Reviewerly/exHarmony huggingface dataset:
The future of computing and wisdom: Insights from Human–Computer Interaction

In this paper, we present a structured report on a dialogue on the Future of Computing and Wisdom. The dialogue consists of a recorded and transcribed discussion between researchers and practitioners in the field of Human–Computer Interaction that was held at workshop in conjunction with the 10th Nordic Conference on Human–Computer Interaction in September 2018. However, the dialogue also encompasses workshop participants’ preparatory work with writing “fictional abstracts” – abstracts of yet-to-be-written research papers that will be published in 2068. The polyvocal dialogue that is reported upon thus includes not just the voices of researchers and practitioners who attended the workshop, but also includes the voices of the future researchers of 2068 who wrote the abstracts in question as well as the voices of the organisms, individuals, intelligent agents and communities who are the subjects, victims, beneficiaries and bystanders of wise (or unwise) future computing systems.
An efficient approach for secure multi-party computation without authenticated channel

Secure multi-party sum is one of the most important secure multi-party computation protocols. It has been widely applied to solve many privacy-preservation problems such as privacy-preserving data mining, secure auction, secure electronic voting, and privacy-preserving statistical data analysis. To guarantee the correctness of the final output and to enhance the security level, the existing secure multi-party sum protocols have to use authenticated channels, even secure channels for participants to communicate, however such a usage requirement significantly reduces their performance. Furthermore, these secure multi-party sum protocols are impossible to run on public networks. In this paper, we propose a new secure multi-party sum protocol that can ensure the correctness of the output result as well as securely protecting the parties’ privacy against attacks without requiring any authenticated/secure channel. The proposed protocol is based on a multi-party sum function employing a variant of ElGamal encryption and a Schnorr signature-derived authentication method, in which both these cryptographic tools use the same private and public keys. Additionally, our comparative evaluation shows that the proposed protocol is efficient and practical.
Length Independent Writer Identification Based on the Fusion of Deep and Hand-Crafted Descriptors

Writer's identification from a handwritten text is one of the most challenging machines learning problems because of the variable handwritten sources, various languages, the similarity between writer's pattern, context variation, and implicit characteristics of handwriting styles. In this paper, a combination of the deep and hand-crafted descriptor is utilized to learn patterns from the handwritten images. First, to do so, the local patches are extracted from the handwritten images. Then, these patches are simultaneously fed to deep and hand-crafted descriptors to generate the local descriptions. The extracted local features are then assembled to make the whole description matrix. Finally, by applying the vector of locally aggregated descriptors (VLAD) encoding on the description matrix, a 1-D feature vector is extracted to represent the writer's pattern. It is worthwhile to mention that the generated description does not rely on any language model or context information. Thus, the proposed approach is language and content independent. In addition, the proposed method does not have any restriction on the input length, hence, the writer's sample can be a passage, paragraph, line, sentence, or even a word. The obtained results on three public benchmark datasets of IAM, CVL, and Khatt indicate that the proposed method has a high-accuracy rate in writing identification task. Furthermore, the performance of the proposed method on CVL dataset using both German and English samples demonstrates that the proposed approach has a high capability in learning a writer's pattern from both languages at the same time.
Genus Two Isogeny Cryptography

We study $$(\ell ,\ell )$$ -isogeny graphs of principally polarised supersingular abelian surfaces (PPSSAS). The $$(\ell ,\ell )$$ -isogeny graph has cycles of small length that can be used to break the collision resistance assumption of the genus two isogeny hash function suggested by Takashima. Algorithms for computing (2, 2)-isogenies on the level of Jacobians and (3, 3)-isogenies on the level of Kummers are used to develop a genus two version of the supersingular isogeny Diffie–Hellman protocol of Jao and de Feo. The genus two isogeny Diffie–Hellman protocol achieves the same level of security as SIDH but uses a prime with a third of the bit length.
Cryptocurrencies and Inequality

This chapter seeks to contextualize the nature of cryptocurrencies as an alternate form of capital that, while being inspired by cryptoanarchist thought, has come to embody extreme forms of inequality among its owners. The concentration of wealth produces a “whale effect” that, as the chapter argues, in fact reflects the forms of inequalities that are found in the ownership of traditional forms of capital. The chapter thus alludes to the mismatch between the professed cryptoanarchist philosophical bent of cryptocurrency owners and the reality of capital ownership in the cryptocurrency domain; while cryptoanarchism postulates autonomy, decentralization, and the spread of ownership, the whale effect suggests that cryptocurrencies are insufficiently different from traditional forms of capital in this regard. This challenges the degree to which the praxis of cryptocurrencies coheres with the philosophy of cryptoanarchism.
A New Network Model for Cyber Threat Intelligence Sharing using Blockchain Technology

The aim of this research is to propose a new blockchain network model that facilitates the secure dissemination of Cyber Threat Intelligence (CTI) data. The primary motivations for this study are based around the recent changes to information security legislation in the European Union and the challenges that Computer Security and Incident Response Teams (CSIRT) face when trying to share actionable and highly sensitive data within systems where participants do not always share the same interests or motivations. We discuss the common problems within the domain of CTI sharing and we propose a new model, that leverages the security properties of blockchain. Our model provides a more effective and efficient framework for a CTI sharing network that has the potential to overcome the trust barriers and data privacy issues inherent in this domain. We implemented a testbed using Hyperledger Fabric and the STIX 2.0 protocol and validated the efficacy of the segmentation, implemented using smart contracts and Fabric channels.
U-Net Based Convolutional Neural Network for Skeleton Extraction

Skeletonization is a process aimed to extract a line-like object shape representation, skeleton, which is of great interest for optical character recognition, shape-based object matching, recognition, biomedical image analysis, etc.. Existing methods for skeleton extraction are typically based on topological, morphological or distance transform and are known to be sensitive to the noise on the boundary and require post-processing procedure for redundant branches pruning. In this work, we introduce U-net based approach for direct skeleton extraction of the object within Pixel SkelNetOn - CVPR 2019 challenge, inspired by CNNs success in skeleton extraction from real images task. The main idea of our approach is to consistently edit a skeleton mask by feature propagation through different scale layers. It opposes final skeleton generation from different scale object shape representations as occurs in approaches with deep supervision for skeleton extraction from the real image. Our U-net based model showed ~0.75 F1-score on the validation set and the ensemble of eight identical models, trained on different data subsets, got 0.7846 F1-score on the test data.
Deep Learning Application to Ensemble Learning—The Simple, but Effective, Approach to Sentiment Classifying

Sentiment analysis is an active research area in natural language processing. The task aims at identifying, extracting, and classifying sentiments from user texts in post blogs, product reviews, or social networks. In this paper, the ensemble learning model of sentiment classification is presented, also known as CEM (classifier ensemble model). The model contains various data feature types, including language features, sentiment shifting, and statistical techniques. A deep learning model is adopted with word embedding representation to address explicit, implicit, and abstract sentiment factors in textual data. The experiments conducted based on different real datasets found that our sentiment classification system is better than traditional machine learning techniques, such as Support Vector Machines and other ensemble learning systems, as well as the deep learning model, Long Short-Term Memory network, which has shown state-of-the-art results for sentiment analysis in almost corpuses. Our model’s distinguishing point consists in its effective application to different languages and different domains.
A Face Morphing Detection Concept with a Frequency and a Spatial Domain Feature Space for Images on eMRTD

Since the face morphing attack was introduced by Ferrara et al. in 2014, the detection of face morphings has become a wide spread topic in image forensics. By now, the community is very active and has reported diverse detection approaches. So far, the evaluations are mostly performed on images without post-processing. Face images stored within electronic machine readable documents (eMRTD) are ICAO-passport-scaled to a resolution of 413x531 and a JPG or JP2 lesize of 15 kilobytes. This paper introduces a face morphing detection concept with 3 modules (ICAO-aligned pre- processing module, feature extraction module and classi cation module), tailored for such images on eMRTD. In this work we exemplary design and evaluate two feature spaces for the feature extraction module, a frequency domain and a spatial domain feature space. Our evaluation will compare both feature spaces and is carried out with 66,229 passport-scaled images (64,363 morphed face images and 1,866 authentic face images) which are completly independent from training and include all images provided for the IHMMSEC'19 special session: "Media Forensics - Fake or Real?". Furthermore, we investigate the in uence of di erent morph gen- eration pipelines to the detection accuracies of the concept and we analyse the impact of neutral and smiling genuine faces to the morph detector performance. The evaluation determines a detection rate of 86.0% for passport-scaled morphed images with a false alarm rate of 4.4% for genuine images for the spatial domain feature space
Web genre classification with methods for structured output prediction

The increase of the number of web pages prompts for improvement of the search engines. One such improvement is specifying the desired web genre of the resulting web pages. The prediction of web genres triggers expectations about the type of information contained in a given web page. More specifically, web genres can be seen as textual categories such as scientific papers, home pages or eshops. Arguably, in the context of web search, specifying genre beside topical keywords enables a user to easily find a scientific paper (genre) about text mining (topic). Typically, web genre prediction is treated as a predictive modelling task of multi-class classification, with some recent studies advocating the introduction of a structure in the output space: either by considering multiple web genres per web page or exploiting a hierarchy of web genres. We investigate the structuring of the output space by constructing hierarchies using data-driven methods, experts or even randomly. We also use 10 different representations of the web pages. We use predictive clustering trees and ensembles thereof to properly assess the influence of the different information sources. The experimental evaluation is performed on two benchmark corpora: 20-genre and SANTINIS-ML. The results reveal that exploiting a hierarchy of web genres yields best predictive performance across both datasets, all predictive models, all feature sets and all hierarchies. Next, data-driven hierarchy construction is at least as good as expert-constructed hierarchy with the added value that the hierarchy construction is automatic and fast. Furthermore, ensembles offer state-of-the-art predictive performance and they have a superior performance than single tree models.
Privacy-Preserving Electric Vehicle Charging for Peer-to-Peer Energy Trading Ecosystems

The proliferation of renewable energy systems and high-capacity batteries has enabled customers to trade their excess energy on the market in a peer-to-peer manner through the smart grid. At the same time, electric vehicles (EVs) are enjoying widespread acceptance, leading to a higher demand for charging stations. In this paper, we propose a system where energy traders and EV owners collectively work to satisfy the energy demands of EVs. Specifically, energy traders make bids to EV owners who, in turn, reserve their preferred charging station for a specific period of time. To protect the privacy of EV owners, we also introduce an anonymous payment system that cannot link individual owners to specific charging locations. Finally, to guarantee the security and transparency of the entire system, we store all transactions on a consortium blockchain that is managed by the energy traders and the financial institutions that support the anonymous payment system. Our experimental results indicate that the overhead of the cryptographic operations involved in the major transactions is low, in terms of both computational and communication cost.
Finding Closest Lattice Vectors Using Approximate Voronoi Cells

The two traditional hard problems underlying the security of lattice-based cryptography are the shortest vector problem (SVP) and the closest vector problem (CVP). For a long time, lattice enumeration was considered the fastest method for solving these problems in high dimensions, but recent work on memory-intensive methods has resulted in lattice sieving overtaking enumeration both in theory and in practice. Some of the recent improvements [Ducas, Eurocrypt 2018; Laarhoven–Mariano, PQCrypto 2018; Albrecht–Ducas–Herold–Kirshanova–Postlethwaite–Stevens, 2018] are based on the fact that these methods find more than just one short lattice vector, and this additional data can be reused effectively later on to solve other, closely related problems faster. Similarly, results for the preprocessing version of CVP (CVPP) have demonstrated that once this initial data has been generated, instances of CVP can be solved faster than when solving them directly, albeit with worse memory complexities [Laarhoven, SAC 2016]. In this work we study CVPP in terms of approximate Voronoi cells, and obtain better time and space complexities using randomized slicing, which is similar in spirit to using randomized bases in lattice enumeration [Gama–Nguyen–Regev, Eurocrypt 2010]. With this approach, we improve upon the state-of-the-art complexities for CVPP, both theoretically and experimentally, with a practical speedup of several orders of magnitude compared to non-preprocessed SVP or CVP. Such a fast CVPP solver may give rise to faster enumeration methods, where the CVPP solver is used to replace the bottom part of the enumeration tree, consisting of a batch of CVP instances in the same lattice. Asymptotically, we further show that we can solve an exponential number of instances of CVP in a lattice in essentially the same amount of time and space as the fastest method for solving just one CVP instance. This is in line with various recent results, showing that perhaps the biggest strength of memory-intensive methods lies in being able to reuse the generated data several times. Similar to [Ducas, Eurocrypt 2018], this further means that we can achieve a "few dimensions for free" for sieving for SVP or CVP, by doing $$\varTheta (d/\log d)$$ levels of enumeration on top of a CVPP solver based on approximate Voronoi cells.
‘Internet of Things’: How Abuse is Getting Smarter

From home thermostats you can control from your car, to home assistants ready to organise your diary at a spoken word, technology is playing a more central role in our daily lives. However, while networked home devices provide many advantages, they also offer abusers an abundance of opportunities to control, harass and stalk their victims. The Gender and Internet of Things project at University College London has been investigating how these devices are being misused, and what support survivors and services need to navigate these emerging risks.
Thermal Imaging Dataset for Person Detection

In this paper will be presented an original thermal dataset designed for training machine learning models for person detection. The dataset contains 7412 thermal images of humans captured in various scenarios while walking, running, or sneaking. The recordings are captured in the LWIR segment of the electromagnetic (EM) in various weather condition-clear, fog and rain at different distances from the camera, different body positions (upright, hunched) and movement speeds (regular walking, running). In addition to the standard lens of the camera, we used a telephoto lens for recording, and we compared the image quality at different weather conditions and at different distances in both cases to set parameters that provide the level of detail that is enough to detect the person.
Analysis of Cybersecurity Threats in Cloud Applications Using Deep Learning Techniques

In this paper we present techniques based on machine learning techniques on monitoring data for analysis of cybersecurity threats in cloud environments that incorporate enterprise applications from the fields of telecommunications and IoT. Cybersecurity is a term describing techniques for protecting computers, telecommunications equipment, applications, environments and data. In modern networks enormous volume of generated traffic can be observed. We propose several techniques such as Support Vector Machines, Neural networks and Deep Neural Networks in combination for analysis of monitoring data. An approach for combining classifier results based on performance weights is proposed. The proposed approach delivers promising results comparable to existing algorithms and is suitable for enterprise grade security applications.
Ranking architecturally critical agglomerations of code smells

Code smells are symptoms in the source code that could help to identify architectural problems. However, developers may feel discouraged to analyze multiple smells if they are not able to focus their attention on a small set of source code locations. Unfortunately, current techniques fall short in assisting developers to prioritize smelly locations that are likely to indicate architectural problems. Furthermore, developers often have trouble analyzing interconnected smells that contribute together to realize an architectural problem. To deal with these issues, this work presents and evaluates a suite of five criteria for ranking groups of code smells as indicators of architectural problems in evolving systems. These criteria were implemented in a tool called JSpIRIT. In a first experiment, we have assessed the criteria in the context of 23 versions of 4 systems and analyzed their effectiveness for revealing architectural problem locations. In addition, we conducted a second experiment for analyzing similarities between the prioritization provided by developers and the prioritization provided by our best performing criterion. The results provide evidence that one of the proposed criteria helped to correctly prioritize more than 80 code locations of architectural problems, alleviating tedious manual inspection of the source code vis-a-vis with the architecture.
Using Latent Semantic Analysis to Score Short Answer Constructed Responses: Automated Scoring of the Consequences Test

Automated scoring based on Latent Semantic Analysis (LSA) has been successfully used to score essays and constrained short answer responses. Scoring tests that capture open-ended, short answer responses poses some challenges for machine learning approaches. We used LSA techniques to score short answer responses to the Consequences Test, a measure of creativity and divergent thinking that encourages a wide range of potential responses. Analyses demonstrated that the LSA scores were highly correlated with conventional Consequence Test scores, reaching a correlation of .94 with human raters and were moderately correlated with performance criteria. This approach to scoring short answer constructed responses solves many practical problems including the time for humans to rate open-ended responses and the difficulty in achieving reliable scoring.
Sensor-based activity recognition: One picture is worth a thousand words

In several domains, including healthcare and home automation, it is important to unobtrusively monitor the activities of daily living (ADLs) carried out by people at home. A popular approach consists in the use of sensors attached to everyday objects to capture user interaction, and ADL models to recognize the current activity based on the temporal sequence of used objects. Often, ADL models are automatically extracted from labeled datasets of activities and sensor events, using supervised learning techniques. Unfortunately, acquiring such datasets in smart homes is expensive and violates users’ privacy. Hence, an alternative solution consists in manually defining ADL models based on common sense, exploiting logic languages such as description logics. However, manual specification of ADL ontologies is cumbersome, and rigid ontological definitions fail to capture the variability of activity execution. In this paper, we introduce a radically new approach enabled by the recent proliferation of tagged visual contents available on the Web. Indeed, thanks to the popularity of social network applications, people increasingly share pictures and videos taken during the execution of every kind of activity. Often, shared contents are tagged with metadata, manually specified by their owners, that concisely describe the depicted activity. Those metadata represent an implicit activity label of the picture or video. Moreover, today’s computer vision tools support accurate extraction of tags describing the situation and the objects that appear in the visual content. By reasoning with those tags and their corresponding activity labels, we can reconstruct accurate models of a comprehensive set of human activities executed in the most disparate situations. This approach overcomes the main shortcomings of existing techniques. Compared to supervised learning methods, it does not require the acquisition of training sets of sensor events and activities. Compared to knowledge-based methods, it does not involve any manual modeling effort, and it captures a comprehensive array of execution modalities. Through extensive experiments with large datasets of real-world ADLs, we show that this approach is practical and effective.