Reviewerly/exHarmony huggingface dataset:
A Survey of Procedural Dungeon Generation

Procedural content generation (PCG) is a method of content creation fully or semi-performed by computers. PCG is widely used in game development to generate game content, from Rogue (1998) to No Man's Sky (2016). PCG generates final contents, which are ready to be added to a game, or intermediate contents, which are might work as content sketch to be polished by human designers. In this paper we survey the current state of procedural dungeon generation (PDG) research, a subarea of PCG. We analyzed the works according to the game features they generate, the solution strategy employed and the taxonomy of procedural content generation. Some of the relevant findings of the survey are: (1) PDG for 3D levels has been little explored; (2) few works supported levels with barriers, a game mechanic which blocks, temporarily, the player progression, and; (3) and just a few solutions relied on mixed-initiative approach, where a human design content is combined with a computer generated level.
Using Boosting Approaches to Detect Spam Reviews

When it comes down to buying products from online shops, one of the key factor that influences a buyer are the reviews associated with a product. While buying people try to understand the quality and authenticity of the product by reading the previous user feedback. And sellers have started taking advantage of it. Putting fake and spam reviews to deceive the buyers is a common strategy mostly used by newcomers. But these reviews are important when it comes to deciding whether to buy a product or not. We propose a method to detect these fake reviews from Amazon Review Dataset. Rather than using traditional machine learning classifiers we have used boosting algorithms to improve the accuracy of the traditional approach. In this approach, a significant increase in accuracy has been achieved by boosting weak learners. Up to 93% accuracy has been achieved when tried to detect fake reviews where traditional machine learning algorithms achieve an accuracy of up to 89%.
The education logistic in digital school

The authors propose to consider the digital transformation of education as logistics view, looking at educational logistics like an activity to provide the student with all the necessary educational resources. Usually the problems of education are considered from the standpoint of pedagogy, didactics, and psychology. Logistics is usually associated with the tasks of supply of products. However, logistics is the solution of optimization problems of different processes. The subject of optimization can be the educational process. The advantage of the logistics approach in education is that it is possible to move away from the traditional humanitarian aspects of the relations of the participants of the educational process in order to focus on structural problems and management features. Traditional educational logistics involves the logistics of study groups. Pedagogy works to align all students in the study group. All curricula are based on the study group as a unit of development. If the student fails to integrate into the study group, then he or she is individually pulled up by personal teacher, or picked up by another study group. Digital technologies allow to develop the personal educational logistics — individual development of each student as a new task of new education. Digital technologies blur the boundaries of educational organizations, so there is a wide range of groups of different levels and composition. Instead of fitting the student to the study group, it is possible to fit the groups to the student.
Implementation-Centric Classification of Business Rules from Documents

In large multi-site multi-vendor projects, studying requirement documents to understand the problem domain and inferring possible solution to the posed problem is an important activity in Requirements Engineering. The process of reading User require-ments Specification (URS) to create Software Requirement Speci-fication (SRS) is a knowledge intensive activity that precedes sev-eral other important Software Engineering (SE) activities such as design and test plans. Automated Interpretation of the URS in terms of implementation-specific knowledge elements for software engineers' consumption has been reported in the past. The aim of such an interpretation is to reduce the effort associated with a manual extraction of knowledge elements and subsequently, their "translation" into primitives understood by those who must build the intended software. In this paper, we present a deep learning model for an implementation-centric classification of one such knowledge element, namely, business rules. We discuss an approach based on a Bidirectional Long Short Term Memory Network (BiLSTM) to capture the context information for each word, followed by an attention model to aggregate useful infor-mation from these words to get to the final classification. Our model adopts an end-to-end architecture that does not rely on any handcrafted features.
Deep Learning-Enabled Real-Time Recognition of Wireless Signals

In this demonstration, we perform a software defined radio (SDR) prototype of recognizing wireless signals in real-time. Convolutional Neural Network (CNN), a deep learning algorithm, is applied. Universal Software Radio Peripheral is used as RF front-end to transmit and receive over-the-air signals. MATLAB toolboxes are used to generate waveform for training, build learning models, train and test recognition and classification. Digital modulation types are recognized and classified in this demo. Meanwhile, we propose to demonstrate the classification of multi-carrier waveforms and multiple-in-multiple-out (MIMO) communications.
We are All Amina Filali: Social Media, Civil Society, and Rape legislation reform in Morocco

In 2012 Amina Filali, a 16-year-old girl from Larache, Morocco was kidnaped, raped, and forced to marry her rapist under Article 475 of the Moroccan penal code. The decision by the courts to impose this law on Amina Filali forced her into an abusive relationship with her 'husband' and eventual suicide through the consumption of rat poison. This case was one of the first mediatized criminal events in post-Arab Spring Morocco and produced a domestic and global movement to repeal Article 475 and protect victims of rape. The paper will examine the case of Amina Filali and how digital mediums can be useful in promoting legal changes.
A Comparative Review of ISMS Implementation Based on ISO 27000 Series in Organizations of Different Business Sectors

Abstract Organizations have different takes on Information Security Management Systems (ISMS) since security measurements vary according to their business relevance. One way to assure ISMS is being well-implemented is by having a standard compliance such as the ISO 27000 series. The ISO 27000 series is a family of standards that provides a framework for best practice ISMS that helps organizations keep their information assets secure. This paper intends to seek how organizations in different business sectors implement ISMS in their practices. By identifying which organization attains a higher number of ISO requirements, it is anticipated that the characteristics that increase the chances of an organization being certified can be distinguished. This paper reviews case studies regarding the ISMS implementation based on ISO 27000 series between organizations in different business sectors. The result of this paper presents the state of ISO compliance of the organizations. The findings also discussed the characteristics of organizations that are applicable for certification. Through the findings, it is found that the organization, which fulfilled the highest number of ISO requirement, has a stronger possibility of being certified. However, ISO standards should be more dynamic to support diverse business environment thus avoiding generalization to get compliance.
Usage-Based Learning in Human Interaction With an Adaptive Virtual Assistant

Today users can interact with popular virtual assistants such as Siri to accomplish their tasks on a digital environment. In these systems, links between natural language requests and their concrete realizations are specified at the conception phase. A more adaptive approach would be to allow the user to provide natural language instructions or demonstrations when a task is unknown by the assistant. An adaptive solution should allow the virtual assistant to operate a much larger digital environment composed of multiple application domains and providers and better match user needs. We have previously developed robotic systems, inspired by human language developmental studies, that provide such a usage-based adaptive capacity. Here, we extend this approach to human interaction with a virtual assistant that can first learn the mapping between verbal commands and basic action semantics of a specific domain. Then, it can learn higher level mapping by combining previously learned procedural knowledge in interaction with the user. The flexibility of the system is demonstrated as the virtual assistant can learn actions in new domains (e-mail, Wikipedia, etc.), and then can learn how e-mail and Wikipedia basic procedures can be combined to form hybrid procedural knowledge.
Difficulty of Decentralized Structure Due to Rational User Behavior on Blockchain

Blockchain is an autonomous decentralized system, the operation of which depends on the motivation of the users who provide the nodes for managing the accurate distributed ledger. For the management of an accurate ledger, a strong limitation exists that requires a large amount of storage, and it binds the node specifications to force users to perform rational management on the network. These are designed into the system as invisible rules that are not described in the program. Therefore, monitoring the dynamism of nodes on the network can aid in understanding the potential system mechanisms. In this study, by monitoring Bitcoin and Ethereum over a long comparative period of 455 days as a system based on the main blockchains actually used, we demonstrate that it is difficult to balance the distributed structure owing to the rational behavior of users. We empirically provide the following evidence: (1) the node density of a specific hosting region increases; and (2) a local hosting region with less than 1% of the total nodes closes. We conclude that this is a security risk problem that results in a weak network structure, and discuss possible solutions.
An Approach for Designing Low Cost Deep Neural Network based Biometric Authentication Model for Smartphone User

With the increasing use of smartphones, lots of smartphone based applications have been developed. Smart-phones are used in personal health care or monitoring activities of elderly persons. These types of smartphone applications require continuous authentication of the user for taking action in case of detachment of the smartphone from the user due to forgetfulness or theft. Continuous authentication on smartphone requires authentication process having low computational overhead. In this work, the objective is to develop low cost user authentication algorithm from time series data of user activities taken from sensors like accelerometer or gyroscope. Deep neural networks are used for user authentication. A two-step authentication process has been developed in which sensor data has been first classified into different activities and activity dependent authentication is proposed. For lowering computational cost of classifier, knowledge distillation is used to reduce the model parameters. Fine tuning is used to cope with the limited number of training data. As a result the authentication accuracy has been improved by 5% to 10%, also authentication time of 0.032 sec has been achieved which is useful for real time authentication. Simulation studies have been done by several bench mark data sets to evaluate the efficiency of the proposed approach.
Historical Improvement Optimal Motion Planning with Model Predictive Trajectory Optimization for On-road Autonomous Vehicle

This paper presents an efficient, robust, comfortable, and real-time motion planning framework for on-road autonomous vehicles. This proposed framework aims to enhance the performance of motion planning in complex environments such as driving in the urban area. It uses a path velocity decomposition method to separate the motion planning problem into path planning and velocity planning. The novelty lies in the use of Historical data in the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$SL$</tex> coordinate in the framework of a tree version of Rapidly-exploring Random Graph (RRT*) technique in path planner, called HSL-RRT*, which grows the path tree efficiently by the data from previous planning cycle. The velocity planner uses a Nonlinear Model Predictive Controller (NMPC) to generate optimal velocity along the path generated from the path planner, taking account of vehicle constraints and comfort. Analytic and simulation results are presented to validate the approach, with a special focus on the robustness and efficiency of the algorithm operating in complex scenarios.
Rating Prediction Algorithm Based on User Time-Sensitivity

Rating prediction is an important technology in the personalized recommendation field. Prediction results are influenced by many factors, such as time, and their accuracy directly affects the quality of the recommendation. Current time-based collaborative filtering (CF) algorithms have improved the technology of prediction accuracy to a certain extent, but they fail to differentiate the time-sensitivity of different users, which further affects prediction accuracy. To address this issue, we have proposed a rating prediction algorithm based on user time-sensitivity differences. First, we analyzed and modeled the time sensitivities of users, utilized cosine distance and relative entropy to build a judgment function, and then judged the time sensitivities of users based on a voting strategy. Next, we applied the time-sensitivity difference to improve the traditional CF algorithm and optimized the combination of parameters. Finally, we tested our algorithm on standard datasets. The experimental results showed that there are many users who have different sensitivities to time. According to these experimental results, our proposed algorithm has achieved a higher prediction accuracy than other state-of-the-art algorithms.
Culturally Polite Communication: Enhancing the Effectiveness of the Localization Strategy

Although global brands entering local markets often use localized communication (i.e., incorporation of local cultural elements in their marketing communications), the fundamental question of when and why the local community would react favorably to this strategy is still not fully answered. This research draws on the communication accommodation theory to address this question. Results from four studies show that local consumers evaluate a global brand less positively when it incorporates high-symbolic (vs. low-symbolic) local cultural elements in its marketing communication. Notably, the positive effect of culturally polite communication on consumers’ evaluations of a global brand occurs only in the local market, but not when the communication occurs in another market. Moreover, localization efforts by a global brand result in a strong localness perception, which has a positive effect on brand evaluation. Indeed, a strong localness perception of the global brand could even overshadow the need for culturally polite communication.
A Simple Authentication Method with Multilayer Feedforward Neural Network Using Keystroke Dynamics

Keystroke dynamics is a widely accepted user recognition and verification behavioral biometric, which has been studied nearly for a century. Intrinsically, this biometric is used together with id/password authentication forming multi-factor authentication. There are several anomaly detection algorithms that have been proposed for this task. While some proposals handle this problem with measuring data distance by taking correlation and dependence into account, some models use complex and time-consuming models deep neural networks to train to reach the right approximation. Our paper addresses a simple, accurate and lightweight method for user authentication. We show the effectiveness of our approach through comparisons with existing methods, which have also used the CMU keystroke dynamics benchmark dataset used here too. Using feed forward multilayer neural network with resilient backpropagation, we obtained an Equal Error Rate (ERR) equal to 0.049 for authentication with overall identification accuracy of 94.7%.
Interpreting Intentionally Flawed Models with Linear Probes

The representational differences between generalizing networks and intentionally flawed models can be insightful on the dynamics of network training. Do memorizing networks, e.g. networks that learn random label correspondences, focus on specific patterns in the data to memorize the labels? Are the features learned by a generalizing network affected by randomization of the model parameters? In high-risk applications such as medical, legal or financial domains, highlighting the representational differences that help generalization may be even more important than the model performance itself. In this paper, we probe the activations of intermediate layers with linear classification and regression. Results show that the bias towards simple solutions of generalizing networks is maintained even when statistical irregularities are intentionally introduced.
Large Scale Multimodal Data Capture, Evaluation and Maintenance Framework for Autonomous Driving Datasets

Autonomous driving (AD) programs today primarily depend on one or the other form of supervised Deep Learning (DL) models for their behavioral success. However, these DL models are only as good as the data on which they are trained, and their success depends immensely on their training data. Hence it is imperative that we create datasets of good quality. However, the process of collecting this real-world driving data and the infrastructure needed to evaluate and manage this large data is commonly unspoken and is challenging. To address this issue, we have developed an open-source framework and infrastructure to capture, evaluate, and maintain such multi-sensor data. In this paper, we discuss the motive for this framework, a process for evaluation and quality analysis, insights on data storage, distribution and management for large multimodal data and the key lessons learned collecting and maintaining huge volumes of data from long driving distances.
The Effort of Implementing of The Big Book as A Media to Improve The Intelligence of Linguistic Verbals for Early Childhood

Abstract Children’s vocabulary or verbal linguistics can be improved through several media including the big book. Big book is a learning media that is very easy to understand by early childhood. Big book has the characteristics of short stories, clear sentence patterns, images that have meaning, type and font size clearly read, and the storyline is easy to understand. This certainly makes it easy for early childhood to improve their linguistic verbal. This research is library research. The method used in this study is a philosophical and pedagogical approach that is to analyze more deeply about the application of big book media as an effort to improve verbal linguistics in early childhood. The purpose of this study is to formulate how big book media can improve intelligence of linguistic verbal in early childhood.
Deep Learning on Big, Sparse, Behavioral Data

The outstanding performance of deep learning (DL) for computer vision and natural language processing has fueled increased interest in applying these algorithms more broadly in both research and practice. This study investigates the application of DL techniques to classification of large sparse behavioral data-which has become ubiquitous in the age of big data collection. We report on an extensive search through DL architecture variants and compare the predictive performance of DL with that of carefully regularized logistic regression (LR), which previously (and repeatedly) has been found to be the most accurate machine learning technique generally for sparse behavioral data. At a high level, we demonstrate that by following recommendations from the literature, researchers and practitioners who are not DL experts can achieve world-class performance using DL. More specifically, we report several findings. As a main result, applying DL on 39 big sparse behavioral classification tasks demonstrates a significant performance improvement compared with LR. A follow-up result suggests that if one were to choose the best shallow technique (rather than just LR), there still would often be an improvement from using DL, but that in this case the magnitude of the improvement might not justify the high cost. Investigating when DL performs better, we find that worse performance is obtained for data sets with low signal-from-noise separability-in line with prior results comparing linear and nonlinear classifiers. Exploring why the deep architectures work well, we show that using the first-layer features learned by DL yields better generalization performance for a linear model than do unsupervised feature-reduction methods (e.g., singular-value decomposition). However, to do well enough to beat well-regularized LR with the original sparse representation, more layers from the deep distributed architecture are needed. With respect to interpreting how deep models come to their decisions, we demonstrate how the neurons on the lowest layer of the deep architecture capture nuances from the raw fine-grained features and allow intuitive interpretation. Looking forward, we propose the use of instance-level counterfactual explanations to gain insight into why deep models classify individual data instances the way they do.
Bi-Connect Net for salient object detection

As a challenging task for pixel-wise image analysis, salient object detection has made huge progress in recent years. However, there still exists a difficult problem: detection of distinguishing a salient and non-salient object in multiple objects under complex background (e.g. blur, translucent, light reflection, etc.). Our proposed method cast this difficulty as information dissolve problem in deep convolutional network, which is manifested as: first, the model cannot grab whole details of a salient object at training phrase; second, due to the isolation between layers and blocks, the valued information is blocked within a block, which leads to the difficulty in obtaining the position and the edge of salient objects simultaneously; third, the output of the network is a low-resolution saliency map, which cannot accurately express the edge of salient objects. To address information dissolve problems, we construct a Bi-Connect Net (BCN) composed of forward connection subnet and reverse side connection subnet. Besides, the proposed adaptive learning fusion method not only stress all blocks contribution but also combine multiple features with different scale, so that grab more details on the right salient location and precise edges at the same time. Extensive experiments show that our proposed Bi-Connect Net can outperform the state-of-the-art methods.