Reviewerly/exHarmony huggingface dataset:
LSB Based Image Steganography With The Aid of Secret Key and Enhance its Capacity via Reducing Bit String Length

Data hiding is an approach in which one can transmit the information to the exact user without notice to third person. Nowadays, the all information is available across the web has easily accessible, so it is necessary to improve the security mechanism. Steganography is the technology which uses images to conceal the information inside them. To unleash a secure transmission Steganography make latent the existence of information into images. In this paper image steganography by the use of secrete key is demonstrated. The information has been made latent in LSB of RGB true color Image. To avoid unintended access from illegal user the Secret information resides within LSB of image where a secret key protects the information. In LSB Technique, at a peculiar position of LSB of the image the Secret information is stacked. In this proposed technique, a message bits stream has been reduced from each character having seven bits in ASCII to each character having five bits stream. In conclusion of the results, the capacity of image is increases up to 30% and it can improves Peak Signal to Noise ratio up to 27% compared to the existing technique.
Beyond individuals

Much of the research in wearable technology focuses on the primary user's experiences and interactions. However, many wearables are inherently social - even public - by nature as they are visible to nearby others. Wearables carry meanings about the wearer (e.g., lifestyle, attitudes, interests, social status). Some wearables are even designed to enable interaction between collocated users or enhance group-experience. While the functions of the technology are often seen to justify its existence, reaching high acceptance of technology requires that also social and cultural aspects are considered. In this workshop, we look into the dynamic and communicative nature of wearable technology designed for both individuals and groups. We are particularly interested in social experiences emerging around personal wearables and the possibilities for technology enhancing group experiences. The goal of this workshop is to bring together a community of researchers, designers, and practitioners who have designed or are interested in designing wearable technology to discuss research agenda and challenges in designing wearable technology as social, communicative artefacts.
Detecting abnormal behavior in the transportation planning using long short term memories and a contextualized dynamic threshold

Unsupervised anomaly detection in time-series data is crucial for both machine learning research and industrial applications. Over the past few years, the operational efficiencies of logistics agencies have decreased because of a lack of understanding on how best to address potential client requests. However, current anomaly detection approaches have been inefficient in distinguishing normal and abnormal behaviors from high dimensional data. In this study, we aimed to assist decision makers and improve anomaly detection by proposing a Long Short Term Memory (LSTM) approach with dynamic threshold detection. In the proposed methodology, first, data were processed and inputted into an LSTM network to determine temporal dependency. Second, a contextualized dynamic threshold was determined to detect anomalies. To demonstrate the practicality of our model, real operational data were used for evaluation and our model was shown to more accurately detect anomalies, with values of 0.836 and 0.842 for precision and recall, respectively.
Basic Evaluation Scenarios for Incrementally Trained Classifiers

Evaluation of incremental classification algorithms is a complex task because there are many aspects to evaluate. Besides the aspects such as accuracy and generalization that are usually evaluated in the context of classification, we also need to assess how the algorithm handles two main challenges of the incremental learning: the concept drift and the catastrophic forgetting. However, only catastrophic forgetting is evaluated by the current methodology, where the classifier is evaluated in two scenarios for class addition and expansion. We generalize the methodology by proposing two new scenarios of incremental learning for class inclusion and separation that evaluate the handling of the concept drift. We demonstrate the proposed methodology on the evaluation of three different incremental classifiers, where we show that the proposed methodology provides a more complete and finer evaluation.
Assembling Convolution Neural Networks for Automatic Viewing Transformation

Images taken under different camera poses are rotated or distorted, which leads to poor perception experiences. This article proposes a new framework to automatically transform the images to the conformable view setting by assembling different convolution neural networks. Specifically, a referential three-dimensional ground plane is first derived from the color image and a novel projection mapping algorithm is developed to achieve automatic viewing transformation. Extensive experimental results demonstrate that the proposed method outperforms the state-of-the-art vanishing points based methods by a large margin in terms of accuracy and robustness.
Prototypes Within Minimum Enclosing Balls

We revisit the kernel minimum enclosing ball problem and show that it can be solved using simple recurrent neural networks. Once solved, the interior of a ball can be characterized in terms of a function of a set of support vectors and local minima of this function can be thought of as prototypes of the data at hand. For Gaussian kernels, these minima can be naturally found via a mean shift procedure and thus via another recurrent neurocomputing process. Practical results demonstrate that prototypes found this way are descriptive, meaningful, and interpretable.
Heuristics and Models for Evaluating the Usability of Security Measures

Security mechanisms are nowadays part of almost every software. At the same time, they are typically sociotechnical and require involvement of end users to be effective. The usability of security measures is thus an essential factor. Despite this importance, this aspect often does not receive the necessary attention, for example due to short resources like time, budget, or usability experts. In the worst-case, users reject or circumvent even strong security measures and technically secure systems become insecure. To tackle the problem of unusable security measures, we developed a heuristics-based usability evaluation and optimization approach for security measures. In order to make heuristics applicable also for non-usability experts, we enrich them with information from a joint model for usability and security. In particular, this approach allows developers and administrators to perform usability evaluations and thus enables an early tailoring to the user, complementary to expert or user reviews. In this paper, we present our approach, including an initial set of heuristics, a joint model for usability and security and a set of mapping rules that combine heuristics and model. We evaluated the applicability of our approach, which we present in this paper.
Unsupervised domain adaptation for robust sensory systems

Despite significant advances in the performance of sensory inference models, their poor robustness to changing environmental conditions and hardware remains a major hurdle for widespread adoption. In this paper, we introduce the concept of unsupervised domain adaptation which is a technique to adapt sensory inference models to new domains only using unlabeled data from the target domain. We present two case-studies to motivate the problem and highlight some of our recent work in this space. Finally, we discuss the core challenges in this space that can trigger further ubicomp research on this topic.
Performance Evaluation of Learners for Analyzing the Hotel Customer Sentiments Based on Text Reviews

The world is in the midst of a digital revolution, and thus, it is natural that businesses are leveraging technology to position themselves well in the digital market. Travel planning and hotel bookings have become significant commercial applications. In recent years, there has been a rapid growth in online review sites and discussion forums where the critical characteristics of a customer review are drawn from their overall opinion/sentiments. Customer reviews play a significant role in a hotel’s persona which directly affects its valuation. This research work is intended to address the problem of analyzing the inundation of opinions and reviews of hotel services publicly available over the Web. Availability of large datasets containing such texts has allowed us to automate the task of sentiment profiling and opinion mining. In this study, over 800 hotel reviews are collected from travel information and review aggregator site like Trip Advisor, and after pre-processing of collected raw text reviews, various features are extracted using unigram, bigram, and trigram methods. The labeled feature vectors are used to train binary classifiers. The results are compared and contrasted among ensemble classifiers, support vector machines, and linear models using performance measures such as accuracy, F-measure, precision, and recall.
Phase extraction from a single closed interferogram with quadratic phase based on fractional Fourier transform method

Phase extraction from a single closed interferogram with a quadratic phase plays an important role in optical interferometry. Based on the energy of the interference images of this type is concentrated in a very narrow range around a point in the fractional Fourier transform (FRFT) domain under matched angle, the FRFT technique is a useful parameter estimator for the fringe pattern, but it has not been used to retrieve the desired phase from the fringe pattern. Thus, the parameter estimation approach based on FRFT is extended to the extract phase. The phase extraction can be done without using a phase unwrapping algorithm. Moreover, for ensuring both the estimation precision and speed, a coarse-to-fine searching strategy that includes a direct searching process implemented with a large step size and an iterative searching based on quasi-Newton method is presented in this paper to implement the FRFT method. The feasibility and applicability of the proposed approach are demonstrated using simulation and experimental results. The experimental results show that the proposed method is robust to noise and obstacles.
Incremental feature selection for efficient classification of dynamic graph bags

Summary Learning and analyzing graph data is one of the most fundamental research areas in machine learning and data mining. Among numerous graph‐based data structures, this paper focuses on a graph bag (simply, bag ), which corresponds to a training object containing one or more graphs, and a label is available only for a bag. This type of a bag can represent various real‐world objects such as drugs, web pages, XML documents, and images, among many others, and there have been many researches on models for learning this type of bag data. Within this research context, we define a novel problem of dynamic graph bag classification , and propose an algorithm to solve this problem. Dynamic bag classification aims to build a classification model for bags, which are presented in a streaming fashion, ie, frequent emerging of new bags or graphs over time. Given such changes made to the bag dataset, our proposed algorithm aims to update incrementally the top‐ m most discriminative features instead of searching for them from scratch. Incremental gSpan and incremental gScore are proposed as core parts of our algorithm to deal with a stream of bags efficiently. We evaluate our algorithm on two real‐world datasets in terms of both feature selection time and classification accuracy. The experimental results demonstrate that our algorithm derives an informative feature set much faster than the existing one originally designed for targeting static bag data, with little accuracy loss.
Drift-Correction Techniques for Scale-Adaptive VR Navigation

Scale adaptive techniques for VR navigation enable users to navigate spaces larger than the real space available, while allowing precise interaction when required. However, due to these techniques gradually scaling displacements as the user moves (changing user's speed), they introduce a Drift effect. That is, a user returning to the same point in VR will not return to the same point in the real space. This mismatch between the real/virtual spaces can grow over time, and turn the techniques unusable (i.e., users cannot reach their target locations). In this paper, we characterise and analyse the effects of Drift, highlighting its potential detrimental effects. We then propose two techniques to correct Drift effects and use a data driven approach (using navigation data from real users with a specific scale adaptive technique) to tune them, compare their performance and chose an optimum correction technique and configuration. Our user study, applying our technique in a different environment and with two different scale adaptive navigation techniques, shows that our correction technique can significantly reduce Drift effects and extend the life-span of the navigation techniques (i.e., time that they can be used before Drift draws targets unreachable), while not hindering users' experience.
Depth image upsampling based on guided filter with low gradient minimization

In this paper, we present a novel upsampling framework to enhance the spatial resolution of the depth image. In our framework, the upscaling of a low-resolution depth image is guided by a corresponding intensity images; we formulate it as a cost aggregation problem with the guided filter. However, the guided filter does not make full use of the information of the depth image. Since depth images have quite sparse gradients, it inspires us to regularize the gradients for improving depth upscaling results. Statistics show a special property of depth images, that is, there is a non-ignorable part of pixels whose horizontal or vertical derivatives are equal to $$\pm 1$$. Based on this special property, we propose a low gradient regularization method which reduces the penalty for horizontal or vertical derivative $$\pm 1$$, and well describes the statistics of the depth image gradients. Then, we present a solution to the low gradient minimization problem based on threshold shrinkage. Finally, the proposed low gradient regularization is integrated with the guided filter into the depth image upsampling method. Experimental results demonstrate the effectiveness of our proposed approach both qualitatively and quantitatively compared with the state-of-the-art methods.
Models of recognition operators defined in the space of large dimension attributes

Abstract The problems of constructing modified recognition operators are considered taking into account the high dimensionality of the feature space. As a basic algorithm, models of recognition algorithms based on radial functions are considered. The main idea of the model of modified recognition operators is the formation of independent subsets of interrelated objects and the allocation of basic objects for each subset of tightly coupled objects. A distinctive feature of the proposed model of algorithms is to determine the appropriate set of distance functions when building a model of discriminating operators. To test the performance of the proposed object model, experimental studies were carried out in solving a number of problems. The main advantage of the proposed recognition operators is the improvement of accuracy and a significant reduction in the volume of computational operations in recognition of unknown objects, which allows them to be used in the construction of recognition systems operating in real time.
Segmentation and recognition of continuous gesture based on chaotic theory

The performance of conventional continuous gesture recognition algorithms is mainly affected by factors such as incomplete keyframe detection, input of unconscious gestures, or variations in duration and movement range. The hypothesis is made that the irregularly sampled data of continuous gestures can be approximated with a particular type of dynamic system, and the characterization of these nonlinear dynamics will help with the trajectory partition and establishment of feature vectors. Finally, the proposed algorithm is evaluated with a database of alphabetic gestures, and the experiment results indicate that our framework has a high recognition rate of around 93.6% while maintaining its performance in the segmentation of continuous gestures.
Interactive evaluation of recommender systems with SNIPER

Recommender systems are typically evaluated using either offline methods, online methods, or through user studies. In this paper we take an episode mining approach to analysing recommender system data and we demonstrate how we can use SNIPER, a tool for interactive pattern mining, to analyse and understand the behaviour of recommender systems. We describe the required data format, and present a useful scenario of how a user can interact with the system to answer questions about the quality of recommendations.
An Improved Technique to Estimate the Total Generated Power by Neighboring Photovoltaic Systems Using Single-Point Irradiance Measurement and Correlational Models

A major operational challenge of photovoltaic (PV) systems is the variability in their output power, which is mainly caused by the movements of clouds. This variability can be reduced through building several smaller PV systems, distributed within an area, rather than a single, large PV system, often referred to as the geographic smoothing. This smoothing can be expressed mathematically by the variability reduction index (VRI), which denotes the reduced level of variations in the output power of a group of neighboring PV systems. VRI considers the size of the PV systems, the distances between them, as well as the mathematical correlation model that reflects the timescale, along with the speed, direction, and density of the clouds passing over the PV systems. The VRI and solar irradiance, measured at one location, can be used to estimate the power generated by a group of neighboring PV systems. This article initially compares and assesses various VRI models through their application on a group of 16 small-scale neighboring rooftop PV systems, distributed over a square kilometer area in Brisbane, Australia. Then, a technique is proposed to improve the estimation accuracy of the overall generated power by a group of neighboring small-scale rooftop PV systems. This new technique is validated by comparing the estimated power with the measured power, generated by the investigated PV systems.
Merging DBSCAN and Density Peak for Robust Clustering

In data clustering, density based algorithms are well known for the ability of detecting clusters of arbitrary shapes. DBSCAN is a widely used density based clustering approach, and the recently proposed density peak algorithm has shown significant potential in experiments. However, the DBSCAN algorithm may misclassify border data points of small density as noises and does not work well with large density variance across clusters, and the density peak algorithm has a large dependence on the detected cluster centers. To circumvent these problems, we make a study of these two algorithms and find that they have some complementary properties. We then propose to combine these two algorithms to overcome their problems. Specifically, we use the DP algorithm to detect cluster centers and then determine the parameters for DBSCAN adaptively. After DBSCAN clustering, we further use the DP algorithm to include border data points of small density into clusters. By combining the complementary properties of these two algorithms, we manage to relieve the problems of DBSCAN and avoid the drawbacks of the density peak algorithm in the meanwhile. Our algorithm is tested with synthetic and real datasets, and is demonstrated to perform better than DBSCAN and density peak algorithms, as well as some other clustering algorithms.
Image Identification of Animal Tumor Models for Human Breast Cancer Research

Murine (mouse) mammary tumor models as analogs of human breast cancer are invaluable in cancer research but distinguishing murine sarcoma tumors from carcinoma tumors has been problematic. We present an image classification method with complete separation in projected parameter space.
Leadership and Innovative Approaches in Higher Education

The place of leadership support for technological innovation in advancing quality management in higher education cannot be underrated in the fourth industrial revolution. This chapter examines the role of leadership in higher education and innovative teaching and learning methods for quality assurance in higher education system. The literature review approach and author observation were adopted to cross-examine the influence of leadership on innovative teaching/learning methods and quality assurance in higher education. This chapter shows that leadership support for innovative teaching and learning methods is a benchmark for quality assurance in higher education in recent times. Therefore, no meaningful change will happen in any higher institution without a strong leadership support for innovation and quality management. Policymakers in higher education should create a climate that promotes creativity and innovation by ensuring that transformational leaders are at the helm of affairs for quality management.
A survey of senior practitioners regarding most desirable qualifications for hiring and advancement within forensic science

In today's environment in the field of forensic science where continual advancements in technology and analytical approaches are the norm, the need for forensic practitioners with more specialized and subject-specific knowledge is critical. An updated survey targeting the preferred educational requirements by senior practitioners, crime laboratory directors and managers for entry level applicants was conducted. Results underscored a preference for specialized coursework within specific disciplines in preparing the next generation of forensic scientists while maintaining a strong foundation in the natural sciences at the undergraduate level. Practitioners, regardless of discipline, are seeking applicants with exposure to advanced curriculum content in addition to refined professional skills and critical thinking capabilities. The results of this survey reflect a transition in the needs of crime laboratory employers from a general, broad based criminalistics curriculum as described under current accreditation guidelines, to a focused subject matter rich curriculum with additional management and professional content.
Structured query construction via knowledge graph embedding

In order to facilitate the accesses of general users to knowledge graphs, an increasing effort is being exerted to construct graph-structured queries of given natural language questions. At the core of the construction is to deduce the structure of the target query and determine the vertices/edges which constitute the query. Existing query construction methods rely on question understanding and conventional graph-based algorithms which lead to inefficient and degraded performances facing complex natural language questions over knowledge graphs with large scales. In this paper, we focus on this problem and propose a novel framework standing on recent knowledge graph embedding techniques. Our framework first encodes the underlying knowledge graph into a low-dimensional embedding space by leveraging generalized local knowledge graphs. Given a natural language question, the learned embedding representations of the knowledge graph are utilized to compute the query structure and assemble vertices/edges into the target query. Extensive experiments were conducted on the benchmark dataset, and the results demonstrate that our framework outperforms state-of-the-art baseline models regarding effectiveness and efficiency.