Reviewerly/exHarmony huggingface dataset:
SHeFU: Secure Hardware-Enabled Protocol for Firmware Updates

Firmware updates are often termed as a panacea to vulnerable Internet-of-Things (IoT) networks, as firmware updates can fix the exposed bugs and prevent them from being exploited in the future. However, a secure firmware update is a challenging task as IoT devices are often employed in unattended networks. Moreover, malicious updates of firmware in any of the devices of a network, or the non-execution of an update, can create havoc. Although security mechanisms like remote attestation (RA) are quite popular to identify malicious nodes in a network, they are costly in terms of computation/memory usage and communication overhead. To overcome these issues, we propose a “Secure Hardware-enabled Protocol for Firmware Updates (SHeFU)”. The aim of the proposed protocol is two-fold: 1) we obviate the need for remote attestation, and 2) we make sure that malicious nodes are isolated from benign nodes. Assuming a restricted threat model and network constellation, SHeFU ensures secure firmware updates and prevents compromised nodes from communicating with benign nodes in a network.
Diagnostic Prediction with Sequence-of-sets Representation Learning for Clinical Events

Electronic health records (EHRs) contain both ordered and unordered chronologies of clinical events that occur during a patient encounter. However, during data preprocessing steps, many predictive models impose a predefined order on unordered clinical events sets (e.g., alphabetical, natural order from the chart, etc.), which is potentially incompatible with the temporal nature of the sequence and predictive task. To address this issue, we propose DPSS, which seeks to capture each patient's clinical event records as sequences of event sets. For each clinical event set, we assume that the predictive model should be invariant to the order of concurrent events and thus employ a novel permutation sampling mechanism. This paper evaluates the use of this permuted sampling method given different data-driven models for predicting a heart failure (HF) diagnosis in subsequent patient visits. Experimental results using the MIMIC-III dataset show that the permutation sampling mechanism offers improved discriminative power based on the area under the receiver operating curve (AUROC) and precision-recall curve (pr-AUC) metrics as HF diagnosis prediction becomes more robust to different data ordering schemes.
Circular Rejoin in 3D Using Bézier Paths

In this paper, the interception of a target vehicle is presented utilizing a Bézier path. Utilizing the Bézier path, the controlled vehicle is navigated from an initial point and velocity vector to a desired target waypoint and velocity vector. To ensure a flyable path, control points of the Bézier are adjusted using a nonlinear program that takes into account maximum path curvature and vehicle performance criteria. A model predictive controller is designed to provide the real-time controls that drive the controlled vehicle along the Bézier path with minimal flight path error and control effort. A numerical simulation, which demonstrates the ability to navigate to a desired waypoint and velocity vector for circular rejoin, is provided as an example.
KNOCAP

It is well known that it is desirable to capture the most essential parts of software design meetings that take place at the whiteboard. It is equally well known, however, that actual capture rarely takes place. A few photos may be taken, informal notes might be scribbled down, and at best one of the developers may be tasked with creating a summary. Regardless, problems persist with important information being lost and, even when information is captured, that information not being easily located and accessed. To address these problems, I propose to design and evaluate a novel suite of tools that enables software designers working at the whiteboard to: (1) efficiently and in-the-moment capture important information produced during that meeting, and (2) be delivered, either by request or proactively by the tools, relevant information captured in the past when it is needed in a future design meeting.
A Comparative Approach between Different Computer Vision Tools, Including Commercial and Open-source, for Improving Cultural Image Access and Analysis

Digital cultural heritage objects can benefit greatly from the application of Artificial Intelligence such as computer vision based tools to automatically extract valuable information from them. Novel methods and technologies have been used in the last few years to perform image classification, object detection, caption generation, and other techniques on different types of digital objects from different disciplines. In this pilot study, carried out in the context of the Digital Humanities project ChIA, we present an approach for testing different commercial (Clarifai, IBM Watson, Microsoft Cognitive Services, Google Cloud Vision) and open-source (YOLO) computer vision (CV) tools on a set of selected cultural food images from the Europeana collection with regard to producing relevant concepts. The project generally aims at improving access to implicit cultural knowledge contained in images, and increase analysis possibilities for scientific research as well as for content providers and educational purposes. Preliminary results showed that not only quantitative output results are important, but also the quality of concepts generated. Types of digital objects can pose a challenge to CV solutions.
A Path Planning Method of Logistics Robot Based on Improved Ant Colony Algorithm

One of the key technologies of logistics robot indoor navigation is path planning. Traditional ant colony algorithm is often used in the path planning of logistics robot, which has the problems of slow convergence and local optimization. We proposed an improved algorithm, that was used in path planning for logistics robots. Introducing a heuristic factor in the path transition probability can dynamically adjust the state transition probability, so that the algorithm avoids stagnation and improves the strategy of pheromone update. The paper carried out simulation experiments on the improved algorithm through the matlab software platform. From the simulation results, we can see that the path length of the improved algorithm is shorter, and the number of iterations is reduced by 23%.
KNOWLEDGECHECKR: Intelligent Techniques for Counteracting Forgetting

Existing e-learning environments primarily focus on the aspect of providing intuitive learning contents and to recommend learning units in a personalized fashion. The major focus of the KnowledgeCheckR environment is to take into account forgetting processes which immediately start after a learning unit has been completed. In this context, techniques are needed that are able to predict which learning units are the most relevant ones to be repeated in future learning sessions. In this paper, we provide an overview of the recommendation approaches integrated in KnowledgeCheckR. Examples thereof are utility-based recommendation that helps to identify learning contents to be repeated in the future, collaborative filtering approaches that help to implement session-based recommendation, and content-based recommendation that supports intelligent question answering. In order to show the applicability of the presented techniques, we provide an overview of the results of empirical studies that have been conducted in real-world scenarios.
Joint Image Super-Resolution Via Recurrent Convolutional Neural Networks With Coupled Sparse Priors

Joint image super-resolution (SR) refers to the reconstruction of a high-resolution image from its low-resolution version with the aid of a high-resolution image from another modality. Inspired by the recent success of recurrent neural networks in single image SR, we propose a novel multimodal recurrent convolutional neural network with coupled sparse priors for joint image SR. Our network fuses representations of the two image modalities at input layers using a learned multimodal convolutional sparse coding network. Additional recurrent convolutional stages are performed to further learn the mapping between the input modalities and the desired highresolution estimate. We apply the proposed network to the tasks of near-infrared image SR and multi-spectral image SR using RGB images as the guidance modality. Experimental results show the superior performance of the proposed multimodal recurrent convolutional network against several state-of-the-art single-modal and multimodal image SR methods.
Identification of Frame-Rate Up-Conversion Based on Spatial-Temporal Edge and Occlusion with Convolutional Neural Network

Frame-Rate Up-Conversion (FRUC) is a frame-based video manipulation where synthesized frames are periodic inserted into two consecutive frames to increase the frame-rate of videos. Thus, it can be employed by counterfeiters for falsifying high frame rate videos or splicing videos with different frame-rates. Automatically identifying the adopted FRUC techniques from a serial of faked frame-rate videos while achieving a stable recognition rate is a challenging task in video forensics. We propose a new forensic framework, named Spatial-Temporal Edge-Occlusion Identification of FRUC (STEO-IoF), by considering the capabilities of both clues: spatial-temporal edge and occlusion maps, to guide a forensic model using prior knowledge transferred from the existing steganalysis convolutional neural network (CNN) model for the recognition of adopted FRUC techniques. Three open FRUC softwares and four representative FRUC techniques have been evaluated, and experimental results clearly show that the efficacy of the proposed approach.
The position of object pronouns in the German middlefield

Abstract This paper presents a corpus study of the position of object pronouns relative to a non-pronominal subject in embedded clauses of German. A total of 4322 embedded clauses from the deWaC corpus (Baroni, Marco, Silvia Bernardini, Adriano Ferraresi &amp; Eros Zanchetta. 2009. The WaCky Wide Web: A collection of very large linguistically processed web-crawled corpora. Language Resources and Evaluation Journal 23(3). 209–226), a corpus of written German Internet texts, were analyzed. In 67.0% of all clauses, the object pronoun occurred in front of the subject. Several factors that have been proposed in the literature on word order alternations were found to govern the choice between subject–object and object–subject order in the corpus under investigation. The most important findings are: (i) The Extended Animacy Hierarchy and the Semantic Role Hierarchy independently contribute to the choice of word order. (ii) The Definiteness Hierarchy has a strong effect on the position of the object pronoun. (iii) Word order effects of constituent weight, measured as length in number of words, cannot be reduced to effects of grammatical factors, nor can effects of grammatical factors be reduced to effects of weight.
Undecided Voters as Set-Valued Information – Towards Forecasts Under Epistemic Imprecision

Increasing numbers of undecided individuals in pre-election polls throughout western democracies impose a severe challenge for election forecasting. While conventionally these voters are neglected relying on presumably unjustified assumptions, we sketch more nuanced approaches incorporating the potential valuable information in a set-valued manner. Hereby, each undecided voter is represented by the set of parties he or she is incapable to choose from. This set, containing one true, but unknown element, enables modelling under so-called epistemic imprecision. Depending on further assumptions, (imprecise) transition probabilities between the options can be estimated in order to achieve election forecasting. Starting with Dempster’s upper and lower probabilities as the most cautious approach, two further ideas are introduced, providing initial methodology. Furthermore, extensions including Bayesian modeling are sketched. The theory is applied using data from the German Longitudinal Election Study for forecasting concerning the most recent German federal election of 2017. The results are promising, laying the groundwork for further research.
HyperIoT: Securing Transactions in IoT through Private Permissioned Blockchain

The Internet of Things (IoT) is growing at a lightning pace every year. There are more connected devices on the internet than people in the world today. However, the security and privacy of these networks are a primary concern for IoT applications which still faces some enormous challenges. The rise in the number of connected devices has led to more access points in the network making the systems vulnerable to cyber-attacks. Most customers are also typically uninformed of how much data is collected and how the data is used by the service providers. We propose HyperIoT as a decentralized, distributed, private blockchain network on the current IoT architecture to address these issues. The proposed architecture is based on Hyperledger Fabric framework. The model uses the concept of Fabric organizations and channels to create private sub-network per confidential domain where only permitted peers may subscribe to, thus restricting access to data. In addition to this, the proposed model has other security features such as permissioned membership, digital identity, and access control policies. The architecture proposed makes IoT data and transactions more secure, restricted, and transparent over a traditional IoT network.
User detection of threats with different security measures

Cyber attacks and the associated costs made cybersecurity a vital part of any system. User behavior and decisions are still a major part in the coping with these risks. We developed a model of optimal investment and human decisions with security measures, given that the effectiveness of each measure depends partly on the performance of the others. In an online experiment, participants classified events as malicious or non-malicious, based on the value of an observed variable. Prior to making the decisions, they had invested in three security measures - a firewall, an IDS or insurance. In three experimental conditions, maximal investment in only one of the measures was optimal, while in a fourth condition, participants should not have invested in any of the measures. A previous paper presents the analysis of the investment decisions. This paper reports users' classifications of events when interacting with these systems. The use of security mechanisms helped participants gain higher scores. Participants benefited in particular from purchasing IDS and/or Cyber Insurance. Participants also showed higher sensitivity and compliance with the alerting system when they could benefit from investing in the IDS. Participants, however, did not adjust their behavior optimally to the security settings they had chosen. The results demonstrate the complex nature of risk-related behaviors and the need to consider human abilities and biases when designing cyber security systems.
A Transformer based Approach for Identification of Tweet Acts

Speech acts help in uncovering and understanding the communicative intention and behavior of a speaker utterance. This is pertinent for communication on any platform that embodies social media platforms like Twitter. This paper presents a supervised speech act (tweet act in our case) classifier for tweets for assessing the content and intent of tweets, thereby, exploring the valuable communication amongst the tweeters. With the recent success of Bi-directional Encoder Representations from Transformers (BERT), a newly introduced language representation model that provides pre-trained deep bi-directional representations of vast unlabeled data, we introduce BERT-extended that is built on top of BERT. Our model is based on calculating attention weights over the representations of tokens of a sequence to identify tweet acts. The proposed model attained a benchmark accuracy of 75.97% and outperformed several strong baselines and state-of-the-art approaches on an open-source, tweet act annotated Twitter dataset.
Virtual Savannah: An Effective Therapeutic and Relaxing Treatment for People with Subjective Cognitive Decline

In an attempt to make the therapeutic aspect less aversive, more attractive and engaging, virtual reality, an increasingly popular application in healthcare, offers an interesting alternative to pharmacological treatments. Positive emotions may improve the cognitive abilities of people suffering from cognitive impairment. Virtual reality can provide immersive and efficient relaxation tool. This paper presents an experiment where 19 people with Subjective Cognitive Decline (SCD) were immersed in a virtual environment representing a savannah. The hypothesis is that the environment may help them reducing their frustration by relaxing. Participants’ brain activity was recorded using the Emotiv Epoc headset and the virtual savannah experience lasted 10 min. Results suggest that frustration decreased when participants were surrounded by the virtual savannah and that the positive effects continued afterwards.
Channel-Wise Progressive Learning For Lossless Image Compression

This paper presents a channel-wise progressive coding system for lossless compression of color images. We follow the classical lossless compression scheme of LOCO-I and CALIC, where pixel values and coding contexts are predicted and forwarded to the entropy coder for compression. The contribution is that we jointly estimate the pixel values and coding contexts from neighboring pixels by training a simple multilayer perceptron in a residual and channel-wise progressive manner. Specifically, we obtain accurate pixel prediction along with coding contexts that reflect the magnitude of local activity very well. These results are sent to an adaptive arithmetic coder that appropriately encodes the prediction error according to the corresponding coding context. Experimental results demonstrate the effectiveness of the proposed method in high-resolution datasets.
A Novel Approach for Measurement of Source Code Similarity

Plagiarism is really a matter of concern. Plagiarism detection is a major issue while detecting at various levels. Finding plagiarism in source codes that can either be written in same language or different language, increases the complexity of plagiarism detection. People dealing with software designing and academic works are majorly affected by the plagiarism. In this paper we present a novel approach for measurement of code similarity to detect similarity between codes. With the help of the proposed approach, by using methods and functions we can compare the two source codes. This will lead to fair evaluation of assignments, as with the help of this tool we can easily detect Programming Assignment and copying of codes or function.
Method of Sentiment Preservation in the Kazakh-Turkish Machine Translation

This paper describes characteristics which affect the sentiment analysis in the Kazakh language texts, models of morphological rules and morphological analysis algorithms, formal models of simple sentence structures in the Kazakh-Turkish combination, models and methods of sentiment analysis of texts in the Kazakh language. The studies carried out to compare the morphological and syntactic rules of the Kazakh and Turkish languages prove their closeness by structure. In this respect, we can assume that taking into account sentiment in machine translation for these combinations of languages will give a good result at preserving the text meaning.
Multi-hop quantum key distribution with passive relays over underwater turbulence channels

Absorption, scattering, and turbulence experienced in underwater channels severely limit the range of quantum communication links. In this paper, as a potential solution to overcome range limitations, we investigate a multi-hop underwater quantum key distribution (QKD) where intermediate nodes between the source and destination nodes help the key distribution. We consider the deployment of passive relays that simply redirect the qubits to the next relay node or the receiver without any measurement. Based on the near-field analysis, we present the performance of a relay-assisted QKD scheme in terms of quantum bit error rate and secret key rate in different water types and turbulence conditions. We further investigate the effect of system parameters such as aperture size and detector field of view on the performance. Our results demonstrate under what conditions relay-assisted QKD can be beneficial and what end-to-end transmission distances can be supported with a multi-hop underwater QKD system.
SD-FB-GAN: Saliency-Driven Feedback Gan for Remote Sensing Image Super-Resolution Reconstruction

The visual characteristics of different regions in remote sensing images are significantly versatile, which poses a huge challenge to single image super-resolution. Although generative adversarial network (GAN) has shown great potential in generating photo-realistic results, it provides unsatisfactory performance in objective metrics owning to pseudo textures brought by adversarial learning. In this paper, we propose a new saliency-driven feedback GAN to cope with these problems. We design a saliency-driven feedback generator based on paired-feedback blocks (PFBBs) and recurrent structure to provide strong reconstruction ability. In the PFBB, the saliency map serves as an indicator to reflect the texture complexity, so different reconstruction principles can be applied to restore areas with varying levels of saliency. Besides, we propose to measure the visual quality of salient areas, non-salient areas, and the whole image with multi-discriminators, which can dramatically eliminate pseudo textures. Comprehensive evaluations and ablation studies validate the superiority of our proposal.
A Constructive Induction of Feature using Random Forest Approach

Machine learning, including the selection of important features, has a very wide and important application in medicine. Feature selection is an important stage in the analysis of medical data and aims to simplify the problem, reduce dimensionality but also to identify important parameters, symptoms that are important in diagnosing diseases. The presented approach is a kind of concept for automation of the process of determining the relationship between the attributes describing the problem. It can also be a way to define features that are interdependent. In this way it is possible to identify redundant features existing in the dataset. The method is based on the construction of new features based on the analysis of the relevance of the original features. The initial results obtained are an interesting introduction to further extensive research on constructive induction of feature.