Reviewerly/exHarmony huggingface dataset:
A series of developmental validation tests for Number of Contributors platforms: Exemplars using NOCIt and a neural network

Complex DNA mixtures are challenging to interpret and require computational tools that aid in that interpretation. Recently, several computational methods that estimate the number of contributors (NOC) to a sample have been developed. Unlike analogous tools that interpret profiles and report LRs, NOC tools vary widely in their operational principle where some are Bayesian and others are machine learning tools. Conjunctionally, NOC tools may return a single n estimate, or a distribution on n. This vast array of constructs, coupled with a gap in standardized methods by which to validate NOC systems, warrants an exploration into the measures by which differing NOC systems might be tested for operations. In the current paper, we use two exemplar NOC systems: a probabilistic system named NOCIt, which renders an a posteriori probability (APP) distribution on the number of contributors given an electropherogram and an artificial neural network (ANN). NOCIt is a continuous Bayesian inference system incorporating models of peak height, degradation, differential degradation, forward and reverse stutter, noise and allelic drop-out while considering allele frequencies in a reference population. The ANN is also a continuous method, taking all the same features (barring degradation) into account. Unlike its Bayesian counterpart, it demands substantively more data to parameterize, requiring synthetic data. We explore each system’s performance by conducting tests on 214 PROVEDIt mixtures where the limit of detection was 1-copy of DNA. We found that after a lengthy training period of approximately 24 h, the ANN’s evaluation process was very fast and perfectly repeatable. In contrast, NOCIt only took a few minutes to train but took tens of minutes to complete each sample and was less repeatable. In addition, it rendered a probability distribution that was more sensitive and specific, affording a reasonable method by which to report all reasonable n that explain the evidence for a given sample. Whatever the method, by acknowledging the inherent differences between NOC systems, we demonstrate that validation constructs will necessarily be guided by the needs of the forensic domain and be dependent upon whether the laboratory seeks to assign a single n or range of n.
image2emmet: Automatic code generation from web user interface image

Abstract Web development usually follows with analyzing the functionality, designing the user interface (UI) prototype, implementing the UI by front‐end (FE) developers and implementing the REpresentational State Transfer (RESTful) application programming interface (API) by back‐end (BE) programmers. Unfortunately, web development is a tedious, cumbersome, and time‐consuming task, which makes it a challenge for the FE programmers to work in an efficient way. In this paper, we propose an approach, image2emmet, to assist FE programmers in implementing the UI. First, we collect HyperText Markup Language, Cascading Style Sheets (HTML‐CSS) dataset in an automatic and efficient way. The HTML‐CSS dataset used for model training consists of HTML‐CSS code and its display images. Second, the faster region‐based convolutional neural network (CNN) (R‐CNN) is utilized to detect the UI component. Finally, we build a model combining CNN and long short‐term memory (LSTM) to transform the UI component into the HTML‐CSS code. The empirical study demonstrates that image2emmet can achieve a precision of 80% on the UI component detection and 60% on the transformation of UI component into HTML‐CSS code.
The Taxonomy of Writing Systems: How to Measure How Logographic a System Is

Taxonomies of writing systems since Gelb (1952) have classified systems based on what the written symbols represent: if they represent words or morphemes, they are logographic; if syllables, syllabic; if segments, alphabetic; and so forth. Sproat (2000) and Rogers (2005) broke with tradition by splitting the logographic and phonographic aspects into two dimensions, with logography being graded rather than a categorical distinction. A system could be syllabic, and highly logographic; or alphabetic, and mostly non-logographic. This accords better with how writing systems actually work, but neither author proposed a method for measuring logography. In this article we propose a novel measure of the degree of logography that uses an attention-based sequence-to-sequence model trained to predict the spelling of a token from its pronunciation in context. In an ideal phonographic system, the model should need to attend to only the current token in order to compute how to spell it, and this would show in the attention matrix activations. In contrast, with a logographic system, where a given pronunciation might correspond to several different spellings, the model would need to attend to a broader context. The ratio of the activation outside the token and the total activation forms the basis of our measure. We compare this with a simple lexical measure, and an entropic measure, as well as several other neural models, and argue that on balance our attention-based measure accords best with intuition about how logographic various systems are. Our work provides the first quantifiable measure of the notion of logography that accords with linguistic intuition and, we argue, provides better insight into what this notion means.
A Deep Multimodal Learning Approach to Perceive Basic Needs of Humans From Instagram Profile

Nowadays, a significant part of our time is spent sharing multimodal data on social media sites such as Instagram, Facebook and Twitter.The particular way in which users present themselves to social media can provide useful insights into their behaviours, personalities, perspectives, motives and needs.This paper proposes to use multimodal data collected from Instagram accounts to predict the five basic prototypical needs described in Glasser's choice theory (i.e., Survival, Power, Freedom, Belonging, and Fun).We automate the identification of the unconsciously perceived needs from Instagram profiles by using both visual and textual contents.The proposed approach aggregates the visual and textual features extracted using deep learning and constructs a homogeneous representation for each profile through the proposed Bag-of-Content.Finally, we perform multi-label classification on the fusion of both modalities.We validate our proposal on a large database, consensually annotated by two expert psychologists, with more than 30,000 images, captions and comments.Experiments show promising accuracy and complementary information between visual and textual cues.
The Use of Video Captioning for Fostering Physical Activity

Video Captioning is considered to be one of the most challenging problems in the field of computer vision. Video Captioning involves the combination of different deep learning models to perform object detection, action detection, and localization by processing a sequence of image frames. It is crucial to consider the sequence of actions in a video in order to generate a meaningful description of the overall action event. A reliable, accurate, and real-time video captioning method can be used in many applications. However, this paper focuses on one application: video captioning for fostering and facilitating physical activities. In broad terms, the work can be considered to be assistive technology. Lack of physical activity appears to be increasingly widespread in many nations due to many factors, the most important being the convenience that technology has provided in workplaces. The adopted sedentary lifestyle is becoming a significant public health issue. Therefore, it is essential to incorporate more physical movements into our daily lives. Tracking one’s daily physical activities would offer a base for comparison with activities performed in subsequent days. With the above in mind, this paper proposes a video captioning framework that aims to describe the activities in a video and estimate a person’s daily physical activity level. This framework could potentially help people trace their daily movements to reduce an inactive lifestyle’s health risks. The work presented in this paper is still in its infancy. The initial steps of the application are outlined in this paper. Based on our preliminary research, this project has great merit.
Exploiting the Unique Expression for Improved Sentiment Analysis in Software Engineering Text

Sentiment analysis on software engineering (SE) texts has been widely used in the SE research, such as evaluating app reviews or analyzing developers' sentiments in commit messages. To better support the use of automated sentiment analysis for SE tasks, researchers built an SE-domain-specified sentiment dictionary to further improve the accuracy of the results. Unfortunately, recent work reported that current mainstream tools for sentiment analysis still cannot provide reliable results when analyzing the sentiments in SE texts. We suggest that the reason for this situation is because the way of expressing sentiments in SE texts is largely different from the way in social network or movie comments. In this paper, we propose to improve sentiment analysis in SE texts by using sentence structures, a different perspective from building a domain dictionary. Specifically, we use sentence structures to first identify whether the author is expressing her sentiment in a given clause of an SE text, and to further adjust the calculation of sentiments which are confirmed in the clause. An empirical evaluation based on four different datasets shows that our approach can outperform two dictionary-based baseline approaches, and is more generalizable compared to a learning-based baseline approach.
Taking Heuristic Based Graph Edge Partitioning One Step Ahead via OffStream Partitioning Approach

In the modern era of big data, large-scale graph computing has become challenging because of the dramatic rise in graph data size. Graph edge partitioning (GEP) is a crucial preprocessing step to distributed graph platforms, yet it is challenging to partition the large-scale graphs. GEP has shown better partition quality than the graph vertex partitioning for the graph's skewed degree distribution. Existing GEP approaches are classified into two as stream and offline. The former category assigns edges to the partitions based on the previously received edge information. It has less partitioning quality and is affected by stream order compared to the latter while supporting big graph partitioning. The latter uses complete knowledge of a graph during partitioning and hence has a better partitioning quality than the former; however, it does not support large-scale graphs. In this study, we propose a novel OffStream partitioning approach (OSPA) and hybrid graph edge partitioner OffStreamNH. OSPA leverages both the offline and stream graph partitioning approaches through stateful partitioning by introducing a state layer. This stateful partition state is recorded while offline is partitioning its input graph. It contains partial knowledge of previously partitioned data and is used by the stream partitioner. The OffStreamNH uses Neighborhood Expansion (NE) and Higher Degree Replicated First (HDRF) algorithms for the offline and online; respectively, with minor modifications of both algorithms. Experimental results show that OffStreamNH outperforms the state of the art stream partitioners in terms of replication factor, load balance and tolerates the effect of stream orders.
Multiple Similarity-based Features Blending for Detecting Code Clones using Consensus-Driven Classification

Code clone detection helps to reduce the costs associated with software maintenance and bug prevention. Machine learning methods have previously suggested many ways by which to detect code clones. The majority of clone detectors are traditional in their approach, they can detect syntactic clones but are poor at detecting semantic clones. Researchers use machine learning to detect semantic clones and automatically scan the data to learn latent semantic features. In this study, we have introduced a new formal model of similarity which combines similarity measures so that method blocks can measure both the syntactic and semantic distances between method block pairs. The uniqueness of our study is in the use of different similarity measures, and similarity scores as features in machine learning, to detect code clones. We use a number of similarity measure computations to extract similarity score features, these features are then represented as vectors. Using ensemble classification models, we perform extensive comparisons and evaluations of the effectiveness of our proposed idea. The results indicate that our approach is significantly better at detecting clone types compared to contemporary code clone detectors. We achieved a 99% success rate in detecting cloned codes based on F-score, recall, and precision. Our approach achieves 98–100% accuracy in the majority of cases.
A New Method of Halftoning and Inverse Halftoning Based on GAN Network

Halftoning is a method of quantifying a continuous tone image into a binary image. Halftone methods can be categorized into dithering, error diffusion and iterative method. We present a new method different from traditional halftoning algorithms for learning the mapping between continuous images and halftone images using conditional generative adversarial networks (conditional GANs). We regard halftoning and inverse halftoning as the process of image-to-image translation, and use the classic pix2pixHD network. In this work, we use multi-scale generator and discriminator architectures to perform both halftoning and its structural reconstruction. The experimental results show that this method can better fit some classical halftoning algorithms to realize halftoning and inverse halftoning. Compared with the existing methods, our method can better realize reconstruction of halftone images.
Cost-Effective Solutions in Cloud Computing Security

The popularity and advances in technology, recently have created a great deal of interest in cloud computing, especially for enterprises. Although the cloud computing platform offers a cost-efficient solution, there is a big drawback when it comes to security and the real costs behind it. Depending on the type of deployment and service, outsourcing security is a big concern for the provider, as well as for the client regarding the security itself and the impact on the overall performance of the cloud. Most of the security designers propose high-level assurances using cryptography while making sure to maintain the benefits of outsourcing. Anyhow, there are many challenges and issues with respect to privacy and security of the cloud that need to be addressed and analyzed. This work aims to present some of the most efficient existing solutions for security in cloud computing, present a short overview of the progress in literature and research that tackle such issues, and finally investigate the models and methods of calculating the costs of security in cloud computing. While engaging qualitative and quantitative methods, different analyses were carried out to finally present the state-of-art in cloud security research and, present the existing cost calculation methods.
CFFNet: Coordinated feature fusion network for crowd counting

Long skip connection or encoder-decoder networks for crowd counting have proven to be effective methods to generate high-resolution density maps. However, the simple and coarse feature fusion ignores the disharmony between features, that is, spatial misalignment and semantic inconsistency, which will weaken feature representation and degrade network performance. In this paper, we propose an end-to-end trainable architecture called Coordinated Feature Fusion Network (CFFNet) to tackle the aforementioned problems. The proposed model contains a powerful baseline network and embeds two primary modules: Spatial Alignment Module (SAM) and Semantic Consistency Module (SCM). Specifically, the SAM can learn the transformation offset of pixels to alleviate the spatial misalignment caused by the feature resolution difference; the SCM based on the multi-scale attention mechanism can capture pixel-wise weight to alleviate the semantic inconsistency due to the feature level gap. Extensive experiments on four benchmark crowd datasets (the ShanghaiTech, the UCF-QNRF, the JHU-CRWORD ++ and the NWPU-Crowd), indicate that CFFNet can achieve state-of-the-art counting performance and high robustness.
An Alternative To Likert Scale: Emoji

In the twenty-first century, the wide use of emojis in communication platforms has emerged. As a result, emojis have started to be used in scales. However, there are a limited number of studies in the literature that focuses on the effect of using emojis instead of Likert-type response categories in scales. Therefore, the focus of this study is to examine the differences that may arise from using emoji and Likert-type response categories in scales. For this purpose, the 3, 5, and 7-point Likert-type and 3, 5, and 7 emoji response categories Psychological Well-Being Scale was applied to 341 students studying at two state universities located in different regions of Turkey. Exploratory and confirmatory factor analyses and reliability analyses were carried out on the data of the participants who answered the six forms with different response categories. As a result, it was determined that there were no significant differences in exploratory and confirmatory factor analyses and reliability analyses. However, when correlational analyses were examined, it was observed that as the number of reaction categories increased, the correlation scores of emoji and Likert-type scales decreased.
Designing Games for and with Children. Co-design Methodologies for playful activities using AR/VR and Social Agents

Playing games is an inherent part of children's lives as it impacts several aspects of their physical and mental development. Technological advances have been manifesting new and exciting avenues of interaction when children play games, ranging from board and card games, to videogames that are played on mobile devices, virtual and augmented reality (VR/AR) headsets, robotic systems, and social agents. These games encompass a wide range of applications aiming to provide educational benefits, promote development, enhance well-being, or simply enjoy leisure time. Along with the fun and excitement, these advancements also bring unique challenges in the game design process due to the inclusion of complex technology, the maximization of the players' engagement and expectations and interests of the children. The player-centric approach of co-designing games with the target audience has a unique position as it involves creating the games for and with the children, allowing them to act as an equal stakeholder rather than simple users or informants. This half-day workshop aims to expose the researchers to collaborative techniques used in game design to create interactive and playful activities for children that involve contemporary technologies such as AR/VR and social agents.
Federated Machine Learning as a Self-Adaptive Problem

Machine Learning (ML) enables the creation of a new generation of applications that “learn” from collected data, transferred and analyzed on centralized servers. Moving data may imply a significant overhead and may also undermine users' privacy. Federated Machine Learning (FedML) tries to address these issues by means of local training phases on client devices: only lightweight aggregated data are then sent to the centralized server. FedML solutions must offer response times and accuracy similar to traditional ML applications, but their management is distributed on devices that may be heterogeneous, may become unavailable, and are not as powerful as (cloud-based) servers. This paper considers FedML systems a novel example of self-adaptive applications, where clients and servers must cooperate to provide required results. In particular, this paper proposes: i) the formalization of FedML applications as self-adaptive systems, ii) an initial prototype that shows the feasibility of the approach, and iii) a preliminary evaluation that demonstrates the benefit of the proposed solution.
Boomerang: Proactive Insight-Based Recommendations for Guiding Conversational Data Analysis

Natural-language interfaces are gaining popularity due to their potential to democratize access to data and insights by making the interaction with data more natural and accessible for a wide range of business users. To fully embrace the goal of democratization, it is also necessary to provide effective and continuous guidance support for data exploration. Conversational interfaces enable exploration of the data and insights search space in small incremental steps as the conversation with the data progresses. In this demo, we describe Boomerang, a system that recommends data-driven insights to guide exploration of datasets through a conversational interface. Boomerang aggregates recommendations from a variety of statistical, collaborative, and content-based recommenders, and selects insights that match closely to the user's current state of data exploration, represented as the \em conversational context. Boomerang combines various metrics, such as \em relevance, \em interestingness and \em timeliness, to rank the insights and recommends the insights based on current conversational context. In the demo, we will show how Boomerang enables guided data exploration on a sales dataset, containing information about products, retailers, sales, orders, inventory levels and regions.
Replay Spoof Attack Detection using Deep Neural Networks for Classification

In this paper, we explore the use of the deep learning approach for replay spoof detection in speaker verification systems. Automatic speaker verifications (ASVs) can be easily spoofed by previously recorded genuine speech. In order to counter the issues of spoofing, detecting spoofing attacks play an important role. Hence, we consider the detection of replay attack spoofing that is the most easily accomplished spoofing attack. In this light, we propose a deep neural network-based (DNN) classifier using a hybrid feature from Mel-frequency cepstral coefficient (MFCC) and constant Q cepstral coefficient (CQCC). Several experiments were conducted on the latest version of ASVspoof 2017 dataset. The results are compared with a base line system that uses the Gaussian mixture model (GMM) classifier with different features that include MFCC, CQCC, and the hybrid feature of the two. The experiment results reveal that the DNN classifier outperforms the conventional GMM classifier. It was found that the hybrid-based features are superior to single features, such as CQCC and MFCC in terms of equal error rate (ERR). In addition, like many previous researchers have found, it turned out that high-frequency regions of speech utterance convey much more discriminative information for replay attack detection.
Development of Large Chaotic S-boxes for Image Encryption

In this work, we suggest a novel algorithm for construction of large chaotic confusion and diffusion S-boxes for gray scale and color image scrambling. The modification of the value of a pixel by these S-boxes, depend not only on its gray level intensity but also on its position in the original image. In order to raise the impact of avalanche effect and to make our method robust against differential attack, we have developed a strong link between the ciphered pixel and the next original. The simulations carried out by our system on a large number of randomly selected images show the effectiveness of this crypto system against any known attack.