Reviewerly/exHarmony huggingface dataset:
Anchored Model Transfer and Soft Instance Transfer for Cross-Task Cross-Domain Learning: A Study Through Aspect-Level Sentiment Classification

Supervised learning relies heavily on readily available labelled data to infer an effective classification function. However, proposed methods under the supervised learning paradigm are faced with the scarcity of labelled data within domains, and are not generalized enough to adapt to other tasks. Transfer learning has proved to be a worthy choice to address these issues, by allowing knowledge to be shared across domains and tasks. In this paper, we propose two transfer learning methods Anchored Model Transfer (AMT) and Soft Instance Transfer (SIT), which are both based on multi-task learning, and account for model transfer and instance transfer, and can be combined into a common framework. We demonstrate the effectiveness of AMT and SIT for aspect-level sentiment classification showing the competitive performance against baseline models on benchmark datasets. Interestingly, we show that the integration of both methods AMT+SIT achieves state-of-the-art performance on the same task.
Joint vehicle detection and distance prediction via monocular depth estimation

Vehicle detection and distance estimation are critical components of driver assistance system and self-driving system, and considerable different frameworks have been investigated such as radar, laser and camera-based. Among them, camera-based vehicle detection and distance estimation have an obvious advantage over other systems in that it needs lower cost. However, existing camera-based methods are not robust enough under complex driving scenes. In this work, an end-to-end deep convolutional neural network framework is proposed to jointly detect vehicles and estimate vehicle distance efficiently. Specifically, a monocular depth estimation method is designed to transform the RGB appearance information into depth modality information. Then the vehicle detection module takes the RGB and depth image as inputs to improve the detection performance. Finally, the distance estimation module employs the detection results and the estimated depth information to predict the distance more precisely. The whole network can be trained in an end-to-end manner with the multi-task loss function. The proposed framework is evaluated on the public vehicle detection benchmark KITTI to show the effectiveness of the proposed framework. Moreover, the performance of three proposed sub-modules are also analysed separately to give a more comprehensive evaluation of the designed framework.
Functionality Based Code Smell Detection and Severity Classification

The Long Method code smell is a symptom of design defects caused by implementing multiple tasks within a single method. It limits reusability, evolvability and maintainability of a method. In this paper, we present a functionality based approach for detecting long methods. Functionalities are identified through a novel block based dependency analysis technique called Segmentation. It clusters sets of statements into extract method opportunities (or tasks). The approach uses interdependencies among various extract method opportunities identified within the method as a means to measure severity of the long method smell. The approach is validated over a Java based open source code. A comparison with expert's assessment shows that the approach is promising in detecting severe methods irrespective of their sizes.
Instance Hard Triplet Loss for In-video Person Re-identification

Traditional Person Re-identification (ReID) methods mainly focus on cross-camera scenarios, while identifying a person in the same video/camera from adjacent subsequent frames is also an important question, for example, in human tracking and pose tracking. We try to address this unexplored in-video ReID problem with a new large-scale video-based ReID dataset called PoseTrack-ReID with full images available and a new network structure called ReID-Head, which can extract multi-person features efficiently in real time and can be integrated with both one-stage and two-stage human or pose detectors. A new loss function is also required to solve this new in-video problem. Hence, a triplet-based loss function with an online hard example mining designed to distinguish persons in the same video/group is proposed, called instance hard triplet loss, which can be applied in both cross-camera ReID and in-video ReID. Compared with the widely-used batch hard triplet loss, our proposed loss achieves competitive performance and saves more than 30% of the training time. We also propose an automatic reciprocal identity association method, so we can train our model in an unsupervised way, which further extends the potential applications of in-video ReID. The PoseTrack-ReID dataset and code will be publicly released.
Deep Learning-based Mammogram Classification using Small Dataset

Breast Cancer is one of the most diagnosed cancer and the leading cause of death among women worldwide, second only to lung cancer. Mammographic screening has been the most successful screening technology capable of detecting up to 90% of all breast cancer even before a lump growth can be felt using breast exam. However, mammogram is a low intensity image and the heterogeneous nature of breast can make healthy breast tissue appears as cancerous, this is most common among women with dense breast (aged 40-44). Thus, the sensitivity for early detection of breast cancer from mammogram has been estimated to 85-90%. This result can be improved by Deep CNN, however, to achieve good generalization, it must be train with high voluminous dataset whereas, mammographic dataset exists in smaller volume. In this paper, we present a method of training deep CNN with few datasets to achieve high training result and good generalization. An augmentation technique that increase both size and variance of the dataset is presented herewith, the augmented dataset was used to train five state of the art models. Highest training and validation accuracy (99.01% and 99.99% respectively) were achieved with DensNet. Meanwhile, SqueezeNet, a deep CNN model with fewer parameter also shows promising result, which means soon this model can be deployed into microcontroller and FPGAs for clinical applications.
Classification of handwritten names of cities using various deep learning models

The digitized text of handwriting would conduce to automate the business processes of many companies, simplifying the work of human being. For example, our state postal service does not have an automated mail processing system that recognizes handwritten addresses on an envelope. Each incoming correspondence is registered in the system by the operator. Automation of this business process on registering post mailing will significantly reduce expenses of postal service on mail delivery.There are two main approaches to handwriting recognition, namely hidden Markov models (HMM) and artificial neural networks (ANN). The methods proposed in this article are based on ANN. The first model is based on deep convolutional neural networks (DCNN) [1] for feature extraction and a fully connected multilayer perceptron (fully connected MLP) for word classification. The next model under consideration, called SimpleHTR, proposed by Harald Scheidl [2], has layers of a convolutional neural network (CNN) and layers of a recurrent neural network (RNN) for disseminating information through an image. Finally, the Connectionist Temporal Classification (CTC) decoding algorithm is executed, which adduces the text to the final version.Models were learned on the dataset of handwritten city names from Cyrillic words. 21,000 images were collected (42 classes of 500 handwriting samples). To increase the data set for training, 207,438 images from available samples were generated.As a result, two approaches for handwriting recognition were analyzed and the SimpleHTR model showed the best results over all.
Design of a reservoir for cloud-enabled echo state network with high clustering coefficient

Abstract Reservoir computing (RC) is considered as a suitable alternative for descending gradient methods in recursive neural networks (RNNs) training. The echo state network (ESN) is a platform for RC and nonlinear system simulation in the cloud environment with many external users. In the past researches, the highest eigenvalue of reservoir connection weight (spectral radius) was used to predict reservoir dynamics. Some researchers have illustrated; the characteristics of scale-free and small-world can improve the approximation capability in echo state networks; however, recent studies have shown importance of the infrastructures such as clusters and the stability criteria of these reservoirs as altered. In this research, we suggest a high clustered ESN called HCESN that its internal neurons are interconnected in form of clusters. Each of the clusters contains one backbone and a number of local nodes. We implemented a classical clustering algorithm, called K-means, and three optimization algorithms including genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO) to improve the clustering efficiency of the new reservoir and compared them with each other. For investigating the spectral radius and predictive power of the resulting reservoirs, we also applied them to the laser time series and the Mackey-Glass dynamical system. It is demonstrated that new clustered reservoirs have some specifications of biologic neural systems and complex networks like average short path length, high clustering coefficient, and power-law distribution. The empirical results illustrated that the ESN based on PSO could strikingly enhance echo state property (ESP) and obtains less chaotic time series prediction error compared with other works and the original version of ESN. Therefore, it can approximate nonlinear dynamical systems and predict the chaotic time series.
Stateful Hash-based Digital Signature Schemes for Bitcoin Cryptocurrency

Modern computing devices use classical algorithms such as Rivest Shamir Adleman (RSA) and Elliptic Curve Digital Signature Algorithm (ECDSA) for their security. The securities of these algorithms relied on the problem and difficulty of integer factorization and also calculating the Discrete Logarithm Problems. With the introduction of quantum computers, recent research is focusing on developing alternative algorithms which are supposed to withstand attacks from quantum computers. One of such alternatives is the Hash-based Digital Signature Schemes. Chosen hash-based signature schemes over classical algorithms is because their security is on the hash function used and that they are metaheuristic in nature. This research work presents basic analysis and the background understanding of Stateful Hash-based Signature Schemes, particularly the Lamport One-Time Signature Scheme, Winternitz One-Time Signature Scheme, and the Merkle Signature Scheme. The three schemes selected are stateful, hence has common features and are few-time hash-based signature schemes. The selected Stateful Hash-based Digital Signature Schemes were analyzed based on their respective key generation, signature generation, signature verification, and their security levels. Practical working examples were given for better understanding. With the analyses, Merkle Signature Scheme proves to be the best candidate to be used in the Bitcoin Proof of Work protocol because of its security and its advantage of signing many messages.
StarGAN Based Facial Expression Transfer for Anime Characters

Human facial expression transfer has been well explored using Generative Adversarial Networks. Also, in case of anime style images, several successful attempts have been made to generate high-quality anime face images using GAN approach. However, the task of anime facial expression transfer is not well studied yet due to the lack of a clean labeled anime dataset. We address this issue from both data and model perspectives, by providing a clean labeled anime dataset and leveraging the use of the StarGAN image-to-image translation framework. Our collected dataset consists of about 5k high- quality anime face images including five major emotions collected from online image boards. We preprocessed our dataset by CARN super-resolution technique to improve quality of the images, and applied tuned StarGAN model to learn the mapping of an input anime image with arbitrary expression to the target expression. We evaluate our work by visually comparing the output translated results with the baseline model. Moreover, we provide a quantitative analysis of our proposed approach by computing the confusion matrix of expression transfer accuracy.
Incremental Training and Group Convolution Pruning for Runtime DNN Performance Scaling on Heterogeneous Embedded Platforms

Inference for Deep Neural Networks is increasingly being executed locally on mobile and embedded platforms due to its advantages in latency, privacy and connectivity. Since modern System on Chips typically execute a combination of different and dynamic workloads concurrently, it is challenging to consistently meet inference time/energy budget at runtime because of the local computing resources available to the DNNs vary considerably. To address this challenge, a variety of dynamic DNNs were proposed. However, these works have significant memory overhead, limited runtime recoverable compression rate and narrow dynamic ranges of performance scaling. In this paper, we present a dynamic DNN using incremental training and group convolution pruning. The channels of the DNN convolution layer are divided into groups, which are then trained incrementally. At runtime, following groups can be pruned for inference time/energy reduction or added back for accuracy recovery without model retraining. In addition, we combine task mapping and Dynamic Voltage Frequency Scaling (DVFS) with our dynamic DNN to deliver finer trade-off between accuracy and time/power/energy over a wider dynamic range. We illustrate the approach by modifying AlexNet for the CIFAR10 image dataset and evaluate our work on two heterogeneous hardware platforms: Odroid XU3 (ARM big.LITTLE CPUs) and Nvidia Jetson Nano (CPU and GPU). Compared to the existing works, our approach can provide up to 2.36× (energy) and 2.73× (time) wider dynamic range with a 2.4× smaller memory footprint at the same compression rate. It achieved 10.6× (energy) and 41.6× (time) wider dynamic range by combining with task mapping and DVFS.
Adversarial Robustness Curves

The existence of adversarial examples has led to considerable uncertainty regarding the trust one can justifiably put in predictions produced by automated systems. This uncertainty has, in turn, lead to considerable research effort in understanding adversarial robustness. In this work, we take first steps towards separating robustness analysis from the choice of robustness threshold and norm. We propose robustness curves as a more general view of the robustness behavior of a model and investigate under which circumstances they can qualitatively depend on the chosen norm.
How Do Quantifiers Affect the Quality of Requirements?

[Context] Requirements quality can have a substantial impact on the effectiveness and efficiency of using requirements artifacts in a development process. Quantifiers such as "at least", "all", or "exactly" are common language constructs used to express requirements. Quantifiers can be formulated by affirmative phrases ("At least") or negative phrases ("Not less than"). [Problem] It is long assumed that negation in quantification negatively affects the readability of requirements, however, empirical research on these topics remains sparse. [Principal Idea] In a web-based experiment with 51 participants, we compare the impact of negations and quantifiers on readability in terms of reading effort, reading error rate and perceived reading difficulty of requirements. [Results] For 5 out of 9 quantifiers, our participants performed better on the affirmative phrase compared to the negative phrase. Only for one quantifier, the negative phrase was more effective. [Contribution] This research focuses on creating an empirical understanding of the effect of language in Requirements Engineering. It furthermore provides concrete advice on how to phrase requirements.
A Method for Transforming Object-relational to Document-oriented Databases

Object-relational databases have emerged to improve relational ones by adding properties of object-oriented approach such as references, polymorphism, inheritance, etc. However, these extended relational databases have become a huge amount of data and the database management systems (DBMS) cannot handle them. Due to the emergence of NoSQL databases for ensuring the storage and the processing of large data scale, it is necessary to propose a method for transforming object-relational to NoSQL databases. This paper presents a new method for transforming the object-relational database to one of the popular NoSQL data stores so-called document-oriented database. The proposed method is based on a set of matching between the schemata of object-relational and document-oriented databases. The method is terminated by the generation of a set of JSON files which represent collections of semi-structured documents. These files can be imported and represented by BSON format that will be managed by document-oriented DBMS such as MongoDB.
Evolutionary intelligence for breast lesion detection in ultrasound images: A wavelet modulus maxima and SVM based approach

Intelligent lesion detection system for medical ultrasound images are aimed at reducing physicians’ effort during cancer diagnosis process. Automatic separation and classification of tumours in ultrasound images is challenging owing to the low contrast and noisy behavior of the image. A Computer ai ded detection (CAD) system that automatically segment and classify breast tumours in ultrasound (US) images is proposed in this paper. The proposed method is invariant to scale changes and does not require an operator defined initial region of interest. Wavelet modulus maxima points of the US image are analyzed to extract the tumour seed point. The lesions segmented using a region-based approach are classified using a support vector machine (SVM) classifier. Evaluation of various performance measures show that the performance of the proposed CAD system is promising.
Formalizing a Seligman-Style Tableau System for Hybrid Logic

Hybrid logic is modal logic enriched with names for worlds. We formalize soundness and completeness proofs for a Seligman-style tableau system for hybrid logic in the proof assistant Isabelle/HOL. The formalization shows how to lift certain rule restrictions, thereby simplifying the original un-formalized proof. Moreover, the completeness proof we formalize is synthetic which suggests we can extend this work to prove a wider range of results about hybrid logic.
An Improved Vehicle Detection Algorithm Based on YOLOV3

With the development of science and technology, the application of vehicle object detection in intelligent video monitoring and vehicle assisted driving has become more extensive. Traditional vehicle object detection algorithms have some inefficiencies in generalization ability and recognition rate. To solve this problem, this paper proposes an improved vehicle detection method based on YOLO(You Only Look Once) V3. Our model improves the YOLOV3 algorithm, integrating the label smoothing, and adopts K-means++ algorithm to analyze the data aggregation and avoid the disappearance of the gradient, make our train model has better generalization ability. Finally, we use the UA-DETRAC data set for training, and the experimental results showed that our method has a better detection speed and a higher recognition rate for vehicle detection, which is 6% mAP higher than the original YOLOV3 algorithm.
Behavioral Security in Covert Communication Systems

The purpose of the covert communication system is to implement the communication process without causing third party perception. In order to achieve complete covert communication, two aspects of security issues need to be considered. The first one is to cover up the existence of information, that is, to ensure the content security of information; the second one is to cover up the behavior of transmitting information, that is, to ensure the behavioral security of communication. However, most of the existing information hiding models are based on the “Prisoners’ Model”, which only considers the content security of carriers, while ignoring the behavioral security of the sender and receiver. We think that this is incomplete for the security of covert communication. In this paper, we propose a new covert communication framework, which considers both content security and behavioral security in the process of information transmission. In the experimental part, we analyzed a large amount of collected real Twitter data to illustrate the security risks that may be brought to covert communication if we only consider content security and neglect behavioral security. Finally, we designed a toy experiment, pointing out that in addition to most of the existing content steganography, under the proposed new framework of covert communication, we can also use user’s behavior to implement behavioral steganography. We hope this new proposed framework will help researchers to design better covert communication systems.
A Privacy Preserving Blockchain-based Reward Solution for Vehicular Networks

Vehicular networks use a vehicle to infrastructure (V2I) to improve traffic safety by gathering information from vehicles such as traffic conditions and accidents. This approach is based on the vehicle's location information and the reliability of the data collected from the vehicle. At the same time, the voluntary participation of drivers requires privacy protection and compensation for sharing trusted information. In this paper, we propose a blockchain-based decentralized reward solution considering drivers’ privacy. Using a unique identifier has a vulnerability of exposing the vehicle’s location to attackers and hence the attackers can easily trace any vehicles. By employing changing pseudonyms instead of using a unique identifier, the driver can protect their location privacy from other vehicles and Roadside Units (RSUs) that could be a potential attacker. The proposed solutions also enable service providers can verify the reliability and integrity of the messages transmitted by legitimate vehicles
Non-Functional Requirements in Robotic Systems: Challenges and State of the Art

The design of the software architecture is driven by two types of requirements: Functional Requirements specify what the software system does, while Non-Functional Requirements (NFRs) express desired qualities, called Non-Functional Properties (NFPs). This paper illustrates three research challenges for addressing NFRs in the engineering process of autonomous robotic systems related to identification, modeling, and analysis of robotic-specific NFRs, reports on partial state-of-the-art solutions currently documented in the literature, discusses possible steps to overcome current limitations.
Toward growable computer games

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