EEF: Exponentially Embedded Families with Class-Specific Features for Classification

In this letter, we present a novel exponentially embedded families (EEF) based classification method, in which the probability density function (PDF) on raw data is estimated from the PDF on features. With the PDF construction, we show that class-specific features can be used in the proposed classification method, instead of a common feature subset for all classes as used in conventional approaches. We apply the proposed EEF classifier for text categorization as a case study and derive an optimal Bayesian classification rule with class-specific feature selection based on the Information Gain (IG) score. The promising performance on real-life data sets demonstrates the effectiveness of the proposed approach and indicates its wide potential applications.Comment: 9 pages, 3 figures, to be published in IEEE Signal Processing Letter. IEEE Signal Processing Letter, 201

Model Order Selection Rules For Covariance Structure Classification

The adaptive classification of the interference covariance matrix structure for radar signal processing applications is addressed in this paper. This represents a key issue because many detection architectures are synthesized assuming a specific covariance structure which may not necessarily coincide with the actual one due to the joint action of the system and environment uncertainties. The considered classification problem is cast in terms of a multiple hypotheses test with some nested alternatives and the theory of Model Order Selection (MOS) is exploited to devise suitable decision rules. Several MOS techniques, such as the Akaike, Takeuchi, and Bayesian information criteria are adopted and the corresponding merits and drawbacks are discussed. At the analysis stage, illustrating examples for the probability of correct model selection are presented showing the effectiveness of the proposed rules

AoA-aware Probabilistic Indoor Location Fingerprinting using Channel State Information

With expeditious development of wireless communications, location fingerprinting (LF) has nurtured considerable indoor location based services (ILBSs) in the field of Internet of Things (IoT). For most pattern-matching based LF solutions, previous works either appeal to the simple received signal strength (RSS), which suffers from dramatic performance degradation due to sophisticated environmental dynamics, or rely on the fine-grained physical layer channel state information (CSI), whose intricate structure leads to an increased computational complexity. Meanwhile, the harsh indoor environment can also breed similar radio signatures among certain predefined reference points (RPs), which may be randomly distributed in the area of interest, thus mightily tampering the location mapping accuracy. To work out these dilemmas, during the offline site survey, we first adopt autoregressive (AR) modeling entropy of CSI amplitude as location fingerprint, which shares the structural simplicity of RSS while reserving the most location-specific statistical channel information. Moreover, an additional angle of arrival (AoA) fingerprint can be accurately retrieved from CSI phase through an enhanced subspace based algorithm, which serves to further eliminate the error-prone RP candidates. In the online phase, by exploiting both CSI amplitude and phase information, a novel bivariate kernel regression scheme is proposed to precisely infer the target's location. Results from extensive indoor experiments validate the superior localization performance of our proposed system over previous approaches

Subspace averaging and order determination for source enumeration

In this paper, we address the problem of subspace averaging, with special emphasis placed on the question of estimating the dimension of the average. The results suggest that the enumeration of sources in a multi-sensor array, which is a problem of estimating the dimension of the array manifold, and as a consequence the number of radiating sources, may be cast as a problem of averaging subspaces. This point of view stands in contrast to conventional approaches, which cast the problem as one of identifiying covariance models in a factor model. We present a robust formulation of the proposed order fitting rule based on majorization-minimization algorithms. A key element of the proposed method is to construct a bootstrap procedure, based on a newly proposed discrete distribution on the manifold of projection matrices, for stochastically generating subspaces from a function of experimentally determined eigenvalues. In this way, the proposed subspace averaging (SA) technique determines the order based on the eigenvalues of an average projection matrix, rather than on the likelihood of a covariance model, penalized by functions of the model order. By means of simulation examples, we show that the proposed SA criterion is especially effective in high-dimensional scenarios with low sample support.The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Yuejie Chi. The work of V. Garg and I. Santamaria was supported in part by the Ministerio de Economía y Competitividad (MINECO) of Spain, and in part by the AEI/FEDER funds of the E.U., under Grants TEC2016-75067-C4-4-R (CARMEN), TEC2015-69648-REDC, and BES-2017-080542. The work of D. Ramírez was supported in part by the Ministerio de Ciencia, Innovación y Universidades under Grant TEC2017-92552-EXP (aMBITION), in part by the Ministerio de Ciencia, Innovación y Universidades, jointly with the European Commission (ERDF), under Grants TEC2015-69868-C2-1-R (ADVENTURE) and TEC2017-86921-C2-2-R (CAIMAN), and in part by The Comunidad de Madrid under Grant Y2018/TCS-4705 (PRACTICOCM). The work of L. L. Scharf was supported in part by the U.S. NSF under Contract CISE-1712788

Sex in the city: the rise of soft-erotic film culture in Cinema Leopold, Ghent, 1945-1954

Since the 1990s, film studies saw a disciplinary shift from approaches favoring a textual and ideological analysis of films to a broader understanding of the socio-cultural history of cinema under the banner of new cinema history. This turn not only allowed for ‘niche’ research domains to flourish such as film economics or cinema memory research, or for new empirical and critical methodologies to be applied to film and cinema history. This change in researching and writing film/cinema history also shed light on previously marginalized, neglected or uncharted film cultures and histories, burgeoning scholarship in for instance (s)exploitation cinema. This contribution examines a peculiar part of post-war local film culture in the Belgian city of Ghent, more precisely the one around the city-center soft-erotic cinema Cinema Leopold (1945-54). The research is based on a programming and box-office database compiled from archival sources and contextualized by other data (internal and external correspondence, posters,…) coming from the business archive of Octave Bonnevalle, Cinema Leopold’s founding pater familias (material kept in the State Archives of Belgium; RAB/B70/1928-1977). The database now contains information on 625 film titles shown between 1945 and 1954, out of which 233 were unidentified (due to lack of information). Although the database is at times crippled by source inconsistencies, it is extremely rich in documenting the everyday practices of a cinema that gradually turned into a soft-erotic movie theater. The database allows for some remarkable findings concerning shifts in the origin of films, their production years, genres, censorship and popularity. The key finding is that Cinema Leopold started out after the Second World War with a child-friendly, mainstream Hollywood-oriented film program, as did most cinemas in Ghent, but its profile slowly tilted towards more mature audiences and provocative film genres. These included French ‘risqué’ feature films containing some forms of nudity like Perfectionist/Un Grand Patron (Ciampi, 1951) and documentaries on venereal diseases like the successful Austrian Creeping Poison/Schleichendes Gift (Wallbrück, 1946), but also auteur movies such as Bergman’s Port of Call/Hamnstad (1948) were shown. It is interesting how Leopold walked a fine line between innovative, bold European art-house cinema, soft-erotic ‘didactic’ movies and flat-out commercial soft-porn. By 1954, Leopold had gathered a loyal crowd, which kept the cinema alive until 1981 despite the several law suits and trials. This micro-history offers a remarkable example of the post-war flourishing of alternative, yet profit-driven cinema circuits, riddled with media controversies and censorship

Algorithmic aspects of resource allocation and multiwinner voting: theory and experiments

This thesis is concerned with investigating elements of computational social choice in the light of real-world applications. We contribute to a better understanding of the areas of fair allocation and multiwinner voting. For both areas, inspired by real-world scenarios, we propose several new notions and extensions of existing models. Then, we analyze the complexity of answering the computational questions raised by the introduced concepts. To this end, we look through the lens of parameterized complexity. We identify different parameters which describe natural features specific to the computational problems we investigate. Exploiting the parameters, we successfully develop efficient algorithms for spe- cific cases of the studied problems. We complement our analysis by showing which parameters presumably cannot be utilized for seeking efficient algorithms. Thereby, we provide comprehensive pictures of the computational complexity of the studied problems. Specifically, we concentrate on four topics that we present below, grouped by our two areas of interest. For all but one topic, we present experimental studies based on implementations of newly developed algorithms. We first focus on fair allocation of indivisible resources. In this setting, we consider a collection of indivisible resources and a group of agents. Each agent reports its utility evaluation of every resource and the task is to “fairly” allocate the resources such that each resource is allocated to at most one agent. We concentrate on the two following issues regarding this scenario. The social context in fair allocation of indivisible resources. In many fair allocation settings, it is unlikely that every agent knows all other agents. For example, consider a scenario where the agents represent employees of a large corporation. It is highly unlikely that every employee knows every other employee. Motivated by such settings, we come up with a new model of graph envy-freeness by adapting the classical envy-freeness notion to account for social relations of agents modeled as social networks. We show that if the given social network of agents is simple (for example, if it is a directed acyclic graph), then indeed we can sometimes find fair allocations efficiently. However, we contrast tractability results with showing NP-hardness for several cases, including those in which the given social network has a constant degree. Fair allocations among few agents with bounded rationality. Bounded rationality is the idea that humans, due to cognitive limitations, tend to simplify problems that they face. One of its emanations is that human agents usually tend to report simple utilities over the resources that they want to allocate; for example, agents may categorize the available resources only into two groups of desirable and undesirable ones. Applying techniques for solving integer linear programs, we show that exploiting bounded rationality leads to efficient algorithms for finding envy-free and Pareto-efficient allocations, assuming a small number of agents. Further, we demonstrate that our result actually forms a framework that can be applied to a number of different fairness concepts like envy-freeness up to one good or envy-freeness up to any good. This way, we obtain efficient algorithms for a number of fair allocation problems (assuming few agents with bounded rationality). We also empirically show that our technique is applicable in practice. Further, we study multiwinner voting, where we are given a collection of voters and their preferences over a set of candidates. The outcome of a multiwinner voting rule is a group (or a set of groups in case of ties) of candidates that reflect the voters’ preferences best according to some objective. In this context, we investigate the following themes. The robustness of election outcomes. We study how robust outcomes of multiwinner elections are against possible mistakes made by voters. Assuming that each voter casts a ballot in a form of a ranking of candidates, we represent a mistake by a swap of adjacent candidates in a ballot. We find that for rules such as SNTV, k-Approval, and k-Borda, it is computationally easy to find the minimum number of swaps resulting in a change of an outcome. This task is, however, NP-hard for STV and the Chamberlin-Courant rule. We conclude our study of robustness with experimentally studying the average number of random swaps leading to a change of an outcome for several rules. Strategic voting in multiwinner elections. We ask whether a given group of cooperating voters can manipulate an election outcome in a favorable way. We focus on the k-Approval voting rule and we show that the computational complexity of answering the posed question has a rich structure. We spot several cases for which our problem is polynomial-time solvable. However, we also identify NP-hard cases. For several of them, we show how to circumvent the hardness by fixed-parameter tractability. We also present experimental studies indicating that our algorithms are applicable in practice

Energy Efficient and Cooperative Solutions for Next-Generation Wireless Networks

Energy efficiency is increasingly important for next-generation wireless systems due to the limited battery resources of mobile clients. While fourth generation cellular standards emphasize low client battery consumption, existing techniques do not explicitly focus on reducing power that is consumed when a client is actively communicating with the network. Based on high data rate demands of modern multimedia applications, active mode power consumption is expected to become a critical consideration for the development and deployment of future wireless technologies. Another reason for focusing more attention on energy efficient studies is given by the relatively slow progress in battery technology and the growing quality of service requirements of multimedia applications. The disproportion between demanded and available battery capacity is becoming especially significant for small-scale mobile client devices, where wireless power consumption dominates within the total device power budget. To compensate for this growing gap, aggressive improvements in all aspects of wireless system design are necessary. Recent work in this area indicates that joint link adaptation and resource allocation techniques optimizing energy efficient metrics can provide a considerable gain in client power consumption. Consequently, it is crucial to adapt state-of-the-art energy efficient approaches for practical use, as well as to illustrate the pros and cons associated with applying power-bandwidth optimization to improve client energy efficiency and develop insights for future research in this area. This constitutes the first objective of the present research. Together with energy efficiency, next-generation cellular technologies are emphasizing stronger support for heterogeneous multimedia applications. Since the integration of diverse services within a single radio platform is expected to result in higher operator profits and, at the same time, reduce network management expenses, intensive research efforts have been invested into design principles of such networks. However, as wireless resources are limited and shared by clients, service integration may become challenging. A key element in such systems is the packet scheduler, which typically helps ensure that the individual quality of service requirements of wireless clients are satisfied. In contrastingly different distributed wireless environments, random multiple access protocols are beginning to provide mechanisms for statistical quality of service assurance. However, there is currently a lack of comprehensive analytical frameworks which allow reliable control of the quality of service parameters for both cellular and local area networks. Providing such frameworks is therefore the second objective of this thesis. Additionally, the study addresses the simultaneous operation of a cellular and a local area network in spectrally intense metropolitan deployments and solves some related problems. Further improving the performance of battery-driven mobile clients, cooperative communications are sought as a promising and practical concept. In particular, they are capable of mitigating the negative effects of fading in a wireless channel and are thus expected to enhance next-generation cellular networks in terms of client spectral and energy efficiencies. At the cell edges or in areas missing any supportive relaying infrastructure, client-based cooperative techniques are becoming even more important. As such, a mobile client with poor channel quality may take advantage of neighboring clients which would relay data on its behalf. The key idea behind the concept of client relay is to provide flexible and distributed control over cooperative communications by the wireless clients themselves. By contrast to fully centralized control, this is expected to minimize overhead protocol signaling and hence ensure simpler implementation. Compared to infrastructure relay, client relay will also be cheaper to deploy. Developing the novel concept of client relay, proposing simple and feasible cooperation protocols, and analyzing the basic trade-offs behind client relay functionality become the third objective of this research. Envisioning the evolution of cellular technologies beyond their fourth generation, it appears important to study a wireless network capable of supporting machine-to-machine applications. Recent standardization documents cover a plethora of machine-to-machine use cases, as they also outline the respective technical requirements and features according to the application or network environment. As follows from this activity, a smart grid is one of the primary machine-to-machine use cases that involves meters autonomously reporting usage and alarm information to the grid infrastructure to help reduce operational cost, as well as regulate a customer's utility usage. The preliminary analysis of the reference smart grid scenario indicates weak system architecture components. For instance, the large population of machine-to-machine devices may connect nearly simultaneously to the wireless infrastructure and, consequently, suffer from excessive network entry delays. Another concern is the performance of cell-edge machine-to-machine devices with weak wireless links. Therefore, mitigating the above architecture vulnerabilities and improving the performance of future smart grid deployments is the fourth objective of this thesis. Summarizing, this thesis is generally aimed at the improvement of energy efficient properties of mobile devices in next-generation wireless networks. The related research also embraces a novel cooperation technique where clients may assist each other to increase per-client and network-wide performance. Applying the proposed solutions, the operation time of mobile clients without recharging may be increased dramatically. Our approach incorporates both analytical and simulation components to evaluate complex interactions between the studied objectives. It brings important conclusions about energy efficient and cooperative client behaviors, which is crucial for further development of wireless communications technologies