Few-shot Bioacoustic Event Detection with Machine Learning Methods

Few-shot learning is a type of classification through which predictions are made based on a limited number of samples for each class. This type of classification is sometimes referred to as a meta-learning problem, in which the model learns how to learn to identify rare cases. We seek to extract information from five exemplar vocalisations of mammals or birds and detect and classify these sounds in field recordings [2]. This task was provided in the Detection and Classification of Acoustic Scenes and Events (DCASE) Challenge of 2021. Rather than utilize deep learning, as is most commonly done, we formulated a novel solution using only machine learning methods. Various models were tested, and it was found that logistic regression outperformed both linear regression and template matching. However, all of these methods over-predicted the number of events in the field recordings.Comment: 7 pages, 6 tables, 1 figur

pFedDef: Defending Grey-Box Attacks for Personalized Federated Learning

Personalized federated learning allows for clients in a distributed system to train a neural network tailored to their unique local data while leveraging information at other clients. However, clients' models are vulnerable to attacks during both the training and testing phases. In this paper we address the issue of adversarial clients crafting evasion attacks at test time to deceive other clients. For example, adversaries may aim to deceive spam filters and recommendation systems trained with personalized federated learning for monetary gain. The adversarial clients have varying degrees of personalization based on the method of distributed learning, leading to a "grey-box" situation. We are the first to characterize the transferability of such internal evasion attacks for different learning methods and analyze the trade-off between model accuracy and robustness depending on the degree of personalization and similarities in client data. We introduce a defense mechanism, pFedDef, that performs personalized federated adversarial training while respecting resource limitations at clients that inhibit adversarial training. Overall, pFedDef increases relative grey-box adversarial robustness by 62% compared to federated adversarial training and performs well even under limited system resources.Comment: 16 pages, 5 figures (11 images if counting sub-figures separately), longer version of paper submitted to CrossFL 2022 poster workshop, code available at (https://github.com/tj-kim/pFedDef_v1

Adversarial Robustness Unhardening via Backdoor Attacks in Federated Learning

In today's data-driven landscape, the delicate equilibrium between safeguarding user privacy and unleashing data potential stands as a paramount concern. Federated learning, which enables collaborative model training without necessitating data sharing, has emerged as a privacy-centric solution. This decentralized approach brings forth security challenges, notably poisoning and backdoor attacks where malicious entities inject corrupted data. Our research, initially spurred by test-time evasion attacks, investigates the intersection of adversarial training and backdoor attacks within federated learning, introducing Adversarial Robustness Unhardening (ARU). ARU is employed by a subset of adversaries to intentionally undermine model robustness during decentralized training, rendering models susceptible to a broader range of evasion attacks. We present extensive empirical experiments evaluating ARU's impact on adversarial training and existing robust aggregation defenses against poisoning and backdoor attacks. Our findings inform strategies for enhancing ARU to counter current defensive measures and highlight the limitations of existing defenses, offering insights into bolstering defenses against ARU.Comment: 8 pages, 6 main pages of text, 4 figures, 2 tables. Made for a Neurips workshop on backdoor attack

D2.1 Performance evaluation framework

This deliverable contains a proposal for a performance evaluation framework that aims at ensuring that multiple projects within 5G-PPP wireless strand can quantitatively assess and compare the performance of different 5G RAN design concepts. The report collects the vision of several 5G-PPP projects and is conceived as a living document to be further elaborated along with the 5G-PPP framework workshops planned during 2016.Weber, A.; Agyapong, P.; Rosowski, T.; Zimmerman, G.; Fallgren, M.; Sharma, S.; Kousaridas, A.... (2016). D2.1 Performance evaluation framework. https://doi.org/10.13140/RG.2.2.35447.2192

D2.2 Draft Overall 5G RAN Design

This deliverable provides the consolidated preliminary view of the METIS-II partners on the 5 th generation (5G) radio access network (RAN) design at a mid-point of the project. The overall 5G RAN is envisaged to operate over a wide range of spectrum bands comprising of heterogeneous spectrum usage scenarios. More precisely, the 5G air interface (AI) is expected to be composed of multiple so-called AI variants (AIVs), which include evolved legacy technology such as Long Term Evolution Advanced (LTE-A) as well as novel AIVs, which may be tailored to particular services or frequency bands.Arnold, P.; Bayer, N.; Belschner, J.; Rosowski, T.; Zimmermann, G.; Ericson, M.; Da Silva, IL.... (2016). D2.2 Draft Overall 5G RAN Design. https://doi.org/10.13140/RG.2.2.17831.1424

Essays on Corruptible Markets, Strategic Certification and Online Peer Effects

Emerging markets offer significant business opportunities. However, local and foreign firms selling in these markets are often faced with corrupt agents. The first essay investigates the marketing strategy implications for firms competing for business in a corruptible market. We consider a setting in which a buyer (a firm or government) seeks to purchase a good through a corruptible agent. Supplier firms, that may or may not be a good fit, compete to be selected by the agent. Only the agent observes whether or not a firm is a good fit. Corruption arises due to incentive of the agent to select a non-deserving firm in exchange for bribes. Intuitively and as expected, a sufficiently large monitoring of the agent eradicates corruption. But the interesting point is that increasing the monitoring from an initial low level can backfire, making the agent more likely to select a non-deserving firm. As firms become reluctant to offer bribes in response to higher monitoring, it now becomes likely that the agent receives a bribe offer, in equilibrium, only from a non-deserving firm. This nonmonotonic agent behavior makes it difficult to reduce corruption. The implication is that the buyer should choose either to be ignorant or to take drastic measures to limit corruption. Further, We show that unilateral anti-corruption controls, such as the Foreign Corrupt Practices Act of 1977, on a U.S. firm seeking business in a corrupt foreign market can actually increase the profits of the U.S. firm. This is because such a control on the U.S. firm puts pressure on the buyer to set monitoring at higher levels and reduces corruption.The second essay describes how market forces create incentives for firms to seek product safety certifications. We consider a firm which makes the decision of whether or not to seek certification prior to selling the product. Consumers choose to be careful or negligent while using the product. The probability of an accident depends on both the consumer's effort and the product safety. We show that, even when both the firm and consumers have same beliefs about the product safety, the presence of consumer moral hazard can create incentives for certification. Consumers' choice of effort may change as they update their beliefs upon observing a certification outcome. The consumer surplus that is thereby generated may be extracted by the firm through higher prices creating incentives for certification. Interestingly, if the certification decision is private information to the firm, the presence of consumer moral hazard may lead to more certification if safety and effort are substitutes but less certification if they are complements. If safety and effort are substitutes, a negligent product use hurts the consumer more when using a non-certified product compared to when using a certified product. This makes the certified product more valuable to the consumer. As a result, the certification equilibrium exists over a larger set of conditions. On the other hand, if safety and effort are complements, a negligent product use hurts the consumer more when using a certified product. The certified product becomes less valuable causing the certification equilibrium to exist over a smaller set of conditions.The third essay empirically investigates the effect of consumers' product evaluations on the judgments of other consumers in an online setting. Consumers routinely get exposed to others' opinions, most often in the form of average of prior ratings, when reporting their own. It is not obvious if consumers incorporate these prominently displayed average ratings in their own evaluations. By use of movie ratings data from Netflix we find that consumers incorporate the average of the prior ratings displayed on screen in their evaluations. Simulations using the estimated parameter values indicate that this behavior changes the resulting pattern of the ratings. We also find that a small number of early extreme ratings has a long lasting impact on the average rating of a movie. Average ratings remain inflated even after 1000 periods when only the first 5 ratings are changed to the highest possible rating

Essays on Corruptible Markets, Strategic Certification and Online Peer Effects

Emerging markets offer significant business opportunities. However, local and foreign firms selling in these markets are often faced with corrupt agents. The first essay investigates the marketing strategy implications for firms competing for business in a corruptible market. We consider a setting in which a buyer (a firm or government) seeks to purchase a good through a corruptible agent. Supplier firms, that may or may not be a good fit, compete to be selected by the agent. Only the agent observes whether or not a firm is a good fit. Corruption arises due to incentive of the agent to select a non-deserving firm in exchange for bribes. Intuitively and as expected, a sufficiently large monitoring of the agent eradicates corruption. But the interesting point is that increasing the monitoring from an initial low level can backfire, making the agent more likely to select a non-deserving firm. As firms become reluctant to offer bribes in response to higher monitoring, it now becomes likely that the agent receives a bribe offer, in equilibrium, only from a non-deserving firm. This nonmonotonic agent behavior makes it difficult to reduce corruption. The implication is that the buyer should choose either to be ignorant or to take drastic measures to limit corruption. Further, We show that unilateral anti-corruption controls, such as the Foreign Corrupt Practices Act of 1977, on a U.S. firm seeking business in a corrupt foreign market can actually increase the profits of the U.S. firm. This is because such a control on the U.S. firm puts pressure on the buyer to set monitoring at higher levels and reduces corruption.The second essay describes how market forces create incentives for firms to seek product safety certifications. We consider a firm which makes the decision of whether or not to seek certification prior to selling the product. Consumers choose to be careful or negligent while using the product. The probability of an accident depends on both the consumer's effort and the product safety. We show that, even when both the firm and consumers have same beliefs about the product safety, the presence of consumer moral hazard can create incentives for certification. Consumers' choice of effort may change as they update their beliefs upon observing a certification outcome. The consumer surplus that is thereby generated may be extracted by the firm through higher prices creating incentives for certification. Interestingly, if the certification decision is private information to the firm, the presence of consumer moral hazard may lead to more certification if safety and effort are substitutes but less certification if they are complements. If safety and effort are substitutes, a negligent product use hurts the consumer more when using a non-certified product compared to when using a certified product. This makes the certified product more valuable to the consumer. As a result, the certification equilibrium exists over a larger set of conditions. On the other hand, if safety and effort are complements, a negligent product use hurts the consumer more when using a certified product. The certified product becomes less valuable causing the certification equilibrium to exist over a smaller set of conditions.The third essay empirically investigates the effect of consumers' product evaluations on the judgments of other consumers in an online setting. Consumers routinely get exposed to others' opinions, most often in the form of average of prior ratings, when reporting their own. It is not obvious if consumers incorporate these prominently displayed average ratings in their own evaluations. By use of movie ratings data from Netflix we find that consumers incorporate the average of the prior ratings displayed on screen in their evaluations. Simulations using the estimated parameter values indicate that this behavior changes the resulting pattern of the ratings. We also find that a small number of early extreme ratings has a long lasting impact on the average rating of a movie. Average ratings remain inflated even after 1000 periods when only the first 5 ratings are changed to the highest possible rating

Service Provision in Distribution Channels

Consumers may need help using an inherently complex product after purchase. This article studies a manufacturer's and a retailer's incentives to provide presales service and after-sales support in a distribution channel. The authors consider a model in which a manufacturer makes wholesale price and channel service decisions. Subsequently, a retailer makes retail price and channel service decisions. They find that, in equilibrium, both channel members provide presales service. If the fixed-cost investment needed to enhance the effectiveness of after-sales support is small, the manufacturer lets the retailer provide after-sales support. Yet when it is above a threshold and the retailer becomes unwilling to invest in providing after-sales support, the manufacturer steps in and does so. As expected, when the fixed cost is too large, the manufacturer also opts out of providing after-sales support. Interestingly, when the retailer provides after-sales support, the level of presales service and the demand for after-sales support can simultaneously be the highest among all configurations. Finally, the authors demonstrate the robustness of their main results by studying alternative channel service configurations

SPECTROGRAPHIC SEAM PATTERNS FOR DISCRIMINATIVE WORD SPOTTING

This paper presents a novel method for deriving patterns for classification of speech sounds. In contrast to conventional methods that attempt to capture time-frequency patterns as represented by spectral envelopes or peaks, our method captures patterns of high-energy tracks, or seams, of maximum “whiteness ” across frequency in spectrograms. Our hypothesis is that these seams could potentially carry relatively invariant signatures of underlying sounds. We present a method to derive feature vectors from seam patterns for discriminative word spotting. We show experimentally that spectrographic seam patterns are indeed distinctive for different spoken words, and are effective for word spotting