When standard RANSAC is not enough: cross-media visual matching with hypothesis relevancy

The same scene can be depicted by multiple visual media. For example, the same event can be captured by a comic image or a movie frame; the same object can be represented by a photograph or by a 3D computer graphics model. In order to extract the visual analogies that are at the heart of cross-media analysis, spatial matching is required. This matching is commonly achieved by extracting key points and scoring multiple, randomly generated mapping hypotheses. The more consensus a hypothesis can draw, the higher its score. In this paper, we go beyond the conventional set-size measure for the quality of a match and present a more general hypothesis score that attempts to reflect how likely is each hypothesized transformation to be the correct one for the matching task at hand. This is achieved by considering additional, contextual cues for the relevance of a hypothesized transformation. This context changes from one matching task to another and reflects different properties of the match, beyond the size of a consensus set. We demonstrate that by learning how to correctly score each hypothesis based on these features we are able to deal much more robustly with the challenges required to allow cross-media analysis, leading to correct matches where conventional methods fail.     

Collaboration within large groups in the classroom

The purpose of this paper is to show how a large group of students can work collaboratively in a synchronous way within the classroom using the cheapest possible technological support. Making use of the features of Single Display Groupware and of Multiple Mice we propose a computer-supported collaborative learning approach for big groups within the classroom. The approach uses a multiple classification matrix and our application was built for language-learning (in this case Spanish). The basic collaboration mechanism that the approach is based upon is “silent collaboration,” in which students—through suggestions and exchanges—must compare their ideas to those of their classmates. An exploratory experimental study was performed along with a quantitative and qualitative study that analyzed ease of use of the software, described how the conditions for collaborative learning were achieved, evaluated the achievements in learning under the defined language objectives, and analyzed the impact of silent and spoken collaboration. Our initial findings are that silent collaboration proved to be an effective mechanism to achieve learning in large groups in the classroom.     

Basing cryptographic protocols on tamper-evident seals

In this article, we attempt to formally study two very intuitive physical models: sealed envelopes and locked boxes, often used as illustrations for common cryptographic operations. We relax the security properties usually required from locked boxes [such as in bit-commitment (BC) protocols] and require only that a broken lock or torn envelope be identifiable to the original sender. Unlike the completely impregnable locked box, this functionality may be achievable in real life, where containers having this property are called “tamper-evident seals”. Another physical object with this property is the “scratch-off card”, often used in lottery tickets. We consider three variations of tamper-evident seals, and show that under some conditions they can be used to implement oblivious transfer, BC and coin flipping (CF). We also show a separation between the three models. One of our results is a strongly fair CF protocol with bias bounded by O(1/r) (where r is the number of rounds); this was a stepping stone towards achieving such a protocol in the standard model (in subsequent work).     

Using obstacle analysis to identify contingency requirements on an unpiloted aerial vehicle

This paper describes the use of Obstacle Analysis to identify anomaly handling requirements for a safety-critical, autonomous system. The software requirements for the system evolved during operations due to an on-going effort to increase the autonomous system’s robustness. The resulting increase in autonomy also increased system complexity. This investigation used Obstacle Analysis to identify and to reason incrementally about new requirements for handling failures and other anomalous events. Results reported in the paper show that Obstacle Analysis complemented standard safety-analysis techniques in identifying undesirable behaviors and ways to resolve them. The step-by-step use of Obstacle Analysis identified potential side effects and missing monitoring and control requirements. Adding an Availability Indicator and feature-interaction patterns proved useful for the analysis of obstacle resolutions. The paper discusses the consequences of these results in terms of the adoption of Obstacle Analysis to analyze anomaly handling requirements in evolving systems.     

Chosen ciphertext attacks on lattice-based public key encryption and modern (non-quantum) cryptography in a quantum environment

Modern cryptography is based on various building blocks such as one way functions with or without trapdoors, pseudo-random functions, one way permutations with or without trapdoors, etc. In a quantum world some of the main candidates for these building blocks are broken. For instance, the security of the most popular public-key cryptosystem—RSA—is related to the difficulty of factoring large numbers, and is broken (in principle) by a quantum computer. We investigate some of the remaining candidates, and discuss the resulting “Post-Quantum Cryptography” (namely, the resulting “modern cryptography in a quantum environment”).     

Automatic inference of complex affective states

Affective states and their non-verbal expressions are an important aspect of human reasoning, communication and social life. Automated recognition of affective states can be integrated into a wide variety of applications for various fields. Therefore, it is of interest to design systems that can infer the affective states of speakers from the non-verbal expressions in speech, occurring in real scenarios. This paper presents such a system and the framework for its design and validation. The framework defines a representation method that comprises a set of affective-state groups or archetypes that often appear in everyday life. The inference system is designed to infer combinations of affective states that can occur simultaneously and whose level of expression can change over time. The framework considers also the validation and generalisation of the system. The system was built of 36 independent pair-wise comparison machines, with average accuracy (tenfold cross-validation) of 75%. The accumulated inference system yielded total accuracy of 83% and recognised combinations for different nuances within the affective-state groups. In addition to the ability to recognise these affective-state groups, the inference system was applied to characterisation of a very large variety of affective state concepts (549 concepts) as combinations of the affective-state groups. The system was also applied to annotation of affective states that were naturally evoked during sustained human–computer interactions and multi-modal analysis of the interactions, to new speakers and to a different language, with no additional training. The system provides a powerful tool for recognition, characterisation, annotation (interpretation) and analysis of affective states. In addition, the results inferred from speech in both English and Hebrew, indicate that the vocal expressions of complex affective states such as thinking, certainty and interest transcend language boundaries.     

Semantic-oriented 3d shape retrieval using relevance feedback

Shape-based retrieval of 3D models has become an important challenge in computer graphics. Object similarity, however, is a subjective matter, dependent on the human viewer, since objects have semantics and are not mere geometric entities. Relevance feedback aims at addressing the subjectivity of similarity. This paper presents a novel relevance feedback algorithm that is based on supervised as well as unsupervised feature extraction techniques. It also proposes a novel signature for 3D models, the sphere projection. A Web search engine that realizes the signature and the relevance feedback algorithm is presented. We show that the proposed approach produces good results and outperforms previous techniques.     

Mesh segmentation using feature point and core extraction

Mesh segmentation has become a necessary ingredient in many applications in computer graphics. This paper proposes a novel hierarchical mesh segmentation algorithm, which is based on new methods for prominent feature point and core extraction. The algorithm has several benefits. First, it is invariant both to the pose of the model and to different proportions between the model’s components. Second, it produces correct hierarchical segmentations of meshes, both in the coarse levels of the hierarchy and in the fine levels, where tiny segments are extracted. Finally, the boundaries between the segments go along the natural seams of the models.     

Enantiomeric Control of Intrinsically Chiral Nanocrystals

The chiral aspect of inorganic crystals that crystallize in chiral space groups has been largely ignored until recently, partly due to difficulties in characterizing the chiroptical properties of bulk crystals, and also due to the difficulty in separating (sub)micrometer-scale chiral crystal enantiomers. In recent years, the colloidal synthesis of intrinsically chiral nanocrystals (NCs) of several chiral inorganic compounds with significant enantiomeric excess has been demonstrated. This is achieved through the use of chiral molecular ligands, which bind to the atomic/ionic components of the crystals, preferentially forming one crystal enantiomorph. Here, recent progress on several aspects of these NCs is described, including the connection between ligand structure and its ability to direct NC handedness, chiral amplification in the synthesis leading to enantiopure NC samples, spontaneous symmetry breaking, the formation of NCs with chiral shapes, the connection between lattice and shape chirality and mixed contributions of atomic-scale and shape chirality to the chiroptical properties.     

Control and component selection for incremental motion systems

The main problem in the design of an incremental motion system is the heating of the motor which results from the start/stop type of operation. The question answered in this paper is how to select the components for this system and how to use them in order to minimize the operating temperature of the motor. In the analysis of the control selection, the optimal control was determined which minimizes the energy dissipation. It is shown how a triangular velocity profile, obtained using Bang-Bang control, corresponds to an efficiency of 0.75, whereas a slight change to trapezoidal velocity, increases the efficiency to 0.89 when compared with the optimal control. The optimal gear ratio will differ from the “inertial match” due to the presence of constant load torque. For a capstan drive, the inertia of the capstan must be much smaller than that of the motor, and not “inertially matched”.