Reviewerly/exHarmony huggingface dataset:
Electromagnetic Side Channel Information Leakage Created by Execution of Series of Instructions in a Computer Processor

The side-channel leakage is a consequence of program execution in a computer processor, and understanding relationship between code execution and information leakage is a necessary step in estimating information leakage and its capacity limits. This paper proposes a methodology to relate program execution to electromagnetic side-channel emanations and estimates side-channel information capacity created by execution of series of instructions (e.g., a function, a procedure, or a program) in a processor. To model dependence among program instructions in a code, we propose to use Markov source model, which includes the dependencies among sequence of instructions as well as dependencies among instructions as they pass through a pipeline of the processor. The emitted electromagnetic (EM) signals during instruction executions are natural choice for the inputs into the model. To obtain the channel inputs for the proposed model, we derive a mathematical relationship between the emanated instruction signal power (ESP) and total emanated signal power while running a program. Then, we derive the leakage capacity of EM side channels created by execution of series of instructions in a processor. Finally, we provide experimental results to demonstrate that leakages could be severe and that a dedicated attacker could obtain important information.
A Keyword-Searchable ABE Scheme From Lattice in Cloud Storage Environment

Currently, the security situation of data security and user privacy protection is increasingly serious in cloud environment. Ciphertext data storage can prevent the risk of user's privacy disclosure. But how to search keyword on ciphertext data without revealing keyword information becomes a new challenge. Searchable encryption (SE) is put forward for this reason, which can be used to realize ciphertext-search directly. In terms of multi-user data sharing, public-key encryption with keywords search (PEKS) is more widely used than symmetric searchable encryption. PEKS has been widely studied and developed by researchers in recent years. However, the existing PEKS scheme often lacks flexible access policy. Therefore, combining the advantage of policy control on attribute-based encryption (ABE), and as that the bilinear pairing related assumption is fragile in post-quantum era, lattice-based cryptography is considered as one of secure encryption technology against quantum attack. With this background, in this paper, we give a keyword-searchable ABE scheme based on the hardness of lattice problems, our scheme supports flexible attribute control policy by integrating ABE and PEKS, and the security of new scheme is proved under the learning with errors (LWE) assumption. As lattice-based cryptographic technology is currently thought to be resistant to quantum attacks, so the new scheme has stronger security in a quantum era.
Dynamic gesture recognition by directional pulse coupled neural networks for human-robot interaction in real time

Dynamic gesture recognition is a major topic for most real-time human-robot interaction applications. Recently, increasingly people have focused on dynamic gesture recognition based on 3D representation. In this paper, the gesture recognition is converted into the shortest path problem by transforming the feature matrix to an undirected graph and a novel dynamic gesture recognition algorithm of directional pulse coupled neuron network (DPCNN) is proposed for real time human-robot interactions. The DPCNN can select the firing direction by giving different excitations to neighbor neurons and reduce the effects of useless neurons. Furthermore, to reduce the recognition time, an early gesture recognition method based on the adaptive window is introduced to recognize the unfinished gestures. DPCNN reduces the computation time and achieves a high recognition rate on three public datasets compared with other algorithms which improves the efficiency of real-time dynamic gesture recognition and ensures a friendly experience for human-robot interactions.
Kinship Verification using Deep Siamese Convolutional Neural Network

Recognizing Families In the Wild (RFIW) is a large-scale kinship recognition challenge based on the FIW dataset. This dataset is the largest databases for kinship recognition, consisting of more than 13,000 family photos and 1,000 families. The number of members in each family range from 4 to 38. One of the tasks for the database is, given photos of two individuals, predict whether they have any kin relationship or not. In this paper, we present a deep learning approach using Siamese Convolutional Neural Network Architecture to quantify the similarity between two given photos. We use two parallel SqueezeNet Networks, initialized with weights obtained after training the SqueezeNet on the VGGFace2 Dataset, and use a similarity metric and fully connected networks to merge the two networks to a single output. We use different similarity metric such as L1 norm, L2 Norm, and Cosine Similarity. Our network gives good accuracy and AUC scores.
Cyberbiosecurity Implications for the Laboratory of the Future

Technological innovation has become an integral and inescapable aspect of our daily existence as almost everything of significance in our world now has a cyber (i.e., relating to, or involving computers, computer networks, information technology, and virtual reality) component associated with it. Every facet of our lives is now touched by technology. As such, we're experiencing a digital transformation. Unfortunately, both as individuals and as a society, we're inadequately prepared to embrace the myriad of vulnerabilities presented by cybertechnologies. Unintended cyber vulnerabilities present significant risks to individuals, organizations, governments and economies. Here, we identify current cybersecurity vulnerabilities found in the life science enterprise and discuss the many ways in which these vulnerabilities present risk to laboratory workers in these facilities, the surrounding community and the environment. We also consider the cyberbiosecurity benefits associated with numerous innovations likely to be present in the laboratory of the future. The challenges associated with cyberbiosecurity vulnerabilities are not insurmountable; they simply require thoughtful consideration by equipment designers, software and control systems developers, and by end users. Organizations and the individuals that comprise them must respect, value, and protect their data. End users must train themselves to look at every piece of laboratory equipment and every process from a cyberbiosecurity perspective. With this approach, cyberbiosecurity vulnerabilities can be minimized or eliminated to the benefit of workers, life science organizations, and national security.
A Novel Method for Estimating Distances from a Robot to Humans Using Egocentric RGB Camera

Estimating distances between people and robots plays a crucial role in understanding social Human-Robot Interaction (HRI) from an egocentric view. It is a key step if robots should engage in social interactions, and to collaborate with people as part of human-robot teams. For distance estimation between a person and a robot, different sensors can be employed, and the number of challenges to be addressed by the distance estimation methods rise with the simplicity of the technology of a sensor. In the case of estimating distances using individual images from a single camera in a egocentric position, it is often required that individuals in the scene are facing the camera, do not occlude each other, and are fairly visible so specific facial or body features can be identified. In this paper, we propose a novel method for estimating distances between a robot and people using single images from a single egocentric camera. The method is based on previously proven 2D pose estimation, which allows partial occlusions, cluttered background, and relatively low resolution. The method estimates distance with respect to the camera based on the Euclidean distance between ear and torso of people in the image plane. Ear and torso characteristic points has been selected based on their relatively high visibility regardless of a person orientation and a certain degree of uniformity with regard to the age and gender. Experimental validation demonstrates effectiveness of the proposed method.
Context Aware Image Annotation in Active Learning with Batch Mode

Bags are not isolated in the Multiple-Instance Active Learning process, especially for image as bag, because each picture has its inherent background or metainformation, such as its taken time, taken place, the topic, and they have possible associations. With context associations, we can build the annotation tool providing more interactively user experience and thus increase the annotation efficiency. In this paper, we propose a context aware images annotation framework that selects the images that are context related to query in the multiple-instance active learning with batch mode. Experiments show that it takes less time for annotation with the proposed framework compared to traditional ones, and improve the labeling efficiency.
FbHash: A New Similarity Hashing Scheme for Digital Forensics

With the rapid growth of the World Wide Web and Internet of Things, a huge amount of digital data is being produced every day. Digital forensics investigators face an uphill task when they have to manually screen through and examine tons of such data during a crime investigation. To expedite this process, several automated techniques have been proposed and are being used in practice. Among which tools based on Approximate Matching algorithms have gained prominence, e.g., ssdeep, sdhash, mvHash etc. These tools produce hash signatures for all the files to be examined, compute a similarity score and then compare it with a known reference set to filter out known good as well as bad files. In this way, exact as well as similar matches can be screened out. However, all of these schemes have been shown to be prone to active adversary attack, whereby an attacker, by making feasible changes in the content of the file, intelligently modifies the final hash signature produced to evade detection. Thus, an alternate hashing scheme is required which can resist this attack. In this work, we propose a new Approximate Matching scheme termed as - FbHash. We show that our scheme is secure against active attacks and detects similarity with 98% accuracy. We also provide a detailed comparative analysis with other existing schemes and show that our scheme has a 28% higher accuracy rate than other schemes for uncompressed file format (e.g., text files) and 50% higher accuracy rate for compressed file format (e.g., docx etc.). Our proposed scheme is able to correlate a fragment as small as 1% to the source file with 100% detection rate and able to detect commonality as small as 1% between two documents with appropriate similarity score. Further, our scheme also produces the least false negatives in comparison to other schemes.
Hierarchical Sensor Fusion for Micro-Gesture Recognition With Pressure Sensor Array and Radar

This paper presents a hierarchical sensor fusion approach for human micro-gesture recognition by combining an Ultra Wide Band (UWB) Doppler radar and wearable pressure sensors. First, the wrist-worn pressure sensor array (PSA) and Doppler radar are used to respectively identify static and dynamic gestures through a Quadratic-kernel SVM (Support Vector Machine) classifier. Then, a robust wrapper method is applied on the features from both sensors to search the optimal combination. Subsequently, two hierarchical approaches where one sensor acts as “enhancer” of the other are explored. In the first case, scores from Doppler radar related to the confidence level of its classifier and the prediction label corresponding to the posterior probabilities are utilized to maximize the static hand gestures classification performance by hierarchical combination with PSA data. In the second case, the PSA acts as an “enhancer” for radar to improve the dynamic gesture recognition. In this regard, different weights of the “enhancer” sensor in the fusion process have been evaluated and compared in terms of classification accuracy. A realistic cross-validation method is chosen to test one unknown participant with the model trained by data from others, demonstrating that this hierarchical fusion approach for static and dynamic gestures yields approximately 15% improvement in classification accuracy in the best cases.
The BonaRes metadata schema for geospatial soil-agricultural research data – Merging INSPIRE and DataCite metadata schemes

A spatial data infrastructure (SDI) for the upload and provision of soil-agricultural research data in Germany was developed and launched in 2017. The precondition for the new SDI were to be compliant with the European initiative for spatial information (INSPIRE), to consider FAIR data principles, to be interoperable with other disciplinary national and international SDIs and to support dataset registrations with digital object identifiers (DOI). To meet these requirements, the new SDI had to support both the INSPIRE and DataCite metadata standards. As there was no metadata standard available that supported both metadata schemas from different disciplines, it became necessary to merge these two existing standards. The objective of this paper is to describe the developmental steps of the newly created BonaRes metadata schema for geospatial soil-agricultural research data. First, we analyzed the INSPIRE and DataCite metadata schemas, identified and compared semantically equivalent metadata elements, including properties and allowed content values, for potential mapping. In the next step, we specified the new metadata model, whose elements were derived from INSPIRE or DataCite under consideration of the previously developed mapping. Third, additional metadata elements were defined to directly describe the underlying data model. The advantages and disadvantages of the method that was applied to define the BonaRes metadata schema elements originating from INSPIRE and DataCite schemas are presented and discussed. By implementing the new metadata model, the SDIs can achieve cross-portal metadata interoperability with other INSPIRE-compliant SDIs. Furthermore, researchers who store their geospatial research data in SDIs to implement the BonaRes metadata schema can increase the visibility and findability of their research, as the data can be assigned a DOI, which is a prerequisite for data citations and data publications. The new metadata elements of the BonaRes metadata schema allows targeted dataset queries, e.g., soil pH values, yields or parameters, which were measured by a specific method, using the underlying data model, and improves the discovery and reusability of research data.
Representation Learning Through Cross-Modality Supervision

Learning robust representations for applications with multiple modalities of input can have a significant impact on its performance. Traditional representation learning methods rely on projecting the input modalities to a common subspace to maximize agreement amongst the modalities for a particular task. We propose a novel approach to representation learning that uses a latent representation decoder to reconstruct the target modality and thereby employs the target modality purely as a supervision signal for discovering correlations between the modalities. Through cross modality supervision, we demonstrate that the learnt representation is able to improve the performance of the task of facial action unit (AU) recognition when compared with the modality specific representations and even their fused counterparts. Our experiments on three AU recognition datasets - MMSE, BP4D and DISFA, show strong performance gains producing state-of-the-art results in spite of the absence of a modality.
Characterization and Prediction of Popular Projects on GitHub

GitHub is a large and popular open source project platform, which hosts various open source projects. Despite the prevalence of GitHub platform, not every project has gained high popularity. Identification of popular projects on GitHub can help developers choose proper projects to follow or contribute to, as well as provide guidance in building a popular project. In this paper, we propose an approach to predict the popularity of GitHub projects. We first conducted online surveys with GitHub users to determine the threshold (the number of stars of a project) of popular and unpopular projects. Next, we extract 35 features from both GitHub and Stack Overflow, which are divided into three dimensions: project, evolutionary, and project owner. A random forest classifier is built based on these features to identify popular GitHub projects. To evaluate the performance of our approach, we collect a large-scale dataset from GitHub which contains a total of 409,784 GitHub projects and 174,784 GitHub users. Our model achieves an average AUC of 0.76, which statistically significantly improves state-of-the-art by a substantial margin. We also study which features are of the most importance in distinguishing popular projects from unpopular ones. Experimental results show that number of branches, number of open issues, and number of contributors play the most important roles in identification of popular projects, and all of them have large effect size.
Blockchain-based photo forensics with permissible transformations

Thanks to the popularity of smartphones with megapixel cameras, people would like to capture and share their daily life by means of photos, which results billions of photos uploaded onto Internet every day. However, photo-faking is an extremely easy task at present, since digital photo editing tools are more and more popular and powerful. Besides, due to the lack of copyright protection mechanisms, photo owners lose control of their photos after uploading the photos to Internet. In this paper, we propose a blockchain-based photo forensics scheme in order to uniformly solve photo-faking problems, photo-tracing problems and copyright dispute problems. We combine blockchain technology with image authentication technologies and propose a new mining protocol to stimulate miners to verify photos for users. The scheme allows users to define their own access control rules and provides on-chain photo proofs with which one can verify the copyright of photos and trace the operating histories of them. We implement the system on Ethereum platform and analyze the security of the system. Compared with other schemes, our scheme has the ability of photo forensics with authentication, copyright protection and traceability. In addition, our system allows a variety of permissible transformations without adding additional storage overhead.
Modeling and adjusting in-game difficulty based on facial expression analysis

In this paper we introduce Facial Expression Analysis (FEA) both as a means of predicting in-game difficulty and as a modeling mechanism, based on which we develop in-game difficulty adjustment algorithms for single player arcade games. Our main contribution is the implementation of an online and unobtrusive game personalisation system. On the basis of FEA, our system is able to adapt the difficulty level of the game to the individual player, without interruptions, during actual gameplay. Specifically, we study (a) how perceived in-game difficulty can be measured through facial expression analysis, and (b) how facial expression data can model player behavior and predict their affective state. Experimental findings reveal that different in-game difficulty settings can be correlated to distinct player emotions (revealed in facial expressions). Furthermore, a model based on facial expression analysis is successfully applied to calculate an appropriate difficulty setting, tailored to the individual player. From these results, we may conclude that efficient game personalisation is achievable through FEA.
Fuzzy neural network with support vector-based learning for classification and regression

Fuzzy neural network (FNN) and support vector machine (SVM) are two prominent and powerful learning models broadly used for classification and regression. FNN has a significant local representation and human reasoning advantage. However, the drawback of such a network is that the focal point of the learning algorithms is minimizing empirical risk. In contrary to FNN, SVM emphasizes simultaneously on minimizing empirical and expected risks, which theoretically leads to an excellent generalization performance power. In this paper, we show that a Takagi–Sugeno–Kang (TSK)-type-based fuzzy neural network is, in fact, equivalent to an SVM with an adaptive kernel based on fuzzy rules generated in this FNN. Consequently, it is possible to learn the last layer of the FNN using the concepts of SVM and thus taking the advantage of SVM in generalization. In fact, on the one side, the proposed method is an SVM with an adaptive kernel based on fuzzy rules and, on the other side, it is a TSK–FNN with SVM-based learning. As a matter of fact, the defined kernel in FNN is an adaptive kernel based on data characteristics, which is derived from the fuzzy rules generated by the FNN itself. The results obtained from the proposed method for classification and regression outperform the results of SVM with the conventional kernels and fuzzy neural network trained in the traditional way.
A Public Auditing Protocol for Cloud Storage System With Intrusion-Resilience

Cloud storage auditing helps users to check the integrity of their data stored on the cloud. However, if the client's auditing secret key is exposed to the malicious cloud, the client's data may be deleted by the malicious cloud without being detected. In this paper, we propose a public auditing protocol with intrusion-resilience to relieve damage caused by the key-exposure problem. The proposed protocol divides the lifetime of files stored on the cloud into several time periods, and each time period is further divided into several refreshing periods. The auditing secret key is updated in each time period, and secret values used to update the auditing secret key change in every refreshing period. These two update operations are completed by the client and the third party auditor (TPA). This protocol is secure against the adversary as long as the client and TPA are not compromised in the same refreshing period. Security proof under random oracle model proves the protocol is secure, and the experimental results indicate that the performance of the protocol is acceptable.
A survey of formal theoretical advances regarding XCS

Learning Classifier Systems (LCSs) are a unique machine learning paradigm. The probably most well-known and investigated instance of these is XCS. LCSs, and with them, XCS, have developed in parallel to mathematically more rigorously founded paradigms such as today's reinforcement learning. This is probably the reason why XCS was initially defined without a formal basis. Nevertheless, the pursuit of a formal understanding of XCS has been one of the primary goals since its invention. Over the years, this led to a large and seemingly underestimated body of formal analysis of it. We present our try at a comprehensive overview of the various angles from which XCS was regarded formally. With this paper, we aim at (1) mitigating the misconception we sometimes observed that research on XCS contains some sort of 'formal theory gap', (2) supporting researchers interested in formal advances regarding XCS and (3) identifying future research directions.