Reviewerly/exHarmony huggingface dataset:
Besi: behavior learning and tracking with wearable and in-home sensors - a dementia case-study

Sensing driven behavior modeling is vital in health applications. Recent advances in machine learning and sensing technologies accelerate such efforts. While wearables facilitate continuous sensing, they lack the computational resources for on-board heavy-weight signal processing and model-based prediction. Moreover, continuous transmission to a remote server drains much energy to achieve reasonable battery life for practical use. The BESI (Behavioral and Environmental Sensing and Intervention) system addresses these challenges to achieve continuous and real-time prediction-based tracking of human behavior. It employs a network of embedded nodes to ensure continuous connection with the wearables, and distributes the feature extraction and the model prediction tasks among these nodes and a local server to achieve real-time performance. In a dementia case-study, the BESI system is used for tracking agitated behavior in patients. It has been deployed in 12 residences of dementia patients, each for 30 days; and is planned for 10 more 60-day deployments. The system operation, behavior modeling method, and some preliminary result on tracking performance are presented here along with a discussion on future plan for platform optimization and model performance improvement.
Questioning Our Attitudes and Feelings Towards Persuasive Technology

Definitions regarding Persuasive Technology are often introduced with the accompanying remark: without using coercion and deception. For this position paper, we investigated, evaluated, and discussed the term Persuasive Technology and its connotations. We invesstigated whether Persuasive Technology is perceived with negative connotations such as coercive and deceptive, and, how in comparison, similar labels (such as Behavior Change Support System and Digital Behavior Change Intervention) were perceived. We conducted an online survey where participants (N = 488) rated their attitude towards these systems in the context of a system description, indicated to what extent they agreed with 10 descriptors (such as, manipulative, motivating, or supportive) in the context of a system description, and whether this system in general, is at risk of being perceived as coercive, manipulative, deceptive, or propagandistic. We found that when considering risks of systems in general, labeling them as PT results in them being perceived significantly more forceful than all other labels, and switching the labeling of a system to Digital Behavior Change Intervention results in them being perceived significantly more captivating compared to a neutral system label. The findings suggest that when choosing labels to use for technology or systems it is essential to consider the impact labels can have on how the system or technology is perceived, regardless of the actual function of the system. These findings are relevant for the field of Persuasive Technology and the Persuasive Technology conference specifically. This paper is meant to spark further discussion in the field and at the conference.
The Negativity‐to‐Violation Map between Wigner Function and Quantum Contextuality Inequality for a Single Qudit

Abstract The relation between discrete Wigner function and quantum contextuality based on graph theory has been studied, following the work in [Nature 510,351(2014)]. To do this, non‐stabilizer projectors have been introduced to a series of non‐contextuality graphs based on stabilizer projectors for a single qudit with odd prime dimension. It has been found that, for a phase space point defined by Wootters, there exists a given set of states for an odd‐prime qudit where the negative discrete Wigner function on the phase space point means its quantum contextuality under measurements on the graphs designed by a specific method. To implement this method, a subset of non‐stabilizer projectors has been found. In the union of the set of states for all phase space points, there exists a negativity‐to‐violation map between Wigner function and quantum contextuality inequality. The robustness of the equivalence under depolarizing noise has been analyzed and discussed. For demonstration purposes, the graphs with different independence numbers and the corresponding set of states have been established on a single qutrit. Different to the cited work, this method involves only a single qudit, then is experimentally feasible for a qutrit.
Performance Modelling of an Anonymous and Failure Resilient Fair-Exchange E-Commerce Protocol

This paper explores a type of non-repudiation protocol, called an anonymous and failure resilient fair-exchange e-commerce protocol, which guarantees a fair-exchange between two parties in an e-commerce environment. Models are formulated using the PEPA formalism to investigate the performance overheads introduced by the security properties and behaviour of the protocol. The PEPA eclipse plug-in is used to support the creation of the PEPA models for the security protocol and the automatic calculation of the performance measures identified for the protocol models.
ASP-CNN: aligning semantic parts for fine-grained image classification

Recently, numerous methods have been proposed to tackle the problem of fine-grained image classification (FGIC). Most of them follow a two-step strategy that contains detecting the object regions and classifying with the features extracted from these regions. For the feature extraction, the most popular method is directly cropping the feature maps according to the location of detected part regions. However, one challenge of such a method is that the direction of the semantic parts may vary in different images, therefore, it is necessary to capture such differences for better classification. We propose a CNN architecture by aligning semantic parts (ASP-CNN) for FGIC, aiming to increase the interclass variance and meanwhile reduce the intraclass variance in fine-grained datasets. Extensive experiments on CUB-200-2011 and CUB-200-2010 show the effectiveness of our ASP-CNN.
SOME ASPECTS OF PRACTICAL USE OF TOURISTIC INFORMATION RESOURCES IN ELECTRONIC EDUCATION

The article reveals the content of the notion “digital educational environment” and different approaches to its definition. The author discusses the possibilities and methods of integrating touristic information resources into digital educational environment. The paper gives examples of the ways of such integration at schools and higher education institutions, and also presents the experience of lecturers at Volgograd State University on touristic information resources use in teaching bachelor degree students majoring in 43.03.02 “Tourism”. The projected results of such an innovation on the part of tourism business, educational environment and society are given.
A Test Collection for Passage Retrieval Evaluation of Spanish Health-Related Resources

This paper describes a new test collection for passage retrieval from health-related Web resources in Spanish. The test collection contains 10,037 health-related documents in Spanish, 37 topics representing complex information needs formulated in a total of 167 natural language questions, and manual relevance assessments of text passages, pooled from multiple systems. This test collection is the first to combine search in a language beyond English, passage retrieval, and health-related resources and topics targeting the general public.
Shared Rules: Knowledge Sharing Service by RIF Rules

In this paper, we introduce a new method for knowledge sharing. We call it shared rules. In the shared rules service, users or organizations represent their knowledge in Rule Interchange Format (RIF) rules and post them on the Web. The rules are shared with everyone and infer useful information for each user. This service can be regarded as implementing a single global rule-based expert system on the Web, and it has an uncertainty problem in rule reasoning, as in existing expert systems. In the existing rule-based systems, the uncertainty problem can be mostly resolved at the design and development stage, but in the shared rules service, the problem inevitably arises because rules are freely written and shared by various users. In this paper, we analyze the uncertainty problem and explain that the problem can be resolved using existing methods.
Efficient Dynamic Bloom Filter Hashing Fragmentation for Cloud Data Storage

Abstract Security is important in cloud data storage while using the cloud services provided by the service provider in the cloud. Most of the research works have been designed for a secure cloud data storage. However, cloud users still have security issues with their outsourced data. In order to overcome such limitations, a Dynamic Bloom Filter Hashing based Cloud Data Storage (DBFH-CDS) Technique is proposed. The main goal of DBFH-CDS Technique is to improve confidentiality and security of data storage in a cloud environment. The proposed Technique is implemented using data fragmentation model and Bloom filter. The DBFH-CDS Technique uses data fragmentation model for fragmenting the large cloud datasets. After that, Bloom Filter is employed in DBFH-CDS Technique for storing the fragmented sensitive data along with higher security. The DBFH-CDS Technique ensures high data confidentiality and security for cloud data storage with the help of Bloom Filter. The performance of proposed DBFH-CDS Technique is measured in terms of Execution time and Data retrieval efficiency. The experimental results show that the DBFH-CDS Technique is able to improve the cloud data storage security with minimum space complexity as compared to state-of-the-art-works.
A Performance and Recommendation System for Parallel Graph Processing Implementations

There are nearly one hundred parallel and distributed graph processing packages. Selecting the best package for a given problem is difficult; some packages require GPUs, some are optimized for distributed or shared memory, and some require proprietary compilers or perform better on different hardware. Furthermore, performance may vary wildly depending on the graph itself. This complexity makes selecting the optimal implementation manually infeasible. We develop an approach to predict the performance of parallel graph processing using both regression models and binary classification by labeling configurations as either well-performing or not. We demonstrate our approach on six graph processing packages: GraphMat, the Graph500, the Graph Algorithm Platform Benchmark Suite, GraphBIG, Galois, and PowerGraph and on four algorithms: PageRank, single-source shortest paths, triangle counting, and breadth first search. Given a graph, our method can estimate execution time or suggest an implementation and thread count expected to perform well. Our method correctly identifies well-performing configurations in 97% of test cases.
Meta-Learning Acquisition Functions for Bayesian Optimization.

Transferring knowledge across tasks to improve data-efficiency is one of the open key challenges in the field of global black-box optimization. Readily available algorithms are typically designed to be universal optimizers and, therefore, often suboptimal for specific tasks. We propose a novel transfer learning method to obtain customized optimizers within the well-established framework of Bayesian optimization, allowing our algorithm to utilize the proven generalization capabilities of Gaussian processes. Using reinforcement learning to meta-train an acquisition function (AF) on a set of related tasks, the proposed method learns to extract implicit structural information and to exploit it for improved data-efficiency. We present experiments on a simulation-to-real transfer task as well as on several synthetic functions and on two hyperparameter search problems. The results show that our algorithm (1) automatically identifies structural properties of objective functions from available source tasks or simulations, (2) performs favourably in settings with both scarse and abundant source data, and (3) falls back to the performance level of general AFs if no particular structure is present.
Automatically Identifying Sufficient Object Builders from Module APIs

Various approaches to software analysis (e.g. test input generation, software model checking) require engineers to (manually) identify a subset of a module’s methods in order to drive the analysis. Given a module to be analyzed, engineers typically select a subset of its methods to be considered as object builders to define a so-called driver, that will be used to automatically build objects for analysis, e.g., combining them non-deterministically, randomly, etc. This requires a careful inspection of the module and its API, since both the relative exhaustiveness of the analysis (leaving important methods out may systematically avoid generating different objects), as well as its efficiency (the different bounded combinations of methods grows exponentially as the number of methods increases), are affected by the selection. We propose an approach for automatically selecting a set of builders from a module’s API, based on an evolutionary algorithm that favors sets of methods whose combinations lead to producing larger sets of objects. The algorithm also takes into account other characteristics of these sets of methods, trying to prioritize the selection of methods with less and simpler parameters. As the implementation of this evolutionary mechanism requires in principle handling and comparing large sets of objects, and this grows very quickly both in terms of space and running times, we employ an abstraction of sets of objects, called field extensions, that involves using the field values of the objects in the set instead of the actual objects, and enables us to effectively implement our mechanism. An experimental assessment on a benchmark of stateful classes shows that our approach can automatically identify sets of builders that are sufficient (can be used to create any instance of the module) and minimal (do not contain superfluous methods), in a reasonable time.
Logo and Brand Recognition from Imbalanced Dataset Using MiniGoogLeNet and MiniVGGNet Models

Deep learning model tends to promote models with deep structure. Despite its high accuracy, the model was not practical when high computing power was not available. Thus, deep model with not-so-deep structure or less number of model parameters is needed for low capacity computer. Logo and brand recognition task is an important and challenging problem in computer vision with wide potential applications. The inherent challenge to address this task is not only due to the presence of logo in various direction and clutters as well as imbalanced dataset but also because of high computing workload when deep learning models were adopted. This paper presents empirical results of logo recognition method using MiniVGGNet and MiniGoogleNet models combined with augmentation technique to increase variation and number of samples. The results show that the proposed model combined with augmentation technique increased accuracy of model accuracies and fasten training convergence of both models.
Keyword Extraction with Character-Level Convolutional Neural Tensor Networks

Keyword extraction is a critical technique in natural language processing. For this essential task we present a simple yet efficient architecture involving character-level convolutional neural tensor networks. The proposed architecture learns the relations between a document and each word within the document and treats keyword extraction as a supervised binary classification problem. In contrast to traditional supervised approaches, our model learns the distributional vector representations for both documents and words, which directly embeds semantic information and background knowledge without the need for handcrafted features. Most importantly, we model semantics down to the character level to capture morphological information about words, which although ignored in related literature effectively mitigates the unknown word problem in supervised learning approaches for keyword extraction. In the experiments, we compare the proposed model with several state-of-the-art supervised and unsupervised approaches for keyword extraction. Experiments conducted on two datasets attest the effectiveness of the proposed deep learning framework in significantly outperforming several baseline methods.
Lessons from Teaching Analytical Performance Modeling

This talk summarizes some lessons from teaching a course in analytical performance modeling, specifically: (1) what can an analytical model offer? (2) how a model can be decomposed into submodels,so as to decouple different forces affecting performance, and thus analyze their interaction; (3) making choices in formulating a model; (4) the role of assumptions; (5) Average Value Approximation (AVA); (6) when bottleneck analysis suffices; (7) reducing the parameter space; (8) the concept of analytic validation; and (9) analysis with an analytical model.
A smart system of 3D liver tumour segmentation

An efficient segmentation of liver tumour and volumetry facilitates medical experts to resolve the rate of tumour growth and follow up treatment. In this paper, a smart system for semi-automatic segmentation of liver tumour from medical images based on Geodesic active contours using level set method is proposed. The system passes through following stages. After the image is loaded, the Region of Interest (ROI) is selected and the Geodesic Active Contours with level set methods are initialised. Then segmentation result is updated to extract the tumour in three dimensions. The liver tumours detected by the system were compared with those delineated manually by experts, used as the ground truth results. The system was evaluated on Computed Tomography images of different datasets of tumours and compared with other methods. The result of proposed system obtained the Dice SC 0.948 and Jaccard SC 0.902 exhibits the steadfastness and effectiveness of the system.
Evolving From Know-How to Know-What

Computing is experiencing a “Cambrian explosion” of new technologies that promise great societal benefits but also create opportunities for misuse and societal harm. How to reduce this potential for harm is the objective of computing codes of ethics. Given the rash of ethical lapses related to computing, we ask whether codes of ethics are effective and, if not, why not?
Understanding Twitter Hashtags from Latent Themes Using Biterm Topic Model

: Social media, in general, and Twitter, in particular, provide a space for discourses, contemporary narratives besides a discussion about few specific social issues. People respond to these events by writing short text messages. Background: Hashtag “#”, a specific way to respond to a given raised discourse, narrative or any contemporary issue is usual to social media. Netizens write a short message as their opinion about any given issue represented using a given Hashtag. These small messages generally tend to have a latent topic (theme) as one’s opinion about it. Objective: This research is aimed to extract, represent and understand those hidden themes. Methods: Biterm Topic Model (BTM) has been used in this study given its ability to deal with the short messages unlike Latent Dirichlet Allocation that expects a document to have a significant length. Results: Twitter Hashtag #M comments. Data has been modelled with ten (10) topic. Conclusion: The experimental results show that the proposed approach to understand the twittter hashtages from latent themes using biterm topic modelling method is very effective as compared to other methods.
Conditional Dilated Convolution Attention Tracking Model

Current commercial tracking systems do not process fast enough to perform in real-time. State-of-the-art (SOTA) methods use entire scenes to locate objects frame by frame and are commonly slowed by large convolutions. Alternatively, attention mechanisms track more efficiently by mimicking human optical cognitive interaction to only process small portions of an image. Thus, we took an attention approach to create a single model that learns to compare features along a sequence using dilated convolutions. Using popular data sets like Modified National Institute of Standards and Technology (MNIST) handwritten digits, we tested our work against previous attention based networks like Deep Recurrent Attentive Writer (DRAW) and Recurrent Attention Tracking Model (RATM) to compare tracking abilities. Here we present a novel Conditional Dilated Convolution Attention Network that builds on previous attention principles to achieve generic, efficient, and recurrent-less object tracking.
Maintenance process modeling and dynamic estimations based on Bayesian networks and association rules

Abstract Managing the maintenance process and estimating accurately the effort and duration required for a new release is considered to be a crucial task as it affects successful software project survival and progress over time. In this study, we propose the combination of two well‐known machine learning (ML) techniques, Bayesian networks (BNs) , and association rules (ARs) for modeling the maintenance process by identifying the relationships among the internal and external quality metrics related to a particular project release to both the maintainability of the project and the maintenance process indicators (ie, effort and duration). We also exploit Bayesian inference , to test the effect of certain changes in internal and external project factors to the maintainability of a project. We evaluate our approach through a case study on 957 releases of five open source JavaScript applications. The results show that the maintainability of a release, the changes observed between subsequent releases, and the time required between two releases can be accurately predicted from size, complexity, and activity metrics. The proposed combined approach achieves higher accuracy when evaluated against the BN model accuracy.
Curriculum for a Master’s Program

The current paper features the specifics of the methodology of a master's program curriculum. To produce highly qualified specialists in interdisciplinary fields, Master's programs should be adapted for future professional activity. The present research describes the technology of creating a Master's program in the conditions of digital economy. It is based on the theory of knowledge and activity, the theory of social and social conditionality of education, modern didactic theories and learning technologies, the methodology of teaching and learning activities, the methodology of developing a digital syllabus. The authors introduce some criteria and forms of knowledge transfer. They prove the efficiency of such forms of control as testing and research work from the point of view of the aspect of informational support and transparency in the control system. One of the mandatory conditions for a Master's degree syllabus is the use of portable digital simulators that allow students to practice at any convenient time and thus create conditions for continuous, individual designed education.
Promotion of Information Technology Teaching Ability of Food Specialty Teachers

Under the background of big data era, information technology has occupied a very important position in modern education, especially in modern vocational education, vocational education informationization has become an inevitable trend of vocational education development, vocational education workers to enhance the ability of informationization has become an inherent requirement of the development of vocational education. Based on discussing the current situation of information-based teaching reform in food specialty, this paper studies the measures to optimize the information-based teaching reform.