Reviewerly/exHarmony huggingface dataset:
FastContainer: A Homeostatic System Architecture High-Speed Adapting Execution Environment Changes

Price reduction and performance improvement of cloud computing and web hosting services by making use of container virtualization technology engender greater demands on the efficiency of highly used multi-tenant user environments and for maintenance, security, and appropriate resource management. However, maintaining the availability and load balancing on access congestion remain dependent on the configurations of the respective systems. As described herein, we propose a homeostatic system architecture that rapidly adapts to execution environment changes. It reactively decides invocation, running periods, simultaneous running numbers, and assigned resources of the containers according to the incoming HTTP requests. Our proposed architecture enables automatic and rapid load balancing by generating and discarding the containers following the access frequency in cases of access congestion. The method improves resource utilization efficiency by automatically discarding the containers within a fixed period, which contributes to increasing opportunities for reflecting the library updates.
FPGA Implementation of a Quantum Cryptography Algorithm

Quantum cryptography is a process for developing a perfectly secret encryption key that can be used with any classical encryption system. This paper presents a study of the EPR state protocol, the first continuous variable quantum key distribution protocol. We propose an algorithm for this protocol and subsequently its implementation on FPGA (Field-Programmable Gate Array). For the implementation, we used Xilinx’s ISE System Edition tool as Software and Xilinx’s Artix7 Nexys4 DDR board as hardware.
Penerapan UML Sebagai Alat Perancang Website Dinas Pertanian Kota Payakumbuh

The agricultural sector is one of the main income sectors of the Payakumbuh City community. Efforts made by the Payakumbuh City Agriculture Service are to market agricultural products produced by the Payakumbuh community. So far, product marketing and information on agriculture have been delivered directly from the heads of the sub-district, neighborhood, RW and Wali Nagari. This is considered to be less effective and efficient. Through the use of current technology, a website-based information system needs to be built by using Unified Modeling Language (UML) system design tools. With the existence of this website-based information system, it helps the Department of Agriculture to market agricultural products and information related to agriculture to the wider community.
On-Line Travel Agencies’ Usability: Evaluator eXperience

It is generally agreed that usability is a basic attribute in software quality. User eXperience (UX) extends the usability concept beyond its traditional dimensions (effectiveness, efficiency and satisfaction). UX refers to all user's perceptions resulting from the use (or even the anticipated use) of a product, system or service. For more than two decades heuristic evaluation proves to be one of the most popular usability inspection methods. When performing a heuristic evaluation, generic or specific heuristics may be used. Nielsen's ten usability heuristics are well known, but many other sets of heuristics were proposed. Based on proper heuristics, the heuristic evaluation may also assess other UX aspects, beside usability. Usability heuristic sets are specific artifacts, so heuristics' "usability" may also be evaluated. If we consider that evaluators are particular "users" of particular "products", the set of usability/UX heuristics and the heuristic evaluation method, we may also analyze Evaluator eXperience as a particular case of UX. We systematically conduct studies on evaluators' perception over generic and specific usability/UX heuristics. The paper presents a follow-up study on the perception of novice evaluators over Nielsen's heuristics, using three online travel agencies as case studies (Atrapalo, TripAdvisor and Expedia). The experiments involved Chilean and Spanish students. We compare new results with our previous findings. Based on empirical results, we think the methodology used when teaching the heuristic evaluation method is highly important.
Can route planning be smarter with transfer optimization?

We aim to showcase the benefit of transfer optimization for route planning problems by illustrating how the solution accuracy of travelling salesman problem instances can be enhanced via autonomous and positive transfer of knowledge from related source problems that have been encountered previously. Our approach is able to achieve better solution accuracy by exploiting useful past experiences at runtime, based on a source-target similarity measure learned online.
Apache Spark as a Tool for Parallel Population-Based Optimization

The paper describes a novel application of Apache Spark in population-based optimization, which facilitates the parallel search for optimal solutions. The model has been tested in solving traveling salesman problem (TSP).
Gems - Geometric Median Shapes

We present an algorithm to compute the geometric median of shapes which is based on the extension of median to high dimensions. The median finding problem is formulated as an optimization over distances and it is solved directly using the watershed method as an optimizer. We show that the geometric median shape faithfully represents the true central tendency of the data, contaminated or not. It is superior to the mean shape which can be negatively affected by the presence of outliers. Our approach can be applied to manifold and non manifold shapes, with single or multiple connected components. The application of distance transform and watershed algorithm, two well established constructs of image processing, lead to an algorithm that can be quickly implemented to generate fast solutions with linear storage requirement. We demonstrate our methods in synthetic and natural shapes and compare median and mean results under increasing outlier contamination.
Explore-a-Nation: Combining Graphical and Alphanumeric Authentication

Graphical authentication has been a proposed solution to the usability and memorability issues seen with traditional alphanumeric passwords. However, graphical authentication schemes are often criticized for their susceptibility to Over-the-Shoulder Attacks (OSAs). This research proposes and evaluates Explore-a-Nation (EaN), a unique hybrid authentication scheme that attempts to bridge the gap between graphical authentication passcodes and strong alphanumeric passwords. EaN takes advantage of the known security and efficiency associated with passwords along with the enhanced recognition benefit of graphical schemes. The EaN scheme provides users with a static image consisting of a map wherein an icon passcode path is hidden amongst other distractor icons. Following the icon path allows users to generate their strong password. This study compared our EaN prototype to alphanumeric password standards and to Use Your Illusion (UYI) across the dimensions of efficiency, accuracy, OSA resistance, and subjective usability. User login times for both EaN and UYI met the efficiency usability standards established by alphanumeric passwords. Results for UYI (99%) login accuracy were significantly better than EaN (91%). And, UYI obtained a significantly higher Subjective Usability Survey score than EaN, with both schemes exceeding our usability requirement. Notably, EaN was shown to be resistant to OSAs while UYI was not. We suggest EaN might prove to be an effective next-generation authentication scheme for both frequent and intermittent users.
Distillation of a CNN for a high accuracy mobile face recognition system

In face recognition systems, the use of convolutional neural networks (CNNs) permits to achieve good accuracy performances, which derive largely from a huge number of well-trained parameters. While using online services any mobile device can suffice for an accurate identification, in the offline scenario, implemented on a wearable mobile hardware, it is difficult to achieve both real-time responsiveness and high accuracy. In this paper we present a solution to replace a large open source face recognizer network (provided as part of the dlib libraries), distilling its learned knowledge into a less demanding CNN. The former is used as an expert oracle that provides the targets, while the latter is trained on the same input image, following a regression approach. In addition to lightness, our CNN is trained to use smaller input images, naturally allowing the recognition of identities in a wider distance range and with a reduced amount of computation. This eventually permits the porting of the network into a dedicated mobile accelerating hardware. The hypothesis we want to demonstrate is that since the feature space topology has been deeply explored during the training of the expert network, and due to the fact that no information is created during the up sampling of a tiny face to the input size of the expert oracle, the smaller network can provide the same accuracy at a reduced computational cost.
Statistical Analysis of Machine Translation Evaluation Systems for English- Hindi Language Pair

Background: Automatic Machine Translation (AMT) Evaluation Metrics have become popular in the Machine Translation Community in recent times. This is because of the popularity of Machine Translation engines and Machine Translation as a field itself. Translator is a very important tool to break barriers between communities especially in countries like India, where people speak 22 different languages and their many variations. With the onset of Machine Translation engines, there is a need for a system that evaluates how well these are performing. This is where machine translation evaluation enters. Objective: This paper discusses the importance of Automatic Machine Translation Evaluation and compares various Machine Translation Evaluation metrics by performing Statistical Analysis on various metrics and human evaluations to find out which metric has the highest correlation with human scores. Methods: The correlation between the Automatic and Human Evaluation Scores and the correlation between the five Automatic evaluation scores are examined at the sentence level. Moreover, a hypothesis is set up and p-values are calculated to find out how significant these correlations are. Results: The results of the statistical analysis of the scores of various metrics and human scores are shown in the form of graphs to see the trend of the correlation between the scores of Automatic Machine Translation Evaluation metrics and human scores. Conclusion: Out of the five metrics considered for the study, METEOR shows the highest correlation with human scores as compared to the other metrics.
Method of Deep Web Collection for Mobile Application Store Based on Category Keyword Searching

With the rapid development of mobile Internet, mobile Internet has come into the era of big data. The demand for data analysis of mobile applications has become more and more obvious, which puts forward higher requirements for the standard of mobile application information collection. Due to the large number of applications, almost all third-party app stores display only a small number of applications, and most of the information is hidden in the Deep Web database behind the query form. The existing crawler strategy cannot meet the demand. In order to solve the above problems, this paper proposes a collection method based on category keywords query to improve the crawl rate and integrity of the mobile app stores information collection. Firstly, get the information of application interfaces that include various kinds of applications by using the vertical crawler. Then extract the keywords that represent each category of applications by TF-IDF algorithm from the application name and description information. Finally, incremental crawling is performed by using keyword query-based acquisition method. Results show that this collection method effectively promoted information integrity and acquisition efficiency.
Multiconstrained routing based on artificial bee colony algorithm and dynamic fireworks algorithm

Abstract In this paper, a mathematical model for multiconstrained routing optimization problem is established. The multi-objective optimization problem is transformed into a single-objective optimization problem by adding a penalty function. Then the artificial bee colony algorithm (ABC) is used for route search. Because the ABC algorithm is easy to fall into the local optimal deficiencies, the dynamic fireworks algorithm (dynFWA) is introduced for local search, which can ensure fast global search and fast guarantee. In the process of searching for the optimal solution, the success rate is improved by about 1.05% compared with the PSO_ACO algorithm optimized by the ant colony algorithm, which is 6.18% higher than the standard PSO algorithm and the standard ABC algorithm. The minimum average cost of the search is about 0.53% higher than the PSO_ACO algorithm, which is about 1.87% higher than the other two algorithms. The simulation results show that the algorithm can effectively solve the multiconstrained routing problem under large-scale networks.
Towards an Integrated Mobility Service Network

The approach of a linear mobility chain changes to a network-based mobility. Mobility not only consists of interactions with transport services. The transport services are enhanced by a variety of services concerning ticketing, information, refreshments, or entertainment. These services are used in parallel with transport and form an individual service network of travelers' journeys. This paper introduces the method of mobility service diaries to collect data about the usage of diverse mobility services. Based on the data analysis, the results present a classification of mobility services, typical patterns of the interplay of mobility services, and a framework of an integrated mobility service network. The aim of this research is to support the user-oriented development of human-computer interactions for mobility services.
Using an Adaptive Intelligent Tutoring System to Promote Learning Affordances for Adults with Low Literacy Skills

One out of six adults in the United States possesses low literacy skills. Many advocates believe that technology can pave the way for these adults to gain the skills that they desire. This article describes an adaptive intelligent tutoring system called AutoTutor that is designed to teach adults comprehension strategies across different levels of discourse processing. AutoTutor was designed with a simple, easy to use interface that caters to the special technological needs of adult learners. Though the interface may be simple, the functionality is not. AutoTutor leverages empirically based learning principles from cognitive psychology to scaffold the acquisition of reading comprehension skills. In particular, it embeds six major learning affordances, or learning opportunities, that help students master difficult material. We provide an overview of AutoTutor, describe its' learning affordances and discuss its potential as a reading comprehension tool. We conclude by considering some of the challenges when building adaptive technologies to support low literacy adults.
Exploration of Ideal Interaction Scheme on Smart TV: Based on User Experience Research of Far-Field Speech and Mid-air Gesture Interaction

TV, as an important entertainment appliance in family, its interaction is typical of other screen-equipped devices. Far-field speech and mid-air gesture are the new trends in smart TV interaction. Previous studies have explored the characteristics of far-field speech and mid-air gesture interactions in TV used scenes, but rarely studied the user experience in the interaction process for the two interactive modes and directly compared them. What is the ideal interactive mode for TV in the future when these two can be realized? We know very little about this. Therefore, in Study 1, we quantitatively compared the user experience of far-field speech and mid-air gesture through experiment. The results showed that there were no significant differences between the two interactive modes, and implicated their advantages and disadvantages for different operations. And then, in study 2, we explored the user preference for interaction in different operations. The results showed that there were certain regularities about the participants’ preference for the two interaction channel in different situations. Based on the results mentioned above, we finally proposed the design principles of multichannel interaction fusion for different operations.
Linear Algebra and Image Processing: A New Teaching Approach

Linear Algebra is one of the main mathematical fields, which is used in many technical subjects. Inability to realize the importance of Linear Algebra and its applications in technical subjects is one of the reasons of poor academic performance of students. The typical teaching approach separates the Linear Algebra's from technical subject. This does not necessarily promote student's awareness in both subjects. This paper presents a new teaching approach which combine Linear Algebra and Image Processing contents. The methodology presented is based in teaching Linear Algebra contents such as matrices and matrix transformation using Image Processing applications. This could lead to a whole new interesting learning environment, new teaching approaches that are more stimulating for students and more productive and dynamic for teachers.
Foundational Principles and Design of a Hybrid Tutor

This paper describes the concept of a hybrid tutor as a type of adaptive instructional system (AIS). A hybrid tutor is a confederation of several digital learning resources and human interactions so that the right resource is available to the learner at the right time. We discuss a method for combining several existing educational technologies into a unified platform that tracks progress on learning a subject matter across several constituent parts and offers recommendations on what to do next. There is a learning record store that keeps track of progress and enables intelligent recommendations at several levels: broad topics, specific knowledge components, material difficulty, and mode of instruction. The fine-grain adaptability allows the incorporation of several cognitive learning principles, such as multiple representations and modalities, mental model construction, item spacing, and support for self-regulated learning. The proposed web-based learning environment can function as a stand-alone instructional platform that is integrated into classrooms with topics assigned according a curriculum-based calendar or an adaptive learning environment that suggests learning activities that are generated by an intelligent recommender system. As a proof of concept, we developed ElectronixTutor, a hybrid tutor designed for introductory and intermediate electrical engineering education. This paper describes the rationale for its design and preliminary results.
Purchasing Behavior Analysis Based on Customer's Data Portrait Model

This paper defines the data label, data portrait, and data portrait causal model, which proves the equivalence between the data portrait causal model and the Bayesian network model. The causal model of constructing data portraits and user behavior prediction algorithm are proposed to verify the effectiveness of the proposed model and algorithm.
Normal bases from 1-dimensional algebraic groups

This paper surveys and illustrates a geometric method for constructing normal bases allowing fast finite field arithmetic. Indeed, Couveignes and Lercier presented in February 2008 a process for constructing efficient normal bases of Fqn over Fq from some degree two functions on an elliptic curve over Fq. We apply the Couveignes-Lercier method to all the remaining 1-dimensional algebraic groups. That results in natural normal bases with quasi-linear complexity.
Health Recommender Systems: A Survey

With the big amount of data that become available on the internet, and with the appearance of the information overload problem, it is becoming essential to use recommender systems (RS). RSs help users to extract relevant information that interests them, also to increase their quality decisions. These systems have proven their effectiveness in several domains, such as: e-commerce, e-learning, etc. Furthermore, they play a very important role in the field of medicine, in which the discovery of a tiny knowledge can save thousands of lives. In this paper, we will present a state of the art on RS approaches, their applications in general, and in the medical field.
When Hate Circulates on Campus to Uphold Free Speech

On Inauguration Day 2017, Milo Yiannopoulos gave a talk sponsored by the University of Washington College Republicans entitled “Cyberbullying Isn’t Real.” This chapter is based on participant-observation conducted in the crowd outside the venue that night and analyzes the violence that occurs when the blurring of the boundaries between “free” and “hate” speech is enacted on the ground. This ethnographic examination rethinks relationships between law, bodies, and infrastructure as it considers debates over free speech on college campuses from the perspectives of legal and public policy, as well as those who supported and protested Yiannopoulos’s right to speak at the University of Washington. First, this analysis uses ethnographic research to critique the absolutist free speech argument presented by the legal scholars Erwin Chemerinsky and Howard Gillman. Second, this essay uses the theoretical work of Judith Butler and Sara Ahmed to make claims concerning relationships between speech, vulnerability, and violence. In so doing, this chapter argues that debates over free speech rights on college campuses need to be situated by processes of neoliberalization in higher education and reconsidered in light of the ways in which an absolutist position disproportionately protects certain people at the expense of certain others.
Blockchain in Smart Power Grid Infrastructure

Internet of Things(IoT) is playing a vital role in making the digital world smarter with widely adopted applications. One of the important application of such a system is Smart Power Grid. A critical system like Smart Power Grid requires stringent security measures like immutability of the data sensed by IoT based sensors, verifiability of records etc. It is also important that the system works in a decentralised manner as there are multiple sources of power in a Grid which are from different administrative domain. This paper represents a system which will help to address concerns about security, privacy, Identity along with immutability and verifiability of records. The proposed system allows exchange and collection of data in a decentralized, peer-to-peer, trustless network of devices using BlockChain.
Generation techniques and a novel on-line adaptation strategy for weight vectors within decomposition-based MOEAs

The success of Multi-Objective Evolutionary Algorithms based on Decomposition (MOEA/D) has generated great interest in the use of a reference set of weight vectors to promote diversity within non-dominated solutions. However, the quality of the solution set obtained heavily depends on the relation between the weight distribution and the Pareto front's shape.