Reviewerly/exHarmony huggingface dataset:
Query Strategies, Assemble! Active Learning with Expert Advice for Low-resource Natural Language Processing

Active learning plays an important role in low-resource scenarios, i.e., when only a small amount of annotated instances is available. However, one does not know what is the best active learning strategy before actually testing a handful of strategies on a labeled set, which might not be viable in a real world low-resource scenario. Instead, it would be desirable to dynamically obtain the results from the best strategy on a given scenario, while using as little annotated resources as possible.In this paper, we present a novel application of prediction with expert advice to combine different query strategies as experts, giving a greater weight to those which select the most useful instances. We evaluated our approach in two Natural Language Processing (NLP) tasks: Part-of-Speech tagging (for English) and Named Entity Recognition (for Portuguese). Results show that our solution keeps up with the results of the best strategy in each scenario, nearly reaching fully supervised performance with only half of the annotated data.
An OWA and Aspect-based approach applied to Rating Prediction

We have witnessed a flourish of review websites where users can buy many products/services and share their opinions about them. Most of those opinions may be broken down into different sub-opinions on the different aspects describing said products/services. This fact makes more complicated the task of computing the overall polarity about the product/service studied.We are presenting a fuzzy aggregation mechanism to compute the overall sentiment conveyed in a opinion/review taking into account the individual ratings for the different aspects commented by the opinion holder. This proposal has been tested using real data from Yelp dataset obtaining promising results.
Fitness Landscape Features and Reward Shaping in Reinforcement Learning Policy Spaces

Reinforcement learning (RL) algorithms have received a lot of attention in recent years. However, relatively little work has been dedicated to analysing RL problems; which are thought to contain unique challenges, such as sparsity of the reward signal. Reward shaping is one approach that may help alleviate the sparse reward problem. In this paper we use fitness landscape features to study how reward shaping affects the underlying optimisation landscape of RL problems. Our results indicate that features such as deception, ruggedness, searchability, and symmetry can all be greatly affected by reward shaping; while neutrality, dispersion, and the number of local optima remain relatively invariant. This may provide some guidance as to the potential effectiveness of reward shaping for different algorithms, depending on what features they are sensitive to. Additionally, all of the reward functions we studied produced policy landscapes that contain a single local optimum and very high neutrality. This suggests that algorithms that explore spaces globally, rather than locally, may perform well on RL problems; and may help explain the success of evolutionary methods on RL problems. Furthermore, we suspect that the high neutrality of these landscapes is connected to the issue of reward sparsity in RL.
An Automated Person Authentication System with Photo to Sketch Matching Technique

Recently, considerable measures are taken to improve security in various locations. Most of the methods use the image-supported identification due to its accuracy and robustness. Usually, the picture assessment engages in capturing the raw image, identifying the vital information, extracting the features, and classification and authentication. This technique is used widely in human recognition and also in criminal science, recognition of the right individual is necessary for the verification necessities. In these applications, usually, the sketch drawn by an expert or a computer is matched alongside the digital photographs accessible in the criminal or the public database. Throughout this assessment, necessary facial features are mined from the photo and compared it with the original picture. This paper aims to evaluate the existing picture improvement and recognition procedures in the literature. After generating the necessary drawing for an individual, a relative examination against the digital picture of the person is executed and the image similarity measures are computed to authenticate photo with the sketch. This work proposes a methodology to evacuate the proposed technique using benchmark datasets and the result demonstrates a better result.
Effects of Language Modelling for Sepedi-English Code-Switched Speech in Automatic Speech Recognition System

Speech is the primary means of communication among people. Spoken dialogue system give out some means for people to be able to interact with computer systems. The automatic speech recognition system itself is forms part a of spoken dialogue systems. This type of system did a great job for European languages with more challenges encountered for the recognition of South African languages. In this study, we investigate the appropriate approaches for the development of language models for the recognition of Sepedi-English code-switched speech and their effect in ASR. The SRI Language Modeling (SRILM) toolkit was used to develop the Language Model (LM). The Kaldi toolkit to develop ASR system was chosen which is specifically used for speech recognition. This toolkit was used to evaluate the effects of the smoothing techniques. We have evaluated Four smoothing techniques namely Good-Turing (GT), Witten-Bell (WB), Modified Kneser-Ney (MKN), and Laplace (LP) Smoothing. The Witten-Bell smoothing technique was found to perform better than the other three smoothing techniques for Sepedi-English CS data in Language Modelling and also in our ASR.
WhatsApp and Cell Phones Among Sex Workers in India: The Impact of ICT in the Banaras Red-Light District

The aim of this work is threefold: first, to show how, in the context of the gentrification process in the city of Banaras (now Varanasi) in India, social networking, and in particular WhatsApp video calls, make up an attempt by young sex workers in the red-light district (i.e., Shivdaspur) to overcome the spatial entrapment, physical and identity displacement issues resulting from the policies of “beautification,” and urban development of the city. For young sex workers, information and communication technology represents an opportunity toward an unprecedented self-reflexive drive which, beyond a misrecognition of their profession and the symbolic violence it represents, could reinforce their understanding of the outside world to position themselves better within their surrounding society; second, to describe the testimony of older sex workers, according to whom, the Internet “normalizes” sexual violence on a national scale where it is strictly connected to the spread of pornography on the web; finally, to analyze, through an emic approach based on informal interviews, the complexities and potentiality of the information and communication technology for development in this context to shed more light on the design of research and intervention.
A benchmark for end-user structured data exploration and search user interfaces

During the years, it has been possible to assess significant improvements in the computational efficiency of Semantic Web search and exploration systems. However, it has been much harder to assess how well different semantic systems’ user interfaces help their users. One of the key factors facilitating the advancement of research in a particular field is the ability to compare the performance of different approaches. Though there are many such benchmarks in Semantic Web fields that have experienced significant improvements, this is not the case for Semantic Web user interfaces for data exploration. We propose and demonstrate the use of a benchmark for evaluating such user interfaces, which includes a set of typical user tasks and a well-defined procedure for assigning a measure of performance on those tasks to a semantic system. We have applied the benchmark to four such systems. Moreover, all the required resources to apply the benchmark are openly available online. We intend to initiate a community conversation that will lead to a generally accepted framework for comparing systems and for measuring, and thus encouraging, progress towards better semantic search and exploration tools.
Design of Hardware Acceleration System Based on FPGA and Deep Learning Algorithm

This paper designs a hardware acceleration system based on FPGA and deep learning algorithm for image recognition tasks. While implementing the convolutional neural network algorithm on FPGA based on the ZYNQ series chip, a hardware real-time recognition system architecture is designed. The ARM embedded in ZYNQ is used to collect, store and display the input image data in real time. The collected and stored data is transmitted through the AXI4 bus to the hardwareized convolutional neural network in the FPGA to complete the real-time recognition of the image. The experimental results show that the hardware acceleration system can complete 528 convolution operations in a single clock cycle. After 11-bit fixed-point quantization of the weight parameters, the network recognition rate is 97.8%. The hardware real-time recognition system designed in this paper can realize the real-time recognition of the images collected by the camera, and can adapt to different convolutional neural network models. It has strong portability and low overall system power consumption.
A Survey on Hybridized Gene Selection Strategies

Due to high dimensionality of gene expression datasets, gene selection is an important step for improving gene expression data classification accuracy. This is true for the case of cancer classification using gene expression data. Gene selection is the identifying process for particular gene set from large no of genes. Gene selection means dimensionality reduction as the data dimensionality and storage requirements are reduced. In data mining, dimensionality is one of a major problem. It may affect the quality of learning model. One disadvantage may lead to have over-fitting to the learning model, and also, it requires high CPU time for testing the accuracy. So, to overcome such situation, traditional approaches (filter, wrapper, embedded and hybrid) are used for selecting genes. Hybrid method is the integration of two or more algorithms applied for gene selection. By using this method, we can able to overcome the disadvantages of other sole methods. From these studies, we got to know that hybrid method selects informative genes for classification and also improves the accuracy and run-time performance of the classifier. This paper describes various implementations of hybrid methods. This paper denotes review on few gene selection methods mainly those that have proposed in last few years.
Delay and Power Efficient FIR Architecture for Noise Suppression of EOG Signal

In this paper, we proposed a modified FIR filter using Canonical signed digit (CSD) technique for de-noising the Electro-Oculography (EOG) signal. A dipole was formed between cornea and retina in an eye, resulting in voltage difference with occurrence of eye movement, which in turn generates an electrical signal. These signals are called as EOG and are useful in many medical and bioelectrical applications. To remove the noise in EOG signal an enhanced 16-bit FIR filter was used. The proposed FIR architecture uses CSD based multipliers, unlike the conventional shift-add the proposed architecture provide greater improvement in power and delay. CSD is used in order to reduce the design complexity of the multiplication by representing the filter coefficients in CSD format. The proposed approach was implemented on Xilinx FPGA development kit. By comparing the synthesis results for existing and proposed methods, which has achieved an improvement of 12.60% and 11.76% in Power Delay Product (PDP) and Area Delay Product (ADP) respectively.
A Study on Prediction and Spreading of Epidemic Diseases

Human population is being affected immensely day by day by different epidemic diseases. The West Nile virus outbreaks are epidemic in mainly suburban and urban areas. This paper is a study on various models that can be used to predict the occurrence of an epidemic disease caused by the West Nile Virus (WNV). It is also possible to separate an individual into different compartments like Susceptible and Infectious depending upon the state of infection spread. This paper also discusses different compartmental models that can be used to determine spreading of the West Nile virus. This can be an aid to the health care systems to prevent such deadly viruses to an extent.
A Damage-Tolerant Task Assignment Algorithm for UAV Swarm in Confrontational Environments

As Unmanned Aerial Vehicles (UAVs) are widely used in many applications, a lot of military missions in confrontational environments are being undertaken by UAV swarm rather than human beings due to its advantages. In confrontational environments, the reliability and availability of UAV swarm would be the major concern because of UAVs’ vulnerability, so damage-tolerant task assigning algorithms are of great importance. In this paper, we come up with a novel damage-tolerant framework for assigning real-time tasks to UAVs with dynamical states in confrontational environments. Different from existing scheduling methods, we not only assign tasks but also back up copies of tasks to UAVs when needed, to promote reliability. Meanwhile, we adopt an overlapping mechanism, including Backup-Primary overlapping and Backup-Backup overlapping, in assignment to save the limited swarm resources. On the basis of the damage-tolerant and overlapping mechanism, for the first time, we propose a new damage-tolerant task assignment algorithm named DTTA, aiming at promoting the task success probability. Extensive experiments are conducted based on random synthetic workloads to compare DTTA with three baseline algorithms. The experimental results indicate that DTTA can efficiently promote the probability of tasks’ success without affecting the effectiveness of swarms in confrontational environments.
A novel Ising model processing achieving all interactions only by adjacent spins for a high-speed solver for versatile Ising machines

To implement the Ising model on hardware and create “things” with optimization capability for a future IoT society, two methods for solving an optimization and recognition problem by using a new general-purpose Ising model suitable for LSI chip implementation are proposed. An ordinary model cannot solve many optimization problems, by searching for the ground-state, because the number of interactions is not enough for mapping the problems to the model. This ordinary Ising model considers only interactions of adjacent spins. Actually, to solve an optimization problem, interactions between all spins must be considered. However, all interactions are impossible to implement in an LSI chip. Therefore, to consider all interactions equivalently by adjacent spins only, two models are proposed. By using these model that implements all interactions, it is possible to obtain good candidates for optimal solutions for the “traveling salesman problem” and the “support vector machine”. Moreover, since the features of original Ising model with all interactions is known as a general-purpose model, this result means that the proposed model can be used to obtain the optimal solution candidates for most optimization problems. Further, LSI block diagrams of the two models are presented.
Using Eye-tracking to Study the Authenticity of Images Produced by Generative Adversarial Networks

Nowadays, Machine Learning algorithms, such as Generative Adversarial Networks (GANs), enable generating content, and especially images, featuring people, objects, or landscapes, with unprecedented levels of accuracy and fidelity. As a result, it is becoming challenging for a viewer to distinguish a picture of a fake profile from one that has a real human in it. In this paper, we present the results of an experimental study in which we investigated the perception of images produced by GANs. Specifically, we focused on the individuals' ability to discriminate between fake and real profiles. Furthermore, we utilized eye-tracking technology to identify the presence of patterns in subjects' gaze, which, in turn, can be useful to optimize the output of GANs and, simultaneously, provide insight on the underlying cognitive dynamics.
Kernel based Matching and a Novel training approach for CNN-based QbE-STD

Query-by-Example based spoken term detection (QbE-STD) to audio search involves matching an audio query with the reference utterances to find the relevant utterances. QbE-STD involves computing a matching matrix between a query and reference utterance using a suitable metric. In this work we propose to use kernel based matching by considering histogram intersection kernel (HIK) as a matching metric. A CNN-based approach to QbE-STD involves first converting a matching matrix to a corresponding size-normalized image and classifying the image as relevant or not [6]. In this work, we propose to train a CNN-based classifier using size-normalized images instead of splitting them into subimages as in [6]. Training approach proposed in this work is expected to be more effective since there is less chance of a CNN based classifier getting confused. The effectiveness of the proposed kernel based matching and novel training approach is studied using TIMIT dataset.
A Front Water Recognition Method Based on Image Data for Off-Road Intelligent Vehicle

Off-road intelligent vehicle is an important application about Internet of Vehicles technology used in the transportation field, and the front obstacle recognition method is the key technology for off-road intelligent vehicle. In this paper, based on smart data aggregation inspired paradigm of IoT applications, we mainly study perception technology in vehicle networking by using image data and one symmetrical speeded-up robust features detector (SURF). By considering symmetry and image data aggregation, we found that data aggregation had the ability of providing global information for Internet of Vehicles systems. After we have built the experiment platform, the experiment results showed that this method is faster than Scale-Invariant Feature Transform algorithm in this case, which can satisfy the water detection accuracy and the real-time requirement. So, this method is effective for the water images detection with great symmetry to off-road intelligent vehicle, and it also gives a useful reference about environment perception technology and smart data aggregation inspired paradigm used in future Internet of Vehicles, intelligent vehicle, and traffic safety applications.
A System implementing Fuzzy Hypothetical Datalog<sup>⋆</sup>

This paper presents a system implementing a novel addition to a fuzzy deductive database: hypothetical queries. Such queries allow users to dynamically make assumptions on a given database instance, either by adding or removing data, without changing the instance. Further, since a fuzzy database includes fuzzy relations, these relations can also be changed with assumptions. This ability for dynamic change seamlessly enables writing "what-if" applications such as decision-support systems. Here, the new language Fuzzy Hypothetical Datalog is presented, along with an operational semantics and stratified inference. It has been implemented in a working system DES readily available on-line.
Hybrid-Attention Enhanced Two-Stream Fusion Network for Video Venue Prediction

Video venue category prediction has been drawing more attention in the multimedia community for various applications such as personalized location recommendation and video verification. Most of existing works resort to the information from either multiple modalities or other platforms for strengthening video representations. However, noisy acoustic information, sparse textual descriptions and incompatible cross-platform data could limit the performance gain and reduce the universality of the model. Therefore, we focus on discriminative visual feature extraction from videos by introducing a hybrid-attention structure. Particularly, we propose a novel Global-Local Attention Module (GLAM), which can be inserted to neural networks to generate enhanced visual features from video content. In GLAM, the Global Attention (GA) is used to catch contextual scene-oriented information via assigning channels with various weights while the Local Attention (LA) is employed to learn salient object-oriented features via allocating different weights for spatial regions. Moreover, GLAM can be extended to ones with multiple GAs and LAs for further visual enhancement. These two types of features respectively captured by GAs and LAs are integrated via convolution layers, and then delivered into convolutional Long Short-Term Memory (convLSTM) to generate spatial-temporal representations, constituting the content stream. In addition, video motions are explored to learn long-term movement variations, which also contributes to video venue prediction. The content and motion stream constitute our proposed Hybrid-Attention Enhanced Two-Stream Fusion Network (HA-TSFN). HA-TSFN finally merges the features from two streams for comprehensive representations. Extensive experiments demonstrate that our method achieves the state-of-the-art performance in the large-scale dataset Vine. The visualization also shows that the proposed GLAM can capture complementary scene-oriented and object-oriented visual features from videos. Our code is available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/zhangyanchao1014/HA-TSFN</uri> .
A Design Approach for General Type-2 Fuzzy Logic Controllers with an Online Scheduling Mechanism

This paper proposes a systematic approach to solve the design problem of General Type-2 (GT2) Fuzzy Logic Controllers (FLCs) with an online scheduling mechanism for performance enhancements. We firstly suggest constructing the GT2-FLC over its baseline type-1 and interval type-2 FLCs, and then to tune a single design parameter which defines the shape of the secondary membership functions. We present how the shape of the secondary membership function changes with respect to the design parameter and show resulting effect on the control surface generation. The presented comparative analysis on the control surfaces show that aggressive and smooth control surfaces can be easily generated by tuning the design parameter. We suggest tuning the design parameter by providing a tradeoff between robustness and performance of the control system. Also, to achieve satisfactory control performances for various steady-state points, we propose an online scheduling mechanism that tunes the design parameter with respect to the operating points. We perform a simulation study on a nonlinear system to validate our analyses and proposed design methods. The simulation results show that GT2-FLC has a potential to improve overall system performances in comparison to its type-1 and interval type-2 counterparts, while the developed scheduling mechanism provides an opportunity to achieve satisfactory results for various operating points.
The Serializable and Incremental Semantic Reasoner fuzzyDL

Serializable and incremental semantic reasoners make it easier to reason on a mobile device with limited resources, as they allow the reuse of previous inferences computed by another device without starting from scratch. This paper describes an extension of the fuzzy ontology reasoner fuzzyDL to make it the first serializable and incremental semantic reasoner. We empirically show that the size of the serialized files is smaller than in another serializable semantic reasoner (JFact), and that there is a significant decrease in the reasoning time.