Reviewerly/exHarmony huggingface dataset:
Fused Deep Convolutional Neural Networks Based on Voting Approach for Efficient Object Classification

Object classification has been one of the main tasks in computer vision. With the fast development of deep learning, its performance in image classification and object recognition has presented dramatic improvements. In this paper, we propose a new deep convolutional neural network (CNN) architecture for robust object classification. The proposed model is fused with three traditional CNN approaches, Densenet201, Resnet50, and our proposed residual CNN. The fused network architecture allows parallel processing of the multiple networks for keeping the system sped up. A single shot deep convolution network is trained as an object detector to generate all possible candidates of different object classes. The output of each neural network is representing a single vote that is used in the classification process. 3-to-1 voting criteria are applied in the final classification decision between the candidate object classes. Several experiments were conducted to evaluate the performance of the proposed network. The experimental results show that the proposed approach has better performance than the networks used in the fusion process when they act individually. It also has lower miss rates when compared to several states of art methodologies.
Commodity and Forex trade automation using Deep Reinforcement Learning

Machine learning is an application of artificial intelligence based on the theory that machines can learn from data, discern patterns and make decisions with negligible human intervention. With today's world being inundated by data, machine learning is very relevant due to the amount of learning potential. Machine learning caters to a variety of applications including image recognition, speech recognition, weather prediction, portfolio optimization and so on. The Forex Exchange is a market that allows traders and investors to buy, sell and exchange currencies of various nations. It is regarded as the largest financial market with over 5 trillion American dollars in daily trades, which is larger than the equity and futures markets combined. The Commodity market is a market that allows buying, selling and exchanging of raw materials or primary products. Using the concept of machine learning, this project aims to develop and introduce an agent to automate the trade of a given commodity or currency in a simulated market with the objectives of maximizing returns and minimizing losses for the trader. The model learns from trends in historical market data and is capable of buying, selling or holding a trade at a given instance. The model is validated by running the agent on unseen market data of a later period and the returns generated are analyzed.
A Scenario-based Analysis of Front-facing Camera Eye Tracker for UX-UI Survey on Mobile Banking App

Recently, User Experience and User Interface (UX-UI) have become important aspects in designing an effective mobile banking application. Traditionally, developers and designers have relied on explicit feedback derived from questionnaires to gain more insights into UX-UI. With the advancement of new technology, eye-tracking device has been introduced, and the approach has been used to provide a digital footprint indicating exact gazing positions of the users when using an application. So far, many studies have acknowledged the benefits of eye movement tracking and exploited such implicit feedback, alongside the result yielded from a survey. Successful uses of this eye-tracking device would further the development of mobile banking application. In this study, we aimed to build a device-free eye tracking software module that would work efficiently on mobile phones. To achieve this goal, we employed an existing Convolutional Neural Network model in our framework and evaluated the model when it was applied to the specific domain, i.e., UX-UI research design for mobile banking apps. We investigated a GazeCapture dataset, the first large-scale dataset for eye tracking, and conducted a data wrangling technique. The results show that fine-tuning the model with our wrangled data can improve the overall eye-tracking performance. Moreover, enabling user calibration can clearly enhance the predicting performance of the model.
A semantic facial expression intensity descriptor based on information granules

This paper investigates a granular description for facial expression intensity in which characterization of motion-based expression features is presented by a collection of information granules. First, the motion-based features are extracted based on the facial landmarks to encode the expression intensity. Secondly, the semantic concepts are then generated by involving various mechanisms of fuzzy clustering based on the motion-based features. The resulting clusters can be viewed as numeric prototypes of the descriptors. The information granules are formed around the prototypes according to the principle of justifiable granularity, which is the fundamental idea of Granular Computing. The proposed granular descriptor for expression intensity is tested on the BU-3DFE database, and the experimental results illustrate that the proposed descriptors based on information granules not only can characterize the semantics of facial changes caused by expressions, but also can facilitate the semantic estimation of facial expression intensity.
A Web Based Digital Repository for Scholarly Publication

Institutional repositories are essential research infrastructure for research-based universities.A properly dimensioned institutional repository has the potential to increase research impact and enhance the visibility of an institution through its scholarly outputs.The aim of the study is to design and develop a web-based digital repository for scholarly materials and publications in a tertiary institution.The motivation for carrying out this project is to provide a lasting solution to the need for an effective, efficient, reliable and easily accessible system for storing and retrieving scholarly materials, which will make study and research work less cumbersome and stressful for academic staff and students, thereby enabling them to easily carry out their day-to-day activities, especially as it concerns obtaining relevant scholarly information quickly.The system will be developed using open source software and OOADM (Object Oriented Analysis and Design Methodology).Findings obtained from system validation tests show that the system is a viable solution to the major challenges encountered in the management and sharing of scholarly information at the institution.
Position and Velocity Estimations of 2D-Moving Object Using Kalman Filter: Literature Review

This paper proposes a position and velocity estimation scheme for 2D-moving object using Kalman filter. First of all, the 2D-moving object is represented by a state-space scheme where sensors are used to sense the system states. The main objective of a 2D-moving object is to estimate the position and velocity of moving object. The optimal signal processing algorithm called Kalman filter is proposed to estimate the position and velocity of the target object. A comprehensive literature review has been analyze by advantages and disadvantages about using optimal signal processing algorithms, Kalman filter, and moving object tracking. Extensive simulation results show that the proposed algorithm can properly track the position and velocity of moving object.
Evaluating Low-Cost in Internal Crowdsourcing for Software Engineering: The Case of Feature Location in an Industrial Environment

Internal crowdsourcing in software engineering is a mechanism for recruiting engineers to carry out more efficiently software engineering tasks. However, engineers are busy resources and time is a valuable asset in industry, which hinders internal crowdsourcing in software engineering from becoming a widespread practice. In this work, we propose a low-cost variant of internal crowdsourcing for locating features in models, which limits the time that engineers can spend for providing knowledge. Our approach uses the knowledge provided by the internal crowd to automatically reformulate an initial feature description. The result is taken as input to automatically locate the relevant model fragment using Latent Semantic Indexing. We evaluate our approach using four query reformulation techniques in a real-world case study from our industrial partner. We compare the results of our approach in terms of recall, precision and F-measure with a baseline by means of statistical methods to show that the impact of the results of our approach is significant. Despite the limitation of time, the results show that low-cost in internal crowdsourcing improves significantly the results in an industrial context where engineers' availability is scarce.
Learn-By-Calibrating: Using Calibration As A Training Objective

Calibration error is commonly adopted for evaluating the quality of uncertainty estimators in deep neural networks. In this paper, we argue that such a metric is highly beneficial for training predictive models, even when we do not explicitly measure the uncertainties. This is conceptually similar to heteroscedastic neural networks that produce variance estimates for each prediction, with the key difference that we do not place a Gaussian prior on the predictions. We propose a novel algorithm that performs simultaneous interval estimation for different calibration levels and effectively leverages the intervals to refine the mean estimates. Our results show that, our approach is consistently superior to existing regularization strategies in deep regression models. Finally, we propose to augment partial dependence plots, a model-agnostic interpretability tool, with expected prediction intervals to reveal interesting dependencies between data and the target.
Modulation Recognition Based on IQ-eyes Diagrams and Deep Learning

This article presents our initial results in deep learning for modulation recognition with the signal IQ-eyes diagrams. we design a representative convolution neural network to distinguish between eight modulation classes which shows more promising when compared to more traditional likelihood-based or feature-based techniques. Furthermore, we discuss the impact of frequency offset and influence of network parameters on recognition performance though the recognition accuracy. Moreover, the method based on multi-terminal processing can mine the intrinsic characteristics of the signal more completely, which reduces the requirement of network and makes the network more robust.
Using Software Metrics to detect Temporary Field code smell

Code smell is a characteristic of the source code which indicates some serious problem in the code which might affect the quality of the source code. There exists a list of 22 code smells as defined by Martin Fowler. But all these code smells have not been worked upon. Temporary field code smell is one of them, which has not been considered for its detection and refactoring. In this paper, we have reconstructed a motivating example of object oriented JAVA code that indicates the impact of code smell and need to remove temporary field based on metrics and rules.We have proposed a method to detect temporary field code smell based on software metrics derived from data flow and control flow graphs. We also proposed the process of refactoring the code to improve the maintainability. Analysis of results has shown that NFM, NMN, NCF metrics can help to detect Temporary field code smell. Extract class is more appropriate refactoring technique than parameter passing to remove Temporary Field code smell.
From Symbols to Signals: Symbolic Variational Autoencoders

We introduce Symbolic Variational Autoencoders which generate images from symbols that represent semantic concepts. Unlike generic Variational Autoencoders (VAEs) or Generative Adversarial Networks (GANs), the latent distribution from the Symbolic Variational Autoencoder is discrete. The symbols are learned in a completely unsupervised manner by reconstructing images from symbolic encodings. We demonstrate the efficacy of our symbolic approach on the MNIST and FashionMNIST datasets. Results indicate that symbolic encodings naturally form a grammar, where unique strings of symbols map to different semantic concepts. We further explore how changing these symbols affects the final image that is generated.
Visual Attention-Based Object Detection in Cluttered Environments

The study of human visual attention is considered a hot topic in the field of activity recognition, experimental psychology research and human computer interaction. The importance of detecting user objects of interest in real time is critical to provide accurate cues about the user intentions.However, current methods for visual attention extraction and object detection suffer from low performance when moving to ongoing condition. Inherent complexity of cluttered environmentsis considered the major barrier to achieve good performances. To address this challenge, we present a novel method that includes head-worn eye tracker and egocentric video. Our method exploits sliding window-based time series approach in conjunction with aHeuristic probabilistic function to analyse user fixations around potential object of interest in an egocentric video. We evaluate the proposed method using a new dataset annotated with user gaze data and object within a frame image. Our experimental results show that our approach can outperforms several state-of-the-art commonality visual attention-based object detection methods.
End to End Speech Recognition Error Prediction with Sequence to Sequence Learning

Simulating the errors made by a speech recognizer on plain text has proven useful to help train downstream NLP tasks to be robust to real ASR errors at test time. Prior work in this domain has focused on modeling confusions at the phonetic level, and using a lexicon to convert from words to phones and back, usually accompanied by an FST Language model. We present a novel end to end model to simulate ASR errors. Our approach trains a convolutional sequence to sequence model to take as direct input a word sequence and predict a word sequence as an output. The end to end modeling improves prior published results for recall of recognition errors made by a Switchboard ASR system on unseen Fisher data; we also demonstrate cross-domain robustness by predicting errors made by an unrelated cloud-based ASR system on a Virtual Patient task.
Research on Emotional State in Online Learning by Eye Tracking Technology

In recent years, the intelligence level of eye tracker is increasing, and the application of eye-tracking technology in the field of multimedia learning research is also increasing. At the same time, many related studies have proved the validity of eye movement information in human emotion detection and understanding. The purpose of this paper is to explore the potential psychological state of learners in online learning using eye-tracking technology in multimedia environment, and then by linking the emotional state of learners with related cognitive processing process, to find the relationship between eye movement information and emotional state. This paper uses online learning videos as stimulus materials for research and a total of 38 subjects participated in the experiment. During the period of watching the video material, the eye movement information was recorded by eye tracker. Though the Pearson correlation coefficient analysis method, it is found that in the process of online learning, there is a high correlation between emotional state and eye movement information. The pupil diameter of the subjects in the negative emotional state is larger than that in the positive emotional state, and the blink frequency of the subjects in the positive emotional state is higher than that in the negative emotional state. This experiment shows the feasibility of using eye movement information to detect different emotions in the learning process.
Side-Channel Power Resistance for Encryption Algorithms Using Implementation Diversity

This paper investigates countermeasures to side-channel attacks. A dynamic partial reconfiguration (DPR) method is proposed for field programmable gate arrays (FPGAs)s to make techniques such as differential power analysis (DPA) and correlation power analysis (CPA) difficult and ineffective. We call the technique side-channel power resistance for encryption algorithms using DPR, or SPREAD. SPREAD is designed to reduce cryptographic key related signal correlations in power supply transients by changing components of the hardware implementation on-the-fly using DPR. Replicated primitives within the advanced encryption standard (AES) algorithm, in particular, the substitution-box (SBOX)s, are synthesized to multiple and distinct gate-level implementations. The different implementations change the delay characteristics of the SBOXs, reducing correlations in the power traces, which, in turn, increases the difficulty of side-channel attacks. The effectiveness of the proposed countermeasures depends greatly on this principle; therefore, the focus of this paper is on the evaluation of implementation diversity techniques.
A Privacy-Preserving Schema for the Detection and Collaborative Mitigation of DNS Water Torture Attacks in Cloud Infrastructures

This paper presents a privacy-preserving schema between Authoritative and Recursive DNS Servers for the efficient detection and collaborative mitigation of DNS Water Torture attacks in cloud environments. Monitoring data are harvested from the victim premises (Authoritative DNS Server and Data Center switches) to detect anomalies with DNS requester IPs classified as legitimate or suspicious. Subsequently, requests are forwarded or redirected for refined inspection to a filtering mechanism. Mitigation may be offered as a service either on-premises or via cloud scrubbing infrastructures. The proposed schema leverages on probabilistic data structures (Bloom Filters, Count-Min Sketches) and related algorithms (SymSpell) to meet time, space and privacy constraints required by cloud services. Notably, Bloom Filters are employed to map Resource Records of large DNS zones in a memory efficient manner; rapid name lookups are possible with zero false negatives and tolerable false positives. Our approach is tested via a proof of concept setup based on traces generated from publicly available DNS traffic datasets.
Auglabel: Exploiting Word Representations to Augment Labels for Face Attribute Classification

Augmenting data in image space (eg. flipping, cropping etc) and activation space (eg. dropout) are being widely used to regularise deep neural networks and have been successfully applied on several computer vision tasks. Unlike previous works, which are mostly focused on doing augmentation in the aforementioned domains, we propose to do augmentation in label space. In this paper, we present a simple, yet effective novel method to generate fixed dimensional labels with continuous values for images by exploiting the word2vec - semantic representations - of the existing categorical labels. We then append these representations to existing categorical labels and train the model. We validated our idea on two challenging face attribute classification data sets viz. CelebA and LFWA. Our extensive experiments show that the augmented labels improve the performance of the competitive deep learning baseline and also attains the state-of-the-art performance.
Stochastic Multi-Scale Aggregation Network for Crowd Counting

Crowd counting from unconstrained and congested scenes is an important task in computer vision. Its main difficulties stem from large scale/density variation and prone to over-fitting. This paper presents a novel end-to-end stochastic multi-scale aggregation network (SMANet) which carefully addresses these issues. Specifically, general features are first extracted by the front-end subnetwork and then fed into the back-end subnetwork which consists of stochastic multi-scale aggregation module, density map generator, and global prior encoder. The stochastic aggregation impels the multi-branch units to learn features at different scales effectively and reduces sensitivity to scale variations, whereas the global prior encoder is designed to encode global contextual information and guarantee density consistency of shared representations. Our proposed SMANet is the first work to fuse multi-scale features in a stochastic manner for crowd counting. Experimental results on four public datasets demonstrate that our SMANet consistently outperforms the state-of-the-arts.
Frontal Facial Expression Recognition using Parallel CNN Model

Facial expression recognition is one of the very important research topics in computer vision. Studies on nonverbal communication have shown that 55% of intentional information is conveyed through facial expressions. Expression recognition has recently found a lot many applications in medical and advertising industries. In this paper we have proposed a parallel Convolutional Neural Network (CNN) structure for detection of expression from frontal faces. The CNNs are trained on two most important subfacial patches. The overall feature vector will be the features concatenated from the parallel models. We have experimentally found applying such a strategy provides better results than the models which take the entire facial image. We have also compared our performance with other benchmark CNN structures like AlexNet and VGG16.
Language Independent Gender Identification from Raw Waveform Using Multi-Scale Convolutional Neural Networks

In this work, we propose a raw waveform based multiscale convolution neural network approach for language-independent gender identification. Our approach uses raw audio waveform as input to the 1-dimensional multi-scale convolutional neural network instead of handcrafted feature for speaker gender classification. The multi-scale CNN has the advantage of using filters of different sizes on the audio waveform to extract features from raw waveform. We have a 3 stream CNN network where each stream contains multiple Residual blocks and we combine all the features from all streams after the last convolution layer to predict the gender label. Our gender identification dataset contains 176Hrs of audio data from 6 Indian languages(Hindi, English, Kannada, Telugu, Tamil, and Gujarati). Our experiments show that learning a gender identification task using a raw waveform gives better performance and speed up during training. Our experiments show that using multi-scale CNN on the raw waveform outperforms the spectrogram based model by an absolute improvement of 2.24%.
PKC‐PC: A variant of the McEliece public‐key cryptosystem based on polar codes

Polar codes are novel and efficient error-correcting codes with low encoding and decoding complexities. These codes have a channel-dependent generator matrix, which is determined by the code dimension, code length and transmission channel parameters. A variant of the McEliece public-key cryptosystem based on polar codes, called the PKC-PC, is studied. Since the structure of the polar codes’ generator matrix depends on the parameters of the channel, the authors have used an efficient approach to conceal their generator matrix. The proposed approach is based on a random selection of rows of the matrix by which a random generator matrix is constructed. Using the characteristics of polar codes and introducing an efficient approach, they could reduce the public and secret key sizes, and computational complexity compared to the McEliece cryptosystem. Moreover, they show that PKC-PC yields an increased security level against conventional attacks as well as possible vulnerabilities to the code-based public-key cryptosystems. Furthermore, they prove the security of the authors’ cryptosystem and show that its security is reduced to solve NP-complete problems, called polar parameterised syndrome decoding and polar parameterised codeword existence.