Miracle-dz/UniFAR-FTUs huggingface dataset:
Limitation of effectiveness in using matlab gpuarray method for calculating products of transpose-symmetrically sized matrices

A research of effectiveness in using MATLAB gpuArray method for calculating products of transpose­symmetrically sized matrices is represented. For this, MATLAB gpuArray method is used on three types of NVIDIA® GPU. It is revealed that, independently of the size, generating matrices directly on GPU is fully inefficient. GeForce GT 610 is inefficient in itself. GeForce GTS 450 is efficient when number of lines and columns of the first matrix is greater than 200 and 50, respectively. The running time efficiency of matrix product calculation for Tesla K40c is stronger, as it comes when number of lines and columns of the first matrix is greater than 70 and 10, respectively.
Adaptive Encryption Techniques In Wireless Communication Channels With Tradeoffs Between Communication Reliability And Security

Encryption is a vital process to ensure the confidentiality of the information transmitted over an insecure wireless channel. However, the nature of the wireless channel tends to deteriorate because of noise, interference and fading. Therefore, a symmetrically encrypted transmitted signal will be received with some amount of error. Consequently, due to the strict avalanche criterion (SAC), this error propagates during the decryption process, resulting in half the bits (on average) after decryption to be in error. In order to alleviate this amount of error, smart coding techniques and/or new encryption algorithms that take into account the nature of wireless channels are required. The solution for this problem could involve increasing the block and key lengths which might degrade the throughput of the channel. Moreover, these solutions might significantly increase the complexity of the encryption algorithms and hence to increase the cost of its implementation and use. Two main approaches have been followed to solve this problem, the first approach is based on developing an effective coding schemes and mechanisms, in order to minimize and correct the errors introduced by the channel. The second approach is more focused on inventing and implementing new encryption algorithms that encounter less error propagation, by alleviating the SAC effect. Most of the research done using these two approaches lacked the comprehensiveness in their designs. Some of these works focused on improving the error performance and/or enhancing the security on the cost of complexity and throughput. In this work, we focus on solving the problem of encryption in wireless channels in a comprehensive way, that considers all of the factors in its structure (error performance, security and complexity). New encryption algorithms are proposed, which are modifications to the Standardized Encryption Algorithms and are shown to outperform the use of the these algorithms in wireless channels in terms of security and error performance with a slight addition in the complexity. We introduce new modifications that improves the error performance for
Wide-coverage parsing for Turkish

Wide-coverage parsing is an area that attracts much attention in natural language processing research. This is due to the fact that it is the first step to many other applications in natural language understanding, such as question answering. Supervised learning using human-labelled data is currently the best performing method. Therefore, there is great demand for annotated data. However, human annotation is very expensive and always, the amount of annotated data is much less than is needed to train well-performing parsers. This is the motivation behind making the best use of data available. Turkish presents a challenge both because syntactically annotated Turkish data is relatively small and Turkish is highly agglutinative, hence unusually sparse at the whole word level. METU-Sabanci Treebank is a dependency treebank of 5620 sentences with surface dependency relations and morphological analyses for words. We show that including even the crudest forms of morphological information extracted from the data boosts the performance of both generative and discriminative parsers, contrary to received opinion concerning English. We induce word-based and morpheme-based CCG grammars from Turkish dependency treebank. We use these grammars to train a state-of-the-art CCG parser that predicts long-distance dependencies in addition to the ones that other parsers are capable of predicting. We also use the correct CCG categories as simple features in a graph-based dependency parser and show that this improves the parsing results. We show that a morpheme-based CCG lexicon for Turkish is able to solve many problems such as conflicts of semantic scope, recovering long-range dependencies, and obtaining smoother statistics from the models. CCG handles linguistic phenomena i.e. local and long-range dependencies more naturally and effectively than other linguistic theories while potentially supporting semantic interpretation in parallel. Using morphological information and a morpheme-cluster based lexicon improve the performance both quantitatively and qualitatively for Turkish. We also provide an improved version of the treebank which will be released by kind permission of METU and Sabanci.
Support vector machines, Mel-Frequency Cepstral Coefficients and the Discrete Cosine Transform applied on voice based biometric authentication

In this paper, the implementation of a Support Vector Machine (SVM) is proposed based on automatic system for voice biometric authentication, recognizing the speaker using Mel-Frequency Cepstral Coefficients and the Discrete Cosine Transform. The voice recognition problem can be modeled as a classification problem, where the objective is to obtain the best degree of separability between the classes which represent the voice. Building an automated speech recognition system capable of identifying the speaker, has many techniques using artificial intelligence and general classification at disposal, Support Vector Machines being the one used in this work. The voice samples used are in the Brazilian Portuguese language and had its features extracted through the Discrete Cosine Transform. Extracted features are applied on the Mel-frequency Cepstral Coefficients to create a two-dimensional matrix used as input to the SVM algorithm. This algorithm generates the pattern to be recognized, leading to a reliable speaker identification using few parameters and a small dataset.
Dialogue-Act Prediction of Future Responses Based on Conversation History

Sequence-to-sequence models are a common approach to develop a chatbot. They can train a conversational model in an end-to-end manner. One significant drawback of such a neural network based approach is that the response generation process is a black-box, and how a specific response is generated is unclear. To tackle this problem, an interpretable response generation mechanism is desired. As a step toward this direction, we focus on dialogue-acts (DAs) that may provide insight to understand the response generation process. In particular, we propose a method to predict a DA of the next response based on the history of previous utterances and their DAs. Experiments using a Switch Board Dialogue Act corpus show that compared to the baseline considering only a single utterance, our model achieves 10.8% higher F1-score and 3.0% higher accuracy on DA prediction.
Inference of stochastic regular languages through simple recurrent networks

Grammatical inference has been recently approached through artificial neural networks. Recurrent connectionist architectures were trained to accept or reject strings belonging to a number of specific regular languages, or to predict the possible successor(s) for each character in the string. On the other hand, for static (non-string) data, M.D. Richard et al. (1991), showed that a nonrecurrent architecture can estimate Bayesian a posteriori probabilities. The authors show empirical evidence supporting this statement which also seems to be verified when simple recurrent networks (SRNs) are used to estimate probabilities of stochastic regular languages. >
Generating Natural Language Adversarial Examples through Probability Weighted Word Saliency

We address the problem of adversarial attacks on text classification, which is rarely studied comparing to attacks on image classification. The challenge of this task is to generate adversarial examples that maintain lexical correctness, grammatical correctness and semantic similarity. Based on the synonyms substitution strategy, we introduce a new word replacement order determined by both the word saliency and the classification probability, and propose a greedy algorithm called probability weighted word saliency (PWWS) for text adversarial attack. Experiments on three popular datasets using convolutional as well as LSTM models show that PWWS reduces the classification accuracy to the most extent, and keeps a very low word substitution rate. A human evaluation study shows that our generated adversarial examples maintain the semantic similarity well and are hard for humans to perceive. Performing adversarial training using our perturbed datasets improves the robustness of the models. At last, our method also exhibits a good transferability on the generated adversarial examples.
Resource-Aware Network Topology Management Framework

Cloud infrastructure provides computing services where computing resources can be adjusted on-demand. However, the adoption of cloud infrastructures brings concerns like reliance on the service provider network, reliability, compliance for service level agreements (SLAs), etc. Software-defined networking (SDN) is a networking concept that suggests the segregation of a network’s data plane from the control plane. This concept improves networking behavior. In this paper, we present an SDN-enabled resource-aware topology framework. The proposed framework employs SLA compliance, Path Computation Element (PCE) and shares fair loading to achieve better topology features. We also present an evaluation, showcasing the potential of our framework.
Evaluation of learning of asymmetrical bimanual tasks and transfer to converse pattern: Load, temporal and spatial asymmetry of hand movements

Background: In most daily activities, we are required to use both hands. In many motor skills like playing guitar, the left and right hand must perform asymmetric movements with different timing. Objective: The aim of the study was to evaluate the effect of learning in various asymmetrical bimanual tasks and to evaluate the transfer to tasks with converse hand movements. Methods: Thirty right-handed male students (age 21.5 ± 1.3 years) who had no motor disorders were divided into three groups. Participants of each group were trained for four days after a pretest. All participants performed asymmetrical bimanual drawing of a circle with each hand. Participants in the first group differed in terms of load in each hand, those in the second group differed in the speed of hand movement and those in the third group differed in the range of motion. The test was carried out in simultaneous bimanual movement both as practiced (learning acquisition test) and substitution of patterns between the two hands (transfer test). To analyze the data, repeated measures analysis of variance was performed. Results: For the acquisition test, significant differences were found between the results of the pretest, the posttest, and the retention test across all three groups. In terms of the transfer test, the first group showed a significantly better performance than their performance on the acquisition (p = .001). No such differences were found between the performance of the second group on the two tests (p = .945). Finally, the third group performed significantly better on the transfer test than on the acquisition test. (p = .047). Conclusions: The present study found similar effects of motor learning on various asymmetrical bimanual motor tasks, but different inter-group performance on learning and transfer tasks.
ScholarWorks at WMU

This project investigated the effects of providing verbal descriptions of intersections and crosswalks on the performance of street-crossing subtasks by individuals who are totally blind. The intersections included crosswalks that varied widely in geometric and operational characteristics, including the presence or absence of accessibility features. In the no-database condition, participants used their typical street-crossing procedures. In the database-condition, participants additionally listened to database-generated descriptions of the intersections and crosswalks before crossing. The database descriptions had significant positive effects on some subtasks (primarily “crossing” subtasks such as deciding when to cross) and not others (primarily “wayfinding” subtasks such as remaining in the crosswalk).
Log2Intent: Towards Interpretable User Modeling via Recurrent Semantics Memory Unit

Modeling user behavior from unstructured software log-trace data is critical in providing personalized service (\emphe.g., cross-platform recommendation). Existing user modeling approaches cannot well handle the long-term temporal information in log data, or produce semantically meaningful results for interpreting user logs. To address these challenges, we propose a Log2Intent framework for interpretable user modeling in this paper. Log2Intent adopts a deep sequential modeling framework that contains a temporal encoder, a semantic encoder and a log action decoder, and it fully captures the long-term temporal information in user sessions. Moreover, to bridge the semantic gap between log-trace data and human language, a recurrent semantics memory unit (RSMU) is proposed to encode the annotation sentences from an auxiliary software tutorial dataset, and the output of RSMU is fed into the semantic encoder of Log2Intent. Comprehensive experiments on a real-world Photoshop log-trace dataset with an auxiliary Photoshop tutorial dataset demonstrate the effectiveness of the proposed Log2Intent framework over the state-of-the-art log-trace user modeling method in three different tasks, including log annotation retrieval, user interest detection and user next action prediction.
Design and Implementation of High Performance Simulation Platform for Switching and Scheduling

Simulation has become a significant way for performance evaluations in switching and scheduling, however, the existing simulation softwares have some limitations in inheritability and extensibility. Based on the current crossbar switching fabric, by employing system level design method, and object oriented technology, a simulation platform called SPES (switching performance evaluation system) for switching fabrics and scheduling policies' developments are designed and implemented. Input queuing, output queuing, combined input-output queuing and combined input-crosspoint queuing and corresponding scheduling policies are integrated. Inheritability and extensibility attributes are obtained by designing traffic sources, switching fabrics and scheduling policies separately, and it exhibits good performances for supporting multi-fabric, variable packets sizes and QoS model's simulations. By configuring the platform through a uniform view, users can fulfill their concrete simulation environment. Besides, it can carry out end to end performances' evaluations with little modification. Finally, this paper presents a simulation case based on combined input-crosspoint queuing switch, displaying the good
Expression Invariant Face Recognition Using Convolutional Neural Networks

: For the past few decades, one of the most extensively studied topics under computer vision is face recognition. This technology has many applications in real life. However, the performance of a face recognition system suffers with variation in lighting, pose and facial expression. Out of these major concerns faced by the recognition system this paper mainly focuses on the impact of expression variation in recognition systems. Facial expression changes have a negative effect on the accuracy of recognition of a face recognition system. Hence this paper proposes an expression invariant face recognition technique using convolutional neural network.
An Efficient and Secure Authentication Scheme for Vehicle Sensor Networks

Vehicle sensor networks (VSNs) have applications in intelligent transportation system and smart cities, as they enable vehicles to exchange information for improving traffic security and efficiency. However, VSNs are also subject to a broad range of attacks, including those typically associated with wireless networks. While a large number of authentication schemes have been proposed in the literature, most of these schemes failed to achieve the claimed security attributes. For example, Zhou et al. (2017) proposed a trust-extended authentication scheme for VSNs. However, their scheme cannot provide user anonymity or security against several known attacks as claimed. Thus, we construct a new authentication scheme based on Elliptic Curve Cryptography (ECC), and demonstrate our scheme is secure in the random oracle model. We also show that our scheme can satisfy a number of security requirements. Finally, findings from the performance evaluation suggest that our scheme is more practical for VSNs deployment.
Joint Type Inference on Entities and Relations via Graph Convolutional Networks

We develop a new paradigm for the task of joint entity relation extraction. It first identifies entity spans, then performs a joint inference on entity types and relation types. To tackle the joint type inference task, we propose a novel graph convolutional network (GCN) running on an entity-relation bipartite graph. By introducing a binary relation classification task, we are able to utilize the structure of entity-relation bipartite graph in a more efficient and interpretable way. Experiments on ACE05 show that our model outperforms existing joint models in entity performance and is competitive with the state-of-the-art in relation performance.
Automated Knowledge Provider System with Natural Language Query Processing

ABSTRACT Knowledge management system (KMS) is an effective tool for knowledge extraction, utilization, and dissemination. When a client or user sends query to the KMS, it is expected to provide response to the query. This paper proposes an automated knowledge provider system (AKPS) that accepts a natural language request of a user in a query–response model where natural language query will be automatically converted to the conceptual form of database. This model is a grammatical rule-based automated model that can extract knowledge from knowledge database resident within the AKPS independently without any manual intervention. The model maps the natural language query into the physical data model, and sends response to the user from a well-organized and well-defined database.
Discovery of autonomous semantic services in mobile ad hoc networks

Mobile Ad Hoc Networks (MANETs) are dynamic networks where mobile devices with wireless communication capabilities cooperate to provide spontaneous connectivity. In particular, a mobile ad hoc network can be considered an open distributed system, where autonomous mobile nodes with limited capabilities can join and leave the network at any time. The resulting topologies are decentralised and differ from those in stationary networks in their high degree of randomness. Although a challenging environment, MANETs find wide applicability in scenarios where spontaneous networking is required, e.g., pervasive computing. This thesis assumes that such scenarios will make use of collaborative applications with functionality that is distributed across nodes. In such a dynamic environment, the location of required functionality cannot be hardwired. Nodes can acquire transient addresses and may fail or partition at any time. The result is that discovery becomes an important process preceding any collaborative effort. In general, discovery attempts to match required and available functionality by using an appropriate representation language and suitable network support. Service Oriented Computing (SOC) has emerged as a paradigm to design distributed applications by representing resources as modular and independent services that can be advertised, discovered and invoked. In MANETs, representation and discovery of services has several unique requirements: the opportunistic nature of the network necessitates reduced human intervention and hence requires automated discovery; node autonomy implies that service representation is difficult to standardise or to guarantee its agreement between providers and consumers; the open and dynamic nature of the network requires discovery mechanisms that scale; and resource constrained devices dictate efficient distribution of discovery load.
Hinglish Text Classification

- Text Classification can be done in easily in English, but it’s difficult to perform in various other languages. Not much of the work is done for Indian languages like Hindi, Marathi, Bengali. Due to the incredible growth in the internet users most of the people are comfortable with Hinglish which is the combination of Hindi and English. This paper identifies of language, sentiment analysis and the new classification based on Hinglish language is proposed.
Optimisation model for scheduling MapReduce jobs in big data processing / Ibrahim Abaker Targio Hashem

With the fast development of Internet-based technologies, data generation has increased drastically over the past few years, coined as big data era. Big data offer a new paradigm shift in data exploration and utilization. The major enabler for underlying many big data platforms is certainly the MapReduce computational paradigm. Scheduling plays an important role in MapReduce, mainly in reducing the execution time of data-intensive jobs. However, despite recent efforts toward improving MapReduce performance, scheduling MapReduce jobs across multiple nodes have shown to be multi-objective optimization problem. The problem is even more complex using virtualized clusters in a cloud computing to execute a large number of tasks. The complexity lies in achieving multiple objectives that may be of conflicting nature. These conflicting requirements and goals are challenging to optimize due to the difficulty of predicting a new incoming jobi? ½s behavior and its completion time. In this study, we aim to optimize task scheduling and resource utilization using an evolutionary algorithm based on the proposed completion time and monetary cost of cloud service models. The multi-objective approaches which are, Sorting Genetic Algorithm II (NSGA-II) and Strength Pareto Evolutionary Algorithm II (SPEA2) are applied to find the Pareto front of the Makespan and total cost. The result of our experiment analysis reveals that the advantage of NSGA-II over the SPEA2 on the tested problems based on the adopted measuring criteria. In addition, NSGA-II algorithm was able to find the optimal solutions. We then proposed a multi-objective scheduling algorithm framework that considers resource allocation and task scheduling in a heterogonous cloud environment. The proposed algorithm is evaluated using tasks scheduling in the scheduling load simulator and validated using statistical modeling. The simulation results acquired from the experiments showed the effectiveness of the proposed framework and algorithm.