Reviewerly/exHarmony huggingface dataset:
A Generic Architecture for Cyber-Physical-Social Space Applications

This paper briefly presents a reference architecture called Virtual Physical Space. The purpose of the architecture is to adapt to the development of various Cyber-Physical-Social applications. In the paper, the basic components of the space are described in more detail. Adapting the proposed architecture to implement an intelligent personal touristic guide is also considered.
Human Activity Recognition Using an Accelerometer Magnitude Value

Human activity recognition (HAR) is important for many applications to help healthcare and support systems due to fast increase of senior population worldwide. This paper describes a human activity recognition framework based on feature selection techniques from a waist single accelerometer. The objective is to identify the most important features to recognize static and dynamic human activities based on module acceleration, since a public database. A set of time and frequency features are getting from the module, so to analyze the impact of the features on the performance of the recognition system, a ReliefF algorithm is applied. Finally, a multiclass classification model is implemented thought Support Vector Machine (SVM). Experimental results indicate that the accuracy of the propose model is over of 85%, this percentage is like other works in which use each axes accelerometer. The advantage of this work is the use of the module value that allow identify the activity independently of the sensor position, also it reduces the computer resources.
Curriculum By Texture.

Convolutional Neural Networks (CNNs) have shown impressive performance in computer vision tasks such as image classification and segmentation. One factor for the success of CNNs is that they have an inductive bias that assumes a certain type of spatial structure is present in the data. Recent work by Geirhos et al. (2018) shows how learning in CNNs causes the learned CNN models to be biased towards high-frequency textural information, compared to low-frequency shape information in images. Many tasks generally requires both shape and textural information. Hence, we propose a simple curriculum based scheme which improves the ability of CNNs to be less biased towards textural information, and at the same time, being able to represent both the shape and textural information. We propose to augment the training of CNNs by controlling the amount of textural information that is available to the CNNs during the training process, by convolving the output of a CNN layer with a low-pass filter, or simply a Gaussian kernel. By reducing the standard deviation of the Gaussian kernel, we are able to gradually increase the amount of textural information available as training progresses, and hence reduce the texture bias. Such an augmented training scheme significantly improves the performance of CNNs on various image classification tasks, while adding no additional trainable parameters or auxiliary regularization objectives. We also observe significant improvements when using the trained CNNs to perform transfer learning on a different dataset, and transferring to a different task which shows how the learned CNNs using the proposed method act as better feature extractors.
Semantic Shot Classification in Baseball Videos Based on Similarities of Visual Features

This paper presents a method of semantic shot classification in baseball videos based on similarities of visual features. Since it is difficult to prepare a large amount of training data with annotation, accurate event detection methods constructed from a small amount of training data are needed. In broadcast baseball video, since view angles of cameras are different for each event, shot change and event change have a close relationship. When visual features from shots are similar, events corresponding to shots are also similar, and a simple distance-based approach only focusing on training data is effective. Therefore, semantic shot classification based on visual features from a small amount of training data can be realized.
The More, the Better? Compensation and Remorse as Data Breach Recovery Actions – An Experimental Scenario-based Investigation

With the increasing number of companies actively collecting data, the number of data breaches has exploded. It can be observed that affected often discontinue their relationship with the company. In order to avoid this kind of response, companies should develop and deploy their own recovery strategies. In our paper, we examined the effectiveness of different recovery strategies geared towards retaining customer satisfaction immediately after a data breach. We examine a data breach of a fitness tracker that varies in severity and tests the recovery actions compensation and remorse. The results found that customer satisfaction depends on the severity of the data breach, while combining compensation and remorse together demonstrates itself as the best strategy for increasing customer satisfaction in almost all cases. However, it was also discovered that in case of a severe data breach, customer satisfaction is difficult to restore and in the end remorse has virtually no effect.
Gazetteer Generation for Neural Named Entity Recognition

We present a way to generate gazetteers from the Wikidata knowledge graph and use the lists to improve a neural NER system by adding an input feature indicating that a word is part of a name in the gazetteer. We empirically show that the approach yields performance gains in two distinct languages: a high-resource, word-based language, English and a high-resource, character-based language, Chinese. We apply the approach to a low-resource language, Russian, using a new annotated Russian NER corpus from Reddit tagged with four core and eleven extended types, and show a baseline score.
Computational perception of information foci produced by Chinese English learners and American English speakers

This study used computational perception, via SVM and Random Forest models, to examine phonetic features used by American English speakers (AE) and Chinese second language learners of English (CE1 with low proficiency and CE2 with high proficiency) in realizing different information foci. For all participant groups, the machine learning models achieved above chance level accuracy. Coda duration and the duration of the rising contour were two phonetic features that ranked top across three participant groups in terms of their importance to the models. The SVM models trained with the AE data classified different foci by CE1 and CE2 with above chance level accuracy, but English proficiency had little effect on the classification results.
Enhancing Deep Knowledge Tracing (DKT) Model by Introducing Extra Student Attributes

Modeling the knowledge of the student is very important in online tutoring systems. It helps the instructor to design effective exercises which vary in difficulty level and design curriculum according to the observed knowledge state of students. But modeling the student knowledge has got its own challenges. The model which uses RNNs (Recurrent Neural Networks) which has proved to be effective in modeling the student knowledge but has got its own limitations. We found that there were some features that were very important in predicting the performance of student but were ignored in the original DKT (Deep knowledge Tracing) model [1]. In this paper, we attempt to consider three more features such as No. of hints accessed, Student First Response Time and Number of Attempts when compared to DKT and use the same model as in DKT where the only change is made in the input vector we provide to the model. We also use certain techniques like dimensionality reduction by using autoencoder which helps in reducing the dimensionality as we are taking extra features into consideration.
A Low-Latency and Low-Complexity Hardware Architecture for CTC Beam Search Decoding

The recurrent neural networks (RNNs) along with connectionist temporal classification (CTC) have been widely used in many sequence to sequence tasks, including automatic speech recognition (ASR), lipreading, and scene text recognition (STR). In these systems, CTC-trained RNNs usually require specific CTC-decoders after their output layers. Many existing CTC-trained RNN inference systems use FPGA to do calculations of RNNs, and decode their outputs on CPU. However, with the development of FPGA-based RNN hardware accelerators, existing CPU-based CTC-decoder can not meet the latency requirement of them. To resolve this issue, this paper proposes an efficient hardware architecture for the CTC beam search decoder based on the decoding method reported in our previous work. The experimental results show that the system latency per sample of the CTC-decoder is only 7.19us on Xilinx xc7vx1140tflg19301 FPGA platform, which is lower than state-of-the-art RNNs. We also implement the origin algorithm on the same FPGA platform. Comparison results show that the improved one reduces the system latency per sample by 63.67%, the LUTRAMs by 97.44%, the FFs by 79.55%, and the DSPs by 50%. To the best of our knowledge, this is the first work on hardware implementation for CTC beam search decoder.
Training Data Reduction using Support Vectors for Neural Networks

In the field of machine learning, deep learning is widely used to improve versatility and accuracy. Deep learning has a higher expression ability compared with conventional models but requires large amounts of data and time for training. To tackle this issue, we propose a training data reduction method using support vectors that are the closest data to the classification boundary obtained by support vector machine. The proposed method chooses a subset of training data consisting of support vectors and uses them for training neural networks. Experimental evaluation shows that it is possible to reduce the number of training data by 12% and reduce the learning time of neural network by 9.5% in a test case of ResNet with CIFAR-10 dataset.
An OAuth-based Authentication Mechanism for Open Messaging Interface Standard

The Internet of Things (IoT) security and privacy remain a major challenge, especially due to the highly scalable and distributed nature of IoT networks.IoT security has an exceptionally wide scope which also leads to the most demanding requirements needed for the widespread realization of many IoT visions.It includes the tasks such as trusted sensing, computation, communication, privacy in terms of security scope.A large number of industry-driven domain specific standards has been developed which hinder the development of a single IoT ecosystem.O-MI and O-DF standards were previously proposed to address the challenge of interoperability in IoT devices.This paper highlights and discusses the measures taken to enhance the security of these standards by integration of OAuth 2.0 service provider functionality with the O-MI authentication module along with the existing security module.The security architecture has been proposed which enhances the security features by providing authorization without use of certificates.The implementation details of the plug-in module for envisioned security model developed for the O-MI and O-DF standards have been discussed.
An Automatic Signature-Based Approach for Polymorphic Worms in Big Data Environment

In a big data environment, the signatures of polymorphic worms need to be extracted accurately and efficiently, which is of great importance to prevent them. At present, however, it is difficult to generate the accurate signature for polymorphic worms, especially under the noise condition. To solve this issue, we propose an automatic signature extraction algorithm for polymorphic worms based on the improved Term Frequency-Inverse Document Frequency (TF-IDF). Firstly, each sample of the dataset is divided into some documents. One document is selected randomly and its fist worm sample is analyzed. Then the suspicious substring is selected by calculating the TF value through traversing the document. Secondly, all the documents are traversed and the IDF value is figured out. Finally, the TD-IDF value is determined and the accurate worm signature is generated. This algorithm is tested by various kinds of worms and compared with the existing methods. The results show that our algorithm can generate polymorphic worm signatures more accurately and efficiently compared with similar methods under the noise condition. It can also save the state of worm signature extraction and has excellent scalability.
Adaptive Multi-Attention Convolutional Neural Network for Fine-Grained Image Recognition

Fine-grained recognition is still a difﬁcult task in pattern recognition applications due to the challenge of accurate localization of discriminative parts. Recent CNN-based methods generally utilize attention mechanism to produce attention masks without part labels/annotations and extract corresponding image parts from them. However, these methods extract the attention parts by using ﬁxed-size rectangles to crop images regardless of the size of objects to be recognized, which will hinder the feature expression of the following Part-CNNs. In this paper, we propose an adaptive cropping module based on the information of attention masks to adjust size of cropping rectangles. The trainingprocessofadaptivecroppingmoduleandPart-CNNscan reinforce each other with the proposed rank loss and the classic softmax loss. To further balance and fuse all attention parts, we propose a part weighting module to evaluate part contributions. Under the optimization of sort loss, the part weighting module will produce part weights in the same order as prediction scores learned by attention parts. The backbone of our network is MA-CNN. Different from MA-CNN, the new proposed adaptive cropping module and part weighting module can jointly guide the framework to produce a more discriminative ﬁne-grained feature. Experiments show that the AMA-CNN outperforms MA-CNN by 1.1% on CUB200-2011 bird dataset.
An Efficient Batch Verification Scheme for Secure Vehicular Communication Using Bilinear Pairings

Sridharan Namboodiri, K. N. Praveen I.Vehicle-to-vehicle (V-to-V) communication is an emerging technology for sensing and collecting issues related to traffic. This method revolutionises the traffic control system and human driving experience. At the same time, there is main security concern in the validation of source of messages. In this communication, verification of information is very important to avoid any malicious attack and resource abuse. Some literature used cryptographic primitives like identity-based encryption (IBE) and signatures to accomplish this need. These primitives use the mathematical function bilinear pairings. We propose a similar scheme for authenticated key agreement with the vehicles and roadside unit (RSU) using vector decomposition problem (VDP) which reduces data exchange traffic considerably. The proposed method supports batch verification.
Multi-Task Based Mispronunciation Detection of Children Speech Using Multi-Lingual Information

In developing a Computer-Aided Pronunciation Training (CAPT) system for Chinese ESL (English as a Second Language) children, we suffered from insufficient task-specific data. To address this issue, we propose to utilize first language (L1) and second language (L2) knowledge from both adult and children data through multitask-based transfer learning according to Speech Learning Model (SLM). Experimental set-up includes the TDNN acoustic modelling using the following training data: 70 hours of English speech by American Children (AC), 100 hours by American Adults (AA), 5 hours of Chinese speech by Chinese Children (CC), and 89 hours by Chinese Adults (CA). Testing data includes 2 hours of ESL speech by Chinese children. Experimental results showed that the inclusion of AA data brought about 13% relative Detection Error Rate (DER) reduction compared to AC only. Further inclusion of CC and CA data through L1 transfer learning brought about a total of 21% relative improvement in DER. These results suggested the proposed method is effective in mitigating insufficient data problem.
A Multiagent Framework for Querying Distributed Digital Collections

Since initial digitization strategies are often inspired by existing usage of the data, and usage of archives often varies among institutes, there is a lot of variation in accessibility of digital collections.We identify four challenges that researchers may encounter when querying such collections in conjunction for research purposes, namely query formulation, alignment, source selection and lack of transparency.We present a multiagent architecture to help overcome these challenges and discuss an prototype implementation of this framework.By means of a query scenario we show the utility of using the framework for humanities researchers.
Empowering Deafblind Communication Capabilities by Means of AI-Based Body Parts Tracking and Remotely Controlled Robotic Arm for Sign Language Speakers

Deafblind people face remarkable challenges in communicating, because of their severe disability. The only way to interact with other people is the usage of the tactile sign language, which consists in understanding the sign language putting their hands on the signer’s hands. But this approach works only when the signers are in the same place. The aim of this project is to reduce the gap between deafblind people and the other ones, giving them the capability to communicate remotely. By collecting images with two cameras, the signer’s body is tracked with a deep neural network. The extracted coordinates of the body parts (chest, shoulders, elbows, wrists, palms and fingers) are used to move one or more robotic arms. The deafblind person can put his hands on the robots to understand the message delivered by the person on the other side. The entire system is based on a cloud architecture.
Learner training in MALL: a Pakistani case study

This study investigated the effect of an innovative learner-training programme conducted with undergraduate students at a university in Pakistan. The MALL-based programme was designed to develop the students’ competence and confidence in using smartphones to make blogposts as a means of increasing their opportunities to practice writing in English independently and beyond their educational setting. During the eight week programme, the 23 participants engaged in training in the use of technology for language learning purposes, in tandem with other technical, pedagogical and strategic training components to use their smartphones for writing blogposts. This report draws on data from pre- and post-programme surveys, interviews, written feedback, and blogpost output. The findings indicate that this combined learner-training model was successful in enhancing the participants’ independent use of technology for English language practice. The programme also fostered their interest in learning more about new technologies and getting further training in using these for language learning. This study offers valuable insights for educators well beyond the Pakistani context, as it illustrates a model of learner training that can support students to utilize communicative affordances of readily available mobile devices for independent language learning.
Exploration of On-device End-to-End Acoustic Modeling with Neural Networks

Real-time speech recognition on mobile and embedded devices is an important application of neural networks. Acoustic modeling is the fundamental part of speech recognition and is usually implemented with long short-term memory (LSTM)-based recurrent neural networks (RNNs). However, the single thread execution of an LSTM RNN is extremely slow in most embedded devices because the algorithm needs to fetch a large number of parameters from the DRAM for computing each output sample. We explore a few acoustic modeling algorithms that can be executed very efficiently on embedded devices. These algorithms reduce the overhead of memory accesses using multi-timestep parallelization that computes multiple output samples at a time by reading the parameters only once from the DRAM. The algorithms considered are the quasi RNNs (QRNNs), Gated ConvNets, and diagonalized LSTMs. In addition, we explore neural networks that equip one-dimensional (1-D) convolution at each layer of these algorithms, and by which can obtain a very large performance increase in QRNNs and Gated ConvNets. The experiments were conducted using the connectionist temporal classification (CTC)-based end-to-end speech recognition on WSJ corpus. We not only significantly increase the execution speed but also obtain a much higher accuracy, compared to LSTM RNN-based modeling. Thus, this work can be applicable not only to embedded system-based implementations but also to server-based ones.
Improvement of P-box Efficiency

Modern block ciphers are built by combining the substitution blocks (S-boxes) with the permutation blocks (P-boxes). The principles of P-box building are considered in the paper. The issue of the impact of P-box on the diffusion and confusion is discussed. The structure of improved P-box is proposed. The possibility of improved P-box using the in modern block ciphers is estimated. The statistical properties of block ciphers with usual and improved P-boxes are shown.
Zero-resource Language Recognition

Language recognition (LRE) can be categorized into two configurations: in the close-set setting, a test segment is classified into one of several pre-defined languages, and in the open-set setting, a segment that is not in any of the pre-defined languages will be labelled as ‘unknown’. In real applications, there is another scenario: we hope to register a new language with several utterances and then this language will be recognized by the system, although this language is not involved when the system is constructed. We call this zero-resource LRE (ZR-LRE). In this paper, we explore the language embedding approach and apply it to tackle the ZR-LRE problem. Specifically, we first train an embedding space of languages based on i-vector or d-vector, and then new languages can be registered and recognized within this space. The experiments were conducted on the AP18-OLR database including 10 languages for training the embedding space and another 8 languages as zero-resource (ZR) languages for registration and recognition. To explore the influence of different test condition to the performance of ZR-LRE, we evaluated various configurations that involve different numbers of enrollment utterances and different duration of test utterances. The results show that embedding based on i-vectors is suitable for ZR-LRE, which achieved an Equal Error Rate (EER) of 8.7%.
Empirical Study: Are Complex Network Features Suitable for Cross-Version Software Defect Prediction?

Software defect prediction can identify possible defective software modules and improve testing efficiency. Traditional software defect prediction mainly focuses on using code features and process-based features for research. The rules of complex network are suitable for software. Using complex network features to represent defect information provides a new idea for software defect prediction. In this paper, we first select 18 versions of 9 open source projects through certain rules and then build a logistic regression model based on three kinds of features (complex network features, traditional code features, merged features) to evaluate the predictive defect ability of complex network features. The results show that: (1) Compared with traditional code features, complex network features have better ability to predict defects for cross-versions software defect prediction; (2) Merged features are not as good as complex network features in defect prediction for cross-version software defect prediction, but still better than traditional code features.