Reviewerly/exHarmony huggingface dataset:
Detecting Malicious URLs Using a Deep Learning Approach Based on Stacked Denoising Autoencoder

As the source of spamming, phishing, malware and many more such attacks, malicious URL is a chronic and complicated problem on the Internet. Machine learning approaches have taken effect and obtained high accuracy in detecting malicious URL. But the tedious process of extracting features from URL and the high dimension of feature vector makes the implementing time consuming. This paper presents a deep learning method using Stacked denoising autoencoders model to learn and detect intrinsic malicious features. We employ an SdA network to analyze URLs and extract features automatically. Then a logistic regression is implemented to detect malicious and benign URLs, which can generate detection models without a manually feature engineering. We have implemented our network model using Keras, a high-level neural networks API with a Tensor-flow backend, an open source deep learning library. 5 datasets were used and 4 other method were compared with our model. In the result, our architecture achieves an accuracy of 98.25% and a micro-averaged F1 score of 0.98, tested on a mixed dataset containing around 2 million samples.
Improving in situ GPU simulation of quantum computing in the D-GM environment

Exponential increase and global access to read/write memory states in quantum computing (QC) simulation limit both the number of qubits and quantum transformations which can be currently simulated. Although QC simulation is parallel by nature, spatial and temporal complexity are major performance hazards, making this a nontrivial application for high performance computing. A new methodology employing reduction and decomposition optimizations has shown relevant results, but its GPU implementation could be further improved. In this work, we develop a new kernel for in situ GPU simulation that better explores its resources without requiring further hardware. Shor’s and Grover’s algorithms are simulated up to 25 and 21 qubits respectively and compared to our previous version, to [Formula: see text] simulator and to ProjectQ framework, showing better results with relative speedups up to 4.38×, 3357.76× and 333× respectively.
Action Research in Software Engineering: Metrics’ Research Perspective (Invited Talk)

Software engineering is an applied discipline of science. Its focus on software development processes, technologies and organizations makes it broad and exciting to work with. However, it makes it also challenging to find the right research method to get the results that have impact on industrial practices and that help software organizations to provide more value to their customers. In this paper, I argue that the traditional empirical software engineering methods must be complemented with action research. In the paper, I provide an overview of the methodology of action research, briefly explain the phases and present my experiences from over a decade long applications of action research in industrial contexts. I focus on how action research helps to provide the most value to the collaborating companies, how it helps to build more robust software engineering theories and how it helps individual researchers to develop their careers. I conclude with a short description of how action research can evolve in the future.
Enhanced Learning to Rank using Cluster-loss Adjustment

Most Learning To Rank (LTR) algorithms like Ranking SVM, RankNet, LambdaRank and LambdaMART use only relevance label judgments as ground truth for training. But in common scenarios like ranking of information cards (google now, other personal assistants), mobile notifications, netflix recommendations, etc. there is additional information which can be captured from user behavior and how user interacts with the retrieved items. Within the relevance labels, there might be different sets whose information (i.e. cluster information) can be derived implicitly from user interaction (positive, negative, neutral, etc.) or from explicit-user feedback ('Do not show again', 'I like this suggestion', etc). This additional information provides significant knowledge for training any ranking algorithm using two-dimensional output variable. This paper proposes a novel method to use the relevance label along with cluster information to better train the ranking models. Results for user-trial Notification Ranking dataset and standard datasets like LETOR 4.0, MSLR-WEB10K and YahooLTR further support this claim.
Ontologies, Repository, and Information Mining in Component-Based Software Engineering Environment

This chapter reviews some ontologies, tools, and editors used in building and maintaining the ontology from those reported in the literature, and the main focus is on the interoperability between them. The essential thing while developing an ontology or using an ontology from world web are tools. Through tools, ontology can either be developed or aligned in a manner that the researcher wants and given direction in term of opinion from the source files as meta data. This chapter presents various editors for building the ontology and various tools for matching between the two ontologies and conclusion based on the repository extracted as from the data in term of mining results. Comparison of various ontologies, tools, and editors are also there in order for the ease of user to access a particular ontology tool for selection of data in term of repository or components from the enormous data.
Nurture

Early detection of developmental and growth deviations enables early intervention so that children and parents suffer minimal irreversible physical and financial damage. In India however, pediatricians operate within acute time constraints that keep them from performing comprehensive developmental assessments for their patients. Parents' lack of awareness regarding growth and development milestones further hinders early detection. Our paper contributes the design and evaluation of a probe that seeks to understand the needs of variously resource-constrained middle-class parents in India for monitoring their children's growth, development, and immunization milestones. Our findings highlight the potential of mobile information-based applications to engage, assist, and empower Indian parents. In addition, we describe the affordances of a design process that originated amidst a richly resourced hackerspace organized to address the needs of under-resourced settings, and discuss the consequences of this process on our design.
On Synergies Between Information Retrieval and Digital Libraries

In this paper we present the results of a longitudinal analysis of ACM SIGIR papers from 2003 to 2017. ACM SIGIR is the main venue where Information Retrieval (IR) research and innovative results are presented yearly; it is a highly competitive venue and only the best and most relevant works are accepted for publication. The analysis of ACM SIGIR papers gives us a unique opportunity to understand where the field is going and what are the most trending topics in information access and search. In particular, we conduct this analysis with a focus on Digital Library (DL) topics to understand what is the relation between these two fields that we know to be closely linked. We see that DL provide document collections and challenging tasks to be addressed by the IR community and in turn exploit the latest advancements in IR to improve the offered services. We also point to the role of public investments in the DL field as one of the core drivers of DL research which in turn may also have a positive effect on information accessing and searching in general.
Advanced Protection for Automobiles Using MSP430

As the demand for automobiles rises for everyone, there has been a continuous increase in the number of car thefts. This paper presents a new mechanism for increasing the security for automobiles by employing fingerprint-based biometric security system. Drunken drivers will not be in stable condition, and so the rash driving is the inconvenience for other road users and also the question of life and death. This paper also presents a new automatic engine-locking system which detects the alcohol content on the driver, and the ignition system automatically turns off if the alcohol content of the driver is more than the threshold level. The entire security system, alcohol detection system, and auto-braking system by detecting sudden and any fast moving objects on roads have been implemented using MSP430. The greater advantage of MSP430 is that consumes low power which can increase battery life. The RISC architecture employed by MSP430 is user-friendly and provides greater scope for new innovations.
Quick response code and interference-based optical asymmetric cryptosystem

In this paper, a novel image encryption scheme based on quick response code and interference in Fresnel domain is proposed. The framework is asymmetric in nature due to amplitude- and phase-truncation operation of encoded spectrum. The input image is scrambled using Arnold cat map and divided into pixel blocks. Each block is encoded into a quick response code, which are then multiplexed to generate a binary matrix. The phase-truncated binary matrix is encoded into analytically generated two phase-only masks. During decryption the phase-only masks are recombined to yield the binary matrix, which is grouped into quick response codes and decoded into pixel blocks. The pixel blocks are recombined and descrambled to retrieve the original image. Numerical simulations have been carried out to demonstrate the effectiveness of the proposed method.
Using Risk Assessments to Assess Insurability in the Context of Cyber Insurance

In the current globalisation framework where electronic transactions and data sharing is a common activity, cyber-risks analysis, protection and avoidance have become a key aspect which must be book and prioritised on the business agenda in companies. Nevertheless, this issue is difficult to analyse given the dimension of the problem and the company units and individuals and infrastructures which are involved. In consequence, cyber-insurance is considered as the appropriate mean to avoid financial losses caused by information technologies infrastructures and procedures security breaches. This paper analyses and describes how costumers and their cyber-risks should be assessed by an insurance company in order to establish the company status and implement the required actions to fix the issue. This work describes the three phases required to complete a full cyber-risk assessment and the risks evaluation. Furthermore, the paper highlights the resources that the insurer should keep in its road-map to implement the risk assessment and, thus, to determine the company insurability, and the requirements to reach such condition. After the risk analysis completion at the customer's premises, it must be evaluated subsequently at all levels. Among other factors, this evaluation is based on 63 question criteria. In the risk assessment criteria weights are not uniformly distributed and weighting is applied according to the relevance. In particular, criteria that should receive a special attention are referred to as showstoppers.
An Overview of Futures Research for Designers

Design impacts the everyday life of people and has the potential to influence their behavior and environment. Hence, a holistic and well-informed approach to designing for the future would benefit the society and its evolution. However, the lack of tools and techniques to help designers engage with the far future has been identified as a gap in the Design process. In order to overcome this, designers need to turn into visionaries and this can only be possible if they enter the field of Futures Research. This paper highlights both the commonality and the disparity of Futures Research with the Design process. In doing so, it aims to explore a complimentary beneficial system that will help not only Designers but also other Futurists.
A Voting Scheme in Blockchain Based on Threshold Group Signature

Traditional voting schemes are used for the credit evaluation and authentication. During the voting process, the contents need to be verified through the signature algorithms. Traditional signature schemes for voting scenes exist several drawbacks such as distrust of central nodes for the group signature and inefficiency for the ring signature. A trusted center selection scheme is proposed based on Dynamic Bayesian Network, which can be adapted in the isomerized blockchain. By introducing the historical interaction window, the aging factor, and the penalty factor, the adaptive trusted metrics can be obtained through aggregating the direct credibility and the indirect credibility. A new threshold group signature scheme is introduced through collaboration between users and the trusted centers. In order to protect the user identities, the blinding process is proposed. In case of compromising, the trusted centers create redundant backup, and can be updated with the proposed selection scheme. Security analysis shows that the proposed signature, whose difficulty is equivalent to the discrete logarithm of the elliptic curve, achieves a high level of anonymity and can resist impersonation attacks. Computational complexity analysis shows that the new method with low computational cost and transmission efficiency can be effectively adapted to the isomerized blockchain scene.
Flight test of Quadcopter Guidance with Vision-Based Reinforcement Learning

Reinforcement Learning (RL) has been applied to teach quadcopters guidance tasks.Most applications rely on position information from an absolute reference system such as Global Positioning System (GPS).The dependence on "absolute position" information is a general limitation in the autonomous flight of Unmanned Aerial Vehicles (UAVs).Environments that have weak or no GPS signals are difficult to traverse for them.Instead of using absolute position, it is possible to sense the environment and the information contained within it in order to come up with a "relative" description of the UAV's position.This paper presents the design of an RL agent with relative vision-based states and rewards for the teaching of a guidance task to a quadcopter.The agent is taught the task of turning towards a red marker and approaching it in simulation and in flight tests.A more complex task of travelling between a blue and a red marker is trained in simulation.This work shows that relative vision-based states and rewards can be used with RL to teach quadcopters simple guidance tasks.The performance of the trained agent is inconsistent in simulation and flight test due to the inherent partial observability in the relative description of the state.
Waypoint Based Online Trajectory Generation and Following Control for the ACT/FHS

New research activities on DLR’s EC135 ACT/FHS research helicopter, has led to new challenges for the automatic flight control software of this testbed. Noise abatement flights, innovative automatic approach trajectories and other topics all require the helicopter to follow a set of waypoints as precisely as possible. This paper presents a new algorithm which is capable of generating curvature-continuous trajectories. A modification of the natural cubic spline algorithm is presented, which allows to incrementally fit cubic splines with defined boundary conditions through subsets of the waypoint list. By joining several of these splines with matching boundary condition, a continuous trajectory is obtained. This approach allows to distribute the computational load of the cubic spline calculation over several execution cycles of the real-time system. Based on these results, two controllers to follow the generated trajectories are developed and compared in terms of their ability to reduce and stabilize the distance between the trajectory and the vehicle. The approach is verified in DLRs Air Vehicle Simulator (AVES).
A Preliminary Study on Identifying the Potential Core Values for Product Branding Based on Malaysian Keris Characteristics

This paper discusses in profiling Malaysian keris DNA and properties, to identify the core values and to build emotionally sustainable heritage brand recognition with the Nostalgia Sentimental Values (NSV) towards product design. In this research, the researcher's focus is to build an emotionally sustainable Malay heritage by profiling Malaysian keris DNA & properties as one of its core value towards nostalgic sentimental values in brand recognition. To do so, the research requires a focus on building characteristics from profiling Malaysian keris properties and stories in tandem by designing a product itself. Hence, to establish Visual Branding Characteristic (VBC) as the core value of the product design where the abstract characteristic is part of the Malaysian Keris properties which can communicate through aesthetic elements either semantically or syntactically as a Visual Brand Language (VBL) cohesively building its Nostalgic Sentimental Values (NSV) as the core values in brand recognition towards Malaysian products. Eventually, it can be used as a future reference and as a benchmarking guideline in branding Malaysian identity towards product design.
Multi‐label automatic image annotation approach based on multiple improvement strategies

Currently, multi-label automatic image annotation (MAIA) approach based on machine learning has been widely applied and developed. Since extreme learning machine (ELM) has the advantages of simple structure, fast learning speed, better generalisation ability and so on, it is used for MAIA in this study. In order to enhance the annotation performance and generalisation ability of MAIA, some work is designed and implemented. First of all, a novel distance metric learning method based on cost-sensitive learning for MAIA task is proposed to reduce the impact of class imbalance of samples. Second, an improved ELM approach based on singular value decomposition is proposed for implementing MAIA task. Finally, the selection of training samples (STS) strategy based on error correlation is also proposed to improve the generalisation ability and annotation performance of MAIA. Based on the above work, a novel MAIA approach is implemented. The experimental results confirm that the proposed cost-sensitive DLM, improved cost-sensitive ELM and STS can obtain the good generalisation ability, and achieve better annotation performance than the existing MAIA approaches.
Efficient Visual Saliency Detection via Multi-Cues

Saliency detection has become a valuable tool in computer vision processing, which has been attracting a good deal of attention. Although a lot of research has been done, it cannot obtain ideal performance. In most saliency detection framework, the single difference cue is often used to detect saliency map, but their results were far from satisfaction for low discriminative power of single cue. In addition to the composition of the detection cues during the whole process, the used cues which could only describe the low level information could also lead to poor detection. In order to solve this problem effectively, the paper proposed a novel saliency detection framework by fusing multi-cue difference with high level difference. Basically, the comprehensive information is coupled into multi-cues vectors to remove the non-salient regions and enhance the brightness of the salient area. Specifically, we utilize the fusion of multi-cues and high-level information to improve the ability of understanding images. To further improve the performance of the proposed framework in AUC, we adopt multiple assignments strategy while enhancing the precision of saliency detection. Extensive experiments indicate that the newly constructed multi-cues with high level information could effectively suppress the influence of background information on salient regions. In order to verify the effectiveness of the new algorithm, our experiment uses several standard benchmark datasets (MSRA, ASD, SED1, SED2, and SOD) to test the performance of the algorithm. The experimental results demonstrate that this method has achieved good saliency detection result and good AUC performance in the test. More importantly, the experimental results also show that the proposed method is well complementary to many existing algorithms.
RST Discourse Parsing with Tree-Structured Neural Networks

Discourse structure has a central role in several NLP tasks, such as document translation, text summarization and dialogue generation. Also, text-level discourse parsing is notoriously difficult for the long distance of discourse and deep structures of discourse trees. In this paper, we build a tree-structured neural network for RST discourse parsing. We also introduce two tracking LSTMs to store long-distance information of a document to strengthen the representations for sentences and the entire document. Experimental results show that our proposed method obtains comparable performance regarding standard discourse parsing evaluations when compared with state-of-the-art systems.
Implicit Secondary Authentication for Sustainable SMS Authentication

Short message service (SMS) is the most widely adopted multi-factor authentication method for consumer-facing accounts. However, SMS authentication is susceptible to vulnerabilities such as man-in-the-middle attack, smishing, and device theft. This study proposes implicit authentication based on behavioral pattern of users when they check an SMS verification code and environmental information of user proximity to detect device theft. User behavioral pattern is collected by using the accelerometer and gyroscope of a smart device such as a smartphone and smart watch. User environmental information is collected using device fingerprint, wireless access point, Bluetooth, and global positioning system information. To evaluate the performance of the proposed scheme, we perform experiments using a total of 1320 behavioral and environmental data collected from 22 participants. The scheme achieves an average equal error rate of 6.27% when using both behavioral and environmental data collected from only a smartphone. Moreover, it achieves an average equal error rate of 0% when using both behavioral and environmental data collected from a smartphone and smart watch. Therefore, the proposed scheme can be employed for more secure SMS authentication.
Using ZDDs in the Mapping of Quantum Circuits

A critical step in quantum compilation is the transformation of a technology-independent quantum circuit into a technology-dependent form for a targeted device. In addition to mapping quantum gates into the supported gate set, it is necessary to map pseudo qubits in the technology-independent circuit into physical qubits of the technology-dependent circuit such that coupling constraints among qubits acting in multiple-qubit gates are satisfied. It is usually not possible to find such a mapping without adding SWAP gates into the circuit. To cope with the technical limitations of NISQ-era quantum devices, it is advantageous to find a mapping that requires as few additional gates as possible. The large search space of possible mappings makes this task a difficult combinatorial optimization problem. In this work, we demonstrate how zero-suppressed decision diagrams (ZDDs) can be used for typical implementation tasks in quantum mapping algorithms. We show how to maximally partition a quantum circuit into blocks of adjacent gates, and if adjacent gates within a circuit do not share common mapping permutations, we attempt to combine them using parallelized SWAP operations represented in a ZDD. Boundaries for the partitions are formed where adjacent gates are unable to be combined. Within each partition block, ZDDs represent all possible mappings of pseudo qubits to physical qubits.