Reviewerly/exHarmony huggingface dataset:
An artificial-vision- and statistical-learning-based method for studying the biodegradation of type I collagen scaffolds in bone regeneration systems

This work proposes a method based on image analysis and machine and statistical learning to model and estimate osteocyte growth (in type I collagen scaffolds for bone regeneration systems) and the collagen degradation degree due to cellular growth. To achieve these aims, the mass of collagen -subjected to the action of osteocyte growth and differentiation from stem cells- was measured on 3 days during each of 2 months, under conditions simulating a tissue in the human body. In addition, optical microscopy was applied to obtain information about cellular growth, cellular differentiation, and collagen degradation. Our first contribution consists of the application of a supervised classification random forest algorithm to image texture features (the structure tensor and entropy) for estimating the different regions of interest in an image obtained by optical microscopy: the extracellular matrix, collagen, and image background, and nuclei. Then, extracellular-matrix and collagen regions of interest were determined by the extraction of features related to the progression of the cellular growth and collagen degradation (e.g., mean area of objects and the mode of an intensity histogram). Finally, these critical features were statistically modeled depending on time via nonparametric and parametric linear and nonlinear models such as those based on logistic functions. Namely, the parametric logistic mixture models provided a way to identify and model the degradation due to biological activity by estimating the corresponding proportion of mass loss. The relation between osteocyte growth and differentiation from stem cells, on the one hand, and collagen degradation, on the other hand, was determined too and modeled through analysis of image objects' circularity and area, in addition to collagen mass loss. This set of imaging techniques, machine learning procedures, and statistical tools allowed us to characterize and parameterize type I collagen biodegradation when collagen acts as a scaffold in bone regeneration tasks. Namely, the parametric logistic mixture models provided a way to identify and model the degradation due to biological activity and thus to estimate the corresponding proportion of mass loss. Moreover, the proposed methodology can help to estimate the degradation degree of scaffolds from the information obtained by optical microscopy.
Exploring Tools and Strategies Used During Regular Expression Composition Tasks

Regular expressions are frequently found in programming projects. Studies have found that developers can accurately determine whether a string matches a regular expression. However, we still do not know the challenges associated with composing regular expressions. We conduct an exploratory case study to reveal the tools and strategies developers use during regular expression composition. In this study, 29 students are tasked with composing regular expressions that pass unit tests illustrating the intended behavior. The tasks are in Java and the Eclipse IDE was set up with JUnit tests. Participants had one hour to work and could use any Eclipse tools, web search, or web-based tools they desired. Screen-capture software recorded all interactions with browsers and the IDE. We analyzed the videos quantitatively by transcribing logs and extracting personas. Our results show that participants were 30% successful (28 of 94 attempts) at achieving a 100% pass rate on the unit tests. When participants used tools frequently, as in the case of the novice tester and the knowledgeable tester personas, or when they guess at a solution prior to searching, they are more likely to pass all the unit tests. We also found that compile errors often arise when participants searched for a result and copy/pasted the regular expression from another language into their Java files. These results point to future research into making regular expression composition easier for programmers, such as integrating visualization into the IDE to reduce context switching or providing language migration support when reusing regular expressions written in another language to reduce compile errors.
Activity recognition in manual manufacturing: Detecting screwing processes from sensor data

Knowledge about the duration of manufacturing processes and operation times is essential for production planning and control. But data acquisition is often difficult and especially challenging if production requires manual activities. This paper presents different data analysis and machine learning approaches to detect manual manufacturing processes from sensor data. As human activity recognition approaches are not necessarily applicable in industrial environments, all sensors are attached to tools, in this case screwdrivers. A dataset covering different tool movements, sensor types and mounting options is created and analyzed. The results are evaluated in terms of feasibility of the approach.
A typed, algebraic approach to parsing

In this paper, we recall the definition of the context-free expressions (or µ-regular expressions), an algebraic presentation of the context-free languages. Then, we define a core type system for the context-free expressions which gives a compositional criterion for identifying those context-free expressions which can be parsed unambiguously by predictive algorithms in the style of recursive descent or LL(1).
Error-tolerant geometric graph similarity and matching

Geometric graph matching is the process of evaluating the similarity between geometric graphs, where each vertex has an associated coordinate point in a plane. Exact matching computes a strict correspondence between two geometric graphs, whereas in error-tolerant matching an approximate similarity between two geometric graphs is computed. In this paper, we present an approach to error-tolerant graph matching using geometric graphs. We introduce the vertex distance (dissimilarity) and edge distance between two geometric graphs and use it to compute graph distance. Finally, we use graph distance to perform error-tolerant geometric graph matching.
Trace element substitution and grain-scale compositional heterogeneity in enargite

Enargite, Cu3AsS4, is a relatively common sulphide mineral and is considered diagnostic for deposits of intermediate- to high-sulphidation type. Analysis of enargite-bearing samples from deposits in the Zijinshan porphyry – high-sulphidation epithermal Cu-Au orefield, southeastern China, provides evidence for the diversity of trace elements that may be hosted within enargite and their range of concentrations. Enargite is shown to host Sb, Te, Sn, Zn and Ge at concentrations up to several thousand ppm. The mineral also incorporates measurable concentrations of Mo, Cd, Bi, Pb, Fe, Se, Ag, Au W, Ga and In. Element mapping (electron probe microanalysis and laser ablation inductively coupled plasma mass spectrometry) provides evidence for grain-scale heterogeneity in enargite in the form of oscillatory, grain-scale compositional zonation with respect to Sb, Sn, Te and several other trace elements. Element mapping also clearly shows an inverse correlation between the concentrations of As and Te, and between As and Sn. Incorporation of Sn and Te into the enargite structure is achieved by substitution of Sn4+ and Te4+ for As5+. Charge balance is maintained by incorporation of Fe2+, Zn2+ and other divalent cations (potentially including Cu2+) into the Cu+ site. The complex intra-grain zoning results from evolving fluids, multiple phases of growth – in turn leading to an overprinting of primary distribution patterns. Observed patterns are also influenced by equilibrium partitioning between enargite and co-existing minerals. Nevertheless, trace element signatures in enargite from different parts of the Zijinshan ore system show notable differences. Enargite from the high-sulphidation stage typically shows a marked enrichment in Te and Sn whereas enargite from intermediate-sulphidation stage is relatively depleted in Te and Sn, and comparatively enriched in Sb and Se. These differences represent a potential vector for exploration within porphyry – high-sulphidation epithermal systems. Furthermore, the presence of Te-rich enargite may be a prospective guide to high Au-grade mineralization. The notable concentrations of precious metals (Au, Ag) and critical elements (notably Te and Ge) within enargite make this mineral of particular interest from the perspective of potential recovery of these economically important elements. The observed grain-scale zoning and inherent variability within any given sample emphasize that spot analysis of trace elements alone without consideration of such heterogeneity may provide quantitative data of limited use and potentially, lead to misleading interpretations.
Anisotropic self-adaptive digital volume correlation with optimal cuboid subvolumes

Conventional digital volume correlation (DVC) approaches all use cubic subvolumes for full-field internal displacement mapping. In practical applications, however, real test samples would undergo heterogeneous deformations in three directions due to anisotropic material properties, complex microstructures and/or nonuniform boundary conditions. Based on the self-adaptive DVC (SA-DVC) algorithm we developed recently, here we propose a novel anisotropic self-adaptive DVC (ASA-DVC) approach using cuboid subvolumes and the first-order shape function to realize accuracy-enhanced 3D internal deformation measurement. We first briefly introduce the V-shaped theoretical error model as a function of subvolume size to facilitate optimal subvolume size identification in x, y and z directions. Then, the basic framework of the presented ASA-DVC method capable of automatically specifying optimal subvolume size in three directions at each calculation point is described. Finally, both numerically simulated and real-world experiments are employed to demonstrate the accuracy advantages of the ASA-DVC over routine DVC.
Adaptive l-norm constrained general nonlocal self-similarity regularizer based sparse representation for single image super-resolution

Sparse representation (SR) with the traditional nonlocal self-similarity (NLSS) regularizer has a brilliant future in handling single image super-resolution (SISR) inverse problem. However, such NLSS regularizers proposed so far always favor the fixed lq-norm constraint, making it difficult to cater the statistical diversity of the image content. As a result, the reconstruction capability of SR model is adversely influenced. To cope with the drawback, we first devise an adaptive lq-norm constrained general NLSS regularizer, which can integrate the advantages of the traditional NLSS prior and the row NLSS prior and adaptively assign different q values to handle the different image contents, and then incorporate it into SR model. Moreover, previous works only consider the additive white Gaussian noise case and neglect to research the impulse noise case in SR based SISR framework. For this purpose, our work will further consider these factors. Finally, both the iteratively reweighted least squares and the standard iterative shrinkage algorithms are adopted for solving our SR model. Experiments reveal that the reconstruction capability of our method is better than many outstanding SISR reconstruction methods.
Dual-Track Agile in Software Engineering Education

The fields of Software Engineering and Human-Computer Interaction have traditionally evolved in parallel, with little cross-pollination, both in industry and academia. However, effectively delivering software products offering superior user experiences requires a tight collaboration between professionals from both fields. In recent years, some approaches combining the two perspectives have been proposed in industry, including dual-track agile software development. Yet, very few courses cover those integrated approaches in academia, and it appears that no publication so far has reported the existence of a scholarly course covering dual-track agile. This paper introduces a course that addresses the divide between Software Engineering and Human-Computer Interaction through an integrated approach to requirements engineering and interaction design, in the context of dual-track agile. The course design combines traditional and flipped-classroom delivery, together with project-based learning. During the course project, students learn to design and implement software systems that address real problems and satisfy real stakeholders' needs by being useful, usable, and enjoyable to use. This paper documents the author's experience designing and teaching the course over the past four years. It aims to convince, inspire, and enable others to teach similar courses, bringing interaction design to the forefront of agile software development.
Multiple kernel dimensionality reduction based on collaborative representation for set oriented image classification

Given that collaborative representation (CR) methods have achieved great success in traditional single image based classification, recently, researchers have exploited the mechanism of collaborative representation to handle the case of image set based classification problem. However, without considering a proper criterion for feature extraction, this extension of collaborative representation mechanism suffers from the misleading coefficients of the incorrect classes on the uncontrolled datasets with small class separability. To address this limitation, inspired by large margin principle in discriminative analysis that aims to separately exploit the inter-class and intra-class variability, this paper proposes a novel theoretical framework of set oriented multiple kernel learning for dimensionality reduction based on collaborative representation classification. To achieve this framework, we integrate the learning of an optimal kernel from the multiple base kernels and a discriminative projection into a unified formulation. Moreover, robust feature information can be effectively extracted by minimizing the intra-class reconstruction residual and maximizing the inter-class reconstruction residual of the regularized hull modeled for the image sets. Since the criterion of feature extraction conforms to the mechanism of the collaborative representation classifier, the collaborative representation coefficients in our model can be much discriminative across classes. Notably, this research has important theoretical significance in improving the classification performance for collaborative representation classifier from the perspective of large margin discriminative learning. By employing the method of trace ratio maximization, we also develop a framework to solve the resulting nonconvex optimization problem efficiently. Extensive experiments on benchmark datasets well demonstrate the effectiveness of the proposed method.
Graph Topology Learning and Signal Recovery Via Bayesian Inference

The estimation of a meaningful affinity graph has become a crucial task for representation of data, since the underlying structure is not readily available in many applications. In this paper, a topology inference framework, called Bayesian Topology Learning, is proposed to estimate the underlying graph topology from a given set of noisy measurements of signals. It is assumed that the graph signals are generated from Gaussian Markov Random Field processes. First, using a factor analysis model, the noisy measured data is represented in a latent space and its posterior probability density function is found. Thereafter, by utilizing the minimum mean square error estimator and the Expectation Maximization (EM) procedure, a filter is proposed to recover the signal from noisy measurements and an optimization problem is formulated to estimate the underlying graph topology. The experimental results show that the proposed method has better performance when compared to the current state-of-the-art algorithms with different performance measures.
Multiple classifier-based fast coding unit partition for intra coding in future video coding

Future Video Coding (FVC) significantly improves the compression efficiency over the preceding High Efficiency Video Coding (HEVC) standard but at the cost of extremely huge computational complexity. The flexible quad-tree plus binary tree (QTBT) block partitioning structure in FVC is largely responsible for the high computational complexity. In order to address the issue of huge computational complexity of QTBT, we propose a multiple classifier-based fast QTBT partitioning algorithm for FVC intra coding. The proposed multiple classifier-based QTBT algorithm contains three stages including horizon binary-tree decision model (HBTDM), vertical binary-tree decision model (VBTDM), and quad-tree decision model (QTDM). Consequently, the computational complexity of FVC intra coding can be drastically reduced by replacing the brute-force search with HBTDM, VBTDM, and QTDM to decide the optimal QTBT partitioning. To achieve high prediction accuracy, the descriptive features of intra QTBT partitioning decision are extracted to train corresponding classifiers. An efficient optimal parameter selection method of training classifiers is introduced to balance the computational complexity and rate distortion (RD) performance. Experimental results show that in comparison with the original FVC reference software, the proposed overall algorithm can reduce 64.54% intra coding time with negligible degradation of RD performance. Meanwhile, the proposed algorithm outperforms four state-of-the-art algorithms in terms of computational complexity reduction.
Augmented Reality for Interactive Robot Control

Robots are widely used to support mission-critical, high-risk and complex operations. Human supervision and remote robot control are often required to operate robots in unpredictable and changing scenarios. Often, robots are controlled remotely by technicians via joystick interfaces which require training and experience to operate. To improve robot usage and practicality, we propose using augmented reality (AR) to create a more intuitive, less training-intensive means of controlling robots than traditional joystick control. AR is a creative platform for developing robot control systems, because AR combines the real world (the environment around the user, the physical robot, etc.) with the digital world (holograms, digital displays, etc.); it can even interpret physical gestures, such as pinching two fingers. In this research, a Microsoft Hololens headset is used to create an AR environment to control a Yaskawa Motoman SIA5D robot. The control process begins with the user placing an interactable holographic robot in 3D space. The user can then select between two control methods: manual control and automatic control. In manual control, the user can move the end effector of the holographic robot and the physical robot will respond immediately. In automatic control, the user can move the end effector of the holographic robot to a desired location, view a holographic preview of the motion, and select execute if the motion plan is satisfactory. In this preview mode, the user is able to preview both the motion of the robot and the torques experienced by the joints of the manipulator. This gives the user additional feedback on the planned motion. In this project we succeeded in creating an AR control system that makes controlling a robotic manipulator intuitive and effective.
Co-Design as a Means of Fostering Appropriation of Conservation Monitoring Technology by Indigenous Communities

A threat to indigenous communities living in north-west Namibia has been the growing human wildlife conflict. Established practices of patrolling and incident reporting have not yielded in mitigations. An evaluation of current conservation management tools, such as the Event Book, has shown that its users have not fully comprehended its use nor meaning nor appropriated the tool. We postulate that through co-design with the indigenous target communities we support the appropriation of the Event Book, creating a meaningful interaction.
Obscure

Despite extensive research on cryptography, secure and efficient query processing over outsourced data remains an open challenge. We develop communication-efficient and information-theoretically secure algorithms for privacy-preserving aggregation queries using multi-party computation (MPC). Specifically, query processing techniques over secret-shared data outsourced by single or multiple database owners are developed. These algorithms allow a user to execute queries on the secret-shared database and also prevent the network and the (adversarial) clouds to learn the user's queries, results, or the database. We further develop (non-mandatory) privacy-preserving result verification algorithms that detect malicious behaviors, and experimentally validate the efficiency of our approach over large datasets, the size of which prior approaches to secret-sharing or MPC systems have not scaled to.
SLING: using dynamic analysis to infer program invariants in separation logic

We introduce a new dynamic analysis technique to discover invariants in separation logic for heap-manipulating programs. First, we use a debugger to obtain rich program execution traces at locations of interest on sample inputs. These traces consist of heap and stack information of variables that point to dynamically allocated data structures. Next, we iteratively analyze separate memory regions related to each pointer variable and search for a formula over predefined heap predicates in separation logic to model these regions. Finally, we combine the computed formulae into an invariant that describes the shape of explored memory regions.
Improvement of Image Steganography Scheme Based on LSB Value with Two Control Random Parameters and Multi-level Encryption

Abstract Recently, steganography has played an important part in the field of communication, especially in image steganography. The major points of image steganography are the image quality (imperceptibility) of the stego image and the security of the system towards stopping the recoverability of the secret data. A new steganography scheme based on two control random parameters and multi-level encryption can address the security challenge while the P_Even/P_Odd classification can ensure the imperceptibility of the stego image. The objectives of study to increase the security and PSNR of the image by using the Huffman coding technique to compress the secret data prior to embedding; this will also ensure an increase in the payload capacity. The proposed scheme takes effect after encrypting and compressing the secret data. It is deployed when matching the secret bits with the LSB during embedding to determine 0 (P_Even) and 1 (P_Odd) while classifying the secret message to track and map each bit in the stego image. The results showed the embedding of the secret message based on P_Even/P_Odd with two control random parameters and multi-level encryption to improve the steganography.