Reviewerly/exHarmony huggingface dataset:
A Dialogue manager for task-oriented agents based on dialogue building-blocks and generic cognitive processing

This paper introduces a novel dialogue manager, called DAISY, for intelligent virtual agents. The proposed approach is based on two central concepts: (i) dialogue building-blocks, which offer a systematic approach to the representation and implementation of human-agent dialogue, and (ii) cognitive processing in the form of sequences of simple, generic cognitive actions. The generic nature of the cognitive actions makes it possible to represent a large variety of cognitive processing (e.g, retrieving and manipulating memory content) by using a rather small set of such actions. DAISY is illustrated by means of a specific example, namely an agent acting as an information system for travel and tourist information. The example highlights the usefulness of the systematic approach offered by DAISY.
Real-Time Image Mosaic based on Optimal Seam and Multiband Blend

Visible seams, exposure difference, blurring, or ghosting effects may occur in the overlapping areas for image mosaic when registration error or moving object exists. An algorithm based on optimal seam and multiband blend is proposed. The original images are decomposed into band-pass sub-images of different frequency bands. Low-frequency images with less details are blended through a wider transition region, while high-frequency images with more details are blended according to the mask images generated by the optimal seam line. An improved dynamic programming algorithm is used to search the optimal seam in irregular overlapping areas. The proposed algorithm can effectively eliminate the gap in real-time image mosaic.
Negotiation protocol based on ordered weighted averaging and Fuzzy metrics

Negotiation Support Systems are used to exchange offers and to improve the negotiation process. Some methods are based on perspectives like heuristics in order to bring the negotiation protocol gradually closer to reality. These approaches can be important in real negotiation as they can help to improve skillsespecially in distributive bargaining. This paper proposes a Negotiation Support framework to provide the negotiator with recommendations on making decisions in a negotiation process. To input this idea into negotiation protocols, this paper suggests that some concepts based on OWA (Ordered Weighted Averaging) and some of its metrics (Orness, Dispersion) be included in the heuristics of a structured negotiation schedule. It is expected that the support provided will aid the negotiator to make decisions during the negotiation process, to learn from the elicitation and his own behavior the results obtained can help the negotiator improving his skills in the negotiation process.
A 923 Gbps/W, 113-Cycle, 2-Sbox Energy-efficient AES Accelerator in 28nm CMOS

An energy-efficient AES hardware accelerator based on 2-Sbox 8-bit datapath is fabricated in 28nm CMOS for IoT and mobile SoC applications. It obtains the smallest encryption cycles of 113 of 8b-AES by 100% utilization of two Sboxes and rearranging data bytes processing order. It also minimizes intermediate data registers (InterReg) to only 40b from 256b by eliminating ShiftRow and MixColumn registers. Along with glitch reduction design of Sbox in native GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> )2 composite-field, it achieves best-in-class efficiency of 257923 Gbps/W and 28-991Mbps throughput rate at 0.41/0.9V with scalable voltage down to near-threshold.
A &lt;word, part of speech&gt; embedding model for text classification

Abstract Existing word embeddings learning algorithms only employ the contexts of words, but different text documents use words and their relevant parts of speech very differently. Based on the preceding assumption, in order to obtain appropriate word embeddings and further improve the effect of text classification, this paper studies in depth a representation of words combined with their parts of speech. First, using the parts of speech and context of words, a more expressive word embeddings can be obtained. Further, to improve the efficiency of look‐up tables, we construct a two‐dimensional table that is in the &lt;word, part of speech&gt; format to represent words in text documents. Finally, the two‐dimensional table and a Bayesian theorem are used for text classification. Experimental results show that our model has achieved more desirable results on standard data sets. And it has more preferable versatility and portability than alternative models.
TARNN: Task‐aware autonomic resource management using neural networks in cloud environment

Summary Resource management is one of the major issue in cloud computing for IaaS. Among several resource management problems allocation, provisioning, and requirement mapping directly affects the performance of cloud. Resource allocation signifies assignment of available resources to different workloads in an economically optimal manner. Precise and accurate allocation is required to maximize the usage of resources. Current method of task allocation do not take previously acquired knowledge, type of the tasks, and the QoS parameters altogether into account in the allocation phase, and it has not been trained for different set of tasks. Furthermore, the self‐optimization of the autonomous system fails to address the task type and identify the relationship between tasks and the resource demands along with its requirements. Important aspects like task management and resource utilization are the primary factors to consider for such a characteristic. This paper will present a novel autonomic resource management framework named task‐aware autonomic resource allocation strategy using neural networks (TARNN), which aims to use knowledge about the behavior of the task over an extended period of time and use this knowledge to allocate resources when a similar task is submitted in future by the user. To effectively do the allocation, a neural network–based approach is adopted to classify the tasks appropriately based on the task parameters, task type, and QoS parameters and allocate the resource optimally for a new task autonomously, without the intervention of the cloud provider. Moreover, to identify and improve the relationship of the tasks with the resources in the context of scheduling, we have proposed a novel modified Particle Swarm Optimization (m‐PSO) algorithm to schedule the tasks to resources based on resource demands. In TARNN, we have separated the collected synthetic dataset into 60‐40 ratio for training and testing purposes. We found that the neural network–based approach provides almost 80% accurate classification w.r.t. task type and QoS parameters. We have also compared our results with support vector machine (SVM) and got 69% accuracy. Since the tasks are classified appropriately, the occurrence of resource reconfiguration and VM migration is drastically reduced. Hence, our system provides better allocation of resources and schedules the tasks appropriately to the resources, thereby improving the performance of the cloud.
Scale-Aware Deep Network with Hole Convolution for Blind Motion Deblurring

The removal of non-uniform motion blur, which is caused by the jittering of the camera and the rapid movement of the target objects, is quite a challenging task. A majority of deblurring works focus on the traditional two-step method: firstly, the motion kernel estimation, and then the energy function minimization. In this paper, we address the problem of single image non-uniform motion blur with deep convolutional neural network (CNN) in a decent end-to-end manner. We proposed a brand-new convolution architecture named as 'hole convolution', of which the kernel takes a rectangular ring of the neighbors to the center pixel into computation and thus the reception field is greatly expanded. Moreover, we present a scale-aware convolutional neural network to recover the latent sharp image. With the carefully designed units, the hole convolution layers as well as the exquisite network structure, our proposed deep CNN can eliminate blur from various scales without upsampling and downsampling. Experimental results show that our method can effectively restore the latent sharp image, which outperforms the state-of-the-art method by a large margin.
Acceleration of Server-side Image Processing by Client-side Pre-processing in Web Application Environment

The goal of this paper is to verify whether the use of client-side TensorFlow.js, a WebGL-accelerated JavaScript library for machine learning, can accelerate the processing of common photos by computer vision cloud services, such as detection and recognition of specific features like age, sex, expression or specific people in the image. This acceleration is based on pre-processing the input image, namely detecting human faces, which greatly changes the amount of input data that need to be uploaded to the cloud service and thus the amount of uploaded data compared to the original photograph. The upload speed of Internet connection often is, in the case of computer vision cloud services, the bottleneck of the whole system. That's why decreasing the amount of uploaded data in time shorter than the difference between the total of upload and cloud service processing time of the original and the pre-processed image leads to acceleration.
Optimal photon pairs for quantum communication protocols

We theoretically investigate the problem of finding optimal characteristics of photon pairs, produced in the spontaneous parametric down-conversion (SPDC) process, for fiber-based quantum communication protocols. By using the accessible setup parameters, the pump pulse duration and the extended phase-matching function width, we minimize the temporal width of SPDC photons within the general scenario. This allows one to perform more effectively the temporal filtering procedure, which aims at reducing the noise acquired by the measurement devices. Moreover, we compare the obtained results with the achievable parameter values for SPDC sources based on [Formula: see text]-Barium Borate crystal. We also investigate the influence of non-zero detection timing jitter. Finally, we apply our optimization strategy to a simple quantum key distribution scheme to show that the full optimization of an SPDC source can potentially extend the maximal security distance by several tens of kilometres, which is around 30% more as compared to previous approaches.
Image Enhancement Technique for Use in Real-Time Mobile Applications

This paper addresses the problem of creation of new advanced technologies of image enhancement aimed at use in real-time mobile applications. The aim of this work is to increase the efficiency of image contrast enhancement by graylevel transformations. In order to achieve this aim, we propose a new approach to images enhancement based on an estimate of the brightness distribution at the boundaries of objects in the image. To demonstrate this approach proposes a new parameter-free technique of gray-level transformation for adaptive nonlinear image contrast stretching in automatic mode. The results of experimental research using known noreference metrics of contrast confirm the effectiveness of the proposed approach.
Video text detection based on multi-feature fusion

Video retrieval technology has drawn considerable attention over the years. Compared with the underlying information such as color, edge, etc., the text in the video contains rich semantic information and can well summarize the video information. Many scholars have proposed methods based on SVM to detect video text. For most of these methods, feature dimension is too large, and the time complexity and detection effect remain to be improved. In this paper, a new method of SVM video text detection based on color, edge and HOG features is proposed. And for the problem of single frame detection, the detection effect is improved based on the detection of three adjacent frames. In this paper, video text detection is implemented through steps such as sample selection, feature extraction, model training, and text detection. Finally, many experiments are performed to compare the proposed method with other literatures. The results show that the proposed single-frame and three-frame detection algorithm has a high recall rate and accuracy, which reduces the false detection rate and improves the effectiveness of video text detection.
Comparison between two methods of optical profilometry on micro-PTC

The development of a new prototype of micro-PTC (Parabolic Trough Collector) system requests to study the optical characteristics of the mirrors in use. Two techniques were developed for this purpose. The first one is based on the projection of a Moiré pattern on the mirror surface covered with an opaque material. It provides a reconstruction of the profile from which it is possible to calculate focus position, vertex position and hence the focal length. This method represents a very fast estimation of the macroscopic defects of parabolic collectors, which is useful to determine the quality of the product. The second method is based on the analysis of the reflection of a laser beam, scanning the surface of the parabolic mirror. Despite this latter method is more time consuming, it is a more accurate technique that allows measuring both position and slope of each point of the mirror. In addition, this second method permits to estimate the intercept factor of the concentrator.
A Framework for Design Identification on Heritage Objects

A challenging problem in modern archaeology is to automatically identify fragmented heritage objects by their decorative full designs, such as the pottery sherds from Southeastern America. The difficulties of this problem lie in: 1) these pottery sherds are usually fragmented so that each sherd only covers a small portion of its underlying full design; 2) these sherds can be so highly degraded that curves may contain missing segments or become very shallow; and 3) curve patterns may overlap with each other from the making of these potteries. This paper presents a deep-learning based framework for matching a sherd with a database of known designs to find its underlying design. This framework contains three steps: 1) extracting curve pattern using an FCN-based curve pattern segmentation method from the digitized sherd's depth map, 2) matching a sherd with a non-composite (single copy of a design) pattern combining template matching algorithm with a dual-source CNN re-ranking method to find its underlying design, and 3) matching a sherd with a composite (multiple copies of a design) pattern using a Chamfer Matching based method. The framework was evaluated on a set of sherds from the heartland of the paddle-stamping tradition with a subset of known paddle-stamped designs of Pre-colonial southeastern North America. Extensive experimental results show the effectiveness of the proposed framework and algorithms.
A Path Planning Algorithm for Space Manipulator Based on Q-Learning

An improved Q-Learning autonomous learning algorithm is proposed to solve the problem of the adaptive path planning of the space manipulator in the unknown environment. After simplification of the manipulator and obstacle model, the grid model of the environment is established, and the position of the manipulator and obstacles are randomly deployed in the grid map. Based on the analysis of the basic principle of reinforcement learning and the state generalization method, the improved Q-Learning algorithm is used to carry out the path planning. In this algorithm, the reward and punishment strategies in the path planning of the manipulator are designed, and the approximate greedy and continuous micro Botlzmann distribution behavior selection strategy is adopted. According to the autonomous learning of Q-table, the manipulator can guide its follow-up action selection and path planning, reduce the number of manipulator movement, and reduce the blindness of the learning process. The results show that the algorithm has the advantages of simple calculation, strong self-learning ability, and can successfully complete the adaptive path planning in unknown environment.
Real-time Model Based Path Planning for Wheeled Vehicles

This work presents a model based traversability analysis method which employs a detailed vehicle model to perform real-time path planning in complex environments. The vehicle model represents the vehicle's wheels and chassis, allowing it to accurately predict the vehicles 3D pose, detailed contact information for each wheel and the occurrence of a chassis collision given a 2D pose on an elevation map. These predictions are weighted, depending on the safety requirements of the vehicle, to provide a scoring function for an A*-like search strategy. The proposed method is designed to run at frame rates of 30Hz on data from a RGB-D sensor to provide reactive planning of safe paths. For evaluation, two wheeled mobile robots in different simulated and real world environment setups were tested to show the reliability and performance of the proposed method.
Radio-Mechanical Model of Epidermal Antenna Stretching during Human Gestures

Recent developments in Materials and Radiofrequency Identification (RFID) technologies are currently stimu-lating the development of new class of flexible epidermal devices for the wireless remote monitoring of biophysical parameters. The natural movements and gestures of the human body will not only produce mechanical stretching of skin antennas but they could also affect their communication performance. In this contribution we evaluate the degradation of the radiation gain of on-skin UHF antennas in common gestures by a combined mechanical-electromagnetic model. The deformation of the skin is firstly quantified by using a 3D scanner and hence the communication impact is evaluated with reference to a typical 867 MHz thin wire split-ring epidermal antenna. Preliminary numerical simulations and experimentations demonstrated that the behavior of the antenna is modified with a maximum 30% degradation of the read distance for the strain orientation producing the minimum mechanical stiffness.
Neuromorphic Photonic Processor Applications

Reconfigurable photonic processors promise orders of magnitude improvements in both speed and energy efficiency over digital electronics for applications in neuromorphic computing and machine learning. We will provide an overview of neuromorphic photonic systems and their application to optimization and machine learning problems.
An Equivalent Penalty Coefficient Method: An Adaptive Penalty Approach for Population-Based Constrained Optimization

The penalty function method has been widely used for solving constrained optimization problems. In the method, an extended objective function, which is the sum of the objective value and the constraint violation weighted by the penalty coeffi-cient, is optimized. However, it is difficult to control the coefficient properly because proper control of the coefficient varies in each problem. In this study, the equivalent penalty coefficient value (EPC) is proposed for population-based optimization algorithms (POAs). EPC can be defined in POAs where a new solution is compared with the old solution. EPC is the penalty coefficient value that makes the two extended objective values of the solutions the same. Search that gives priority to the objective value is realized by selecting a small EPC. Search that gives priority to the constraint violation is realized by selecting a large EPC. The adaptive control of the penalty coefficient can be realized by selecting an appropriate EPC. The proposed method is introduced to differential evolution and the advantage of the proposed method is shown by solving well-known constrained optimization problems.
3D Surface Reconstruction Using A Two-Step Stereo Matching Method Assisted with Five Projected Patterns

Three-dimensional vision plays an important role in robotics. In this paper, we present a 3D surface reconstruction scheme based on combination of stereo matching and pattern projection. A two-step matching scheme is proposed to establish reliable correspondence between stereo images with high computation efficiency and accuracy. The first step (coarse matching) can quickly find the correlation candidates, and the second step (precise matching) is responsible for determining the most precise correspondence within the candidates. Two phase maps serve as codewords and are utilized in the two-step stereo matching, respectively. The phase maps are derived from phase-shifting patterns to provide robustness to the background noises. Only five patterns are required, which reduces the image acquisition time. Moreover, the precision is further enhanced by applying a correspondence refinement algorithm. The precision and accuracy are validated by experiments on standard objects. Furthermore, various experiments are conducted to verify the capability of the proposed method, which includes the complex object reconstruction, the high-resolution reconstruction, and the occlusion avoidance. The real-time experimental results are also provided.
Privacy-aware Tag Recommendation for Accurate Image Privacy Prediction

Online images’ tags are very important for indexing, sharing, and searching of images, as well as surfacing images with private or sensitive content, which needs to be protected. Social media sites such as Flickr generate these metadata from user-contributed tags. However, as the tags are at the sole discretion of users, these tags tend to be noisy and incomplete. In this article, we present a privacy-aware approach to automatic image tagging, which aims at improving the quality of user annotations, while also preserving the images’ original privacy sharing patterns. Precisely, we recommend potential tags for each target image by mining privacy-aware tags from the most similar images of the target image, which are obtained from a large collection. Experimental results show that, although the user-input tags compose noise, our privacy-aware approach is able to predict accurate tags that can improve the performance of a downstream application on image privacy prediction and outperforms an existing privacy-oblivious approach to image tagging. The results also show that, even for images that do not have any user tags, our proposed approach can recommend accurate tags. Crowd-sourcing the predicted tags exhibits the quality of our privacy-aware recommended tags. Our code, features, and the dataset used in experiments are available at: https://github.com/ashwinitonge/privacy-aware-tag-rec.git.
Towards Blockchain Technology to Support Digital Government

Blockchain technology is considered as one of the fourth industrial revolution technologies that is transforming governments. This study systematically reviews literature on research into blockchain technologies for government with the aim to map opportunities, challenges and gaps. The main findings show that research has ignored the commercial value of blockchain for digital government with the emphasis on sharing, trust and security, as well as the ability to enhance government infrastructure and services. There is an opportunity to investigate further how blockchain could be gradually re-inventing the traditional notion of government with its transactional intermediaries that are necessary for the classification of G2G, G2C or G2B. The methodological emphasis has been on the design research method, which unsurprisingly relates to the emerging nature of blockchain technology. There is a clear opportunity to begin more reflective research on emergent patterns. Limited research has been conducted in developing countries despite the leapfrogging and governance opportunities that blockchain technology presents.