperaments. Though the fundamental frequency is usually considered, in real-world sounds several additional frequencies (partials) accompany the respective fundamental, influencing both timber and consonance between simultaneous notes. We use a method based on Helmholtz's consonance approach to quantify the consonances and dissonances between each of the pairs of notes in a given temperament. We adopt two distinct partials structures: (i) harmonic; and (ii) shifted, obtained by taking the harmonic components to a given power $\beta$, which is henceforth called the anharmonicity index. The latter type of sounds is more realistic in the sense that they reflect non-linearities implied by real-world instruments. When these consonances/dissonances are estimated along several octaves, respective syntonets can be obtained, in which nodes and weighted edge represent notes, and consonance/dissonance, respectively. The obtained results are organized into two main groups, those related to network science and musical theory. Regarding the former group, we have that the syntonets can provide, for varying values of $\beta$, a wide range of topologies spanning the space comprised between traditional models. Indeed, it is suggested here that syntony may provide a kind of universal complex network model. The musical interpretations of the results include the confirmation of the more regular consonance pattern of the equal temperament, obtained at the expense of a wider range of consonances such as that in the meantone temperament. We also have that scales derived for shifted partials tend to have a wider range of consonances/dissonances, depending on the temperament and anharmonicity strength.
Performance Evaluation of No-Pairing ECC-Based KPABE on IoT Platforms

Internet of Things (IoT) networks operating in lightweight resource-constrained devices have been growing constantly in a wide range of areas. Such networks collect sensitive information over time that represent the users' behavioral patterns, which necessitates the need of safe data communication. IoT distributed systems are incurred with cyber-security challenges that need to be considered for lightweight heterogonous devices. Attribute-Based Encryption (ABE) schemes have been proposed to provide secure data communication between networked devices in unsecure channels by specifying an access policy to be solely verified when a set of attributes of the decrypting user satisfies the access policy. Primitives based on elliptic curves, on the other hand, provide much smaller key bit sizes than other cryptographic algorithms at the same security strength. Several state-of-the-art works have introduced no-pairing ABE schemes that eliminate the need of the computationally expensive bilinear pairing operations, but few have implemented no-pairing ECC-based ABE schemes on resource-constrained platforms for IoT applications. In this paper, a no-pairing ECC-based KPABE scheme is implemented on different IoT platforms of constrained computing capabilities. The performance evaluation in terms of time execution against increasing number of attributes is presented and benchmarked with the scheme's implementation on a computer. The presented implementation provides an aid for future IoT deployments to adopt the optimal platform solution of the proposed implementation IoT platforms.
Research on the Blended Teaching Model of "Analog Electronic Technology" Course in Institutions of higher learning Based on WeChat Platform

We analyzed the current teaching situation, existing problems and the necessity of developing a Blended teaching mode of "Analog Electronic Technology" course for electromechanical and educational technology majors in Institutions of higher learning. With the help of the functions of voice short message, video, picture and text supported by WeChat, we constructed a mixed teaching mode. In order to improve course teaching effect, the course processing are divided into three stage: Before class, some knowledge are introduced to students; during class, we use on-line and off-line method to demonstrate the knowledge in details; after class, on-line method is used to strengthened knowledge and capacity base.
Design of a Mixed Reality Based System for Multimedia Content Presentation

Traditional architecture student presentations use physical artifacts such as posters and models. These artifacts are displayed in a pin up space: a space where architects pin posters showcasing their work to walls so that their work can be viewed by others. Mixed reality (MR) is a technology that involves the merging of real and virtual entities to produce hybrid environments where physical and digital objects co-exist and interact in real time. We created an application for a MR pin up space for architectural posters and models in order to explore if and how MR technology improves upon the architectural portfolio presentation experience due to a richer set of interactions. These interactions include audio clips of the designers explaining their design, and an interactive 3D model of the design. We conducted a user study where we asked participants to use our MR application to view and interact with the pin up space, and compared it to a traditional physical pin up space.
Epitome Evolution of Sanctuary to Detect the Interloper in Home Automation

Barrier and assurance become the real aftereffects to the IoT applications and calm, experience some huge hardships. In order to support this rising zone, we, to aggregate things up, review the assessment headway of IoT and spotlight on the resistance. Altogether explore by this strategy for security building and features, the barrier necessities are given. In this point of view, we practice about the appraisal status of a key movement including encryption fragments, the ensuring sensor information cryptographic estimations, and likeness security have quickly laid out the difficulties, Over the top precision, ought to be presented in the significance of gauge status (EEG Longitude Signals) and the way where that sidelong afflictions and prescriptions are of basic centrality in the elucidation of results, ought to dependably be considered. Check of results in single appraisals and self-decision tests are required, thusly, the receptiveness of tests is basic. ATmega328P is used to get the info and yield work which are aid of an Arduino UNO IDE. It is associated with access to the OTA (Over-The-Air) preparing to help the system handling. This system guarantees intruders through the keypad security structure exclusively. It is gotten to by a switch lattice framework. What’s more, it ensures enduring without gatecrasher in the framework. Electric power converters are used to create the power supply to the board correspondence between the IDE, at that point, to distinguish and discover the security vitality through the liquid crystal show. It produces the picture relating to prepare for security. At that point, the board has imparted to deliver the key capacity through the presentation which aids in electric power handling. The progress of the order executed by the Door lock for the electric lock will get to the security framework.
Selected Stakeholders’ Views on the Use of Tablets for University Learning: A South African Case Study

The popularity of mobile technologies has greatly influenced people of all ages, especially adolescents.The purpose of this research was to determine the views of students, lecturers, and managers as selected stakeholders to assess the effectiveness of tablet computers in learning at a South African university.Using a mixed-methods approach, this case study focused on Extended Stream students who were enrolled in a degree program in Information and Communication Technology or Electrical Engineering.Survey participants consisted of 155 students and 25 lecturers, and interviews were conducted with 18 students, 5 lecturers, and 9 managers.Data collected from closed-ended questionnaires were entered manually into the Statistical Package for Social Sciences (Version 24) and then analyzed using descriptive and inferential analyses, using an independent samples t-test.Data collected from interviews were transcribed and analyzed using thematic analyses to generate major themes and subthemes.Findings from the statistical analyses of quantitative data revealed that there was no significant difference between the views of students and lecturers on the effectiveness of the use of tablet computers for learning.The thematic analyses of qualitative data revealed that students, lecturers, and managers all concurred that tablets have a positive impact on student learning.
Multiobjective particle swarm community discovery arithmetic based on representation learning

Summary With the continuous increase of the network scale, the structure of the network has also become complicated. The original community discovery algorithm based on small‐scale static networks has been unable to meet our needs. In order to improve the quality of community division, community discovery algorithms based on multiple optimization functions have been proposed. These multiobjective algorithms have continued to increase in time complexity as the optimization functions increase. The time complexity of the multiobjective community discovery algorithm is reduced, and the particle swarm algorithm has higher efficiency and accuracy in solving multiobjective optimization (MOO) problems. Based on the above background, the purpose of this article is to study a multiobjective particle swarm community discovery algorithm based on representation learning. This article uses the network representation learning method for static network community discovery, and designs an improved multiobjective particle swarm‐based community discovery algorithm (MOPSO‐CD). This randomness effectively prevents the algorithm from falling into a local optimum. At the same time, combined with the MOO algorithm, all the Pareto optimal solution sets are retained to adjust the population to correct the lack of accuracy caused by the randomness of the algorithm. In addition, in order to improve the efficiency of the algorithm, this article introduces an efficient Pareto optimal solution set method. Compared with the traditional MOO strategy, the time complexity of the MOO process is O ( n 2 ) Reduced to O ( n log n ). Through experimental analysis, MOPSO‐CD has higher efficiency and community discovery quality.
Spectral equivalence of Gaussian random functions: Operator approach

We introduce a new approach to the spectral equivalence of Gaussian processes and fields, based on the methods of operator theory in Hilbert space. Besides several new results including identities in law of quadratic norms for integrated and multiply integrated Gaussian random functions we give an application to goodness-of-fit testing.
Cost-Effective Active Learning with Informativeness and Representativeness for Semantic Segmentation

Semantic segmentation becomes one of the key topics in the field of computer vision. Many deep learning methods for segmentation are trained on large annotated dataset for good performance. While the annotation of the semantic segmentation data is expensive then we propose a new Cost-Effective Active Learning (CEAL) framework for the semantic segmentation task. In this framework, we exploit new acquisition functions to implement the complementary sample selection. The acquisition functions are consisting of two criteria: informativeness and representativeness. We study how to balance these two types of criteria by the analysis of tradeoff parameter. We evaluate the framework on the CamVid dataset. Simultaneously, the result shows that the framework is helpful for reducing the training data and annotation cost.
An Economical Echo State Network for Time-Series Generation Tasks

This work investigates the benefit of having sparse and restricted connectivity in an Echo State Network (ESN) for time-series generation tasks. The proposed recurrent neural network architecture was constructed based on Watts-Strogatz Small-World algorithm. This setting permits the ESN to obtain an Echo State Property (ESP) without the need for rescaling the whole reservoir's connection weights. Three experiments regarding the networks' topology and connectivity have been conducted. The results reveal that: 1) the proposed network exhibits equivalent performance compared to the conventional ESN despite having a very small number of connection weights within a reservoir. 2) Decreasing the number of input-output units and the size of the reservoir result in the degradation of the generative capability. 3) The proposed network be able to handle another more complex time-series dataset while the conventional has failed. 4) Rewiring the connection randomly causes the proposed network to obtain better generative performance than follow the Small-World algorithm. These findings could give an insight into the construction of a simple, fast, and cost-effective recurrent neural network for time-series generation tasks.
Information Acquisition Under Resource Limitations in a Noisy Environment

We introduce a theoretical model of information acquisition under resource limitations in a noisy environment. An agent must guess the truth value of a given Boolean formula \( \varphi \) after performing a bounded number of noisy tests of the truth values of variables in the formula. We observe that, in general, the problem of finding an optimal testing strategy for \( \varphi \) is hard, but we suggest a useful heuristic. The techniques we use also give insight into two apparently unrelated but well-studied problems: (1) rational inattention , that is, when it is rational to ignore pertinent information (the optimal strategy may involve hardly ever testing variables that are clearly relevant to \( \varphi \) ), and (2) what makes a formula hard to learn/remember.
Non-parametric spatially constrained local prior for scene parsing on real-world data

Scene parsing aims to recognize the object category of every pixel in scene images, and it plays a central role in image content understanding and computer vision applications. However, accurate scene parsing from unconstrained real-world data is still a challenging task. In this paper, we present the non-parametric Spatially Constrained Local Prior (SCLP) for scene parsing on realistic data. For a given query image, the non-parametric SCLP is learnt by first retrieving a subset of most similar training images to the query image and then collecting prior information about object co-occurrence statistics between spatial image blocks and between adjacent superpixels from the retrieved subset. The SCLP is powerful in capturing both long- and short-range context about inter-object correlations in the query image and can be effectively integrated with traditional visual features to refine the classification results. Our experiments on the SIFT Flow and PASCAL-Context benchmark datasets show that the non-parametric SCLP used in conjunction with superpixel-level visual features achieves one of the top performance compared with state-of-the-art approaches.
Teaching Reform of Inorganic Synthesis Chemistry Aiming at Innovative Spirit Cultivation and Scientific Research Ability Training

This paper reports the teaching reform of inorganic synthesis chemistry aiming at innovative spirit cultivation and scientific ability training.In this reform, the specialized course was taken as the carrier to cultivate undergraduates' innovative spirit and scientific research ability, to enhance their dominant role in learning, to cultivate their quality of "diligent in thinking, dare to doubt and innovate", and to promote the cultivation of ability and habit of continuous self-learning.The reform has obtained some results, and the classroom teaching has got effective extension.
An Infrared Image Enhancement Method Based on Power Function Histogram Equalization

Traditional histogram equalization and gray transformation enhancement algorithms cannot effectively enhance infrared images. After comparing the advantages and disadvantages of various algorithms, this paper proposes a histogram equalization algorithm based on power function. The algorithm realizes the redistribution of the gray level of the original image by processing the histogram of the original image with square and threshold translation, reduces the over-merging and over-stretching of the gray level, and can effectively resist the disturbance of the image caused by the sudden entry of bright and dark scenes. The enhancement effect of the infrared image is obvious.
LSTM-based Approach to Monitor Operator Situation Awareness via HMI State Prediction

Situational Awareness is an indispensable barrier against execution human errors from cascading across system processes. Therefore, early detection and intervention is vital for preventing accidents in time-critical scenarios. Evidently, legacy human-machine interfaces especially those prevalent in nuclear power plant and aviation industry, are complex, elaborate and require continual supervision of operator situational aware-ness. In this paper, a novel approach towards achieving non-intrusive real-time monitoring of operator situational awareness is framed as a supervised learning task suitable for applying deep recurrent neural network (RNN) models. Results, include performance evaluation of few typical RNN Long-Short Term Memory (LSTM) based time-series forecast models for predicting expected operator n-step ahead response pattern given current human-machine interface state as inputs.
Performance Analysis on Deep Learning Semantic Segmentation with multivariate Training Procedures

Deep Learning approaches are becoming ubiquitous in many fields of computation, especially for tasks of object detection and classification in images. However, the diversity of architectures, the number and range of training parameters, and the dimension and representativeness of datasets make it very complex to define a unified method or set of techniques to address diverse problems, and it is quite common for the literature to skip valuable implementation details. One of these fields is the Semantic Segmentation with many applications like autonomous driving. In that line, this paper carried out a study using an ENet architecture for semantic segmentation on road images for intelligent vehicles and multiple experiments of deep learning training with a wealth of combinations of training procedures, including multiple loss functions and datasets for out-of-domain performance assessment. Besides some expected outcomes, the results allow identifying a set of combinations with performances that stand out and are advisable for researchers to tackle more efficiently problems of a similar nature.
Handwritten Text Lines Segmentation Using Two Column Projection

Libraries and archives throughout the world keep a great number of historical documents. Many of them are handwritten ones. In many cases they are hardly available. Digitization of such artifacts can make them accessible to the community. But even digitized, they remain unsearchable so the important task is to draw the contents in the computer readable form. One of the first steps on this way is segmentation of the document into the lines. Artificial Intelligence algorithms can be used to solve this problem. In the current paper the projection–based algorithm is presented. Our algorithm finds lines in the left and the right part of the page independently and then associates both sets. Thanks to this, our method can recognize skewed lines better then the algorithms that use global projection. The performance of the algorithm is evaluated on the data-set and with the procedure proposed by the organizers of the ICDAR2009 competition.