78,869 research outputs found
Strategy Selection for Product Service Systems Using Case-based Reasoning
A product service system integrates products and services in order to lower environmental impact. It can achieve good eco-efficiency and has received increase in the last decade. This study focuses on strategy selection for product service system design. Case-based reasoning is utilized to provide suggestions for finding an appropriate strategy. To build a case database, successful PSS cases from the literature and websites were collected and formulated. Twelve indices under three categories were analyzed and selected to describe cases. A lot of successful PSS cases and their information were collected. Forty seven cases were used in this study because of the completeness of information. The analytic hierarchic process is used to find the relative weights of the factors that relate to the selection of customers. These weights are used in calculating the similarity in the case-based reasoning process. The successful strategy of the most similar case is extracted and recommended for PSS strategy determination. More than 90% of tested cases obtained an appropriate strategy from the most similar case. Finally, two new products are introduced to find the best strategy for product service system design and development using the proposed case-based reasoning system
Split Chords: Addressing the Federal Circuit Split in Music Sampling Copyright Infringement Cases
This Note offers a comprehensive analysis of the current circuit split regarding how the de minimis doctrine applies to music sampling in copyright infringement cases. Since the Sixth Circuit\u27s 2005 landmark decision in Bridgeport Music Inc. v. Dimension Films, critics, scholars and even judges have dissected the opinion and its bright line rule of “get a license or do not sample.” In May 2016, the Ninth Circuit issued its opinion in VMG Salsoul v. Ciccione. The Ninth Circuit explicitly declined to follow Bridgeport, holding that analyzing a music sampling copyright infringement case requires a substantial similarity analysis, including applying a de minimis analysis.
The Ninth Circuit’s decision created a circuit split and an unsettled area of intellectual property law. This Note seeks to promote critical analysis of this contested area of law by exploring the underpinnings of the substantial similarity and de minimis doctrines, as well as the holdings of each case and their arguments. The Note offers three proposals regarding how courts should handle the circuit split, and in doing so creates a distinctive way of looking at the music sampling issue to help the federal judiciary frame the problem in a more expansive way
Raising argument strength using negative evidence: A constraint on models of induction
Both intuitively, and according to similarity-based theories of induction, relevant evidence raises argument strength when it is positive and lowers it when it is negative. In three experiments, we tested the hypothesis that argument strength can actually increase when negative evidence is introduced. Two kinds of argument were compared through forced choice or sequential evaluation: single positive arguments (e.g., “Shostakovich’s music causes alpha waves in the brain; therefore, Bach’s music causes alpha waves in the brain”) and double mixed arguments (e.g., “Shostakovich’s music causes alpha waves in the brain, X’s music DOES NOT; therefore, Bach’s music causes alpha waves in the brain”). Negative evidence in the second premise lowered credence when it applied to an item X from the same subcategory (e.g., Haydn) and raised it when it applied to a different subcategory (e.g., AC/DC). The results constitute a new constraint on models of induction
Deductive and Analogical Reasoning on a Semantically Embedded Knowledge Graph
Representing knowledge as high-dimensional vectors in a continuous semantic
vector space can help overcome the brittleness and incompleteness of
traditional knowledge bases. We present a method for performing deductive
reasoning directly in such a vector space, combining analogy, association, and
deduction in a straightforward way at each step in a chain of reasoning,
drawing on knowledge from diverse sources and ontologies.Comment: AGI 201
Compression-based Dependencies Among Rhythmic Motifs in a Score
Music similarity has been widely studied through melodic and harmonic matching, clustering, and using various metrics for measuring distance. Such analyses offer the musicologist a view of the ‘sameness’ of parts of a score. However, similarity alone does not necessarily allow exploitation of that sameness in reasoning about the music. In this paper, we present work in progress to investigate rhythm similarity at various scales, beginning at the smallest (single measures or groups of measures). We use normalised compression distance and variations thereof to derive similarity-based dependencies between parts of the music. Establishing such dependencies may allow software engineering dependence analysis techniques to be applied to music to, e.g. remove from focus aspects not relevant to a particular enquiry (‘slicing’), determine the sensitivity of later parts of the music on former parts (‘impact analysis’), and to find motivic processes and developments within the musical form. The analysis will thus draw on software engineering techniques, information theory, and data compression. Our results thus far show that text-based compressors introduce significant non-linear artefacts at small scales making similarity identification based on compressed lengths difficult. Future work will involve progressively larger scale music to determine the sensitivity of the results to the size of music being analysed in order to guide musicologists wanting to adopt similar approaches. We expect to find that at larger scales, the artefacts in text compression become less significant and identifying the threshold at which this happens is thus important. We discuss tree compression as having the potential to capture musically-important relationships lost by text compression and believe that this approach would be more successful at small scales
Explainable Reasoning over Knowledge Graphs for Recommendation
Incorporating knowledge graph into recommender systems has attracted
increasing attention in recent years. By exploring the interlinks within a
knowledge graph, the connectivity between users and items can be discovered as
paths, which provide rich and complementary information to user-item
interactions. Such connectivity not only reveals the semantics of entities and
relations, but also helps to comprehend a user's interest. However, existing
efforts have not fully explored this connectivity to infer user preferences,
especially in terms of modeling the sequential dependencies within and holistic
semantics of a path. In this paper, we contribute a new model named
Knowledge-aware Path Recurrent Network (KPRN) to exploit knowledge graph for
recommendation. KPRN can generate path representations by composing the
semantics of both entities and relations. By leveraging the sequential
dependencies within a path, we allow effective reasoning on paths to infer the
underlying rationale of a user-item interaction. Furthermore, we design a new
weighted pooling operation to discriminate the strengths of different paths in
connecting a user with an item, endowing our model with a certain level of
explainability. We conduct extensive experiments on two datasets about movie
and music, demonstrating significant improvements over state-of-the-art
solutions Collaborative Knowledge Base Embedding and Neural Factorization
Machine.Comment: 8 pages, 5 figures, AAAI-201
A qualitative approach to the identification, visualisation and interpretation of repetitive motion patterns in groups of moving point objects
Discovering repetitive patterns is important in a wide range of research areas, such as bioinformatics and human movement analysis. This study puts forward a new methodology to identify, visualise and interpret repetitive motion patterns in groups of Moving Point Objects (MPOs). The methodology consists of three steps. First, motion patterns are qualitatively described using the Qualitative Trajectory Calculus (QTC). Second, a similarity analysis is conducted to compare motion patterns and identify repetitive patterns. Third, repetitive motion patterns are represented and interpreted in a continuous triangular model. As an illustration of the usefulness of combining these hitherto separated methods, a specific movement case is examined: Samba dance, a rhythmical dance will? many repetitive movements. The results show that the presented methodology is able to successfully identify, visualize and interpret the contained repetitive motions
Synthesis of variable dancing styles based on a compact spatiotemporal representation of dance
Dance as a complex expressive form of motion is able to convey emotion, meaning and social idiosyncrasies that opens channels for non-verbal communication, and promotes rich cross-modal interactions with music and the environment. As such, realistic dancing characters may incorporate crossmodal information and variability of the dance forms through compact representations that may describe the movement structure in terms of its spatial and temporal organization. In this paper, we propose a novel method for synthesizing beatsynchronous dancing motions based on a compact topological model of dance styles, previously captured with a motion capture system. The model was based on the Topological Gesture Analysis (TGA) which conveys a discrete three-dimensional point-cloud representation of the dance, by describing the spatiotemporal variability of its gestural trajectories into uniform spherical distributions, according to classes of the musical meter. The methodology for synthesizing the modeled dance traces back the topological representations, constrained with definable metrical and spatial parameters, into complete dance instances whose variability is controlled by stochastic processes that considers both TGA distributions and the kinematic constraints of the body morphology. In order to assess the relevance and flexibility of each parameter into feasibly reproducing the style of the captured dance, we correlated both captured and synthesized trajectories of samba dancing sequences in relation to the level of compression of the used model, and report on a subjective evaluation over a set of six tests. The achieved results validated our approach, suggesting that a periodic dancing style, and its musical synchrony, can be feasibly reproduced from a suitably parametrized discrete spatiotemporal representation of the gestural motion trajectories, with a notable degree of compression
Toward a Robust Diversity-Based Model to Detect Changes of Context
Being able to automatically and quickly understand the user context during a
session is a main issue for recommender systems. As a first step toward
achieving that goal, we propose a model that observes in real time the
diversity brought by each item relatively to a short sequence of consultations,
corresponding to the recent user history. Our model has a complexity in
constant time, and is generic since it can apply to any type of items within an
online service (e.g. profiles, products, music tracks) and any application
domain (e-commerce, social network, music streaming), as long as we have
partial item descriptions. The observation of the diversity level over time
allows us to detect implicit changes. In the long term, we plan to characterize
the context, i.e. to find common features among a contiguous sub-sequence of
items between two changes of context determined by our model. This will allow
us to make context-aware and privacy-preserving recommendations, to explain
them to users. As this is an ongoing research, the first step consists here in
studying the robustness of our model while detecting changes of context. In
order to do so, we use a music corpus of 100 users and more than 210,000
consultations (number of songs played in the global history). We validate the
relevancy of our detections by finding connections between changes of context
and events, such as ends of session. Of course, these events are a subset of
the possible changes of context, since there might be several contexts within a
session. We altered the quality of our corpus in several manners, so as to test
the performances of our model when confronted with sparsity and different types
of items. The results show that our model is robust and constitutes a promising
approach.Comment: 27th IEEE International Conference on Tools with Artificial
Intelligence (ICTAI 2015), Nov 2015, Vietri sul Mare, Ital
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