Reviewerly/exHarmony huggingface dataset:
Integrating signals for reasoning about visitors' behavior in cultural heritage

This chapter presents and discusses the application of advanced, mobile and stationary technology for reasoning about visitors' behavior in cultural heritage sites. While museums are large indoors spaces, like shopping malls and different public buildings, they have unique characteristics. Visiting them is a leisure activity; people come as individuals and groups for different reasons. Museums study their visitors in order to provide them a better experience. While traditional museum studies involved manual observations and visitors tracking, nowadays technology enables one to do that automatically and non-obtrusively. However, while multimodal raw data can easily be gathered, reasoning about it and interpreting it has become a maor challenge. This chapter reviews some recent studies and techniques used for automatic tracking museum visitors and discusses how measured signals were fused and interpreted, and it suggests what may be done as mobile, wearable technology becomes a commodity.
Hybrid user action prediction system for automated home using association rules and ontology

Nowadays, with the rapid increase of Internet users, the Internet services dominate a primary part of our lifestyle. Moreover, the evolution of the internet of things has introduced new insights into smart platforms and devices that leads to the new vision of ‘smart homes’. The idea of smart homes is not a recent concept; it has been in high interest by both academia and industry to make smart homes a more convenient technology for human's comfort. In this study, the authors propose a new hybrid prediction system based on the frequent pattern (FP)-growth and ontology graphs for home automation systems. Their proposed system simulates the human prediction actions by adding common sense data by utilizing the advantages of the ontology graph and the FP-growth to find a better solution in predicting home user actions for automated systems. For the evaluation of the proposed system, two ontology graphs are introduced with FP-growth to achieve the best results. Both graphs are tested through multiple weight values with the results of FP-growth. As a result, the best weight distribution selected in this study is (70, 30) for time and location ontology graphs respectively. Their results showed that the proposed prediction system achieved an accuracy of 79% for all weekdays and 81% excluding weekend days.
Loss function, unbiasedness, and optimality of Gaussian graphical model selection

A Gaussian graphical model is a graphical representation of the dependence structure for a Gaussian random vector. Gaussian graphical model selection is a statistical problem that identifies the Gaussian graphical model from observations. There are several statistical approaches for Gaussian graphical model identification. Their properties, such as unbiasedeness and optimality, are not established. In this paper we study these properties. We consider the graphical model selection problem in the framework of multiple decision theory and suggest assessing these procedures using an additive loss function. Associated risk function in this case is a linear combination of the expected numbers of the two types of error (False Positive and False Negative). We combine the tests of a Neyman structure for individual hypotheses with simultaneous inference and prove that the obtained multiple decision procedure is optimal in the class of unbiased multiple decision procedures.
Matching Disparate Image Pairs Using Shape-Aware ConvNets

An end-to-end trainable ConvNet architecture, that learns to harness the power of shape representation for matching disparate image pairs, is proposed. Disparate image pairs are deemed those that exhibit strong affine variations in scale, viewpoint and projection parameters accompanied by the presence of partial or complete occlusion of objects and extreme variations in ambient illumination. Under these challenging conditions, neither local nor global feature-based image matching methods, when used in isolation, have been observed to be effective. The proposed correspondence determination scheme for matching disparate images exploits high-level shape cues that are derived from low-level local feature descriptors, thus combining the best of both worlds. A graph-based representation for the disparate image pair is generated by constructing an affnity matrix that embeds the distances between feature points in two images, thus modeling the correspondence determination problem as one of graph matching. The eigen-spectrum of the affnity matrix, i.e., the learned global shape representation, is then used to further regress the transformation or homography that defnes the correspondence between the source image and target image. The proposed scheme is shown to yield state-of-the-art results for both, coarse-level shape matching as well as fine point-wise correspondence determination.
Multi-bit data de-duplication-based cloud storage channel covert

The purpose of this paper is to improve the efficiency and security of the cloud storage user data, a method of multi-bit data de-duplication-based cloud storage channel covert is proposed. Firstly, a covert channel design framework for cross-users including cloud storage providers, victims and attackers is put forward. By combining multiple bits of message, it can be expressed in file form., and the message selection and upload mechanism is adopted, which eliminates the necessity of "0" file uploading so as to greatly reduce the amount of data uploaded to the server.; secondly, in order to achieve the error-free decoding of multi-bit covert channel, a new synchronization technique is proposed, which uses files with different time stamps and sorts them to improve the order of data transmission; finally, the validity of the algorithm is verified by testing the performance of the proposed convert channel algorithm in the cloud storage service SugarSync and BaiduYun.
A spy search mechanism for memetic algorithm in dynamic environments

Searching within the sample space for optimal solutions is an important part in solving optimization problems. The motivation of this work is that today’s problem environments have increasingly become dynamic with non-stationary optima and in order to improve optima search, memetic algorithm has become a preferred search method because it combines global and local search methods to obtain good solutions. The challenge is that existing search methods perform the search during the iterations without being guided by solid information about the nature of the search environment which affects the quality of a search outcome. In this paper, a spy search mechanism is proposed for memetic algorithm in dynamic environments. The method uses a spy individual to scope out the search environment and collect information for guiding the search. The method combines hyper-mutation, random immigrants, hill climbing local search, crowding and fitness, and steepest mutation with greedy crossover hill climbing to enhance the efficiency of the search. The proposed method is tested on dynamic problems and comparisons with other methods indicate a better performance by the proposed method.
Linked Data

The data on the web is heterogeneous and distributed, which makes its integration a sine qua non-condition for its effective exploitation within the context of the semantic web or the so-called web of data. A promising solution for web data integration is the linked data initiative, which is based on four principles that aim to standardize the publication of structured data on the web. The objective of this chapter is to provide an overview of the essential aspects of this fairly recent and exciting field, including the model of linked data: resource description framework (RDF), its query language: simple protocol, and the RDF query language (SPARQL), the available means of publication and consumption of linked data, and the existing applications and the issues not yet addressed in research.
Evolution of trading strategies with flexible structures: A configuration comparison

Evolutionary Computation is often used in the domain of automated discovery of trading rules. Within this area, both Genetic Programming and Grammatical Evolution offer solutions with similar structures that have two key advantages in common: they are both interpretable and flexible in terms of their structure. The core algorithms can be extended to use automatically defined functions or mechanisms aimed to promote parsimony. The number of references on this topic is ample, but most of the studies focus on a specific setup. This means that it is not clear which is the best alternative. This work intends to fill that gap in the literature presenting a comprehensive set of experiments using both techniques with similar variations, and measuring their sensitivity to an increase in population size and composition of the terminal set. The experimental work, based on three S&P 500 data sets, suggest that Grammatical Evolution generates strategies that are more profitable, more robust and simpler, especially when a parsimony control technique was applied. As for the use of automatically defined function, it improved the performance in some experiments, but the results were inconclusive.
Energy efficiency optimization in big data processing platform by improving resources utilization

Big data creates tremendous value for humanity, but also places a heavy burden on the environment. Energy consumption of computing platforms, especially big data processing platforms, cannot be ignored, and optimization of energy usage is imperative. To the out-of-core data processing, this paper proposes that the energy efficiency of a big data processing platform is closely related to the utilization of its computing resources; and an efficient resource allocation strategy for data processing tasks improves the platform’s resources utilization. To improve the resources utilization, different resources are allocated according to a task-related Best Resource Ratio (BRR for short), such as “cpu, disk, network in the ratio of 1:2:4″, rather than the resource’s quantity, such as “cpu = 1 GHz, network = 20MB/s”. We deduce the BRR of data processing tasks, and design a resource ratio based approach (R2), which includes a task scheduling algorithm and resource allocation algorithm, for energy efficiency optimization. Experiments show that the R2 approach can improve energy efficiency by 10%, 10% and 6% compared to FIFO, Capacity and Fair schedulers respectively, by maximizing resource utilization of a 12-node MapReduce platform.
Development of Standard Format for Pavement Image Data

Because proprietary image data formats are used in the pavement industry, there is a critical need to develop a standard interchangeable format for pavement surface conditions and transverse profiles such that pavement image data from various highway agencies and technology suppliers can be shared across different analyses and software platforms. A comprehensive literature review and a survey of current practices found that two-dimensional (2D) image data compression is primarily based on JPEG and JPEG2000, whereas the three-dimensional (3D) laser scanning and imaging technology has rapidly gained popularity using various data formats and dynamic ranges. In this study, a standard data format for 2D and 3D pavement images is developed to meet different data requirements. The standard contains two parts. The first part is the core section including file header, 2D intensity data, and 3D range data, whose definitions shall not be changed as the data format standard evolves; the second part is primarily user-defined metadata, which can be extended and modified as needed to accommodate individual data collection practices and equipment. The expected benefits include facilitating workable protocols for condition surveys, improving implementation of new technologies, and accelerating the development of analysis tools for pavement condition evaluation.
Efficient Shape Coding for Object-Based 3D Video Applications

Shape is a popular way to define objects and shape coding is a key technique for object-based 3D video applications. In this paper, the issue of efficient shape coding for object-based 3D video applications is addressed, and a novel contour-based and chain-represented scheme is proposed. For a given 3D shape video, contour extraction and preprocessing are first implemented followed by chain-based representation. Then, to achieve high coding efficiency, a chain-based prediction and compensation technique is developed based on joint motion-compensated prediction and disparity-compensated prediction to effectively exploit the intra-view temporal correlation and the inter-view spatial correlation. Experiments are conducted, and the results demonstrate that the proposed scheme is more efficient than the existing methods, including state-of-the-art methods.
CNN Based Dense Underwater 3D Scene Reconstruction by Transfer Learning Using Bubble Database

Dense 3D shape acquisition of swimming human or live fish is an important research topic for sports, biological science and so on. For this purpose, active stereo sensor is usually used in the air, however it cannot be applied to the underwater environment because of refraction, strong light attenuation and severe interference of bubbles. Passive stereo is a simple solution for capturing dynamic scenes at underwater environment, however the shape with textureless surfaces or irregular reflections cannot be recovered. Recently, the stereo camera pair with a pattern projector for adding artificial textures on the objects is proposed. However, to use the system for underwater environment, several problems should be compensated, i.e., disturbance by fluctuation and bubbles. Simple solution is to use convolutional neural network for stereo to cancel the effects of bubbles and/or water fluctuation. Since it is not easy to train CNN with small size of database with large variation, we develop a special bubble generation device to efficiently create real bubble database of multiple size and density. In addition, we propose a transfer learning technique for multi-scale CNN to effectively remove bubbles and projected-patterns on the object. Further, we develop a real system and actually captured live swimming human, which has not been done before. Experiments are conducted to show the effectiveness of our method compared with the state of the art techniques.
Enhanced Graph Transforming V2 Algorithm for Non-Simple Graph in Big Data Pre-Processing

Incapability of relational database in handling large-scale data triggers the development of NoSQL database that becomes part of a big data ecosystem. NoSQL database has different characteristics compared to the relational database. However, NoSQL database requires data from the relational database as one of the structured data sources. Therefore, data pre-processing is required to ensure proper data migration from a relational database to NoSQL database. This data pre-processing is normally called data transformation. One of the simple and understandable transformation algorithms is graph transforming algorithm. However, the algorithm has a problem in solving a non-simple graph (multigraph). This research proposes an algorithm to overcome several multigraph problems. The experimental work confirms that the algorithm proposed in this research is able to transform data from a relational database to NoSQL schema that has a minimum number of redundant attributes while the data completeness is still maintained.
RESTalk Miner: Mining RESTful Conversations, Pattern Discovery and Matching

REST has become the architectural style of choice for APIs, where clients need to instantiate a potentially lengthy sequence of requests to the server in order to achieve their goal, effectively leading to a RESTful conversation between clients and servers. Mining the logs of such RESTful conversations can facilitate knowledge sharing among API designers regarding design best practices as well as API usage and optimization. In this demo paper, we present the RESTalk Miner, which takes logs from RESTful services as an input and uses RESTalk, a domain specific language, to visualize them. It provides interactive coloring to facilitate graph reading, as well as statistics to compare the relative frequency of conversations performed by different clients. Furthermore, it supports searching for predefined patterns as well as pattern discovery.
ELeCTRA: Induced Usage Limitations Calculation in RESTful APIs

As software architecture design is evolving to microservice paradigms, RESTful APIs become the building blocks of applications. In such a scenario, a growing market of APIs is proliferating and developers face the challenges to take advantage of this reality. For example, third-party APIs typically define different usage limitations depending on the purchased Service Level Agreement (SLA) and, consequently, performing a manual analysis of external APIs and their impact in a microservice architecture is a complex and tedious task. In this demonstration paper, we present ELeCTRA, a tool to automate the analysis of induced usage limitations in an API, derived from its usage of external APIs. This tool takes the structural, conversational and SLA specifications of the API, generates a visual dependency graph and translates the problem into a constraint satisfaction optimization problem (CSOP) to obtain the optimal usage limitations.
On Buildings that Compute. A Proposal

We present ideas aimed at bringing revolutionary changes on architectures and buildings of tomorrow by radically advancing the technology for the building material concrete and hence building components. We propose that by using nanotechnology we could embed computation and sensing directly into the material used for construction. Intelligent concrete blocks and panels advanced with stimuli-responsive smart paints are the core of the proposed architecture. In particular, the photo-responsive paint would sense the buildings internal and external environment while the nano-material-concrete composite material would be capable of sensing the building environment and implement massive-parallel information processing resulting in distributed decision making. A calibration of the proposed materials with in-materio suitable computational methods and corresponding building information modelling, computer-aided design and digital manufacturing tools could be achievedvia models and prototypes of information processing at nano-level. The emergent technology sees a building as high-level massive-parallel computer—assembled of computing concrete blocks. Based on the generic principles of neuromorphic computation and reservoir computing we envisage a single building or an urban quarter to turn into a large-scale sensing substrate. It could behave as a universal computer, collecting and processing environmental information in situ enabling appropriate data fusion. The broad range of spatio-temporal effects include infrastructural and human mobility, energy, bio-diversity, digital activity, urban management, art and socializing, robustness with regard to damage and noise or real-time monitoring of environmental changes. The proposed intelligent architectures will increase sustainability and viability in digitised urban environments by decreasing information transfer bandwidth by e.g, utilising 5G networks. The emergence of socio-cultural effect will create a cybernetic relationship with our dwellings and cities.
The monzonitic to dioritic pluton of Cerro Prieto, Durango, Mexico: A Laramide laccolith inferred by nonlinear 3D inversion of aeromagnetic data

The Cerro Prieto pluton is located in the San Juan de Guadalupe municipality, in the eastern limits of the Durango State, in northern Mexico. The monzonite–diorite pluton intruded the Upper Cretaceous Caracol Formation, causing metamorphism of shale and sandstone, producing a hornfels aureole surrounding the pluton body, which hosts metallic mineral deposits (Au, Ag, Pb, Cu, Zn). We infer that the Cerro Prieto pluton causes the intense observed magnetic anomaly with normal dipolar character, with peak-to-peak-amplitude of about 1000 nT. The 3D nonlinear inverse modeling of this magnetic anomaly revealed a large, normally magnetized source body, with a volume of 625 km3, which fits the data with a residual variance less than 1 nT. The geometry of the model is consistent with a two-stage genesis in which the magma was first emplaced and stopped on a horizontal surface, currently at about 2000 m below the surface, and later thickening by lifting the roof and lowering the floor of the magma chamber. The principal dimensions of the pluton correspond with a triaxial ellipsoid elongated along N25°E, with axis lengths: L1 = 20, L2 = 12.5 and L3 = 4 km, and this ellipsoid has a volume reduced to 523.6 km3, which satisfies the power law L3 = 0.88 [(L1 + L2)/2] 0.55, allowing the classification of the Cerro Prieto pluton as a laccolith. Considering the 1D diffusion equation, with a thermal diffusion constant of 0.6 mm2/s, the time for lowering the temperature of the magma at the time of the intrusion, down to quartz α-β transition temperature, lasted about 500,000 years, suggesting that the Cerro Prieto laccolith was emplaced at the end of Cretaceous or at the beginning of the Paleocene, then cooled below its Curie temperature, and was fully magnetized during the geomagnetic polarity sub Chron C29n or C28n time, between 66 and 63.5 Ma.
Affordances in UX Design

In this chapter we discuss affordances, an important concept in UX design. An affordance is a characteristic of an artifact that helps you do something. In UX designs, cognitive affordances help users with their cognitive actions. Physical affordances help users with their physical actions. Sensory affordances help users with their sensory actions (e.g., seeing, hearing, touching). Functional affordances help users do work and get things done. Emotional affordances are design features that provide users with a positive emotional impact. Individual interpretations of cognitive affordances often depend on shared conventions. False affordances are design “features” that look like affordances but which actually misguide or misinform, working against the user. User-created affordances are “stick-ons” that serve as wake-up calls to designers, telling them what users think should have been in the design. When used in UX design, the important thing about affordances is how the different types are used together in the design process. We provide a UX design check-list of affordances to help in this regard.
Cross-modal motion regeneration using Multimodal Deep Belief Network

In this paper, we propose a Multimodal Deep Belief Network (MDBN) for learning a generative model of 2D and 3D skeletal data. The MDBM learns the cross-modal relationship between these data in the form of a joint probability distribution over the space of multimodal inputs. It can regenerate any missing modality by sampling from the conditional distribution over the given data modality. The skeletal sequences are converted into the motion images which help us utilize the impressive power of the generative deep networks in the image processing. Furthermore, we use the variation of information (VI) as the training criterion, instead of the conventional maximum likelihood. It is proven that VI is efficient in cross-modal learning where some data modalities are missing. Our experimental results have shown that the model has an outstanding performance on the over-complete MHAD and CMU Mocap datasets in data-driven motion regeneration on a full-body 2D and 3D skeleton structures.