Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Speaker Recognition: Modular or Monolithic?

Speaker recognition has made extraordinary progress with the advent of deep neural networks. In this work, we analyze the performance of end-to-end deep speaker recognizers on two popular text-independent tasks NIST-SRE 2016 and VoxCeleb. Through a combination of a deep convolutional feature extractor, self-attentive pooling and large-margin loss functions, we achieve state-of-the-art performance on VoxCeleb. Our best individual and ensemble models show a relative improvement of 70% an 82% respectively over the best reported results on this task. On the challenging NIST-SRE 2016 task, our proposed endto-end models show good performance but are unable to match a strong i-vector baseline. State-of-the-art systems for this task use a modular framework that combines neural network embeddings with a probabilistic linear discriminant analysis (PLDA) classifier. Drawing inspiration from this approach we propose to replace the PLDA classifier with a neural network. Our modular neural network approach is able to outperform the i-vector baseline using cosine distance to score verification trials.
Interoceptive inference 1 ACTIVE INTEROCEPTIVE INFERENCE AND THE EMOTIONAL BRAIN

We review a recent shift in conceptions of interoception and its relationship to hierarchical inference in the brain. The notion of interoceptive inference means that bodily states are regulated by autonomic reflexes that are enslaved by descending predictions from deep generative models of our internal and external milieu. This re-conceptualization illuminates several issues in cognitive and clinical neuroscience with implications for experiences of selfhood, and emotion. We first contextualize interoception in terms of active (Bayesian) inference in the brain; highlighting its enactivist (embodied) aspects. We then consider the key role of uncertainty or precision and how this might translate into neuromodulation. We next examine the implications for understanding the functional anatomy of the emotional brain, surveying recent observations on agranular cortex. Finally, we turn to theoretical issues; namely, the role of interoception in shaping a sense of embodied self and feelings. We will draw links between physiological homeostasis and allostasis, early cybernetic ideas of predictive control, and hierarchical generative models in predictive processing. The explanatory scope of interoceptive inference ranges from explanations for autism and depression, through to consciousness. We offer a brief survey of these exciting developments.
MICROBLOGGING CONTENT PROPAGATION MODELING USING VIRALITY AND SUSCEPTIBILITY ANALYSIS

Due to the rise of sites like Facebook, Twitter, and Weibo, political information is more and more frequently encountered in a social context: even stories published by mainstream media sites are often encountered by users after having been shared by others. Clearly, this social context can influence how information is interpreted and re-shared. When a microblogging user adopts some content propagated to her, they can attribute that to three behavioral factors, namely, topic virality, user virality and user susceptibility. Topic virality measures the degree to which a topic attracts propagations by users. User virality and susceptibility refer to the ability of a user to propagate content to other users, and the propensity of a user adopting content propagated to her, respectively. In this work, we study the problem of mining these behavioral factors specific to topics from microblogging content propagation data. It first construct a three dimensional tensor for representing the propagation instances. Then propose a tensor factorization framework to simultaneously derive the three sets of behavioral factors. Based on this framework, it develop a numerical factorization model and another probabilistic factorization variant. The work also develop an efficient algorithm for the models’ parameters learning.
: Practical Cache Attacks from the Network

Increased peripheral performance is causing strain on the memory subsystem of modern processors. For example, available DRAM throughput can no longer sustain the traffic of a modern network card. Scrambling to deliver the promised performance, instead of transferring peripheral data to and from DRAM, modern Intel processors perform I/O operations directly on the Last Level Cache (LLC). While Direct Cache Access (DCA) instead of Direct Memory Access (DMA) is a sensible performance optimization, it is unfortunately implemented without care for security, as the LLC is now shared between the CPU and all the attached devices, including the network card.In this paper, we reverse engineer the behavior of DCA, widely referred to as Data-Direct I/O (DDIO), on recent Intel processors and present its first security analysis. Based on our analysis, we present NetCAT, the first Network-based PRIME+PROBE Cache Attack on the processor’s LLC of a remote machine. We show that NetCAT not only enables attacks in cooperative settings where an attacker can build a covert channel between a network client and a sandboxed server process (without network), but more worryingly, in general adversarial settings. In such settings, NetCAT can enable disclosure of network timing-based sensitive information. As an example, we show a keystroke timing attack on a victim SSH connection belonging to another client on the target server. Our results should caution processor vendors against unsupervised sharing of (additional) microarchitectural components with peripherals exposed to malicious input.
Effects of the Exotic Crustacean, Armadillidium vulgare (Isopoda), and Other Macrofauna on Organic Matter Dynamics in Soil Microcosms in a Hardwood Forest in Central Florida

BioOne sees sustainable scholarly publishing as an inherently collaborative enterprise connecting authors, nonprofit publishers, academic institutions, research libraries, and research funders in the common goal of maximizing access to critical research. Effects of the Exotic Crustacean, Armadillidium vulgare (Isopoda), and Other Macrofauna on Organic Matter Dynamics in Soil Microcosms in a Hardwood Forest in Central Florida
Do Hesitations Facilitate Processing of Partially Defective System Utterances? An Exploratory Eye Tracking Study

Spoken dialogue systems are predominantly evaluated using offline methods such as user ratings or task-oriented measures. Various phenomena in conversational speech, however, are known to affect the way the listener’s comprehension unfolds over time, and not necessarily the final result of the comprehension process. For instance, in human reference comprehension, conversational signals like hesitations have been shown to ease processing of expressions referring to difficult-to-describe targets, as can primarily be observed in listeners’ anticipatory eye movements rather than in their final reference resolution decision. In this study, we explore eye tracking for testing conversational dialogue systems, looking at how listeners process automatically generated referring expressions containing defective attributes. We investigate whether hesitations facilitate the processing of partially defective system utterances and track the user’s eye movements when listening to expressions with: (i) semantically defective but fluently synthesized adjectives, (ii) defective and lengthened adjectives, i.e. containing a conversational uncertainty signal. Our results are encouraging: whereas the offline measure of task success does not show any differences between the two conditions, the listeners’ eye movements suggest that processing of partially defective utterances might be facilitated by conversational hesitations.
EFFECTS OF ADVANCED MANUFACTURING TECHNOLOGY ADOPTION AND MARKET COMPETITION ON MANAGEMENT ACCOUNTING SYSTEM OF MANUFACTURING FIRMS IN MALAYSIA

Contingency theory proposes that advanced manufacturing technology adoption and market competition necessitate the adoption of sophisticated MAS for more accurate and timely accounting information to enhance firms’ decision making and to sustain their long-term success. This study empirically examines the effect of advanced manufacturing technology adoption and market competition on the adoption of sophisticated management accounting system (MAS). Using a structured questionnaire, data from 137 manufacturing companies from Malaysia were obtained for analyses. The regression results show that the sophistication of MAS adopted is significantly and positively related with advanced manufacturing technology adoption and market competition. The findings suggests that the adoption of sophisticated MAS design is parallel with the technological innovations that are incorporated into their manufacturing technologies and the adoption of sophisticated MAS design is able to assist managers in cost and control activities and to provide more relevant information for strategic decision making purposes. Besides, the adoption of sophisticated MAS design is also able to assist firms to cope with the increasing customer demands and competition from their core competitors by providing more relevant information regarding the changes in the business environment. These results have contributed to the management accounting change literature by suggesting that attempts by the MAS designers to improve the timeliness and the scope of the information are of particular relevance to the managers as the firm adopts more advanced manufacturing technology and as the market competition increases.
MANUFACTURING OF MULTIFUNCTIONAL SOFT COMPOSITES WITH INTEGRATED PROXIMITY AND TACTILE SENSING

We describe the manufacturing and function of a multi-functional composite that integrates highdensity proximity, contact, and force sensing with applications in robotic tactile manipulation. The composite consists of a Acrylonitrile Butadiene Styrene (ABS) structure, printed circuit boards (PCB) and metal interconnects, which are embedded in transparent Polydimethylsiloxane (PDMS). The PCBs are populated with infrared proximity sensors and control electronics, allowing the composite to measure proximity to objects outside of the PDMS enclosure. At the same time, external forces acting upon the PDMS layer can be measured due to the deformation of the material. The composite is manufactured by combining 3D printing, PCB manufacturing and automated assembly, assembly and overmolding using a four piece mold. The resulting composite has been shown to dramatically increase the accuracy and robustness of robotic manipulation, which is illustrated with sample results.
The experiences of students participating in an HIV/AIDS teacher education pilot programme

Supervisor: Dr. Kesh Mohangi Department: Educational Psychology Degree: MEd Educational Psychology The current research study was a qualitative investigation to explore and describe how students, registered for the Education 364 module, experienced the HIV/AIDS teacher education pilot programme (TEPP), implemented at the University of Pretoria. The availability of in-depth interview transcriptions, as raw data, permitted analysis of existing data from a different perspective. Transcriptions of a focus group interview and two individual interviews with Afrikaans students were purposefully selected and inductively analysed. Two main themes emerged following the thematic analysis of interview transcriptions. Findings indicate that the content of the HIV/AIDS TEPP provided students with a solid HIV/AIDS-related theoretical basis. Findings furthermore suggest that the HIV/AIDS TEPP could be adapted for different levels of knowledge and teaching phases. Appropriate and sufficient resource materials, in accordance with the explicit needs of each teaching phase, could be developed. Practical examples and clear guidelines may be incorporated into HIV/AIDS programmes to equip teachers to deal with difficult situations. In addition, sufficient time allocation to and early implementation of HIV/AIDS programmes seem necessary. The value of a range of teaching and learning methodologies and the role of HIV/AIDS programme presenters were emphasised. Since existing teacher training may not adequately prepare teachers to address HIV/AIDS challenges, the need for continuous development, lifelong learning as well as support for teachers were also highlighted. Finally, findings indicate that engagement in affective and identity-construction domains may reduce HIV/AIDS fatigue and encourage students to take on active roles to address HIV/AIDSrelated challenges.
Scalable Graph Embedding Enhanced by Content-Preserving Locality Sensitive Hashing

Recent years have seen a massive research on representation learning of information networks, a.k. a, graph embedding. Graph embedding techniques aim at learning distributed representation that captures the semantic role of each vertex in a network, and can be applied to a variety of successor tasks such as natural language processing, image processing, recommender systems, and speech processing, etc. Most previous work on graph embedding only focuses on the structural properties of the graph. The insight behind these work is that the role of each node can be represented by its neighbors. For the high-degree nodes, the representation can be sufﬁ-ciently trained due to the surrounding dense structure. For many real-world information networks, however, the degree distribution is likely to be subject to the Power Law, meaning that a large proportion of vertices are low-degree vertices. Due to the structural sparsity, merely learning the representation through the proximity to the neighbors could lead to poor quality of these low-degree nodes. To address this problem, we incorporate the content information to represent graph nodes and make the embedding generalizes well. We propose a model based on Locality Sensitive Hashing (LSH) to incorporate content features of vertices by preserving the content similarity between high-degree nodes and low-degree nodes. In this way, we improve the quality of the representation of the low-degree nodes by associating them to the high-degree ones with similar content via shared hashing outputs. We re-formulate the representation of each vertex based on its output of hash functions, and then utilize graph structure to learn the representation. The hashing trick in our method can also reduce the redundant space consumption caused by content-homogenous vertices so that it can be scaled up to data of industrial volume. We conduct extensive ofﬂine experiments on public datasets, and deploy our model onto the online recommendation system in Alibaba Group. The results show that our method is both effective and highly scalable.
Analysis of the Evaluation Results for our Tasks in COAE 2009

COAE2009 has five tasks and we take part in Task 3, 4 and 5. Task3 is designed for identification of the opinioned sentence; Task4 is designed for topic identification based on the sentences from Task3 and makes the polarity classification; Task5 is about opinion retrieval plus the sentiment polarity analysis. This paper will present our methods in the three tasks and finally draw our conclusion and present our future work.
Various area based Routing Using Proactive and Reactive Routing Protocol in VANET

VANET is the technology of building a robust Ad-Hoc network between mobile vehicles and each other, between mobile vehicles and roadside units. , there are two category of nodes in VANETs; mobile nodes as On Board Units (OBUs) and static nodes as Road Side Units (RSUs). In this paper rural and urban area scenario created for AODV and DSDV in VANET environment with varying speed i.e. 30m/sec, 60m.sec and 90m/sec, and the performance has been evaluated on the basis of throughput and residual energy.
On the Importance of Audio-Source Separation for Singer Identification in Polyphonic Music

Singer identification is to automatically identify the singer in a music recording, such as a polyphonic song. A song has two major acoustic components that are singing vocals and background accompaniment. Although identifying singers is similar to speaker identification, it is challenging due to the interference of background accompaniment on the singer-specific information in singing vocals. We believe that separating the background accompaniment from the singing vocal will help us to overcome the interference. In this work, we extract the singing vocals from polyphonic songs using Wave-U-Net based audio-source separation approach. The extracted singing vocals are then used in i-vector based singer identification system. Further, we explore different state-of-the-art audio-source separation methods to establish the role of considered method in application to singer identification. The proposed singer identification framework achieves an absolute accuracy improvement of 5.66% over the baseline without audio-source separation.
Privacy Preserving Data Mining Approach for IoT based WSN in Smart City

Wireless Sensor Network (WSN) is one of the most fundamental technologies of Internet of Things (IoT). Various IoT devices are connected to the internet by making use of WSN composed of different sensor nodes and actuators, where these sensor nodes collaborate and accomplish their tasks dynamically. The main objective of deploying WSN-based applications is to make high precision real-time observations, and it is extremely challenging because of the limited computing power of the sensors operating under constrained environments, resource constraints like energy, computation speed, bandwidth and memory, huge volume of high speed, heterogeneous and fast-changing WSN data. These challenges encouraged the researchers to concentrate deeper on exploring data mining techniques to extract the required information from the fast-changing sensor data in WSN and thereby efficiently handle the massive data generated by the WSNs. The increasing need of data mining techniques for WSN has inspired us to propose a distributed data mining technique that effectively handles the data generated by the nodes in the WSN and prolongs the lifespan of the network. Our work provides a novel cluster based scheme to mine the sensors data without moving it to cluster head (CH) or base station (BS) to achieve maximum performance in a WSN environment. The basic idea of the proposed work is that local computations are performed by utilizing the computing power at each sensor node and then the minimum higher level statistical summaries are exchanged, which decreases the energy dissipation in communication as the amount of the sensor data transferred is considerably reduced, and thereby the sensor network lifetime is maximized and also preserve the privacy of the sensor data.
Multimodal Word Discovery and Retrieval with Phone Sequence and Image Concepts

This paper demonstrates three different systems capable of performing the multimodal word discovery task. A multimodal word discovery system accepts, as input, a database of spoken descriptions of images (or a set of corresponding phone transcripts), and learns a lexicon which is a mapping from phone strings to their associated image concepts. Three systems are demonstrated: one based on a statistical machine translation (SMT) model, two based on neural machine translation (NMT). On Flickr8k, the SMT-based model performs much better than the NMT-based one, achieving a 49.6% F1 score. Finally, we apply our word discovery system to the task of image retrieval and achieve 29.1% recall@10 on the standard 1000image Flickr8k tests set.
Scam the Declined or Decline to be Scammed : A Model of Financial Choice in the Presence of Cognitive Decline ∗

We develop a multi-period overlapping generations model in which cognitively sound consumers know that their future selves may suffer from cognitive decline. Consumers with cognitive decline are at risk being taken advantage of by fraudsters who sell wealth-reducing financial products, e.g., products that are inappropriate for the consumer, over-priced, or purely fraudulent. Prior to being hit by cognitive decline, consumers can pay to preemptively protect their future selves from fraudsters by constraining their subsequent financial choices. In practice such protection could take many forms such as hiring a fiduciary advisor who is contracted to participate in a consumer’s future financial decisions or purchasing an income annuity and thereby limiting the ability of the consumers older self to spend large amounts of wealth. In equilibrium, more consumers pay for protection from fraudsters and fraudsters enter the market more intensely when the probability of consumers being afflicted by cognitive decline is initially high and increases more significantly and earlier in life, life expectancy is high, average potential losses to consumers are high, and the cost to fraudsters of meeting more consumers is low. The cost of consumer protection and the intensity with which fraudsters enter the market are determined endogenously. When a higher fraction of consumers protect themselves, fraudsters enter the market with lower intensity. Thus when the probability of a consumer being offered a fraudulent product is high, consumers who purchase protection provide a positive externality to other consumers. Since consumers ignore this externality in their individual decisions, in equilibrium fewer consumers purchase protection than is socially optimal.
Liquidity Supply by Broker-Dealers and Real Activity

Abstract This paper develops a novel measure of liquidity supply by broker-dealers using data on their trading positions in Treasury bonds and the deviations of Treasury yields from a fitted yield curve. The measure is informative about liquidity in other asset classes, including corporate bonds and equities. A decline in liquidity supply predicts reduced debt issuance and investment by nonfinancial firms and reduced aggregate economic activity. The sensitivity of liquidity, issuance, and investment to broker-dealers’ liquidity supply is larger for firms with low credit quality. The results suggest that securities intermediaries are important for understanding liquidity commonality, corporate financing, and real activity.
Ethics in Clinical Practice: An Inter-Professional Approach

Preface Chapter 1 Start at 'Go' /Introduction Chapter 2 Learning to understand ethical issues and problems Chapter 3. Where did you get your values and beliefs? Chapter 4. Being part of a team: Interdisciplinary care. Chapter 5. he relationship of ethics to the Western Tradition of philosophy Chapter 6. Eastern philosophy, ethics, religions, and care Chapter 7. Client and patients' rights and vulnerable people E Chapter 8. Ideas for decision-making when ethical problems are involved Chapter 9. Who gets what? The allocation of resources Chapter 10. 'Why do they make me suffer? ' The ethical problems of pain and resuscitation Chapter 11. How to do ethical health research Chapter 12. Feminine and Masculine Sexuality Chapter 13. The experiences of illness and loss Chapter 14. Ethical problems in Primary Health Care Chapter 15. Ethical problems in Mental Health and Psychiatric care Chapter 16. Complex Care: Emergency Department, Perioperative Suite, and ICU and CCU units Chapter 17. It isn't easy, but it is essential! /Conclusion References Index