Miracle-dz/UniFAR-FTUs huggingface dataset:
SEGMENTATION

: Few-shot segmentation (FSS) uses only a few labeled images to learn the method of segmentation rather than training on huge dataset to find satisfactory parameters. Existing methods pay more attention on labeled data leading to the waste of unlabeled data, i.e., potential novel classes are treated as background on training phase. Our method aims to lighten this problem and improve the feature embedding space of all classes. In our work, we propose a novel framework to learn prototypes of labeled and unlabeled classes. When training on support-query pairs, we introduce an additional prototype generating branch that exploits unlabeled classes using a clustering-based method and a new method for prototype rectification on unlabeled classes to promote stability of prototypes. Above all, our prototype generating technique has the ability to utilize unlabeled classes improving the embedding space. Extensive experiments on two FSS benchmarks demonstrate that our method outperforms the previous state-of-the-art by a margin of 1.13% mIOU on PASCAL-(cid:887) (cid:2919) and 2.15% mIOU on COCO-(cid:884)(cid:882) (cid:2919) , according to our extensive experiments on two FSS benchmarks.
Hybrid Deep Learning Model for Sarcasm Detection in Indian Indigenous Language Using Word-Emoji Embeddings

copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. © 2022 Association for Computing Machinery. 2375-4699/2022/1-ART1 $15.00 http://dx.doi. org/10.1145/3519299 ACM Trans. Asian Low-Resour. Lang. Inf. Process. Hybrid Deep Learning Model for Sarcasm Detection in Indian Indigenous Language
RDC: Energy-Efficient Data Center Network Congestion Relief with Topological Reconfigurability at the Edge

The rackless data center (RDC) is a novel network architecture that logically removes the rack boundary of traditional data centers and the inefficiencies that come with it. As modern applications generate more and more inter-rack traffic, the traditional architecture suffers from contention at the core, imbalanced bandwidth utilization across racks, and longer network paths. RDC addresses these limitations by enabling servers to logically move across the rack boundary at runtime. Our design achieves this by inserting circuit switches at the network edge between the ToR switches and the servers, and by reconfiguring the circuits to regroup servers across racks based on the traffic patterns. We have performed extensive evaluations of RDC both in a hardware testbed and packet-level simulations and show that RDC can speed up a 4:1 oversubscribed network by 1.78×∼ 3.9× for realistic applications and more than 10× in large-scale simulation; furthermore, RDC is up to 2.4× better in performance per watt than a conventional non-blocking network.
of Electrical , Electronic and C University of Birmingham , Birmingh

We investigate recognition of spontaneous speech using the focus of visual attention as a secondary cue to speech. In our experiment we collected a corpus of eye and speech data where one participant describes a geographical map to another while having their eye movements tracked. Using this corpus we characterise the coupling between eye movement and speech. Speech recognition results are presented to demonstrate proof of concept for development of a bimodal ASR using focus of visual attention to drive a dynamic language model. Marginal improvement in WER is observed.
Edinburgh Research Explorer Online banking adoption

During the 20 year lifetime of the Journal of Financial Services Marketing the study of online banking adoption has emerged and matured as a field. Now 20 years on, we reflect on the accumulated online banking adoption knowledge and consider what this tells us. Based on an audit of published research over a ten-year period, 1998-2008, we identify the core theories and approaches utilised to study online banking. The findings reveal the widespread application of the Technology Adoption Model (TAM). Drawing on the current debate regarding TAM within the Information Systems (IS) domain, we critically evaluate the ongoing appropriateness of TAM for online banking adoption research, and call for a refreshed approach to the study of bank technology adoption. The paper concludes by highlighting other theories that offer potential to extend knowledge in this area.
Fog Computing: Terminologies, Architecture and Challenges

: Internet of things (IOT) made the world connected to each other through Internet. These gadgets are important to store data, to exchange data and to collect data from other sources. These devices are not perfectly capable to cooperate with data centers directly based on some parameters such as latency, resource availability, load balancing, scheduling and security. Fog computing (FC) paradigm is introduced to overcome the problems of these parameters. As it cooperate with centralized data centers. This paper presents a survey on Fog computing terminology. Here, the term fog computing has been discussed. Further its architecture, its challenges are highlighted. An overview of further research work related to dynamic job scheduling has been discussed.
Hidden Markov Models for Activity Recognition in Ambient Intelligence Environments

Context-aware computing offers several advantages for human computer interaction by augmenting ambient intelligence environments with computational artifacts that can be responsive to the needs of users. One of the main challenges in context-aware computing is context recognition. While some contextual variables, such as location, can be easily recognized, others, such as activity are more complex to estimate. This paper describes an approach to estimate activities in a working environment. The approach is based on information gathered from a workplace study, in which 196 hours of detailed observation of hospital workers were recorded. This data is used to train a Hidden Markov Model to estimate user activity. The results indicate that the user activity can be correctly estimated 92.6% of the time. We compare our results with the use of neuronal networks and human observers familiar with those work practice. We discuss how these results can be used for context-aware applications.
False Positives vs. False Negatives: The Effects of Recovery Time and Cognitive Costs on Input Error Preference

copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org. UIST '21, October 10–14, 2021, Virtual Event, USA © 2021 Copyright is held by the owner/author(s). Publication rights licensed to ACM. ACM ISBN 978-1-4503-8635-7/21/10. $15.00 https://doi.org/10.1145/3472749.3474735 False Positives vs. False Negatives The Effects of Recovery Time and Cognitive Costs on Input Error Preference
0 1 2 3 4 5 6 7 8 9 10 1 2 4 8 16 32 64

As a programming abstraction, the transaction lies typically between the developer of a concurrent application and the operating system. It encapsulates the complex internals of synchronization, thereby simplifying the life of a non-expert programmer. Yet, some programmers are indeed experts. For those, the transaction abstraction turns out sometimes to be over restrictive: it does not allow such programmers to express sophisticated synchronization schemes that would significantly enhance concurrency, and hence performance, while preserving correctness. We show in this paper that this limitation is inherent to the transactional concept in its classic form. We then argue for a genuine democratization of the paradigm, with polymorphic transactional semantics to be used by different programmers, according to their skills, and composed within the same application.
I 2 R Text-to-Speech System for Blizzard Challenge 2011

This paper describes I 2 R‟s submission to the Blizzard Challenge 2011 speech synthesis evaluation. This is our fourth participation in the challenge. In this paper, we will describe our main approaches to building the required voices. We will describe our definitions of the acoustic, prosodic and linguistic parameters, procedure of candidate unit selection, components of cost functions, etc. Finally, we will also present the results of the listening test conducted.
Seeing in the dark: Testing deep neural network and analysis-by-synthesis accounts of 3D shape perception with highly degraded images

The visual system does not require extensive signal in its inputs to compute rich, three-dimensional (3D) shape percepts. Even under highly degraded stimuli conditions, we can accurately interpret images in terms of volumetric objects. What computations support such broad generalization in the visual system? To answer, we exploit two degraded image modalities – silhouettes and two-tone “Mooney” images – alongside regular shaded images. We test two distinct approaches to vision: deep networks for classification and analysis-by-synthesis for scene inference. Deep networks perform substantially sub-human even after training on 18 times more images per category compared to the existing large-scale image sets for object classification. We also present a novel analysis-by-synthesis architecture that infers 3D scenes from images via optimization in a differentiable, physically-based renderer. This model also performs substantially sub-human. Nevertheless, both approaches can explain some of the key behavioral patterns. We discuss the insights these results provide for reverse-engineering visual cognition.
Basin filling evolution of the central basins of Mallorca since the Pliocene

A new compilation of data from 436 drill cores using decompaction and backstripping techniques was used to reconstruct the basin filling history from the Pliocene until the present day in the Palma, Inca and Sa Pobla Basins on the island of Mallorca (Spain). Calcareous rocks dominate the source area and provide a limited amount of clastic input to the basins that has resulted in an average accumulation rate of between 5 and 20 m/Ma during the last 5.3 Ma. Carbonate sediment production dominated the basin filling history during early‐mid Pliocene, but during the Quaternary, the sedimentation processes in the Palma Basin were probably enhanced by an evolution in the drainage network that increased the sediment supply and the accumulated thickness caused by stream capture. However, the maximum sedimentation rate filling the depocentres of the three basins has been decreasing since the Pliocene, showing that not only the catchment transport efficiency but also the relative sea level have been controlling the sediment accumulation in these carbonate basins. The isopach cross‐sections support the idea that a palaeorelief was generated during the Messinian sea level drop and that heterogeneities were filled in from the Pliocene to the Quaternary. We conclude that the central basins of Mallorca were filled heterogeneously due to tectonic and geomorphic processes that controlled sediment transport and production, resulting in different average sedimentation thicknesses that decreased since the Pliocene as the accommodation space became filled and the relative sea level dropped.
Dwell-and-spring: undo for direct manipulation

In graphical user interfaces, direct manipulation consists in incremental actions that should be reversible. Typical examples include manipulating geometrical shapes in a vector graphics editor, navigating a document using a scrollbar, or moving and resizing windows on the desktop. As in many such cases, there will not be any mechanism to undo them, requiring users to manually revert to the previous state using a similar sequence of direct manipulation actions. The associated motor and cognitive costs can be high. We argue that proper and consistent mechanisms to support undo in this context are lacking, and present Dwell-and-Spring, an interaction technique that uses the metaphor of springs to enable users to undo direct manipulations. A spring widget pops up whenever the user dwells during a press-drag-release interaction, giving her the opportunity to either cancel the current manipulation or undo the last one. The technique is generic and can easily be implemented on top of existing applications to complement the traditional undo command. Empirical evaluation shows that users quickly adopt it as soon as they discover it.
Scheduling Storms and Streams in the Cloud

Motivated by emerging big streaming data processing paradigms (e.g., Twitter Storm, Streaming MapReduce), we investigate the problem of scheduling graphs over a large cluster of servers. Each graph is a job, where nodes represent compute tasks and edges indicate data flows between these compute tasks. Jobs (graphs) arrive randomly over time and, upon completion, leave the system. When a job arrives, the scheduler needs to partition the graph and distribute it over the servers to satisfy load balancing and cost considerations. Specifically, neighboring compute tasks in the graph that are mapped to different servers incur load on the network; thus a mapping of the jobs among the servers incurs a cost that is proportional to the number of “broken edges. ” We propose a low-complexity randomized scheduling algorithm that, without service preemptions, stabilizes the system with graph arrivals/departures; more importantly, it allows a smooth tradeoff between minimizing average partitioning cost and average queue lengths. Interestingly, to avoid service preemptions, our approach does not rely on a Gibbs sampler; instead, we show that the corresponding limiting invariant measure has an interpretation stemming from a loss system.
Fruit recognition based on convolution neural network

Computer vision is widely used at present. However, fruit recognition is still a problem for the stacked fruits on weighing scale because of complexity and overlap. In this paper, a fruit recognition algorithm based on convolution neural network(CNN) is proposed. At first the image regions are extracted using selective search algorithm, then the regions have been selected by means of an entropy of fruit images, and finally these regions are regarded as input of CNN neural network for training and recognition. The final decision is made based on a fusion of all region classifications using voting mechanism. In order to achieve the actual application in supermarket, we have considered the variety of fruit, stack of fruits, the changes of fruit number and position, and have made a multifarious training set of fruits. After the network has been trained with an optimal training set, it has obtained a remarkable recognition rates for the fruits stacked on a weighing scale.
An Efficient Biometric Attendance System using Fingerprint Verification

Biometrics based technologies are supposed to be ve ry efficient personal identifiers as they can keep track of characteristics believed to be unique to each pe rson. Among these technologies, Fingerprint identif ication is one of the most popular and reliable personal biometric id entification methods. The main aim of this paper i an accurate, efficient automatic attendance system using fingerprint verification technique. We propos e a system in which fingerprint verification is done by using ext raction of minutiae technique and the system that a utomates the s of taking attendance, Manually which is a laborio us and troublesome work and waste a lot of time, with its managing and maintaining the records for a period of time is also a burdensome task. For this purpose we use fingerprint verification system using extraction of minutiae techniques and Embedded system. The experimental result shows that our proposed system is efficient and low cost in verification of user fingerprint. Fingerprint Recognition; Bifurcation; Ridge; Minuti a .
An Eecient Protocol for Authenticated Key Agreement 1 2

This paper proposes a new and e cient two-pass protocol for authenticated key agreement in the asymmetric (public-key) setting. The protocol is based on Di e-Hellman key agreement and can be modi ed to work in an arbitrary nite group and, in particular, elliptic curve groups. Two modi cations of this protocol are also presented: a one-pass authenticated key agreement protocol suitable for environments where only one entity is on-line, and a three-pass protocol in which key con rmation is additionally provided. The protocols are currently under consideration for standardization in ANSI X9.42 [2], ANSI X9.63 [4] and IEEE P1363 [18].
Developing Business And Information Strategy Alignment: A Study In The Banking Industry

This paper reports part of an empirical study which explored business and information strategy alignment in the Australian banking industry. The aim of the study was to identify organizational policies and practices which contribute to and enhance alignment. The case-study research design involved multiple sources of evidence collected in a structured manner from the major firms which dominate Australian banking. These sources included both written and interview-based executive manager participation examination of board-level strategic planning documentation, and an analysis of five years of annual reports. Evidence for the alignment of business and information strategies was sought in the use of information and/or information technology which provided a comparative advantage to an organization over its competitors. There was evidence to indicate a positive relationship to business and information strategy alignment in the four areas in the case study banks: firm-wide strategy formation processes, organizational structure and accountabilities, information systems responsibilities and policies and technology strategy. Fifteen indicators within these four related areas were depicted in an alignment model.
Efficient similarity joins for near-duplicate detection

With the increasing amount of data and the need to integrate data from multiple data sources, one of the challenging issues is to identify near-duplicate records efficiently. In this article, we focus on efficient algorithms to find a pair of records such that their similarities are no less than a given threshold. Several existing algorithms rely on the prefix filtering principle to avoid computing similarity values for all possible pairs of records. We propose new filtering techniques by exploiting the token ordering information; they are integrated into the existing methods and drastically reduce the candidate sizes and hence improve the efficiency. We have also studied the implementation of our proposed algorithm in stand-alone and RDBMS-based settings. Experimental results show our proposed algorithms can outperform previous algorithms on several real datasets.
Purdue e-Pubs Purdue e-Pubs

In 2014, over one billion smartphones have been sold worldwide, around 3 trillion search queries were entered solely on Google, a growing part from mobile devices. With rapidly growing digitization, enormous changes accompany almost all areas of our lives and define fundamentally new ways we deal with information, education, and culture. At the same time, cultural institutions increasingly assume social responsibilities that go beyond the mere mediation of knowledge. For this purpose, infrastructures are necessary that allow to archive digital content independent from their media type and domain and make it available to the public; on the other hand, it is necessary to semantically link this content and present it to a more and more demanding audience that is already used to interactive and participatory forms of knowledge dissemination and information research. The cloud media projects in Hesse created a platform to give access to heterogeneous content types and domains and link them semantically. Two strategic partners, the Städel Museum in Frankfurt and the Darmstadt University and State Library, provide use cases for 2 applications based on the platform: a discovery service for literature, and a virtual museum application for objets d’art, both implemented with responsive design and touch screen support to enable mobile usage. The first provides a search and discovery in the library’s resources using a topic pie to support an efficient and intuitive drill-down of extensive search results. The second allows an exploration of the Städel's collection on mobile devices; users are able to find similar artworks considering different features such as artist, theme, motif, era, atmosphere, etc. Both take advantage of touch-sensitive displays and allow an intuitive and ludic handling. They break fresh ground by leaving the classic search and find paradigm towards digital strolling: just like a physical visit to a library or museum, strolling around, discovering things, and getting inspired.
Cost-Effective Authentic and Anonymous Data Sharing with Forward Security

Data sharing has is not easier with the advances of cloud computing, and an accurate analysis on the shared data provides more benefits to both the society and individuals. Data sharing with a large number of participants must take into account several issues, including efficiency, data integrity and privacy of data owner. Ring signature is a promising candidate to construct an anonymous and authentic data sharing system. Ring signature showing possibility of achievement to construct an anonymous and authentic data sharing system. It allows a data owner to anonymously authenticate his data which can be put into the cloud for storage or analysis purpose. Yet the costly certificate verification in the traditional public key infrastructure (PKI setting becomes a waist for this solution to be scalable. Identity-based (ID-based) ring signature, which eliminates the process of certificate verification, can be used instead of traditional public key infrastructure. In this paper, we further enhance the security of ID-based ring signature by providing forward security: If a secret key of any user has been dedicated, all previous generated signatures that include this user still remain valid. This property is especially important to any large scale data sharing system, as it is impossible to ask all data owners to reauthenticate their data even if a secret key of one single user has been dedicated. We provide a concrete and efficient instantiation of our scheme, prove its security and provide an implementation to show its practicality.