Reviewerly/exHarmony huggingface dataset:
Facts and Stories in Phishing Training: A Replication and Extension

The number of phishing attacks has increased each year, becoming more sophisticated and creating the need for practical training in identifying these scams. A recent study explored the potential of training staff at a university to recognize phishing attacks through either facts or utilizing stories shared by peers or experts. The study found that facts from an expert, rather than stories from experts or peers, remain the most effective form of training. Here we replicate and extend this study by targeting current college students. The results show that college students respond differently than university staff and are more receptive to social stories or facts from peers. These results indicate the importance of taking the target demographic into account when designing and conducting phishing training.
Search Support Tools

This chapter presents in-depth reviews of search tools for supporting information search. With the brief introduction of cutting-edge search support tools, we describe the key ideas behind the tools and implications for design. We also discuss the limitations of conventional search interfaces to explore directions for future research on search support tools.
Introducing an Edge-Native Deep Learning Platform for Exergames

The recent advancements in the areas of computer vision and deep learning with the development of convolutional neural networks and the profusion of highly accurate general purpose pre-trained models, create new opportunities for the interaction of humans with systems and facilitate the development of advanced features for all types of platforms and applications. Research, consumer and industrial applications increasingly integrate deep learning frameworks into their operational flow, and as a result of the availability of high performance hardware (Computer Boards, GPUs, TPUs) also for individual consumers and home use, this functionality has been moved closer to the end-users, at the edge of the network. In this work, we exploit the aforementioned approaches and tools for the development of an edge-native platform for exergames, which includes innovative gameplay and features for the users. A prototype game was created using the platform that was deployed in the real-world scenario of a rehabilitation center. The proposed approach provides advanced user experience based on the automated, real-time pose and gesture detection, and in parallel maintains low-cost to enable wide adoption in multiple applications across domains and usage scenarios.
Graph Regularized Unsupervised Deep Hashing for Large Scale Image Retrieval

With the development of technology, the number of data is growing rapidly day by day. How to perform efficient big data retrieval becomes a critical problem. Similarity-preserving hashing has been widely used in large-scale information retrieval because of its low storage cost and high computation efficiency. It maps the data from high-dimensional feature space into binary hamming space while preserving the similarity. Particularly, deep learning based hashing methods have shown their significantly advantages in both effectiveness and accuracy. However, the performance of unsupervised deep hashing algorithms is still unsatisfactory because semantic labels are not available in unsupervised learning. In this paper, we propose an end-to-end unsupervised deep hashing model to simultaneously learn image representation and generate compact hash codes. Intuitively, we consider that the convolutional neural networks can capture high level semantic information. Therefore, we explore the semantic relations between images by utilizing data mining techniques on deep features. Specifically, we first extract the features from pretrained CNN model and conducting K-means and k-NN graph construction on these features. Then we train the deep network using the pseudo labels. In addition, due to the binary constrain of hash codes, we iteratively update the binary hash codes using cyclic coordinate descent method. Extensive experiments validate the performance of our method, which outperforms previous state-of-art methods in image retrieval task.
Enforcing Architectural Security Decisions

Software architects should specify security measures for a software system on an architectural level. However, the implementation often diverges from this intended architecture including its security measures. This may lead to severe vulnerabilities that have a wide impact on the system and are hard to fix afterwards. In this paper, we propose an approach for checking the implementation's conformance with the defined security measures using architectural security rules: We extend a controlled natural language approach to formalize these rules and use dynamic analysis techniques to extract information on the actual system behavior for the conformance check. We evaluate our approach by an industrial case study to show the applicability and flexibility of our conformance checking approach.
Simplified long short-term memory model for robust and fast prediction

Long short-term memory(LSTM) is an effective solution to time sequence prediction. Considering the data perturbations, in this letter, a variant model of LSTM is proposed to achieve robustness of prediction. Specifically, data processing procedure in the recurrent unit of proposed model is reformulated, the gates are controlled by only one variable, and the variable is the sum of long-term memory and the current input. Due to the simplified two-gate structure of proposed model, the speed of prediction is improved as well. The experiments on three datasets verify that the proposed model with simplified structure has higher robustness and shorter running time than the traditional LSTM model.
How to curtail oversensing in the home

Limiting sensitive information leakage via smart-home sensor data.
Dynamic distance learning for joint assessment of visual and semantic similarities within the framework of medical image retrieval

The similarity measure is an essential part of medical image retrieval systems for assisting in radiological diagnosis. Attempts have been made to use distance metric learning approaches to improve the retrieval performance while decreasing the semantic gap. However, existing approaches did not resolve the problem of dependency between images (e.g. normal and abnormal images are compared with the same distance). This affects the semantic and the visual similarity. Thus, this work aims at learning a distance metric which preserves both visual resemblance and semantic similarity and modeling this distance in order to treat each query independently. The proposed method is described in three stages: (1) low-level image feature extraction, (2) offline distance metric modeling, and (3) online retrieval. The first stage exploits transform-domain texture descriptors based on local binary pattern histogram Fourier, shearlet, and curvelet transforms. The second stage is carried out using low-level features and machine learning. Given a query image, the online retrieval is based on the evaluation of the similarity between this image and each image within the dataset, while using a distance that is dynamically defined according to the query image. Realized experiments on the challenging Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM) datasets prove the effectiveness of the proposed method in determining dynamically the adequate distance and retrieving the most semantically similar images, while investigating single low-level features as well as fused ones.
Learning to complete partial observations from unpaired prior knowledge

We present a novel training strategy that allows convolutional encoder-decoder networks, to complete partially observed data by means of hallucination. As input, it takes data from a partially observed domain, for which no complete ground truth is available, and data from an unpaired prior knowledge domain and trains the network in an end-to-end manner. This strategy is demonstrated for the task of completing 2-D road layouts as well as 3-D vehicle shapes. In contrast to alternative approaches, our strategy is compatible with networks that use skip connections, to improve detail in the completed output, while not requiring adversarial supervision. To demonstrate its benefits, our training strategy is benchmarked against two state-of-the-art baselines, one using a two-step auto-encoder training strategy and one using an adversarial strategy. Our novel strategy achieves an improvement up to +12% F-measure on the Cityscapes dataset. The learned network intrinsically generalizes better than the baselines on unseen datasets, which is demonstrated by an improvement up to +24% F-measure on the unseen KITTI dataset. Moreover, our approach outperforms the baselines using the same backbone network on the 3-D shape completion benchmark by reducing the Hamming distance with 15%.
Efficient BIKE Hardware Design with Constant-Time Decoder

BIKE (Bit-flipping Key Encapsulation) is a promising candidate under consideration in the NIST Post-Quantum Cryptography Standardization process. It is a code-based cryptosystem with a simple definition and well-understood underlying security. The most critical step in this cryptosystem consists of correcting errors with a QC-MDPC linear code. Published performance on 64-bit processors shows this decode procedure takes 20M cycles for the software implementation, and worse for embedded devices. In this paper, we propose a BIKE coprocessor that accelerates the operations least suitable to embedded software implementation, enabling power-constrained SoC devices to directly implement BIKE with good performance. We propose a simplification of the grey-black decoder in the BIKE spec, which is more friendly to hardware implementations, and demonstrate the performance of this decoder on an Intel Arria 10 FPGA platform. Our implementation has a cycle-area performance comparable to other MDPC decoders in the literature, while including design elements which may facilitate power side channel resilience. The main result is to enable BIKE to be run on small SoC platforms, and therefore for those devices to resist attacks by quantum computers.
Enhanced Road Construction Process with Machine Learning and Blockchain Technology

In India, we are facing various challenges due to improper management of road construction process. Mostly the contracts of road constructions are manipulated by political leaders or some corrupted personalities. This leads to low quality roads, traffic issues due to extended work on road and various frauds during procurement of raw material and wages of labors. With the help of artificial intelligence, we can determine the duration within which road construction will be completed for particular area, how much raw material is expected, how much labors should be appointed and many more. Blockchain will assure the integrity of contracts within stakeholders. After completion of road, by reviewing user's feedback we can determine quality of road or any maintenance related things by applying Machine learning strategy. Ultimately it will explore an expert system for whole life process of Road Construction which leads to Industry 4.0 in India.
On the Digital Pontryagin Algebras

In the current study, we explore digital homology modules, and investigate their fundamental properties on (pointed) digital images as one of the developments of symmetries. We also examine pointed digital Hopf spaces and base point preserving digital Hopf functions between pointed digital Hopf spaces with suitable digital multiplications, and explore the digital primitive homology classes, digital Pontryagin algebras on digital Hopf spaces as a symmetric phenomenon in mathematics and computer science.
An EMR Sharing and Privacy Protection Mechanism Based on Medical Consortium Blockchain

The sharing of Electronic Medical Record(EMR) plays an important role in improving the quality of medical care, reducing costs and enhancing patient experience. But it also raises issues such as privacy leaks and uncontrollable data dissemination. To address these issues, we propose an EMR sharing mechanism based on the medical consortium blockchain. The hospital joins the medical consortium blockchain as a node, storing and maintaining the EMR data it generates. At the same time, the index of EMR is stored on the medical consortium blockchain for query, call and access control. Before accessing the EMR, the patient's authorization needs to be obtained on the medical consortium blockchain. Compared with the traditional mechanism, this mechanism has certain advantages in privacy protection, access control and data security while implementing EMR sharing.
Sya: Enabling Spatial Awareness inside Probabilistic Knowledge Base Construction

This paper presents Sya; the first spatial probabilistic knowledge base construction system, based on Markov Logic Networks (MLN). Sya injects the awareness of spatial relationships inside the MLN grounding and inference phases, which are the pillars of the knowledge base construction process, and hence results in a better knowledge base output. In particular, Sya generates a probabilistic model that captures both logical and spatial correlations among knowledge base relations. Sya provides a simple spatial high-level language, a spatial variation of factor graph, a spatial rules-query translator, and a spatially-equipped statistical inference technique to infer the factual scores of relations. In addition, Sya provides an optimization that ensures scalable grounding and inference for large-scale knowledge bases. Experimental evidence, based on building two real knowledge bases with spatial nature, shows that Sya can achieve 70% higher F1-score on average over the state-of-the-art DeepDive system, while achieving at least 20% reduction in the execution times.
Notice of Removal: Reinforcement Learning and Additional Rewardsfor the Traveling Salesman Problem

Removed.
Zero-Shot Classification Based on Word Vector Enhancement and Distance Metric Learning

The zero-shot classification algorithm has been widely concerned in recent years, in which the labeling of samples of a new category is unnecessary and the cost of annotations can be reduced in applications. This paper presents a zero-shot method for image classification based on word vectors enhancement and distance metric learning. Specifically, the convolutional neural network (CNN) is employed to extract image feature vectors which have the same dimension as semantic feature vectors. Then, an unsupervised learning method is applied on Wikipedia corpus for extracting word vectors and the skip-gram is used to obtain word vectors. The model of analysis dictionary learning is improved by reducing redundant information in word vectors. The obtained sparse vectors are used as semantic features and a distance metric learning method is employed to measure the distance between image features and semantic features. Finally, the classification is implemented by a nearest neighbor based classifier. The effectiveness of the proposed algorithm is validated on the AwA and CUB data sets. Experimental results demonstrate that the proposed method has good performance in terms of both accuracy and robustness.
Analysis of academic service cybersecurity in university based on framework COBIT 5 using CMMI

Abstract A secure academic information system is part of the college. The security of academic information systems is very important to maintain information optimally and safely. Along with the development of technology, academic information systems are often misused by some irresponsible parties that can cause threats. To prevent these things from happening, it is necessary to know the extent to which the security of the academic information system of universities is conducted by evaluating. So the research was conducted to determine the Maturity Level on the governance of the security of University Ahmad Dahlan academic information system by using the COBIT 5 framework on the DSS05 domain. The DSS05 domain on COBIT 5 is a good framework to be used in implementing and evaluating related to the security of academic information systems. Whereas to find out the achievement of evaluation of academic information system security level, CMMI method is needed. The combination of the COBIT 5 framework on the DSS05 domain using the CMMI method in academic information system security is able to provide a level of achievement in the form of a Maturity Level value. The results of the COBIT 5 framework analysis of the DSS05 domain use the CMMI method to get a Maturity level of 4,458 so that it determines the achievement of the evaluation of academic information systems at the tertiary level is Managed and Measurable. This level, universities are increasingly open to technological developments. Universities have applied the quantification concept in each process, and are always monitored and controlled for performance in the security of academic information systems.
Multi-Agent Programming Contest 2018—The Jason-DTU Team

We provide a brief description of the Jason-DTU system, including the overall system design and the tools that we used in the Multi-Agent Programming Contest. We also provide a detailed evaluation of our system. The strengths of our system include dynamic assignment of agents to groups, enabling flexible use of agents according to the current situation. Our team performed overall very well and we won some matches by either a fairly big margin or closely.
Verification of Integrity in Vehicle Architectures

The draft version of ISO 21434 demands, among others, the verification of the car architecture with respect to the enforcement of the security goals. We present a distinct definition of integrity as a system property as well as a formal modeling and analysis approach to verify integrity in a given architecture. Thereby, we discuss the idea of integrity levels as well as security measure levels as metrics to represent a functions worthiness of protection and the reduction of risk, respectively. We present how the beneficence of security measures may be ranked for gaining the global security measure levels. We assign those levels to the system parts and interpret integrity as a global information flow problem. Formal properties enforce the relation between the integrity levels of a function and the assigned security measure level as well as between the communication links and the receiver functions. The relation between communication functions and functions in the same unit of isolation are enforced to follow the policy of no command-up.
Designing the Smart Health Function towards Puskesmas (Citizen Health Centre) based on Smart City Concept

Abstract According to Regional Regulation No.23 of 2014 Article 386 until 390 which requires all institutions to adopt architectural designs to bring up architectural innovations. A smart city is a concept of the application, development, and implementation of technology that is applied to a region (especially urban area) as a complex interaction between the various systems in it. One important development area to be developed is the health sector. In Indonesian constitution of article 28 H and 28 I that the state must guarantee the lives of all its citizens, include by providing appropriate services such as health services. This study aims to design an enterprise architecture in health functions within Citizen Health Centre management system based on the Smart City based on the TOGAF ADM framework, so that it is expected to help the city government work program in realizing smart city and business activities that currently run can be supported by information systems so as to increase satisfaction Bandung City communities. Based on the results of research on public health functions at the Bandung City Health Office obtained a design or blueprint that focuses on the preliminary phase, architecture vision, business architecture, system architecture, information technology, opportunities and solutions, and migration planning.
Investigation of image forgery based on multiscale retinex under illumination variations

The number of forged images is currently expanding vastly over the Internet. Therefore, image authenticity represents a globally challenging issue that must be addressed. Emerging tools for image editing have been developed to manipulate and enhance digital images; however, forgers can exploit these tools to achieve their destructive purposes. Forgers often use a common method of image forgery called region duplication forgery. In this method, the copied region in the fake image can appear identical to the original region of the image. This paper aims to target this issue by developing an algorithm that can detect suspected images through localizing small duplicated regions. These regions can be described by multiscale features, which are invariant with illumination variations. The proposed method begins with segmenting suspected images using an adaptive statistical region merging. The goal of the segmentation method is to discover small regions. The method then targets the small regions based on color correction to represent their illumination features. Experiments are also conducted to validate the proposed method on two image datasets, Media Integration and Communication Center (MICC) and Image Data Manipulation, yielding positive results. A comparative study of the most recent methods is carried out.
Unsupervised learning of textual pattern based on Propagation in Bipartite Graph

Graph-based algorithms have aroused considerable interests in recent years by facilitating pattern recognition and learning via information propagation process through the graph. Here, we propose an unsupervised learning algorithm based on propagation on bipartite graph, referred to as Propagation in Bipartite Graph (PBG) algorithm. The contributions of this approach are threefold: 1) we present an iterative graph-based algorithm and a straight-forward bipartite representation for textual data, in which vertices represent documents and words, and edges between documents and words represent the occurrences of the words in the documents. Additionally, 2) we show that PBG is more flexible and easier to be adapted for different applications than the mathematical formalism of the generative models, and 3) we present a comprehensive evaluation and comparison of PBG to other topic extraction techniques. Here, we describe the strategy employed in PBG algorithm as a problem of maximization of similarity between latent vectors assigned to vertices and edges and demonstrate that the proposed strategy can be improved by assigning good initial values for the vectors. We notice that PBG can be parallelized by a simple adjustment in the algorithm. We also show that the proposed algorithm is competitive with LDA and NMF in the task of textual collection modelling, returning coherent topics, and in the dimensionality reduction task.