Reviewerly/exHarmony huggingface dataset:
Low Congestion Cycle Covers and Their Applications

A cycle cover of a bridgeless graph G is a collection of simple cycles in G such that each edge e appears on at least one cycle. The common objective in cycle cover computation is to minimize the total lengths of all cycles. Motivated by applications to distributed computation, we introduce the notion of low-congestion cycle covers, in which all cycles in the cycle collection are both short and nearly edge-disjoint. Formally, a (d, c)-cycle cover of a graph G is a collection of cycles in G in which each cycle is of length at most d and each edge participates in at least one cycle and at most c cycles.A-priori, it is not clear that cycle covers that enjoy both a small overlap and a short cycle length even exist, nor if it is possible to efficiently find them. Perhaps quite surprisingly, we prove the following: Every bridgeless graph of diameter D admits a (d, c)-cycle cover where d = Õ(D) and c = Õ(1). That is, the edges of G can be covered by cycles such that each cycle is of length at most Õ(D) and each edge participates in at most Õ(1) cycles. These parameters are existentially tight up to polylogarithmic terms.Furthermore, we show how to extend our result to achieve universally optimal cycle covers. Let Ce is the shortest cycle that covers e, and let OPT(G) = maxe∊G |Ce|. We show that every bridgeless graph admits a (d, c)-cycle cover where d = Õ(OPT(G)) and c = Õ(1).We demonstrate the usefulness of low congestion cycle covers in different settings of resilient computation. For instance, we consider a Byzantine fault model where in each round, the adversary chooses a single message and corrupt in an arbitrarily manner. We provide a compiler that turns any r-round distributed algorithm for a graph G with diameter D, into an equivalent fault tolerant algorithm with r·poly(D) rounds.
Memory Segment Matching Network Based Image Geo-Localization

Humans and other animals can easily perform self-localization by means of vision. However, that remains a challenging task for computer vision algorithms with traditional image matching methods. In this paper, we propose a memory segment matching network for image geo-localization that is inspired by the discovery of the place cell in the brain by using artificial intelligence. The place cell becomes active when an animal enters a particular location, where the external sensory information in the environment matches features stored in the hippocampus. In order to emulate the operation of the place cell, we employ a convolutional neural network (CNN) and a long-short term memory (LSTM) to extract the visual features of the environment. The extracted features are stored as segmented memory bounded with a location tag. A matching network is utilized to calculate the cross firing probability of the memory segment and the current input visual data. The final prediction of the location is obtained by sending the cross firing probability to an inference engine that uses a hidden Markov model (HMM). According to the simulation results, the localization accuracy reaches up to 95% for the datasets tested, which outperforms the state-of-the-art by 17% in localization detection accuracy.
QR Code Based Secure Billing System for Shops using Cued Click Points

In today's Mall shopping system, the users usually visit the mall for shopping different products and spend their valuable time in searching the products and the products in their price range.After selecting the product the users have to wait in a long queue for billing which is another time-consuming part of shopping, and moving the cart from one place to another place is another hectic task.In this system the user's time and searching for products in the entire mall increases.So, we are working on this system to avoid these drawbacks.The drawbacks in the existing system are, firstly the time required for bill payment is more as the users have to wait in a long queue.Secondly, the users have to move the cart through the entire mall and carry a load of products at their home.One more drawback is regarding the barcode in which less data is stored.In view of the mobile phone has become popular consumer products, a simple optimization method was given to design shopping guide systems run on smartphones, with the help of QR code generation and recognition technology, wherein, we can store more data using the QR code.The proposed programs can show accurate and real-time shopping destination, thus help shopping mall to mine customer data more accurately and scientifically.In our system we are going to use QR code for every product using QR code generator, a user goes for shopping and wants to purchase a product, he will scan the QR code of the product using QR code scanner, from his Android device and he will get list of all the similar products in that range and any offers and discounts related to that product, this is done using searching and sorting algorithms.Once the user scans and adds the product to the cart the data gets fed to the server and the bill gets generated after adding all the products to the cart.The bill is sent to the user on his device and the products are delivered home and also bill payment is done online.Nowadays it is difficult to deal with the text-based password.As text can be easily detected, or if it is known then it's risky to deal with such things when we have confidential systems with us.We can overcome this problem with the help of a graphical password with cued click points.This graphical password will be really helpful to secure the confidential systems.Cued click points are the concept in which Persuasive Cued Click points graphical password scheme which includes usability as well as security evaluations.There are a lot of effects that are most well-known about passwords; such as that user cannot memorize complicated passwords which are simple to identify.
LOOKING BENEATH THE TIP OF THE ICEBERG: THE TWO-SIDED NATURE OF CHATBOTS AND THEIR ROLES FOR DIGITAL FEEDBACK EXCHANGE

Enterprises are forecasted to spend more on chatbots than on mobile app development by 2021. Up to today little is known on the roles chatbots play in facilitating feedback exchange. However, digitization and automation put pressure on companies to setup digital work environments that enable reskilling of employees. Therefore, a structured analysis of feedback-related chatbots for Slack was conducted. Our results propose six archetypes that reveal the roles of chatbots in facilitating feedback exchange on performance, culture and ideas. We show that chatbots do not only consist of conversational agents integrated into instant messenger but are tightly linked to complementary front- end systems such as mobile and web apps. Like the upper part of an iceberg, the conversational agent is above water and visible within the chat, whereas many user interactions of feedback-related chatbots are only possible outside of the instant messenger. Further, we extract six design principles for chatbots as digital feedback systems. We do this by analyzing chatbots and linking empirically observed design features to (meta-)requirements derived from explanatory theory on feedback, self- determination and persuasive systems. The results suggest that chatbots benefit the social environment of conversation agents and the richness of the graphical user interface of external applications.
Approaches to Crafting English as a Second Language on Social Media: An Ethnographic Case Study from Saudi Arabia

Saudi female undergraduates perform various activities in English, their second language (L2), on social media.They engage in different types of writing and reading on social media in everyday life.But little is known about the material resources and concrete activities they drew on as they read and write in English on their preferred social media.This ethnographic case study aims to examine the material resources and concrete activities of a group of Saudi female undergraduates majoring in English Translation in a university in Saudi Arabia as they use English on social media.This study particularly aims to answer this research question: What material resources and concrete activities do Saudi female undergraduates employ as they read and write in English on social media?A total of eleven female Saudi undergraduates took part in this research.Online observation, focus group interviewing, semi-structured interviews, informal conversations and online logbook of English usage were employed over a three-months period to collect data.This study draws on Literacy Studies (LS), that understand literacies as social practices involving more than technical skills.Analysing the data thematically revealed that these undergraduates drew on three main approaches: English-spelling checking approaches, English grammar-checking approaches and English-meaning checking approaches.The study recommends that language learners are encouraged to use Google app, Google Translate app, Dictionary apps and the COCA website to correct, craft and improve their English.They are also encouraged to utilise the built-in smartphone technologies that supports English spelling.
Information retrieval with semantic annotation

The processing of information with semantic annotation allows to identify the intention of search of the user and to adjust the result according to the context of the information. The present research proposes a model for the retrieval of information with semantic annotation that allows to help the user to retrieve the most relevant information among all the information available on the web. In the model, three components (Crawler-Indexing, Processing and Presentation) are developed that allow identifying the need for user information through the processing, selection and subsequent publication of the retrieved information. The crawling and indexing component allows the identification of available websites to extract information and perform semantic annotation by applying different information processing techniques. The processing component analyzes the user's preferences and processes the query performed to calculate the similarity of the indexed information. Subsequently the results are sorted according to the relevance to show in the Presentation component a quantity of information that can be assimilated by the users. For the validation of the proposal we used the metrics of precision and exhaustiveness that allowed to demonstrate the quality, relevance and relevance of the information retrieval with semantic annotation.
A Global Variational Filter for Restoring Noised Images with Gamma Multiplicative Noise

In this paper, we focus on a globally variational method to restore noisy images corrupted by multiplicative gamma noise. Our problem is assumed as a regularization problem in total variation (TV) framework with data fitting term which is deduced by maximizing the a-posteriori probability density (MAP estimation). We need to evaluate the proximal operator of a data fitting term then we numerically adapt the Douglas-Rachford (DR) splitting method to solve the problem. Our experiments use real images with different levels of noise. To validate the effectiveness of the proposed method, we compare the proposed method with other variational models. Our method shows effective suppression of noise, excellent edge preservation, and the measures of image quality such as PSNR (peak signal-to-noise ratio), VSNR (visual signal-to-noise ratio) and SSIM (structural similarity index) explain the proposed model΄s good performance.
Automatic level Generation for Tower Defense Games

We created a new method to automatically generate levels for a commercial-grade tower defense gameKingdom Rush:Frontiers (KRF) by means of Procedural Content Generation(PCG). Our research focuses on path generation and monster sequence generation. Firstly, we designed a mathematical representation of the game’s path using geometric rules, and then we used search algorithms to develop an algorithm, which can generate new paths that are similar to the paths in original games. We implemented monster sequence generation with genetic algorithm in which we designed a representation of genes that reused some human-designed elements. In addition, we also designed a fitness function to make sure the generated level has moderate difficulty and playability. Finally, we automatically generated new levels for KRF with the combine of path generation and monster sequence generation. We used a turing test to show that our PCG levels are hard to be distinguished from the original levels.
College Information System using Android

Nowadays, Android is the newest mobile device Operating System (OS), which is the more popular in mobile device field than any other mobile OS such as i-Phone OS, Windows mobile OS and so on.This has been designed to the best first step toward the exciting new open source mobile development.The format of this paper is such that it will take through Android application development.Android applications are developed in Java, especially android studio software and run on the Windows OS.In this paper, the user will be understand about the creation of college information system software such as the java programming coding, Android SDK management and android studio after learning of this application programming coding.This software will be absolutely useful for students and teachers in educational fields because it is included the college information that show in detail.
Virtual and Augmented Reality: An Overview

Augmented reality is an innovation which allows a user to the computersimulated environment, regardless of whether that condition is a reproduction of this present reality or a conjured up universe.It is the way to encountering, feeling and contacting the past, present and what's to come.It is the medium of making our very own reality, our own customized reality.It could go from making a computer game to having a virtual walk around the universe, from strolling through our very own fantasy house to encountering a stroll on an outsider planet.With computer-generated reality, we can encounter the scariest and exhausting circumstances by playing safe and with a learning point of view.Not many individuals, be that as it may, truly realize what VR is, the thing that it is fundamental standards and its open issues are.In this paper a chronicled outline of computer generated reality is displayed, essential wording and classes of VR frameworks are recorded.A savvy investigation of normal VR frameworks is done and finds the difficulties of Virtual Reality.Augmented reality, in which virtual substance is reliably planned with grandstands of genuine scenes, is a creating zone of natural arrangement.With the climb of individual cellphones prepared for making charming augmented reality conditions, the tremendous ability of AR has begun to be examined.This paper audits the present forefront in expanded reality.It delineates work performed in different application territories and clears up the leaving issues experienced when building extended reality applications considering the ergonomic besides, specialized confinements of cell phones.
TG-SPSR: A Systematic Targeted Password Attacking Model

Identity authentication is a crucial line of defense for network security, and passwords are still the mainstream of identity authentication.So far trawling password attacking has been extensively studied, but the research related with personal information is always sporadic.Probabilistic context-free grammar (PCFG) and Markov chain-based models perform greatly well in trawling guessing.In this paper we propose a systematic targeted attacking model based on structure partition and string reorganization by migrating the above two models to targeted attacking, denoted as TG-SPSR.In structure partition phase, besides dividing passwords to basic structure similar to PCFG, we additionally define a trajectory-based keyboard pattern in the basic grammar and introduce index bits to accurately characterize the position of special characters.Moreover, we also construct a BiLSTM recurrent neural network classifier to characterize the behavior of password reuse and modification after defining nine kinds of modification rules.Extensive experimental results indicate that in online attacking, TG-SPSR outperforms traditional trawling attacking algorithms by average about 275%, and respectively outperforms its foremost counterparts, Personal-PCFG, TarGuess-I, by about 70% and 19%; In offline attacking, TG-SPSR outperforms traditional trawling attacking algorithms by average about 90%, outperforms Personal-PCFG and TarGuess-I by 85% and 30%, respectively.
Improving Generative and Discriminative Modelling Performance by Implementing Learning Constraints in Encapsulated Variational Autoencoders

Learning latent representations of observed data that can favour both discriminative and generative tasks remains a challenging task in artificial-intelligence (AI) research. Previous attempts that ranged from the convex binding of discriminative and generative models to the semisupervised learning paradigm could hardly yield optimal performance on both generative and discriminative tasks. To this end, in this research, we harness the power of two neuroscience-inspired learning constraints, that is, dependence minimisation and regularisation constraints, to improve generative and discriminative modelling performance of a deep generative model. To demonstrate the usage of these learning constraints, we introduce a novel deep generative model: encapsulated variational autoencoders (EVAEs) to stack two different variational autoencoders together with their learning algorithm. Using the MNIST digits dataset as a demonstration, the generative modelling performance of EVAEs was improved with the imposed dependence-minimisation constraint, encouraging our derived deep generative model to produce various patterns of MNIST-like digits. Using CIFAR-10(4K) as an example, a semisupervised EVAE with an imposed regularisation learning constraint was able to achieve competitive discriminative performance on the classification benchmark, even in the face of state-of-the-art semisupervised learning approaches.
Genetic Programming over Spark for Higgs Boson Classification

With the growing number of available databases having a very large number of records, existing knowledge discovery tools need to be adapted to this shift and new tools need to be created. Genetic Programming (GP) has been proven as an efficient algorithm in particular for classification problems. Notwithstanding, GP is impaired with its computing cost that is more acute with large datasets. This paper, presents how an existing GP implementation (DEAP) can be adapted by distributing evaluations on a Spark cluster. Then, an additional sampling step is applied to fit tiny clusters. Experiments are accomplished on Higgs Boson classification with different settings. They show the benefits of using Spark as parallelization technology for GP.
Privacy Preserving in Social Networks Using Combining Cuckoo Optimization Algorithm and Graph Clustering for Anonymization

Recently, social networks have received dramatic interest. The speed of the development and expansion of the Internet has created a new topic of research called social networks or online virtual communities on the Internet. Today, social networking sites such as Facebook, Twitter, Instagram and so forth are dramatically used by many people. Since people publish a lot of information about themselves on these networks, this information may be attacked by the intruders, so the need of preserving privacy is necessary on these networks. One of the approaches for preserving privacy is the K-anonymity. Anonymization always faces the challenge of data lost, therefore, an approach is required for anonymization of data and meanwhile maintaining the usefulness of the data. In this research, by combining the k-anonymity priority clustering method and Cuckoo optimization algorithm, an appropriate model is developed to maintain the privacy of the data and its usefulness. The average path length, average clustering coefficient and the transitivity criteria have been used to evaluate the proposed algorithm. The results of the experiments show that the proposed method in most cases has 1 unit superiority in terms of k-anonymity and 2 units superiority in terms of usefulness in comparison with similar methods.
Quantity Tagger: A Latent-Variable Sequence Labeling Approach to Solving Addition-Subtraction Word Problems

An arithmetic word problem typically includes a textual description containing several constant quantities.The key to solving the problem is to reveal the underlying mathematical relations (such as addition and subtraction) among quantities, and then generate equations to find solutions.This work presents a novel approach, Quantity Tagger, that automatically discovers such hidden relations by tagging each quantity with a sign corresponding to one type of mathematical operation.For each quantity, we assume there exists a latent, variable-sized quantity span surrounding the quantity token in the text, which conveys information useful for determining its sign.Empirical results show that our method achieves 5 and 8 points of accuracy gains on two datasets respectively, compared to prior approaches.
Comparative Study of Data Compression Techniques

This document discusses data compression and some of the data compression techniques.Data Compression is a technique of reducing the amount of space data occupies, to ease the process of storage and communication.This involves but is not limited to interpretation and elimination of redundancy in data.The fundamental process of compression involves using a well drafted technique to convert the actual data into the compressed data (smaller size).Depending upon how well a compression technique works and how much data can be regenerated from the compressed data given by a certain technique, the technique is classified as either as a lossy data compression technique or lossless data compression technique.
Automatic classification of product reviews into interrogative and noninterrogative: Generating real time answer

Posted reviews on the relevant webpages about a product not only motivate the company to enhance quality but also it helps users to decide in favor of (or against) purchasing the product.These reviews are classified by different researchers through subjectivity based, entity based, or aspect based to find the polarity using the supervised or unsupervised technique.However, classification based on interrogatives and non-interrogatives is not handled yet.Datasets of interrogatives are analyzed as identifying Answer Seeking questions from Arabic tweets, question conveying and not conveying Information, Rhetorical Questions while here classifying the sentences into interrogatives and non-interrogatives is the preliminary step, which is a core contribution of proposed work.If detected questions are answered and moreover real time, it could not only motivate a user positively to buy the product but also users feel full duplex communication.In this work, we formulated this problem proposing linguistic and heuristic rules that automatically senses the interrogative and answer promptly based on the aforementioned aspect.If there is no aspect in an asked question, then LSI (Latent Semantic Indexing) generate answer using classified noninterrogatives.LSI is an efficient information retrieval algorithm, which finds the closest document to a given query.Experimental results using two publically available datasets show a precision of 95% and 96% which has 10% increased performance than alternatives machine learning methods Meta Filtered Classifier and Naive Bayes.