Reviewerly/exHarmony huggingface dataset:
CamCutter: Impromptu Vision-Based Cross-Device Application Sharing

Abstract As the range of handheld, mobile and desktop devices expands and worldwide demand for collaborative application tools increases, there is a growing need for higher speed impromptu cross-device application sharing to keep up with workplace requirements for on-site or remote collaborations. To address this, we have developed CamCutter, a cross-device interaction technique enabling a user to quickly select and share an application running on another screen using the camera of a handheld device. This technique can accurately identify the targeted application on a display using our adapted computer vision algorithm, system architecture and software implementation, allowing impromptu real-time and synchronized application sharing between devices. For desktop and meeting room set-ups, we performed a technical evaluation, measuring accuracy and speed of migration. For a single-user reading task and a collaborative composition task, we carried out a user study comparing our technique with commercial screen sharing applications. The results of this study showed both higher performance and preference for our system. Finally, we discuss CamCutter’s limitations and present insights for future vision-based cross-device application sharing.
FETCH: A cloud‐native searchable encryption scheme enabling efficient pattern search on encrypted data within cloud services

Summary Searchable encryption (SE) is considered important as it provides both confidentiality and searchability for the data stored in semi‐trusted environments such as cloud. However, it is rarely deployed because most SE schemes are not native to cloud services as they require database modifications. In this paper, we present an SE scheme called Frequency‐Eliminated Trapdoor‐Character Hopping (FETCH) that, based on novel common‐conditioned‐subsequence‐preserving (CCSP) techniques, is able to work natively with off‐the‐shelf databases and supports wildcard‐based pattern search on encrypted data thereof. In fact, with the CCSP techniques, we transform the problem of wildcard SE searching into a problem of subsequence searching, which is solved fast in most databases and thus fits well with cloud services in general. Although in our security analysis, CCSP removes the possibility of obtaining theoretical indistinguishability between indexed items, we show that FETCH does provide adequate confidentiality protection and fares much better than other existing wildcard SE schemes in terms of query performance, storage overhead, and deployment complexity. In particular, FETCH is able to efficiently handle data sets whose size is multiple orders of magnitude larger than those that existing schemes can comfortably support.
Predicting GDP of Indonesia Using K-Nearest Neighbour Regression

Abstract The impact of the global recession in 1998 that originated from the recession in the US will affect the projected economies in Asia, including Indonesia, both direct and indirect nature. In this study, we predicted Indonesia’s GDP in the event of the economic crisis that hit Indonesia starting in 1998. Instead of using the famous prediction algorithm as a neural network and linear regression. K-Nearest Neighbour is selected because it is easy and fast to use in the small dataset. We use a dataset from 1980-2002, consisting of rice prices, premium prices, GDP of Japanese country, American GDP, currency exchange rates, Indonesian government consumption, and the value of Indonesia’s oil exports. For evaluation, we compare k-NN regression prediction result with prediction result using back propagation neural network and multiple linear regression algorithm. Result show, k-NN regression is able to predict Indonesia’s GDP using small dataset better than the neural network, and multiple linear regression method.
Unsupervised Content-Based Image Retrieval Technique Using Global and Local Features

Content-based image retrieval is an application of retrieving similar images of a query image. In this paper, global and local features are used to describe the content of an image. Color, texture and shape features are used as global features and SURF, FAST and BRISK are used as local features to retrieve similar images from the database. Different similarity checking methods are also tested to find the best approach. Combined approach using global features (obtained by using modified gray-level co-occurrence matrix) and local features (obtained by using SURF and color moments) produce better accuracy than some existing methods. Here, local features are obtained around the blob points detected by SURF detector. SURF descriptor and color moments which are calculated within a region of size 6s (s is the scale of the image at which the blob point is detected) around the detected blob points are used for extracting local features. Color moments are used because SURF only works on grayscale images. Thus, local color information is achieved. This combination of global and local features result in better performance in term of accuracy compared to some current methods.
A Booting Fingerprint of Device for Network Access Control

The rapid growth of the Internet of Things (IoT) is enhancing customers' experience with an ever-increasing mass of intelligent devices. However, the security doesn't get enough attention compared with novel concepts and features by manufactures which even not provide service of updates and patches. These issues could have been mitigated by the mechanism of the network access control. But traditional authentication mechanisms based on computational complexity of cryptographic protocols cannot be implemented on IoT devices with limited physical and computational resources. Therefore, we turn our attention to the device fingerprint which is like a biometric fingerprint that be able to provide identity authentication. The fingerprint proposed in this paper generated by network traffic features divided into the packet-feature and the IAT-feature. These features are extracted from the traffic when the device is booted, because at this time the device's network behavior is stable compared to other times. Experiments illustrate that the average identification accuracy of our device fingerprint reaches 92.4%.
Learning multiple instance deep representation for objects tracking

Object tracking has been widely used in various intelligent systems, such as pedestrian tracking, autonomous vehicles. To solve the problem that appearance changes and occlusion may lead to poor tracking performance, we propose a multiple instance learning (MIL) based method for object tracking. To achieve this task, we first manually label the first several frames of video stream in image level, which can indicate that whether a target object in the video stream. Then, we leverage a pre-trained convolutional neural network that has rich prior information to extract deep representation of target object. Since the location of the same object in adjacent frames is similar, we introduce a particle filter to predict the location of target object within a specific region. Comprehensive experiments have shown the effectiveness of our proposed method.
Software Failures Forecasting by Holt - Winters, ARIMA and NNAR Methods

This paper describes the results of software failures prediction using regression (Auto-Regressive Integrated Moving Average, ARIMA), smoothing (Holt - Winters) and neural networks (Neural Network Auto-Regressive, NNAR) models. GitHub service was used as a data source, and the failures time-series of the Kubernetes project was chosen for this study. Both cumulative and noncumulative, 7- and 14-days' time series were used. The best forecasting accuracy was reached using the NNAR method for noncumulative 14-days' time-series.
Using blockchain in the follow‐up of emergency situations related to events

Summary This paper describes a decentralized low‐cost system designed to reinforce personal security in big events in case of emergency. The proposal consists of using smart contracts supported by blockchain in the management of events. An alternative communication channel that does not require any cloud service is also provided with the aim of improving the coordination of emergency services. Peers may use this emergency support tool to interact with each other through a chat when additional support is required. Since information security is mandatory in this scenario, identity‐based signcryption schemes are here used to guarantee communication confidentiality, authenticity, and integrity. Depending on the communication mode (peer‐to‐peer or broadcast), different signcryption methods are used. A first implementation of the proposal has produced promising results.
Adaptive Generation of Characters for Tabletop Role Playing Games

This paper tries to solve the problem where automated character creation for Tabletop RPGs is completely random and does not fully pleases certain players, especially those that have difficulty in being creative and adapting characters to their personal tastes. To accomplish that, the usage of a Evolutionary Algorithm (EA) was proposed, in conjunction with the user's input (positive or negative) during generations, to create characters focused on having traits that are catered to each individual's taste. The generated traits are "Race", "Class", "Size", "Visual Trait", "Personality" and "Quirk". The fitness calculation for each individual was made using trait tables with weights previously defined by the programmer, and posteriorly by the user's evaluation of the characters during execution. The project was made using the Unity game engine with C# language, to take advantage of the tool's easy interface creation. It was produced as a phone application, for ease of distribution and general use. The application was tested with a variety of users, and the results, presented in the form of graphs, indicate that the rejection rate was almost null, with the great majority of users recognizing the gradual improvement of suggestions between generations, and evaluating positively the characters generated by the algorithm.
A Digital Transformation in International Transport and Logistics

Firms must deal with the factors that increase performance, which support the delivery of the produced goods at minimal cost and time. In this respect, the advantages of digitization have been studied. The path to international trade in its quest to reduce logistics and supply chain costs cross with Blockchain technology. Blockchain technology isn't only an inter-user money transfer technology, but it also includes all the trade supply chain actors. Thus, a visible “supply chain network” that's directed by blockchain, which holds the record in real time and doesn't change pursuant to the sequence occurs. All actors on the network can access and track the flow and distribution of the transaction across borders. How does blockchain technology improve yield and reduce costs? To find the answer, the application areas of digital technology in trade have been investigated. In international trade, a transaction draft was created and the advantages and disadvantages of blockchain were exposed.
Deep reinforcement learning with external control

A Self-driving car using an end-to-end deep reinforcement learning[1] algorithms trained on lane-keeping task performs well in circuits that don't need decision making but cannot deal with situations like choosing to turn left or right in an upcoming crossroads, deciding when to leave a traffic circle or toward which path/destination to go. In this paper we propose a new Deep Reinforcement Learning architecture that supports external command as high-level input, that we call Steered Deep Reinforcement Learning (SDRL), we apply the SDRL architecture on the Deep Deterministic Policy Gradient algorithm DDPG and use CARLA a High-fidelity realistic driving simulator as a testbed environment to train and experiment the new model, since testing in ground truth turns out to be costly and risky. The Steered DDPG (SDDPG) model performs well on the road/roundabouts and responds correctly to the external commands that allow the driving agent to take the right turns.
Low cost Smart Refrigerator

Emergence of smart systems has created an environment with improved monitoring enabling ease of control. Any smart system has a communication module which gives access to the control of the system even remotely. These systems have emerged as a new area into the development of smart home appliances. Many smart home appliances like television, lighting etc. have emerged. A commercially available smart appliance is quite expensive and not very accessible to even some part of the working community. The following paper discusses the concept of a smart refrigerator developed for an existing one with a rule based algorithm. When making a new appliance the rules for building a customized device have to be defined.
Laser-speckle-projection-based handheld anthropometric measurement system with synchronous redundancy reduction

Human body measurement is essential in modern rehabilitation medicine, which can be effectively combined with the technology of additive manufacturing. Digital image correlation based on laser speckle projection is a single-shot, accurate, and robust technique for human body measurement. In this paper, we present a handheld anthropometric measurement system based on laser speckle projection. A flexible retroreflective marker target is designed for multi-view data registration. Meanwhile, a synchronous redundancy-reduction algorithm based on a re-projected global disparity map is proposed. Experiment results validate that the proposed system is effective and accurate for different human body part measurements. Comparative experiments show that the proposed redundancy-reduction algorithm has high efficiency and can effectively preserve the features of complex shapes. The comprehensive performance of the algorithm is better than the other two tested methods.
Food retail expansion patterns in Sub‐Saharan Africa and the Middle East and North Africa: Institutional and regional perspectives

Abstract Food retail transformation in developing countries continues to receive attention due to its implications for business expansion and food system development. We assess the dynamism of supermarketization in two relatively understudied regions—Sub‐Saharan Africa and the Middle East and North Africa—applying the institution‐based view and econometric analysis of a pooled cross‐sectional dataset of over 700 country‐year observations. While modern food retail has shown rapid growth, the traditional food retail segment is still dominant in both regions. The observed food retail patterns may be attributed to economic factors (income levels), sociodemographic (female labor force participation, urbanization, and religion), and institutional factors (control of corruption, ease of starting a business, time to enforce a contract, and World Trade Organization membership). However, while economic and sociodemographic variables exhibit similar effects in both regions, results are mixed regarding the institutional factors; potential explanations are suggested [EconLit citations: O100, L100].
Cross-Domain Variational Autoencoder for Recommender Systems

Aiming at the problem of sparse data and user cold start in a single domain for collaborative filtering, this paper proposes a cross-domain recommendation model called cross-domain variational autoencoder (CDVAE) based on Bayesian theory. The model uses rating data to construct latent representations of users and items in multiple domains, utilizes overlapping users in different domains, and draws on the idea of variational autoencoder (VAE) to train a user generating networks cross-domains based on user preferences to different domains. Bayesian theory is introduced to ensure the generation ability and robustness of the model. Finally, based on the user’s preference to the source domain, the user’s preference prediction in the target domain is obtained. The generation of the recommendation result is obtained by the user’s preference weighting in the source domain and the target domain, and the user’s multi-source interest preference is considered to improve the accuracy. The experimental results show that CDVAE is able to significantly outperform the state-of-the-art recommendation methods of more robust performance.
Eye Tracking in Framework for the Development of Games for People with Motor Disabilities

In research carried out by the Brazilian Institute of Geography and Statistics (IBGE), alarming data on motor diseases are disclosed, in which it is shown that 7% of the Brazilian population has some type of motor deficiency. The gaming industry is the most profitable entertainment industry today, yet the area of gaming adapted for people with motor disabilities is under-explored. Thinking about it, this work proposes to develop a work environment (Framework) to facilitate the creation of accessible games using eye tracking, thus giving the possibility of playing with just the eyes.
Assessing the current state of information security policies in academic organizations

Purpose Colleges and universities across the USA have seen data breaches and intellectual property theft rise at a heightened rate over the past several years. An integral step in the first line of defense against various forms of attacks are (written) security policies designed to prescribe the construction and function of a technical system, while simultaneously guiding the actions of individuals operating within said system. Unfortunately, policy analysis is an insufficiently discussed topic in many academic communities with very little research being conducted in this space. Design/methodology/approach This work aims to assess the current state of information security policies by analyzing in-use policies from 200 universities and colleges in the USA with the goal of identifying important features and general attributes of these documents. The authors accomplish this through a series of analyzes designed to examine the language and construction of these policies. Findings To summarize high-level results, the authors found that only 54 per cent of the top 200 universities had publicly accessible information security policies, and the policies that were examined lacked consistency with little shared source material. The authors also found that the tonal makeup of these policies lacked a great deal of emotion, but contained a high amount of tentative or ambiguous language leading toward policies that could be viewed as “unclear.” Originality/value This work is an extension of a paper that was presented at ECIS 2018. The authors have added additional analyzes including a cross-policy content and tonal analysis to strengthen the findings and implications of this work for the wider research audience.
Application Weighted Voronoi Diagrams to Determine the Areas of Responsibility of Transformer Substation, with Accounting Heterogeneity of Load Density

This article deals with the accounting for inhomogeneity of the load density in determining the parameters of the elements of urban power supply systems.In the comparative example shows that accounting load density nonuniformity can more accurately determine the values of these parameters.To determine the optimal location of transformer substations and their areas of responsibility proposed to use multiplicative weighted Voronoi diagrams.The author shows that the distribution of consumers by polygons weighted Voronoi diagrams, in which the force center are transformer stations, reduces density nonuniformity consumer load in the neighboring ranges diagrams and as a consequence, reduction of electric power losses in the power flow between adjacent polygons.
MOBILE LEARNING, MOBILE APPLICATION, ELECTRONIC EDUCATIONAL RESOURCE, LEARNING TOOL: ESSENCE AND INTERRELATION OF CONCEPTS

The article is devoted to revealing the essence and interrelation of concepts "mobile learning", "educational mobile application", "electronic educational resource", "learning tool", as well as the possibility of using mobile applications as electronic educational resources and learning tools. The definitions of "mobile learning", "learning mobile application", "educational mobile application" and "non-educational mobile application" are given, as well as the classification of mobile applications. The article provides an example of an educational mobile application developed by the authors, which is an electronic educational resource and can be used as a means of teaching informatics.
An Analysis of the Interaction Between Transfer Learning Protocols in Deep Neural Networks

We extend work on the transferability of features in deep neural networks to explore the interaction between training hyperparameters, optimal number of layers to transfer and the size of a target dataset. We show that using the commonly adopted transfer learning protocols results in increased overfitting and significantly decreased accuracy compared to optimal protocols, particularly for very small target datasets. We demonstrate that there is a relationship between fine-tuning hyperparameters used and the optimal number of layers to transfer. Our research shows that if this relationship is not taken into account, the optimal number of layers to transfer to the target dataset will likely be estimated incorrectly. Best practice transfer learning protocols cannot be predicted from existing research that has analysed transfer learning under very specific conditions that are not universally applicable. Extrapolating transfer learning training settings from previous findings can in fact be counterintuitive, particularly in the case of smaller datasets. We present optimal transfer learning protocols for various target dataset sizes from very small to large when source and target datasets and tasks are similar. Our results show that using these settings results in a large increase in accuracy when compared to commonly used transfer learning protocols. These results are most significant with very small target datasets. We observed an increase in accuracy of 47.8% on our smallest dataset which comprised of only 10 training examples per class. These findings are important as they are likely to improve outcomes from past, current and future research in transfer learning. We expect that researchers will want to re-examine their experiments to incorporate our findings and to check the robustness of their existing results.
Robot Path Planning with Low Learning Cost Using a Novel K-means-based Pointer Networks

Robot-path-planning is an important research that seeks the shortest path to optimize the motion cost for robots. In robot-path-planning, the computational time will significantly increase if the moving targets for a robot rise largely, while the current algorithms for the shortest path planning may be invalidated due to large input data. This work thus proposes a hybrid algorithm, called the k-means-based pointer network, to tackle the problem mentioned above. By combining the k-means clustering and pointer network, unsupervised and supervised learning respectively, this work demonstrates how to lower the learning cost drastically with smaller training data. The simulation results show that the computational time cost of the Held-Karp algorithm grows significantly when the input size increases in some amount, while the proposed algorithm climbs slightly during the increments of input size because of using smaller input data for Ptr-Net. In applications, the proposed work can be applied practically to the case of large input size, for example, the employment for the ball-collecting robot in a golf court.