Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Pathway Generative Adversarial Hashing for Unsupervised Cross-Modal Retrieval

Cross-modal hashing aims to map heterogeneous cross-modal data into a common Hamming space, which can realize fast and flexible retrieval across different modalities. Unsupervised cross-modal hashing is more flexible and applicable than supervised methods, since no intensive labeling work is involved. However, existing unsupervised methods learn the hashing functions by preserving inter- and intra-correlations while ignoring the underlying manifold structure across different modalities, which is extremely helpful in capturing the meaningful nearest neighbors of different modalities for cross-modal retrieval. Furthermore, existing works mainly focus on pairwise relation modeling while ignoring the correlations within multiple modalities. To address the above-mentioned problems, in this paper, we propose a multi-pathway generative adversarial hashing approach for unsupervised cross-modal retrieval, which makes full use of a generative adversarial network's ability for unsupervised representation learning to exploit the underlying manifold structure of cross-modal data. The main contributions can be summarized as follows: First, we propose a multi-pathway generative adversarial network to model cross-modal hashing in an unsupervised fashion. In the proposed network, given the data of one modality, the generative model tries to fit the distribution over the manifold structure and selects informative data of other modalities to challenge the discriminative model. The discriminative model learns to distinguish the generated data and the true positive data sampled from the correlation graph to achieve better retrieval accuracy. These two models are trained in an adversarial way to improve each other and promote hashing function learning. Second, we propose a correlation graph-based approach to capture the underlying manifold structure across different modalities so that data of different modalities but within the same manifold can have a smaller Hamming distance to promote retrieval accuracy. Extensive experiments compared with state-of-the-art methods on three widely used datasets verify the effectiveness of our proposed approach.
Computational Forensic Linguistics: An Overview of Computational Applications in Forensic Contexts

The number of computational approaches to forensic linguistics has increased significantly over the last decades, as a result not only of increasing computer processing power, but also of the growing interest of computer scientists in natural language processing and in forensic applications. At the same time, forensic linguists faced the need to use computer resources in both their research and their casework – especially when dealing with large volumes of data. This article presents a brief, non-systematic survey of computational linguistics research in forensic contexts. Given the very large body of research conducted over the years, as well as the speed at which new research is regularly published, a systematic survey is virtually impossible. Therefore, this survey focuses on some of the studies that are relevant in the field of computational forensic linguistics. The research cited is discussed in relation to the aims and objectives of the linguistic analysis in forensic contexts, paying particular attention to both their potential and their limitations for forensic applications. The article ends with a discussion of future implications.
A Traffic Meter Based on a Multicolor Marker for Bandwidth Guarantee and Priority Differentiation in SDN Virtual Networks

Network virtualization is a technology that enables multiple tenants to share a common physical network. A virtual network (VN) needs to have dedicated bandwidth and a priority differentiation capability to provide quality of service (QoS) for its serving flows. In this paper, we propose a traffic meter based on a multicolor marker (MCM) to fulfill both inter-VN and intra-VN QoS requirements. In our system, a VN’s guaranteed bandwidth is dedicated to the VN, and the bandwidth is not influenced by the traffic generated from other VNs in the same physical network. In addition, the high-priority traffic of a VN can always achieve its needed bandwidth unless the total amount of high-priority traffic exceeds the VN’s guaranteed bandwidth. This feature enables a network to provide independent QoS differentiation for each VN. Our MCM meters can be applied in two operation modes. In the remaining bandwidth nonsharing (RBNS) mode, the guaranteed bandwidth is treated as an upper bound for each VN, even if the network has unused capacity remaining. However, in the remaining bandwidth sharing (RBS) mode, the unused network capacity is shared among VNs so that a VN can use more than its guaranteed bandwidth. To validate our design, we implemented our MCM meter in P4 programmable switches. The experimental results on a testbed reveal that our design can accurately achieve bandwidth isolation and QoS differentiation for network virtualization in P4-based software-defined networking (SDN).
Incipient Fault Diagnosis Method for IGBT Drive Circuit Based on Improved SAE

An incipient fault diagnosis method devised for insulated gate bipolar transistor (IGBT) drive circuit based on improved stack auto-encoder (SAE) is recommended. First, the Monte Carlo method is applied to extracting the time domain response signal of the circuit under test as sample data. Then, with SAE used to extract essential features of data, the SAE is employed to extract features of sample data. Meanwhile, multi-classification relevant vector machine (RVM) is involved for fault diagnosis of the acquired features. As the structure of the hidden layer in SAE and the learning rate could exert a significant effect on the feature extraction performance, in this paper, the quantum particle swarm optimization (QPSO) algorithm is used to optimize the above parameters. As revealed by the experimental results, the improved SAE method is effective in the extraction of the essential characteristics of the incipient faults for the IGBT drive circuit. Further with this, the incipient fault multi-classification RVM of the IGBT drive circuit is capable of achieving 100% diagnostic accuracy.
Ideal Decompositions for Vector Addition Systems

Vector addition systems, or equivalently Petri nets, are one of the most popular formal models for the representation and the analysis of parallel processes. Many problems for vector addition systems are known to be decidable thanks to the theory of well-structured transition systems. Indeed, vector addition systems with configurations equipped with the classical point-wise ordering are well-structured transition systems. Based on this observation, problems like coverability or termination can be proven decidable. However, the theory of well-structured transition systems does not explain the decidability of the reachability problem. In this presentation, we show that runs of vector addition systems can also be equipped with a well quasi-order. This observation provides a unified understanding of the data structures involved in solving many problems for vector addition systems, including the central reachability problem.
Design and Development of Vedic Mathematics based BCD Adder

In a conventional Binary Coded Decimal (BCD) representation is used in the scientific and computing calculation. Now they are also started to have impact in the processing unit. The only overhead in the converting the value from decimal to binary, processing and converting back to decimal. The direct reproduction of decimal value in computation produces the significant improvement in conversion and processing time. This paper is the extended version of Alp Arslan Bayracci and Ahmet Akkas et al of reduced delay Binary Coded Decimal (BCD) adder. When the design is simulated for the corner cases, the design was not responding as expected and we have proposed the modified design
Big Data for Traffic Engineering in Software-Defined Networks

Software-defined networking overcomes the limitations of traditional networks by splitting the control plane from the data plane. The network logic is moved to a component called the controller that manages devices in the data plane. To implement this architecture, it has become the norm to use the OpenFlow protocol, which defines several counters that are maintained by network devices. These counters are the starting point for Traffic Engineering activities. Traffic Engineering monitors several network parameters, including network bandwidth utilization. A great challenge for Traffic Engineering is to collect and generate statistics about bandwidth utilization for monitoring and traffic analysis activities. This becomes even more challenging if fine-grained monitoring is required. Network management tasks such as network provisioning, capacity planning, load balancing, and anomaly detection can benefit from this fine-grained monitoring. Because the counters are updated for every packet that crosses the switch, they must be retrieved in a streaming fashion. This scenario suggests the use of Big Data techniques to collect and process counter values. The benefits of Big Data techniques for collecting and processing counter values are twofold. First, Big Data techniques provide streaming processing tools that can deliver outputs in near real time, which can help early detection of anomalous traffic behaviour and possible proactive actions toward resolving the issue. Second, Big Data techniques provide batch processing tools that can deal with a large amount of historical data, which enable the use of Big Data analytics techniques to achieve a better understanding of traffic behaviour over time. This research proposes an approach based on a fine-grained Big Data traffic monitoring method to collect and generate traffic statistics using counter values. This research work can significantly leverage Traffic Engineering. The approach can provide a more detailed view of network resource utilization because it can deliver individual and aggregated statistical analyses of bandwidth consumption. In the context of the proposed monitoring method, this research proposes a new approach to estimate the Traffic Matrix, a repository that maintains information about the traffic volume between all host origin-destination pairs. This research also proposes a traffic analysis method based on batch processing of historical traffic data. Experimental results show the effectiveness and potential of the proposed methods.
A Unified Multi-task Adversarial Learning Framework for Pharmacovigilance Mining

The mining of adverse drug reaction (ADR) has a crucial role in the pharmacovigilance. The traditional ways of identifying ADR are reliable but time-consuming, non-scalable and offer a very limited amount of ADR relevant information. With the unprecedented growth of information sources in the forms of social media texts (Twitter, Blogs, Reviews etc.), biomedical literature, and Electronic Medical Records (EMR), it has become crucial to extract the most pertinent ADR related information from these free-form texts. In this paper, we propose a neural network inspired multi- task learning framework that can simultaneously extract ADRs from various sources. We adopt a novel adversarial learning-based approach to learn features across multiple ADR information sources. Unlike the other existing techniques, our approach is capable to extracting fine-grained information (such as ‘Indications’, ‘Symptoms’, ‘Finding’, ‘Disease’, ‘Drug’) which provide important cues in pharmacovigilance. We evaluate our proposed approach on three publicly available real- world benchmark pharmacovigilance datasets, a Twitter dataset from PSB 2016 Social Me- dia Shared Task, CADEC corpus and Medline ADR corpus. Experiments show that our unified framework achieves state-of-the-art performance on individual tasks associated with the different benchmark datasets. This establishes the fact that our proposed approach is generic, which enables it to achieve high performance on the diverse datasets.
Supervised information retrieval for text and images

Motivation: In recent years, with the continuing increase in available storage space, image repositories have grown to such sizes that it is becoming ever more difficult to keep track of this large amount of data. These images are usually ”unlabeled. ” If these images were categorized and ”labeled,” it would allow for the simple retrieval of a specific image upon request. It is important therefore, to find methods which enable us to automatically categorize and label these images. Say for example, we were requesting an image (photo) of the president of the U.S.A. from a large database of images. If all the images had already been placed into categories and labeled, then it would be trivial to just return all the images which fall into the category of U.S. President. The motivation for this work is the fact that several learning-machine algorithms have already been developed. Our goal is to combine several such algorithms and to use boosting techniques to improve our results.
DS-Vikor: a New Methodology for Supplier Selection

How to select the optimal supplier is an open and important issue in supply chain management (SCM), which needs to solve the problem of assessment and sorting the potential suppliers, and can be considered as a multi-criteria decision-making (MCDM) problem. Experts’ assessment play a very important role in the process of supplier selection, while the subjective judgment of human beings could introduce unpredictable uncertainty. However, existing methods seem powerless to represent and deal with this uncertainty effectively. Dempster-Shafer evidence theory (D- S theory) is widely used to uncertainty modeling, decision making and conflicts management due to its advantage to handle uncertain information. The VIKOR method has a great advantage to handle MCDM problems with non-commensurable and even conflicting criteria, and to obtain the compromised optimal solution. In this paper, a DS- VIKOR method is proposed for the supplier selection problem which expends the VIKOR method by D-S theory. In this method, the basic probability assignment (BPA) is used to denote the decision makers’ assessment for suppliers, Deng entropy weight-based method is defined and applied to determine the weights of multi-criteria, and VIKOR method is used for getting the final ranking results. An illustrative example under real life is conducted to analyze and demonstrate the practicality and effectiveness of the proposed DS-VIKOR method.
Improved round robin queue management algorithm for elastic and inelastic traffic flows

In current scenario, network traffic flows need a start-time fair queuing algorithm which is computationally efficient and also which can achieve maximum fairness regardless of variation in a network capacity. To enhance the situation of congestion, an improved round robin IRR queue management algorithm for elastic and inelastic traffic flows is proposed. In this approach, the traffic flows are categorised into elastic traffic flows and in-elastic traffic flows. The scheduling process in the inelastic flows is handled by the BRR scheduler algorithm since they have large capacity requirements and delay constraints and elastic flows will be scheduled using DRR-SFF. The results are simulated with NS-2 and they show consistent improvement in the performance of the network.
Transfer Capsule Network for Aspect Level Sentiment Classification

Aspect-level sentiment classification aims to determine the sentiment polarity of a sentence towards an aspect. Due to the high cost in annotation, the lack of aspect-level labeled data becomes a major obstacle in this area. On the other hand, document-level labeled data like reviews are easily accessible from online websites. These reviews encode sentiment knowledge in abundant contexts. In this paper, we propose a Transfer Capsule Network (TransCap) model for transferring document-level knowledge to aspect-level sentiment classification. To this end, we first develop an aspect routing approach to encapsulate the sentence-level semantic representations into semantic capsules from both the aspect-level and document-level data. We then extend the dynamic routing approach to adaptively couple the semantic capsules with the class capsules under the transfer learning framework. Experiments on SemEval datasets demonstrate the effectiveness of TransCap.
Efficient spiking neural network training and inference with reduced precision memory and computing

In this study, reduced precision operations are investigated in order to improve the speed and energy efficiency of SNN implementation. Instead of using the 32-bit single-precision floating-point format, small floating-point format and fixed-point format are used to represent SNN parameters and to perform SNN operations. The analyses are performed on the training and inference of a leaky integrate-and-fire model-based SNN that is trained and used to classify the handwritten digits in MNIST database. The analysis results show that for SNN inference, the floating-point format with 4-bit exponent and 3-bit mantissa or the fixed-point format with 6-bit integer and 7-bit fraction can be used without any accuracy degradation. For training, a floating-point format with 5-bit exponent and 3-bit mantissa or a fixed-point format with 6-bit integer and 10-bit fraction can be used to obtain full accuracy. The proposed reduced precision formats can be used in SNN hardware accelerator design and the selection between floating-point and fixed-point can be determined by design requirements. A case study of SNN implementation on field-programmable gate array device is performed. With reduced precision numerical formats, memory footprint, computing speed, and resource utilisation are improved. As a result, the energy efficiency of SNN implementation is also improved.
Non-Hermiticity and conservation of orthogonal relation in dielectric microcavity

Non-Hermitian properties of open quantum systems and their applications have attracted much attention in recent years. While most of the studies focus on the characteristic nature of non-Hermitian systems, here we focus on the following issue: A non-Hermitian system can be a subsystem of a Hermitian system as one can clearly see in Feshbach projective operator (FPO) formalism. In this case, the orthogonality of the eigenvectors of the total (Hermitian) system must be sustained, despite the eigenvectors of the subsystem (non-Hermitian) satisfy the bi-orthogonal condition. Therefore, one can predict that there must exist some remarkable processes that relate the non-Hermitian subsystem and the rest part, and ultimately preserve the Hermiticity of the total system. In this paper, we study such processes in open elliptical microcavities. The inner part of the cavity is a non-Hermitian system, and the outer part is the coupled bath in FPO formalism. We investigate the correlation between the inner- and the outer-part behaviors associated with the avoided resonance crossings (ARCs), and analyze the results in terms of a trade-off between the relative difference of self-energies and collective Lamb shifts. These results come from the conservation of the orthogonality in the total Hermitian quantum system.
A branch and bound approach for single machine scheduling problem

Last decades, scheduling problems have attracted researchers because of the fact that they play a critical role in production planning. This paper studies to minimize the sum weight of lateness on a single machine scheduling problem. There are given n jobs and for each job we have a release date, a processing time, a due date and weight in a constraint working environment. Single machine models are important for various reasons because of the fact that it not only provides insights into the single machine environment but also bottleneck problem. There are various exact methods in order to solve single machine scheduling problem with make span objective function. However, if the objective functions is tardiness, lateness, weighted tardiness, weighted lateness etc. to find exact solution is very difficult. In this paper, branch and bound method is proposed to solve single machine scheduling problem with the total weighed lateness objective for small number of job. The proposed method has applied on a job size of 4, 5 and 8 and provides optimal result.
Extracting diverse attribute-value information from product catalog text via transfer learning

E-commerce sites are increasingly becoming the norm for how consumers search, purchase, and review products. Such sites internally list millions of products, creating a torrent of product options that can overwhelm a browsing consumer. To facilitate their search, it helps to annotate each product with a table of attributes describing general features such as color, size, etc. However, the tables must be provided by the merchant, so there is a business incentive to automate this task by extracting attribute-value information directly from product titles and descriptions. However, while past methods have done extraction for only a handful of attributes, in practice their exists hundreds of diverse attributes. In this thesis, we present a single model for extracting information on all attributes. In addition, we show that incorporating extra information about intra-attribute similarity improves performance for data-poor attributes. Thesis Supervisor: Regina Barzilay Title: Delta Electronics Professor of Electrical Engineering and Computer Science
Enhancing Unsupervised Generative Dependency Parser with Contextual Information

Most of the unsupervised dependency parsers are based on probabilistic generative models that learn the joint distribution of the given sentence and its parse. Probabilistic generative models usually explicit decompose the desired dependency tree into factorized grammar rules, which lack the global features of the entire sentence. In this paper, we propose a novel probabilistic model called discriminative neural dependency model with valence (D-NDMV) that generates a sentence and its parse from a continuous latent representation, which encodes global contextual information of the generated sentence. We propose two approaches to model the latent representation: the first deterministically summarizes the representation from the sentence and the second probabilistically models the representation conditioned on the sentence. Our approach can be regarded as a new type of autoencoder model to unsupervised dependency parsing that combines the benefits of both generative and discriminative techniques. In particular, our approach breaks the context-free independence assumption in previous generative approaches and therefore becomes more expressive. Our extensive experimental results on seventeen datasets from various sources show that our approach achieves competitive accuracy compared with both generative and discriminative state-of-the-art unsupervised dependency parsers.