Reviewerly/exHarmony huggingface dataset:
RPC Based Framework for Partitioning IoT Security Software for Trusted Execution Environments

Partitioning security components of IoT devices to enable the use of Trusted Execution Environments adds resilience against side-channel attacks. Devices are hardened against extraction of sensitive information, but at the same time additional effort must be spent for the integration of the TEE and software partitioning. To perform partitioning, the developer typically inserts Remote Procedure Calls into the software. Existing RPC-based solutions require the developer to write Interface Definition Language files to generate RPC stubs. In this work, we present an RPC-based framework that supports software partitioning via a graphical user interface. The framework extracts required information about the interfaces from source-code header files to eliminate the need for IDL files. With this approach the TEE integration time is reduced and reuse of existing libraries is supported. We evaluate a Proof-of-Concept by partitioning a TLS library for IoT devices and compare our approach to other RPC-based solutions.
Team Zylinium DARPA Spectrum Collaboration Challenge Radio Design and Implementation

This paper describes the radio entry for Team Zylinium in the DARPA Spectrum Collaboration Challenge (SC2). SC2 is a competitive DARPA grand challenge where each team designs an autonomous software defined radio (SDR) agent with the goal of winning the competition. Our radio design implements many state-of-the-art wireless communications features to create a robust baseline communications platform. We then added to this platform various reasoning and optimization features to allow for nimble spectrum access.
Simulating Human Behaviour in Games using Machine Learning

The study of Artificial Intelligence and, more specifically, Machine learning in games is of great interest to the gaming industry due to their wide application in several scenarios and their capabilities of simulating human behavior by non-playable characters. In online multiplayer scenarios, one of the greatest concerns is player disconnections and how to replace this player with a "good" replacement bot. In this work, we propose a machine learning based methodology to simulate the behavior of players that learns from their gaming history that generates more balance in the game. Our work uses Multi Layer Perceptron Neural Networks evaluated in a classic card game called Hearts, in order to emulate some previously defined behaviors. The results obtained in the experiments indicate that the proposed method has very good performance, since all generated models have managed to approach similar amount of victories when comparing to the behaviors that they were trained with. Through the evaluation of the results of 10,000 matches, with gameseeds of different matches used for training, the best result was for the model Shooter versus 3 Sheriffs getting 95.5% of the amount of wins compared to their particular bot. We also conclude that behavior learning is also clear on the difference of wins in all results depending on opponent skills, i.e., opponents who were difficult to win remain difficult to win in the simulated environment.
Fuzzy Comprehensive Evaluation of Security Maintenance Ability Base on AHP-Entropy

Based on the establishment of the evaluation index system of security maintenance ability, combined with AHP, entropy-weighted method and fuzzy comprehensive evaluation method, a fuzzy comprehensive evaluation model is constructed. Taking an unit as an example, the security maintenance ability evaluation value is obtained and the correctness of the model is verified. Through the evaluation result, the problems existing in the development process of the security maintenance ability are found.
Technical Usability of Wikidata’s Linked Data

Wikidata is an outstanding data source with potential application in many scenarios. Wikidata provides its data openly in RDF. Our study aims to evaluate the usability of Wikidata as a data source for robots operating on the web of data, according to specifications and practices of linked data, the Semantic Web and ontology reasoning. We evaluated from the perspective of two use cases of data crawling robots, which are guided by our general motivation to acquire richer data for Europeana, a data aggregator from the Cultural Heritage domain. The first use case regards general data consumption applications based on RDF, RDF-Schema, OWL, SKOS and linked data. The second case regards applications that explore semantics relying on Schema.org and SKOS. We conclude that a human operator must assist linked data applications to interpret Wikidata's RDF because of the choices that were taken at Wikidata in the definition of its expression in RDF. The semantics of the RDF output from Wikidata is "locked-in" by the usage of Wikidata's own ontology, resulting in the need for human intervention. Wikidata is only a few steps away from high quality machine interpretation, however. It contains extensive alignment data to RDF, RDFS, OWL, SKOS and Schema.org, but a machine interpretation of those alignments can only be done if some essential Wikidata alignment properties are known.
Disk Cluster Allocation Behavior in Windows and NTFS

Abstract The allocation algorithm of a file system has a huge impact on almost all aspects of digital forensics, because it determines where data is placed on storage media. Yet there is only basic information available on the allocation algorithm of the currently most widely spread file system; NTFS. We have therefore studied the NTFS allocation algorithm and its behavior empirically. To do that we used two virtual machines running Windows 7 and 10 on NTFS formatted fixed size virtual hard disks, the first being 64 GiB and the latter 1 TiB in size. Files of different sizes were written to disk using two writing strategies and the $Bitmap files were manipulated to emulate file system fragmentation. Our results show that files written as one large block are allocated areas of decreasing size when the files are fragmented. The decrease in size is seen not only within files, but also between them. Hence a file having smaller fragments than another file is written after the file having larger fragments. We also found that a file written as a stream gets the opposite allocation behavior, i. e. its fragments are increasing in size as the file is written. The first allocated unit of a stream written file is always very small and hence easy to identify. The results of the experiment are of importance to the digital forensics field and will help improve the efficiency of for example file carving and timestamp verification.
On Kummer Lines with Full Rational 2-torsion and Their Usage in Cryptography

A paper by Karati and Sarkar at Asiacrypt’17 has pointed out the potential for Kummer lines in genus 1, by observing that their SIMD-friendly arithmetic is competitive with the status quo. A more recent preprint explores the connection with (twisted) Edwards curves. In this article, we extend this work and significantly simplify the treatment of Karati and Sarkar. We show that their Kummer line is the x -line of a Montgomery curve translated by a point of order two, and exhibit a natural isomorphism to the y -line of a twisted Edwards curve. Moreover, we show that the Kummer line presented by Gaudry and Lubicz can be obtained via the action of a point of order two on the y -line of an Edwards curve. The maps connecting these curves and lines are all very simple. As a result, a cryptographic implementation can use the arithmetic that is optimal for its instruction set at negligible cost.
Face Representation Learning using Composite Mini-Batches

Mini-batch construction strategy is an important part of the deep representation learning. Different strategies have their advantages and limitations. Usually only one of them is selected to create mini-batches for training. However, in many cases their combination can be more efficient than using only one of them. In this paper, we propose Composite Mini-Batches - a technique to combine several mini-batch sampling strategies in one training process. The main idea is to compose mini-batches from several parts, and use different sampling strategy for each part. With this kind of mini-batch construction, we combine the advantages and reduce the limitations of the individual mini-batch sampling strategies. We also propose Interpolated Embeddings and Priority Class Sampling as complementary methods to improve the training of face representations. Our experiments on a challenging task of disguised face recognition confirm the advantages of the proposed methods.
Assessing the impact of knowledge management on total factor productivity

Purpose The purpose of this paper is to examine the impact of knowledge management (KM) on the total factor productivity (TFP) at the organizational level in Egypt. Design/methodology/approach Using the novel available EC 2013 data set, which includes approximately 60,000 private organizations in Egypt, the paper explores the relationship between KM and TFP. For the purpose of dealing with endogeneity, the two-stage least squares econometric model has been implemented. Findings The study reveals that KM impacts positively the TFP of the Egyptian organizations. Conspicuously, each 10 percent increase in KM is associated with 9.3 percent increase in TFP. Originality/value The role of KM in the organizations has been under-researched globally, especially in Africa. This study contributes to the current literature by assessing the impact of KM on TFP, which represents the most comprehensive measure of the firm productivity; by implementing a novel instrumental variable in order to deal with endogeneity between KM and TFP; and by generating a more nuanced measure for the knowledge intensity that is not based on any financial indicator as in the most of the previous studies. Original findings can be highlighted from the paper as it demonstrates that the impact of KM is more important than proposed by the current literature. Conspicuously, the KM does not merely impact the customer satisfaction, the quality improvement and the profit margin, but it also impacts the TFP of the organizations.
Bootstrapped Neuro-Simulation as a method of concurrent neuro-evolution and damage recovery

Bootstrapped Neuro-Simulation (BNS) is a method of concurrent simulator and robot controller evolution. The algorithm requires little domain knowledge and no pre-investigation data gathering. Additionally, it bridges the reality gap effectively, rapidly evolves functional controllers, and recovers from damage automatically. In this paper, the first evidence of the ability of BNS to evolve closed-loop controllers is shown; in this case to solve a light-following problem. The algorithm is then evaluated for its damage recovery ability for these closed-loop controllers and shown to be very effective, with only minor adaptations.
Aligning Competence Hierarchies with Bloom's Taxonomies

We distinguish between cognitive competence, captured in Bloom et al.'s cognitive taxonomy, and operational competence, expressed in Simpson's hierarchy. We argue that the skills gap observed by employers can be addressed by designing computing degrees that focus on developing competence rather than on knowledge.
A fast score test for generalized mixture models

In biomedical studies, testing for homogeneity between two groups, where one group is modeled by mixture models, is often of great interest. This paper considers the semiparametric exponential family mixture model proposed by Hong et al. (2017) and studies the score test for homogeneity under this model. The score test is nonregular in the sense that nuisance parameters disappear under the null hypothesis. To address this difficulty, we propose a modification of the score test, so that the resulting test enjoys the Wilks phenomenon. In finite samples, we show that with fixed nuisance parameters the score test is locally most powerful. In large samples, we establish the asymptotic power functions under two types of local alternative hypotheses. Our simulation studies illustrate that the proposed score test is powerful and computationally fast. We apply the proposed score test to an UK ovarian cancer DNA methylation data for identification of differentially methylated CpG sites.
Evaluating Gradient Perception in Color-Coded Scalar Fields

Color mapping is a commonly used technique for visualizing scalar fields. While there exists advice for choosing effective colormaps, it is unclear if current guidelines apply equally across task types. We study the perception of gradients and evaluate the effectiveness of three colormaps at depicting gradient magnitudes. In a crowd-sourced experiment, we determine the just-noticeable differences (JNDs) at which participants can reliably compare and judge variations in gradient between two scalar fields. We find that participants exhibited lower JNDs with a diverging (cool-warm) or a spectral (rainbow) scheme, as compared with a monotonic-luminance colormap (viridis). The results support a hypothesis that apparent discontinuities in the color ramp may help viewers discern subtle structural differences in gradient. We discuss these findings and highlight future research directions for colormap evaluation.
On the Decision Boundaries of Neural Networks: A Tropical Geometry Perspective

This work tackles the problem of characterizing and understanding the decision boundaries of neural networks with piecewise linear non-linearity activations. We use tropical geometry, a new development in the area of algebraic geometry, to characterize the decision boundaries of a simple network of the form (Affine, ReLU, Affine). Our main finding is that the decision boundaries are a subset of a tropical hypersurface, which is intimately related to a polytope formed by the convex hull of two zonotopes. The generators of these zonotopes are functions of the network parameters. This geometric characterization provides new perspectives to three tasks. (i) We propose a new tropical perspective to the lottery ticket hypothesis, where we view the effect of different initializations on the tropical geometric representation of a network's decision boundaries. (ii) Moreover, we propose new tropical based optimization reformulations that directly influence the decision boundaries of the network for the task of network pruning. (iii) At last, we discuss the reformulation of the generation of adversarial attacks in a tropical sense. We demonstrate that one can construct adversaries in a new tropical setting by perturbing a specific set of decision boundaries by perturbing a set of parameters in the network.
Organization of interaction between the university and employers in teaching students the development and implementation of IT projects

The authors conceptualize the need to use the project approach as the main one for professional activity in the IT sphere in the development and modernization of the main professional educational programs of the relevant areas of training at the university in the conditions of implementation of the Federal State Educational Standards of Higher Education (FSES HE 3++). The article analyzes the role of employers in the processes related to the modernization of the bachelor’s degree programs of IT training implemented by the regional university. It is proved that practice-oriented training in the basics of project activity in the IT sphere in accordance with the industry-standardized stages of IT project implementation is of particular importance for the formation of professional competencies of graduates of IT training areas that meet the requirements of employers. From the positions of key employers the characteristic of stages of the IT project is presented, and correspondence between its stages and components of labor functions of the professional standard “Programmer” is established. Presents the experience of teaching students at a regional university in the implementation stages of an IT project within the framework of formation of professional competences of graduates of the bachelor’s direction “Informatics and computer engineering”, “Informational systems and technologies” with participation of basic chairs, lecturers are representatives of large IT company.
Run-Time Class Generation: Algorithms for Union of Homogeneous and Inhomogeneous Classes

Run-time code (class) generation is a way to develop adaptive intelligent systems, which can dynamically modify their source codes and knowledge bases up to challenges of the working environment. The example of such challenges is a task of integration of new extracted or acquired knowledge into the knowledge base avoiding redundancy of their representation. To execute it effectively the system should be able to analyze and generate (create) new classes of objects dynamically. Therefore algorithms for dynamic creation of new classes of objects via computing the union of homogeneous and inhomogeneous and union of two inhomogeneous classes of objects are proposed in the paper. Proposed algorithms provide an opportunity for knowledge-based systems to generate new classes, which define heterogeneous collections of objects at the run-time; to determine connection between new and previously acquired knowledge; to conclude thematic relevance and connection level of new knowledge with particular theme, category or domain; to integrate new knowledge into the knowledge base. Developed algorithms have quadratic polynomial time complexity and linear space complexity. They can be adapted and integrated into particular object-oriented programming language or knowledge representation model.
Twitter-based Polarised Embeddings for Abusive Language Detection

We present a method to generate polarised word embeddings using controversial topics as search terms in Twitter as proxies for interactions among social media communities that may be liable to use abusive language.We investigate to what extent models trained with these embeddings perform with respect to generic embeddings across four data sets of abusive language, both in the same domain and out of domain, using simple linear classifiers.Our results show that the polarised embeddings are competitive in the same domain data sets, and perform better in out of domain one.
A Survey on Keyword and Key Phrase Extraction with Deep Learning

With the technological developments, a large amount of data has been produced. Tera bytes of data previously recorded by manpower were digitized with the use of personal computers. As a result, rapidly growing data stacks were formed, making it difficult to find information among these unanticipated data. The need to make sense of this data has made predefined statistical methods more important. It is possible to access the required information from a single document or from the document stacks by means of text mining methods. This problem, which was previously solved mostly by statistical methods or Natural Language Processing (NLP) techniques, has been started to be solved by machine learning algorithms and artificial neural networks. In recent years, deep learning, which is a specialized study area of artificial neural networks, gives better results than the current statistical and NLP methods in many problems and has provided the application of these methods in problems such as machine translation, keyword extraction and summarizing. In this study, deep learning methods used in the extraction of keywords and key phrases are examined.
Exploiting Multi-Direction Features in MRF-Based Image Inpainting Approaches

Image inpainting technique recovers the missing regions of an image using information from known regions and it has shown success in various application fields. As a popular kind of methods, Markov Random Field (MRF)-based methods are able to produce better results than earlier diffusion-based and sparse-based methods on inpainting images with big holes. However, for images with complex structures, the results are still not quite pleasant and some inpainting trails exist. The direction feature is an important factor for image understanding and human eye visual requirements, and exploiting multi-direction features is of great potential to further improve inpainting performance. Following the idea, this paper proposes a Structure Offsets Statistics based image inpainting algorithm by exploiting multiple direction features under the framework of MRF-based methods. Specifically, when selecting proper labels, multi-direction features are extracted and applied to construct a structure image and a non-structure image, and the candidate labels are chosen from the offsets of structure and non-structure images. Meanwhile, the multi-direction features are applied to construct a new smooth term for the energy equation which is then solved by graph-cut optimization technology. Experimental results show that on inpainting tasks with various complexities, the proposed method is superior to several state-of-the-art approaches in terms of the abilities of maintaining structure coherence and neighborhood consistence and the computational efficiency.
A Financial Evaluation Framework for Blockchain Implementations

The following topics are dealt with: Internet of Things; learning (artificial intelligence); mobile computing; Internet; feature extraction; data privacy; quality of service; security of data; wireless sensor networks; cryptography.
Deep Transfer Learning For Abnormality Detection

Deep learning has proven to be effective in learning scenarios with massive training data. However, in many real applications (i.e., abnormality detection), there is a lack of sufficient data to achieve a good deep learning model. Considering the fact that collecting massive labeled training data for a new task is often expensive and time-consuming, it is critical to transfer and reuse the knowledge of the labeled data or simulation data to the new task. To tackle this issue, we propose a deep transfer learning method in this paper. On one hand, we pre-train a basic deep learning model with the related training data. Then, we treat the learning model as a starting point for the current problem to train the new deep learning model. On the other hand, we utilize the Generative Adversarial Nets (GAN) in the learning process to transfer knowledge from simulation data and further enhances the discriminative power of the model. Besides, we apply the proposed method to abnormality detection problem. Experiments in Bone X-Ray anomaly detection show that the proposed deep transfer model can significantly improve performance compared to the basic deep learning model.
A Proposal for Improving Estimation Accuracy of Localization Using QR codes and Image Sensors

This paper describes the new ideas for improving methods of the estimation accuracy of localization that uses QR codes and image sensors for videos. The ideas the authors describe localization methods that take accounts of continuity of frames in the video and use multiple QR codes filmed in the image sensor's view.
Extending a Generative Adversarial Network to Produce Medical Records with Demographic Characteristics and Health System Use

Generative adversarial networks use machine learning to generate synthetic data that is similar to real data. This has been widely applied to image data, and is now being applied to electronic medical records. Synthetically generated medical records are promising for many applications where privacy and security issues make using real medical records too risky. This includes software and systems development, training, and health research. Developing upon previous work, we have extended the MEDGAN system to generate records with eight additional variables, including demographic and health system use factors. The records generated are similar in distribution to the underlying dataset for all of these added variables. Finally, we discuss our future plans, with an emphasis on privacy-protecting approaches.