A technique for assessment of torso kinesiology

A technique is described for monitoring and analysis of the angular displacement of the human torso. The technique is based on the use of miniature electromechanical inclinometers and video recording. The system was developed for continuous measurement of the angular displacement of spinal segments in the sagittal plane during all stages of lifting. Angular displacements of the cervical, thoracic and lumbar regions were obtained from a dynamic lifting case study using a digital processing system. The geometrical changes of the torso during five prescribed stages were measured. The system revealed good accuracy with high correlation coefficient scores for a wide range of lifting tasks in a comparative laboratory study. This paper describes the monitoring technique; the system's advantages, disadvantages and measuring accuracy are discussed.     

A New Approach to Two-View Motion Segmentation Using Global Dimension Minimization

We present a new approach to rigid-body motion segmentation from two views. We use a previously developed nonlinear embedding of two-view point correspondences into a 9-dimensional space and identify the different motions by segmenting lower-dimensional subspaces. In order to overcome nonuniform distributions along the subspaces, whose dimensions are unknown, we suggest the novel concept of global dimension and its minimization for clustering subspaces with some theoretical motivation. We propose a fast projected gradient algorithm for minimizing global dimension and thus segmenting motions from 2-views. We develop an outlier detection framework around the proposed method, and we present state-of-the-art results on outlier-free and outlier-corrupted two-view data for segmenting motion.     

Evaluation of Face Datasets as Tools for Assessing the Performance of Face Recognition Methods

Face datasets are considered a primary tool for evaluating the efficacy of face recognition methods. Here we show that in many of the commonly used face datasets, face images can be recognized accurately at a rate significantly higher than random even when no face, hair or clothes features appear in the image. The experiments were done by cutting a small background area from each face image, so that each face dataset provided a new image dataset which included only seemingly blank images. Then, an image classification method was used in order to check the classification accuracy. Experimental results show that the classification accuracy ranged between 13.5% (color FERET) to 99% (YaleB). These results indicate that the performance of face recognition methods measured using face image datasets may be biased. Compilable source code used for this experiment is freely available for download via the Internet.     

Learning dataset representation for automatic machine learning algorithm selection

The algorithm selection problem is defined as identifying the best-performing machine learning (ML) algorithm for a given combination of dataset, task, and evaluation measure. The human expertise required to evaluate the increasing number of ML algorithms available has resulted in the need to automate the algorithm selection task. Various approaches have emerged to handle the automatic algorithm selection challenge, including meta-learning. Meta-learning is a popular approach that leverages accumulated experience for future learning and typically involves dataset characterization. Existing meta-learning methods often represent a dataset using predefined features and thus cannot be generalized across different ML tasks, or alternatively, learn a dataset’s representation in a supervised manner and therefore are unable to deal with unsupervised tasks. In this study, we propose a novel learning-based task-agnostic method for producing dataset representations. Then, we introduce TRIO, a meta-learning approach, that utilizes the proposed dataset representations to accurately recommend top-performing algorithms for previously unseen datasets. TRIO first learns graphical representations for the datasets, using four tools to learn the latent interactions among dataset instances and then utilizes a graph convolutional neural network technique to extract embedding representations from the graphs obtained. We extensively evaluate the effectiveness of our approach on 337 datasets and 195 ML algorithms, demonstrating that TRIO significantly outperforms state-of-the-art methods for algorithm selection for both supervised (classification and regression) and unsupervised (clustering) tasks.

     

Wikiometrics: a Wikipedia based ranking system

We present a new concept—Wikiometrics—the derivation of metrics and indicators from Wikipedia. Wikipedia provides an accurate representation of the real world due to its size, structure, editing policy and popularity. We demonstrate an innovative “mining” methodology, where different elements of Wikipedia – content, structure, editorial actions and reader reviews – are used to rank items in a manner which is by no means inferior to rankings produced by experts or other methods. We test our proposed method by applying it to two real-world ranking problems: top world universities and academic journals. Our proposed ranking methods were compared to leading and widely accepted benchmarks, and were found to be extremely correlative but with the advantage of the data being publically available.     

Effective Oral Administration of an Antitumorigenic Nanoformulated Titanium Complex

Abstract : Orally administered anticancer drugs facilitate treatment, but the acidic conditions in the stomach often challenge their availability. PhenolaTi is a Ti^IV-based nontoxic anticancer drug with marked in-vivo efficacy. We report that nanoformulation protects phenolaTi from decomposition in stomach-like conditions. This is evidenced by similar NMR characteristics and similar in-vitro cytotoxicity toward murine (CT-26) and human (HT-29) colon cancer cells before and after incubation of nanoformulated phenolaTi (phenolaTi-F) at pH 2, unlike results with the unformulated form of the complex. Furthermore, administration of phenolaTi-F in animal drinking water revealed a notable inhibition of tumor growth in Balb/c and immune-deficient (Nude) mice inoculated with CT-26 and HT-29 cells, respectively. In-vivo efficacy was at least similar to that of the corresponding intraperitoneal treatment with phenolaTi-F and the clinically employed oral drug, capecitabine. No body weight loss or clinical signs of toxicity were evident in the phenolaTi-F-treated animals. These findings demonstrate a new convenient mode of cancer treatment through oral administration by safe titanium-based drugs.     

Collective-agreement-based pruning of ensembles

Ensemble methods combine several individual pattern classifiers in order to achieve better classification. The challenge is to choose the minimal number of classifiers that achieve the best performance. An ensemble that contains too many members might incur large storage requirements and even reduce the classification performance. The goal of ensemble pruning is to identify a subset of ensemble members that performs at least as good as the original ensemble and discard any other members.In this paper, we introduce the Collective-Agreement-based Pruning (CAP) method. Rather than ranking individual members, CAP ranks subsets by considering the individual predictive ability of each member along with the degree of redundancy among them. Subsets whose members highly agree with the class while having low inter-agreement are preferred.     

Utility Dependence in Correct and Fair Rational Secret?Sharing

The problem of carrying out cryptographic computations when the participating parties are rational in a game-theoretic sense has recently gained much attention. One problem that has been studied considerably is that of rational secret sharing. In this setting, the aim is to construct a mechanism (protocol) so that parties behaving rationally have incentive to cooperate and provide their shares in the reconstruction phase, even if each party prefers to be the only one to learn the secret.     

On Projection Matrices $$\mathcal{P}^k \to \mathcal{P}^2 ,k = 3,...,6, $$ and their Applications in Computer Vision

Projection matrices from projective spaces have long been used in multiple-view geometry to model the perspective projection created by the pin-hole camera. In this work we introduce higher-dimensional mappings for the representation of various applications in which the world we view is no longer rigid. We also describe the multi-view constraints from these new projection matrices (where k > 3) and methods for extracting the (non-rigid) structure and motion for each application.