Reviewerly/exHarmony huggingface dataset:
Latent association graph inference for binary transaction data

A novel approach to the problem of statistical inference for multivariate binary transaction data is proposed. A fundamental question that arises from this data, often referred to as market basket data, is how the items relate to one another. These relationships are naturally expressed by a graph and transactions can be modeled as samples of cliques from this association graph. A hierarchical model is developed that follows from this generative idea, along with an MCMC sampling procedure that handles large datasets and allows inference on a broad set of parameters. This model provides a sparser representation of associations between items as compared with frequent itemset mining (FIM) output, without sacrificing predictive accuracy. Additionally, by allowing inference on a broad set of parameters, the model provides a deeper level of insight into transaction data. Empirical results are provided on applications of this model to simulated data and real transaction data from Instacart.
Preventing the Insider – Blocking USB Write Capabilities to Prevent IP Theft

The Edward Snowden data breach of 2013 clearly illustrates the damage that insiders can do to an organization. An insider's knowledge of an organization allows them legitimate access to the systems where valuable information is stored. Because they belong within an organizations security perimeter, an insider is inherently difficult to detect and prevent information leakage. To counter this, proactive measures must be deployed to limit the ability of an insider to steal information. Email monitoring at the edge is can easily be monitored for large file exaltation. However, USB drives are ideally suited for large-scale file extraction in a covert manner. This work discusses a process for disabling write-access to USB drives while allowing read-access. Allowing read-access for USB drives allows an organization to adapt to the changing security posture of the organization. People can still bring USB devices into the organization and read data from them, but exfiltration is more difficult.
A Conversational Agent for Database Query: A Use Case for Thai People Map and Analytics Platform

Since 2018, Thai People Map and Analytics Platform (TPMAP) has been developed with the aims of supporting government officials and policy makers with integrated household and community data to analyze strategic plans, implement policies and decisions to alleviate poverty. However, to acquire complex information from the platform, non-technical users with no database background have to ask a programmer or a data scientist to query data for them. Such a process is time-consuming and might result in inaccurate information retrieved due to miscommunication between non-technical and technical users. In this paper, we have developed a Thai conversational agent on top of TPMAP to support self-service data analytics on complex queries. Users can simply use natural language to fetch information from our chatbot and the query results are presented to users in easy-to-use formats such as statistics and charts. The proposed conversational agent retrieves and transforms natural language queries into query representations with relevant entities, query intentions, and output formats of the query. We employ Rasa, an open-source conversational AI engine, for agent development. The results show that our system yields Fl-score of 0.9747 for intent classification and 0.7163 for entity extraction. The obtained intents and entities are then used for query target information from a graph database. Finally, our system achieves end-to-end performance with accuracies ranging from 57.5%-80.0%, depending on query message complexity. The generated answers are then returned to users through a messaging channel.
Analysis of children’s everyday language experiences using longform audio: promises and pitfalls (<i>Análisis de las experiencias lingüísticas cotidianas de niños y niñas utilizando audio de formato largo: posibles ventajas y dificultades</i>)

(2021). Analysis of children’s everyday language experiences using longform audio: promises and pitfalls (Análisis de las experiencias lingüísticas cotidianas de niños y niñas utilizando audio de formato largo: posibles ventajas y dificultades) Journal for the Study of Education and Development: Vol. 44, SPECIAL ISSUE: CHILD LANGUAGE AND DEVELOPMENT CONTEXTS / NÚMERO ESPECIAL: LENGUAJE INFANTIL Y CONTEXTOS DE DESARROLLO, pp. 477-501.
An Improved Approach for Object Proposals Generation

The objectness measure is a significant and effective method used for generic object detection. However, several object detection methods can achieve accurate results by using more than 1000 candidate object proposals. In addition, the weight of each proposal is weak and also cannot distinguish object proposals. These weak proposals have brought difficulties to the subsequent analysis. To significantly reduce the number of proposals, this paper presents an improved generic object detection approach, which predicts candidate object proposals from more than 10,000 proposals. All candidate proposals can be divided, rather than preclassified, into three categories: entire object, partial object, and nonobject. These partial object proposals also display fragmentary information of the objectness feature, which can be used to reconstruct the object boundary. By using partial objectness to enhance the weight of the entire object proposals, we removed a huge number of useless proposals and reduced the space occupied by the true positive object proposals. We designed a neural network with lightweight computation to cluster the most possible object proposals with rerank and box regression. Through joint training, the lightweight network can share the features with other subsequent tasks. The proposed method was validated using experiments with the PASCAL VOC2007 dataset. The results showed that the proposed approach was significantly improved compared with the existing methods and can accurately detect 92.3% of the objects by using less than 200 proposals.
Exploring the relationship between performance metrics and cost saving potential of defect prediction models

Abstract Context: Performance metrics are a core component of the evaluation of any machine learning model and used to compare models and estimate their usefulness. Recent work started to question the validity of many performance metrics for this purpose in the context of software defect prediction. Objective: Within this study, we explore the relationship between performance metrics and the cost saving potential of defect prediction models. We study whether performance metrics are suitable proxies to evaluate the cost saving capabilities and derive a theory for the relationship between performance metrics and cost saving potential. Methods: We measure performance metrics and cost saving potential in defect prediction experiments. We use a multinomial logit model, decision, and random forest to model the relationship between the metrics and the cost savings. Results: We could not find a stable relationship between cost savings and performance metrics. We attribute the lack of the relationship to the inability of performance metrics to account for the property that a small proportion of very large software artifacts are the main driver of the costs. fact that performance metrics are incapable of accurately considering the costs associate with individual artifacts, which is required due to the exponential distribution of artifact sizes. Conclusion: Any defect prediction study interested in finding the best prediction model, must consider cost savings directly, because no reasonable claims regarding the economic benefits of defect prediction can be made otherwise.
Intent Recognition and Unsupervised Slot Identification for Low-Resourced Spoken Dialog Systems

Intent Recognition and Slot Identification are crucial components in spoken language understanding (SLU) systems. In this paper, we present a novel approach towards both these tasks in the context of low-resourced and unwritten languages. We use an acoustic based SLU system that converts speech to its phonetic transcription using a universal phone recognition system. We build a word-free natural language understanding module that does intent recognition and slot identification from these phonetic transcription. Our proposed SLU system performs competitively for resource rich scenarios and significantly outperforms existing approaches as the amount of available data reduces. We train both recurrent and transformer based neural networks and test our system on five natural speech datasets in five different languages. We observe more than 10% improvement for intent classification in Tamil and more than 5% improvement for intent classification in Sinhala. Additionally, we present a novel approach towards unsupervised slot identification using normalized attention scores. This approach can be used for unsupervised slot labelling, data augmentation and to generate data for a new slot in a one-shot way with only one speech recording.
The Semantic Level of Shannon Information: Are Highly Informative Words Good Keywords? A Study on German

This paper reports the results of a study on automatic keyword extraction in German. We employed in general two types of methods: (A) unsupervised, based on information theory, i.e., (i) a bigram model, (ii) a probabilistic parser model, and (iii) a novel model which considers topics within the discourse of target word for the calculation of their information content, and (B) supervised, employing a recurrent neural network (RNN). As baselines, we employed TextRank and the TF-IDF ranking function. The topic model (A)(iii) outperformed clearly all remaining models, even TextRank and TF-IDF. In contrast, RNN performed poorly. We take the results as first evidence that (i) information content can be employed for keyword extraction tasks and has thus a clear correspondence to semantics of natural language, and (ii) that—as a cognitive principle—the information content of words is determined from extra-sentential contexts, i.e., from the discourse of words.
Sharing Hidden Histories: The <i>XRchaeology at Miller Grove</i>, a Free African American Community in Southern Illinois

XRchaeology at Miller Grove is a web-based collection of new media components, including digital storytelling, geographic information systems, virtual tours, three-dimensional objects, and web-based virtual reality presentations. XRchaeology contributes to revisions of traditional narratives about free African Americans and the network of escape routes referred to as the Underground Railroad (UGRR). This project focuses attention on potential African American “conductors” in the UGRR and resituates African Americans as the agents of their own freedom. XRchaeology emphasizes that freedom was achieved across a continuum shaped by social, economic, and legal biases and anti-immigration sentiments. The XRchaeology application is designed for both traditional learners and non-specialist members of the public, as the project facilitates self-directed learning and evidence-based inquiry for all interest levels.
AI Tax

Artificial intelligence and machine learning are experiencing widespread adoption in industry and academia. This has been driven by rapid advances in the applications and accuracy of AI through increasingly complex algorithms and models; this, in turn, has spurred research into specialized hardware AI accelerators. Given the rapid pace of advances, it is easy to forget that they are often developed and evaluated in a vacuum without considering the full application environment. This article emphasizes the need for a holistic, end-to-end analysis of artificial intelligence (AI) workloads and reveals the “AI tax.” We deploy and characterize Face Recognition in an edge data center. The application is an AI-centric edge video analytics application built using popular open source infrastructure and machine learning (ML) tools. Despite using state-of-the-art AI and ML algorithms, the application relies heavily on pre- and post-processing code. As AI-centric applications benefit from the acceleration promised by accelerators, we find they impose stresses on the hardware and software infrastructure: storage and network bandwidth become major bottlenecks with increasing AI acceleration. By specializing for AI applications, we show that a purpose-built edge data center can be designed for the stresses of accelerated AI at 15% lower TCO than one derived from homogeneous servers and infrastructure.
RE-KGR: Relation-Enhanced Knowledge Graph Reasoning for Recommendation

A knowledge graph (KG) has been widely adopted to improve recommendation performance. The multi-hop user-item connections in a KG can provide reasons for recommending an item to a user. However, existing methods do not effectively leverage the relations of entities and interpretable paths in a KG. To address this limitation, in this paper, we propose a novel recommendation framework called relation-enhanced knowledge graph reasoning for recommendation (RE-KGR) that combines recommendation and explainability by reasoning user-item interaction paths (UIIPs). First, instead of applying an alignment algorithm for preprocessing, RE-KGR directly learns the semantic representation of entities from structured knowledge by stacking relation-based convolutional layers to take full advantage of the KG. Moreover, RE-KGR infers user preferences by calculating the sum of all UIIPs between users and items. Finally, RE-KGR selects several UIIPs with the highest probabilities as possible reasons for the recommendations. Extensive experiments on three real-world datasets demonstrate that our proposed method significantly outperforms several state-of-the-art baselines and achieves superior performance and explainability.
Security Challenges in Cloud Computing Networks

Cloud computing is a concept of resource sharing of available resources on the networks accessed remotely by the users. The benefit of cloud computing include cost saving and improved business in secured environment. The data transmission over the network should be quite secured in client server architecture while sharing important data among the users. Network security in cloud computing has always been and issue and to fill the gap the technological concept and their approaches to cloud computing needs to apply.The paper elaborates the process where one can identifies major challenges being faced and how to overcome those challenges.
Maritime biota waste as eco-jewelry materials, potentials and possibilities

Abstract This research aims to describe the process of designing jewelry from marine biota waste. The background of this writing is the utilization of marine biota waste which is still conventional in applying visual quality in the form of the uniqueness of marine biota waste as a personal need. Maritime Biota Waste have potential to be developed into jewelry to meet personal needs as self-charm of lifestyle. Some accessories from those waste are usually in the conventional form without any significant changes. Accessories produced by mass production usually do not have removable parts because they prioritize ease and efficiency of production. This research focuses on the development of the waste potentials which is reviewed with a visual strategy and uses the Interchangeable Elements design method. The method conducted has relevance between fluctuations in market share changes and production flexibility. This helps to find out the parameters for applying visuals to jewelry and enhances self-charm by using removable parts of jewelry as adjustments to achieve optimal visual quality.
Relevance of Color spaces and Color channels in performing Image dehazing

In recent years, image dehazing has became a widely popular research topic because of its societal importance. Proper dehazing of images is very necessary to restore the visibility of images. In order to perform accurate dehazing of images it is very essential to identify the color channels of hazy images in different color spaces which are getting more influenced by the scattering and attenuation of scene light due to the presence of haze particles in the atmosphere in comparison to the other less influenced color channels so that the effect of airlight on the selected (more influenced) color channels can be removed and visibility of degraded hazy images can be fully restored without even processing the least influenced color channels. In this paper, we have performed a comprehensive analyses by comparing the histograms and the intensities of the color channels of different hazy (both nighttime and daytime) images and haze free images of the same scene to identify which of the color channels in RGB, HSV and L*a*b color spaces are getting more influenced by the presence of haze in the atmosphere in both daytime and nighttime hazy images as the characteristics of nighttime hazy images vastly differs from those of daytime hazy images.
Carvers Suite – Smart Application for Data Recovery in SSD

This paper proposes the automation of the information retrieval process in solid-state storage devices - SSD, through an application that has been named as Carvers Suite, which is supported by recovery techniques and methodologies (File Carving); by the affectation factors, whether physical, human or environmental, and developed in the most common scenarios as causes of this loss, impact, dampening, electrocution and magnetization. The methodological proposal was outlined according to the experimentation, based on the work done in SSD and with the use of File Carving forensic recovery techniques. This proposal tries to become the norm that guides the analysis, design, and implementation of software products, whose purpose is the recovery of information in SSD.
Real-time Assistive Reader Pen for Arabic Language

Disability is an impairment affecting an individual's livelihood and independence. Assistive technology enables the disabled cohort of the community to break the barriers to learning, access information, contribute to the community, and live independently. This article proposes an assistive device to enable people with visual disabilities and learning disabilities to access printed Arabic material in real-time, and to help them participate in the education system and the professional workforce. This proposed assistive device employs Optical Character Recognition (OCR) and Text To Speech (TTS) conversion, using concatenation synthesis. OCR is achieved using image processing, character extraction, and classification, while Arabic speech synthesis is achieved through concatenation synthesis, followed by Multi Band Re-synthesis Overlap-Add (MBROLA). Waveform generation in the second phase produces vocal output for the disabled user to hear. OCR character and word accuracy tests were conducted for nine Arabic fonts. The results show that six fonts were recognized with over 60% character accuracy and two fonts were recognized with over 88% accuracy. A Mean Opinion Score (MOS) test for speech quality was conducted. The results showed an overall MOS score of 3.53/5 and indicated that users were able to understand the speech. A real-time usability testing was conducted with 10 subjects. The results showed an overall average of agreements scores of 3.9/5 and indicated that the proposed Arabic reader pen meets the real-time constraints and is pleasant and satisfying to use and can contribute to make printed Arabic material accessible to visually impaired persons and people with learning disabilities.
Spatiotemporal Particle Swarm Optimization with Incremental Deep Learning-Based Salient Multiple Object Detection

The recent developments in the computer vision application will detect the salient object in the videos, which plays a vital role in our day-to-day lives. Difficulty in integrating spatial cues with motion cues makes the process of a salient object detection more difficult. Spatiotemporal constrained optimization model (SCOM) is provided in the previous system. Since the better performance is exhibited in the detection of single salient object, the variation of salient features between different persons is not considered in this method and more general agreement related to their significance is met by some objects. To solve this problem, the proposed system designed a spatiotemporal particle swarm optimization with incremental deep learning-based salient multiple object detection. In this proposed work, incremental deep convolutional neural network (IDCNN) classifier is introduced for a suitable measurement of success in a relative object saliency landscape. Spatiotemporal particle swarm optimization model (SPSOM) is used for performing the ranking method and detection of multiple salient objects. In this system to achieve global saliency optimization, local constraint temporal as well as spatial cues is exploited. Prior video frame saliency map and change detection motion history are done using SPSOM. Moving salient objects are distinguished from diverse changing background regions. When compared with existing methods, better performance is exhibited using proposed method as shown in results of experimentation concerning recall, precision, average run time, accuracy and mean absolute error (MAE).
The development of sign language learning media using multimedia systems

Abstract Language is a means to communicate. The expressions in the world are diverse, one of which is sign language. There are two types of sign languages in Indonesia, namely BISINDO (Indonesian Sign Language) and SIBI (Indonesian Language System). Besides that, few people know the sign language, and the method used still uses conventional or manual methods so that it feels challenging to communicate with people who are deaf speech. This study aims to develop sign language learning applications using multimedia systems that expected to help people with hearing impairment and hearing impairment, the general public, and teachers in communicating with speech deaf people. In this study, using the Multimedia Development Life Cycle research method, Alpha testing with Black Box testing and beta testing as an application testing method to measure user satisfaction. The results of research using beta testing is an android-based sign language learning application suitable for the general public, and the deaf equipped with evaluation features and delivery of material in the form of video.
Planning exploration of the transition from “waste landfill” to “circular economy industrial park”-Take Guangzhou Xingfeng Domestic Waste Landfill Site Closure and Reconstruction Plan as an Example

Abstract Under the background of high-level protection of the ecological environment and the promotion of economic “green recovery” and high-quality development after the epidemic, we will do a good job in the “14th Five-Year” ecological environment protection work, correctly handle the relationship between development and protection, and develop an environmental protection culture. It will definitely become the main line of work during the “14th Five-Year Plan” period. This article takes the closure and renovation plan of the Xingfeng domestic waste landfill in Guangzhou as an example, studies and draws on successful cases at home and abroad, and explores the planning and design of the renovation of the landfill after the closure, combined with the domestic waste landfill Advanced waste treatment technology and the unique natural ecological environment of Jinkeng National Forest Park will transform the Guangzhou Xingfeng Domestic Waste Landfill into a circular economy industrial park with the function of ecological recycling of domestic waste. This transformation plan can guide the public to actively participate in the environment. Cultural construction is of great significance to the construction of ecological civilization; at the same time, it is also an exploration of green recycling, low-carbon, and sustainable planning methods.