Reviewerly/exHarmony huggingface dataset:
Socio-cultural factors and capacity building in Interaction Design: Results of a video diary study in Botswana

This paper presents the results of a video diary study that was conducted with ten novice interaction designers in Botswana, to investigate socio-cultural factors (SCFs) in interaction design. The overall aim of this research is to gain new insights into design behaviour in different cultures in order to inform international interaction design capacity building. To this end, this research investigates what SCFs are considered during the design process, which are accounted for in prototype designs and which interaction design concepts participants use to implement SCFs in their designs. The results reveal 13 categories of SCFs considered during the design process, 10 of which were implemented in prototypes. Participants used five main interaction design concepts to implement SCFs: simplicity, wearable devices, accessibility, universal design, and conversational interaction. In addition, they tended to rely on adaptations to existing products rather than creating new designs. Recommendations for capacity building include the need to identify concrete examples and alternative interaction design concepts that focus on a wider set of SCFs; and to draw upon the synergy between local product design practices and interaction design practices in order to learn how to transition SCFs into interactive products.
Application of Data Mining Prediction of Electricity Deviation Flow Using Metode Backpropogation at PLN Binjai Area

Abstract PLN is the only state-owned electricity company that provides and regulates electricity to the public. However, in the provision of electricity to every community, there are many frauds or irregularities in utilization of electricity by irresponsible parties was found by PLN. Electricity deviation caused by irresponsible parties, like the theft of electricity can have a tremendous impact due to loss of electrical energy. Electricity Deviation, using Electricity Deviation data for 3 years, from 2012 to 2014. Sales data processed by the network, in the form of amount of Kwh deviation per month. The data needed in the process of analysis of this Neural Network is pure database results by taking the input variable in the form of Amount of Kwh Deviation and Amount of Kwh Deviation for the next Month for the following year as output. Electricity for 3 years is processed into Matlab using GUI facilities, to produce information on the predicted results of the Kwh Deviation in November 2014 to March, May, June, August, November, December 2015 experiencing has increase in Kwh, while in April July, September, and Okrober 2015 decreased.
Regularizing Deep Neural Networks by Ensemble-based Low-Level Sample-Variances Method

Deep Neural Networks (DNNs) with a large number of parameters are very powerful machine learning systems. However, overfitting is a serious problem in such networks. Till now, many regularizers such as dropout, data augmentation have been proposed to prevent overfitting. Motivated by ensemble learning, we treat each hidden layer in neural networks as an ensemble of some base learners by dividing hidden units into some non-overlapping groups and each group is considered as a base learner. Based on the theoretical analysis of generalization error of ensemble estimators (bias-variance-covariance decomposition), we find the variance of each base learner plays an important role in preventing overfitting and propose a novel regularizer---\emphEnsemble-based Low-Level Sample-Variances Method (ELSM) to encourage each base learner of hidden layers to have a low-level sample-variance. Experiments across a number of datasets and network architectures show that ELSM can effectively reduce overfitting and improve the generalization ability of DNNs.
Formal Verifications of Call-by-Need and Call-by-Name Evaluations with Mutual Recursion

We present new proofs—formalized in the Coq proof assistant—of the correspondence among call-by-need and (various definitions of) call-by-name evaluations of $$\lambda $$-calculus with mutually recursive bindings. For non-strict languages, the equivalence between high-level specifications (call-by-name) and typical implementations (call-by-need) is of foundational interest. A particular milestone is Launchbury’s natural semantics of call-by-need evaluation and proof of its adequacy with respect to call-by-name denotational semantics, which are recently formalized in Isabelle/HOL by Breitner (2018). Equational theory by Ariola et al. is another well-known formalization of call-by-need. Mutual recursion is especially challenging for their theory: reduction is complicated by the traversal of dependency (the “need” relation), and the correspondence of call-by-name and call-by-need reductions becomes non-trivial, requiring sophisticated structures such as graphs or infinite trees. In this paper, we give arguably simpler proofs solely based on (finite) terms and operational semantics, which are easier to handle for proof assistants (Coq in our case). Our proofs can be summarized as follows: (1) we prove the equivalence between Launchbury’s call-by-need semantics and heap-based call-by-name natural semantics, where we define a sufficiently (but not too) general correspondence between the two heaps, and (2) we also show the correspondence among three styles of call-by-name semantics: (i) the natural semantics used in (1); (ii) closure-based natural semantics that informally corresponds to Launchbury’s denotational semantics; and (iii) conventional substitution-based semantics.
Character Keypoint-Based Homography Estimation in Scanned Documents for Efficient Information Extraction

Precise homography estimation between multiple images is a pre-requisite for many computer vision applications. One application that is particularly relevant in today's digital era is the alignment of scanned or camera-captured document images such as insurance claim forms for information extraction. Traditional learning based approaches perform poorly due to the absence of an appropriate gradient. Feature based keypoint extraction techniques for homography estimation in real scene images either detect an extremely large number of inconsistent keypoints due to sharp textual edges, or produce inaccurate keypoint correspondences due to variations in illumination and viewpoint differences between document images. In this paper, we propose a novel algorithm for aligning scanned or camera-captured document images using character based keypoints and a reference template. The algorithm is both fast and accurate and utilizes a standard Optical character recognition (OCR) engine such as Tesseract to find character based unambiguous keypoints, which are utilized to identify precise keypoint correspondences between two images. Finally, the keypoints are used to compute the homography mapping between a test document and a template. We evaluated the proposed approach for information extraction on two real world anonymized datasets comprised of health insurance claim forms and the results support the viability of the proposed technique.
Redefining Learning in Visual Comparison with Spatio-relational Context-aware Representations

Visual comparison, the task of differentiating between identical patterns, images or scenes, and telling the difference in the form of descriptors is deemed cognitively challenging for humans and computational models alike. This difficulty heightens when the differences in the patterns, images or scenes in context are subtle. Triggering of subtle differences in similar patterns, images or scenes ultimately requires precise visual attention capabilities and an equally good natural language interpretation models to describe the differences found. Unlike previous works that use cluster-based detection approaches, we propose a deep convolution neural network that leverages latent spatial representations to capture and localize differences, while actively aligning the differences to their respective annotations for the task of visual comparison. We introduce an image reconditioning method to enhance images, prior detecting target differences and complement our detection methods with a descriptor generation module to describe coarse to fine-grained differences. Experimental results validate the performance of our proposed model for visual comparison tasks, transcending other models by an appreciable extent.
Online Model Management via Temporally Biased Sampling

To maintain the accuracy of supervised learning models in the presence of evolving data streams, we provide temporallybiased sampling schemes that weight recent data most heavily, with inclusion probabilities for a given data item decaying exponentially over time. We then periodically retrain the models on the current sample. We provide and analyze both a simple sampling scheme (T-TBS) that probabilistically maintains a target sample size and a novel reservoirbased scheme (R-TBS) that is the first to provide both control over the decay rate and a guaranteed upper bound on the sample size. The R-TBS and T-TBS schemes are of independent interest, extending the known set of unequalprobability sampling schemes. We discuss distributed implementation strategies; experiments in Spark show that our approach can increase machine learning accuracy and robustness in the face of evolving data.
Grid-Based Target Estimation Scheme for Passive Coherent Location System

In this paper, two dimensional (2D) target location is estimated in the passive coherent location (PCL) system in noisy and cluttered environment. Compared to the conventional filter based target estimation schemes that require a prior information of target dynamics, the proposed target estimation scheme does not require that information and can track a target with various movements, such as maneuvering or non-maneuvering target, using a grid point (GP)-based estimation method. Bistatic range sum (BRS) measurements from all receivers are combined and processed for target estimation, but some of them are reflected by clutters rather than a target, degrading estimation accuracy. To tackle this problem, the proposed scheme first eliminates â€falseâ€ clutter-reflected BRS measurements by taking multiple time measurements into account. Then, a successive GP update scheme is applied for the remaining sets of BRS measurement, and candidate target locations are determined as a result. In the last step, a soft decision method is applied to determine the target location from the candidates. Simulation results show that our proposed target estimation scheme can track a target with various movements, especially highly-maneuvering target, despite the lack of prior knowledge of target dynamics.
Comparative Analysis on Raster, Spiral, Hilbert, and Peano Mapping Pattern of Fragile Watermarking to Address the Authentication Issue in Healthcare System

Healthcare service is one of the focus areas in supporting the existence of smart city. Nowadays, many procedures are done paperless and fully in digital. Medical scanning is also stored in digital modalities format and transmitted through a hospital management system. An authentication system is needed during the transmission, thus the need of watermarking. Some interesting research directions in watermarking include the embedding pattern of watermark data in the early process of watermarking. The objective of this research is to investigate the best pattern to determine the bit locations for watermark embedding for copyright protection. This paper applies four types of embedding patterns on medical images, which the quality of watermarked images would depend on how the mapping pattern is. It compares the difference of having a straight forward pattern mapping as a raster pattern and a unique pattern mapping like spiral, Hilbert, and Peano patterns. After mapping, all would have the same stages of a watermarking scheme which are embedding, detection and recovery stage. The comparison factors include the peak-signal-noise-ratio, mean-squared-error values of embedded images, and the computational time. From the result, the significant difference is the computational time; the taken time by the unique pattern is significantly longer than raster. When it comes to handling superabundant data, it is very crucial to produce a user-friendly system. However, as a whole, the results from Peano pattern embedding scheme shows it has a unique pattern which hard to be tracked, yet, its computational time to watermark is acceptable.
Public Audit Scheme of Shared Data Based on Blockchain

A cloud platform provides users with shared data storage services. While the cloud protects the privacy of users, it is inevitable that malicious users illegally use shared data. Currently, the audit scheme in which managing users access records by group managers is widely adopted, and this scheme realizes the protection of users’ identity privacy and the traceability of users’ identities. However, this kind of scheme disregards the hidden danger of group managers and has certain limitations. This paper proposes a public audit scheme of shared data based on the blockchain (BBSD). First, by introducing the blockchain technology, this paper realizes the sharing of records information, avoids the hidden security risks of group managers, and simultaneously makes the user identity traceable. Second, this paper constructs a novel audit algorithm to preprocess users’ revocation, which adopts a new resignature algorithm to make management more secure and reliable. Then, this paper introduces an outsourcing algorithm to reduce the computational burden of users. Finally, the theoretical analysis and experimental verification show that BBSD is secure and efficient.
Self Attentive Product Recommender – A Hybrid Approach with Machine Learning and Neural Network

People are choosing products from online ratings and comments. Experience from Amazon, Flipkart and other leading online shopping portals in India, the buyer `s product choice is mostly based on the other buyer's review. We have found an interesting case study of Netflix video recommendation based on so many criteria. Product recommendation is one of the demanding area of recent time where efficiency of prediction of which product a buyer can choose over other hundreds of product is a challenging task. Artificial intelligence has helped researchers in developing algorithm that has self-aware method for machine with machine learning, deep learning and natural language processing. In this research, we are introducing a hybrid approach of recommendation for products. There are many ways to find out the people who have similar choice and combining their choices can lead us to suggestions for other products. Collaborative filtering method has been being used for Recommender frameworks for quite a while now. They have been fruitful in comprehending numerous issues of the systems which are being used in the market. User- behavior analysis, sentiment score, product reviews, popularity score can be a decisive factor along with neural network with classification method can lead to more efficient results. In this research, we present some of more potential areas of working on Collaborative Filtering technique with machine learning and deep learning techniques. Self attentive product recommendation is one such technique which focuses on automated form for recommendation which is independent of a dataset and its data type. We have examined other approaches for joining different calculations for predicting client evaluations and furthermore examine a few outcomes from the investigation of different procedures utilized by earlier analysts and discover answers for them.
Stronger Validity Criteria for Encoding Synchrony

We analyse two translations from the synchronous into the asynchronous $$\pi $$ -calculus, both without choice, that are often quoted as standard examples of valid encodings, showing that the asynchronous $$\pi $$ -calculus is just as expressive as the synchronous one. We examine which of the quality criteria for encodings from the literature support the validity of these translations. Moreover, we prove their validity according to much stronger criteria than considered previously in the literature.
Cooperative reconnaissance mission planning for heterogeneous UAVs with DCSA

In this paper, an improved Discrete Cuckoo Search algorithm (DCSA) is proposed to solve the problem of cooperative reconnaissance mission planning for multi-heterogeneous UAVs, to maximize the reconnaissance revenue of UAVs and achieve the shortest total mission time in the combat areas. DCSA achieves the balance between local and global solutions via Lévy flights in the search space. At the same time, it avoids falling into local optimal solution. Considering the heterogeneity and resource constraints of UAVs, the multi-heterogeneous UAVs cooperative reconnaissance task planning model has been established, which is based on the evaluation criteria of total investigative revenue and the longest time consumption of UAV. Meanwhile, the UAV-task combination sequence is constructed. Therefore, the task planning problem is transformed into Traveling Salesman Problem (TSP). With the aid of DCSA, the task planning problem is simplified effectively. As the result in simulation shows, the improved DCSA can effectively solve this problem.
Towards Blockchain-Based Auditing of Data Exchanges

Auditing operations in multi-party data exchange, and over an arbitrary topology, is a common requirement yet still an open problem especially in the case where no trust on any participating party can be presumed. The challenges range from storage of the audit trail to tampering and collusion of participating entities. In this paper, we propose a blockchain-based auditing scheme. It is designed based on public key infrastructure and Shamir secret sharing scheme.
A Robust Digital Image Watermarking Technique Against Geometrical Attacks Using Support Vector Machine and Glowworm Optimization

The tampering and forgery of digital multimedia data remains as one of the most important challenges all over the internet. Digital image watermarking techniques are used to overcome these challenges. The digital image watermarking techniques provides the security for multimedia data. In this paper, discuss the image features optimization-based digital image watermarking algorithm. The proposed algorithm prevents geometrical attacks during the transmission of data. The proposed algorithm uses support vector machine and glowworm optimization algorithms. The support vector machine classifies the features data of raw image and glowworm optimization used for the reduction cum selection of features for the proceeds of embedded for the extraction of features used SIFT (scale-invariant feature transform) function. The scale-invariant feature transform is major dominated features of critical points and features points. The proposed algorithm implemented in MATLAB software. For the validation and testing purpose used 300 image dataset. For the evaluation of performance measure, these parameters such as PSNR, NC, and SIM. The measured results show better performance instead of SIFT and SVM-GA.
No-reference video quality assessment based on spatiotemporal slice images and deep convolutional neural networks

Most learning-based no-reference (NR) video quality assessment (VQA) needs to be trained with a lot of subjective quality scores. However, it is currently difficult to obtain a large volume of subjective scores for videos. Inspired by the success of full-reference VQA methods based on the spatiotemporal slice (STS) images in the extraction of perceptual features and evaluation of video quality, this paper adopts multi-directional video STS images, which are images composed of multi-directional sections of video data, to deal with the lacking of subjective quality scores. By sampling the STS images of video into image patches and adding noise to the quality labels of patches, a successful NR VQA model based on multi-directional STS images and neural network training is proposed. Specifically, first, we select the subjective database that currently contains the largest number of real distortion videos as the test set. Second, we perform multi-directional STS extraction on the videos and sample the local patches from the multi -directional STS to augment the training sample set. Besides, we add some noise to the quality label of the local patches. Third, a reasonable deep neural network is constructed and trained to obtain a local quality prediction model for each patch in the STS image, and then the quality of an entire video is obtained by averaging the model prediction results of multi -directional STS images. Finally, the experiment results indicate that the proposed method tackles the insufficiency of training samples in small subjective VQA dataset and obtains a high correlation with the subjective evaluation.
Multiobjective model-free learning for robot pathfinding with environmental disturbances

This article addresses the robot pathfinding problem with environmental disturbances, where a solution to this problem must consider potential risks inherent in an uncertain and stochastic environment. For example, the movements of an underwater robot can be seriously disturbed by ocean currents, and thus any applied control actions to the robot cannot exactly lead to the desired locations. Reinforcement learning is a formal methodology that has been extensively studied in many sequential decision-making domains with uncertainty, but most reinforcement learning algorithms consider only a single objective encoded by a scalar reward. However, the robot pathfinding problem with environmental disturbances naturally promotes multiple conflicting objectives. Specifically, in this work, the robot has to minimise its moving distance so as to save energy, and, moreover, it has to keep away from unsafe regions as far as possible. To this end, we first propose a multiobjective model-free learning framework, and then proceed to investigate an appropriate action selection strategy by improving a baseline with respect to two dimensions. To demonstrate the effectiveness of the proposed learning framework and evaluate the performance of three action selection strategies, we also carry out an empirical study in a simulated environment.
Automatic Tuning Methodology of Visual Servoing System Using Predictive Approach

In this paper, a tuning methodology based on predictive model approach for visual servoing system is investigated. The proposed approach uses features prediction based method to estimate the camera velocity and thus to calculate the optimal control tuning parameter. In order to have a faster convergence and in the same time a desired behaviour of the servoing system, a new control parameter is computed every sampling time. To evaluate the designed tuning strategy, a visual servoing architecture with an eye-in-hand configuration has been considered. The experimental results showed that the proposed tuning algorithm based on prediction model has a stable and convergent behavior when dealing with visual servoing applications. To the knowledge of the authors, the proposed methodology is the first approach which enables an automatic selection of control parameter for the proportional visual control law.