Reviewerly/exHarmony huggingface dataset:
What You See is NOT What You Get

Malicious ads often use social engineering (SE) tactics to coax users into downloading unwanted software, purchasing fake products or services, or giving up valuable personal information. These ads are often served by low-tier ad networks that may not have the technical means (or simply the will) to patrol the ad content they serve to curtail abuse.
Multiplierless chaotic Pseudo random number generators

This paper presents a multiplierless based FPGA implementation for six different chaotic Pseudo Random Number Generators (PRNGs) that are based on: Chua, modified Lorenz, modified Rössler, Frequency Dependent Negative Resistor (FDNR) oscillator, and other two systems that are modelled using the simple jerk equation. These chosen systems can be employed in high speed applications because they don’t utilize any hardware multiplier. The proposed PRNGs have been implemented using VHDL, synthesized on Xilinx, using the FPGA: XC5VLX50T, and tested using the NIST statistical suite. Furthermore, a comparison has been established between the performance of all the PRNGs, regarding the implementation area, speed, and the statistical randomness quality. It has been found that the modified Lorenz PRNG has the best randomness quality and the best hardware performance as it can pass all the NIST tests while operating at 298.597 MHz and utilizing only 0.23% and 0.62% from the FPGA’s slice registers and Look-Up-Tables (LUTs) respectively.
VLog: A Rule Engine for Knowledge Graphs

Knowledge graphs are crucial assets for tasks like query answering or data integration. These tasks can be viewed as reasoning problems, which in turn require efficient reasoning systems to be implemented. To this end, we present VLog, a rule-based reasoner designed to satisfy the requirements of modern use cases, with a focus on performance and adaptability to different scenarios. We address the former with a novel vertical storage layout, and the latter by abstracting the access to data sources and providing a platform-independent Java API. Features of VLog include fast Datalog materialisation, support for reasoning with existential rules, stratified negation, and data integration from a variety of sources, such as high-performance RDF stores, relational databases, CSV files, OWL ontologies, and remote SPARQL endpoints.
VisibleV8

Modern web security and privacy research depends on accurate measurement of an often evasive and hostile web. No longer just a network of static, hyperlinked documents, the modern web is alive with JavaScript (JS) loaded from third parties of unknown trustworthiness. Dynamic analysis of potentially hostile JS currently presents a cruel dilemma: use heavyweight in-browser solutions that prove impossible to maintain, or use lightweight inline JS solutions that are detectable by evasive JS and which cannot match the scope of coverage provided by in-browser systems. We present VisibleV8, a dynamic analysis framework hosted inside V8, the JS engine of the Chrome browser, that logs native function or property accesses during any JS execution. At less than 600 lines (only 67 of which modify V8's existing behavior), our patches are lightweight and have been maintained from Chrome versions 63 through 72 without difficulty. VV8 consistently outperforms equivalent inline instrumentation, and it intercepts accesses impossible to instrument inline. This comprehensive coverage allows us to isolate and identify 46 JavaScript namespace artifacts used by JS code in the wild to detect automated browsing platforms and to discover that 29% of the Alexa top 50k sites load content which actively probes these artifacts.
Portal-ble: Intuitive Free-hand Manipulation in Unbounded Smartphone-based Augmented Reality

Smartphone augmented reality (AR) lets users interact with physical and virtual spaces simultaneously. With 3D hand tracking, smartphones become apparatus to grab and move virtual objects directly. Based on design considerations for interaction, mobility, and object appearance and physics, we implemented a prototype for portable 3D hand tracking using a smartphone, a Leap Motion controller, and a computation unit. Following an experience prototyping procedure, 12 researchers used the prototype to help explore usability issues and define the design space. We identified issues in perception (moving to the object, reaching for the object), manipulation (successfully grabbing and orienting the object), and behavioral understanding (knowing how to use the smartphone as a viewport). To overcome these issues, we designed object-based feedback and accommodation mechanisms and studied their perceptual and behavioral effects via two tasks: picking up distant objects, and assembling a virtual house from blocks. Our mechanisms enabled significantly faster and more successful user interaction than the initial prototype in picking up and manipulating stationary and moving objects, with a lower cognitive load and greater user preference. The resulting system---Portal-ble---improves user intuition and aids free-hand interactions in mobile situations.
The Blockchain: Opportunities for Research in Information Systems and Information Technology

Blockchain technology is regarded by many IT innovators and experts as one of the most significant technological innovations in recent years in the field of digitization of secure ownership of asse...
Opisthenar

We introduce a vision-based technique to recognize static hand poses and dynamic finger tapping gestures. Our approach employs a camera on the wrist, with a view of the opisthenar (back of the hand) area. We envisage such cameras being included in a wrist-worn device such as a smartwatch, fitness tracker or wristband. Indeed, selected off-the-shelf smartwatches now incorporate a built-in camera on the side for photography purposes. However, in this configuration, the fingers are occluded from the view of the camera. The oblique angle and placement of the camera make typical vision-based techniques difficult to adopt. Our alternative approach observes small movements and changes in the shape, tendons, skin and bones on the opisthenar area. We train deep neural networks to recognize both hand poses and dynamic finger tapping gestures. While this is a challenging configuration for sensing, we tested the recognition with a real-time user test and achieved a high recognition rate of 89.4% (static poses) and 67.5% (dynamic gestures). Our results further demonstrate that our approach can generalize across sessions and to new users. Namely, users can remove and replace the wrist-worn device while new users can employ a previously trained system, to a certain degree. We conclude by demonstrating three applications and suggest future avenues of work based on sensing the back of the hand.
Predicting Cognitive Load in an Emergency Simulation Based on Behavioral and Physiological Measures

The reliable estimation of cognitive load is an integral step towards real-time adaptivity of learning or gaming environments. We introduce a novel and robust machine learning method for cognitive load assessment based on behavioral and physiological measures in a combined within- and cross-participant approach. 47 participants completed different scenarios of a commercially available emergency personnel simulation game realizing several levels of difficulty based on cognitive load. Using interaction metrics, pupil dilation, eye-fixation behavior, and heart rate data, we trained individual, participant-specific forests of extremely randomized trees differentiating between low and high cognitive load. We achieved an average classification accuracy of 72%. We then apply these participant-specific classifiers in a novel way, using similarity between participants, normalization, and relative importance of individual features to successfully achieve the same level of classification accuracy in cross-participant classification. These results indicate that a combination of behavioral and physiological indicators allows for reliable prediction of cognitive load in an emergency simulation game, opening up new avenues for adaptivity and interaction.
My Algorithms Have Determined You're Not Human

The past decade has seen an exponential growth in the capabilities and deployment of artificial intelligence systems based on deep neural networks. These are visible through the speech recognition and natural language processing of Alexa/Siri/Google that structure many of our everyday interactions, and the promise of SAE Level 5 autonomous driving provided by Tesla and Waze. Aside from these shiny and visible applications of AI-ML are many other uses that are more subtle: AI-ML is now being used to screen job applicants as well as determine which web ads we are shown. And while many vendors of AI-ML technologies have promised that these tools provide for greater access and freedom from human prejudice, disabled users have found that these tools can embed and deploy newer, subtler forms of discrimination against disabled people. At their worst, AI-ML systems can deny disabled people their humanity.
Transferable Semi-Supervised 3D Object Detection From RGB-D Data

We investigate the direction of training a 3D object detector for new object classes from only 2D bounding box labels of these new classes, while simultaneously transferring information from 3D bounding box labels of the existing classes. To this end, we propose a transferable semi-supervised 3D object detection model that learns a 3D object detector network from training data with two disjoint sets of object classes - a set of strong classes with both 2D and 3D box labels, and another set of weak classes with only 2D box labels. In particular, we suggest a relaxed reprojection loss, box prior loss and a Box-to-Point Cloud Fit network that allow us to effectively transfer useful 3D information from the strong classes to the weak classes during training, and consequently, enable the network to detect 3D objects in the weak classes during inference. Experimental results show that our proposed algorithm outperforms baseline approaches and achieves promising results compared to fully-supervised approaches on the SUN-RGBD and KITTI datasets. Furthermore, we show that our Box-to-Point Cloud Fit network improves performances of the fully-supervised approaches on both datasets.
Data money: The socio-technical infrastructure of cryptocurrency blockchains

Drawing on an empirical study of cryptocurrency white papers, this paper proposes an actor-based taxonomy of cryptocurrency blockchains. First, it describes the evolution of blockchain architecture with reference to the economic services that blockchains supply. Second, it discusses the socio-technical platform of blockchains as proposed in cryptocurrency white papers. Third, it analyses the socio-economic consequences of these technically diverse blockchain platforms, by proposing a taxonomy of their digital architectures in reference to two groups of actors that maintain blockchain infrastructure: transactioners and accountants. Defining cryptocurrency as data money, and locating cryptocurrency ownership as the possession of an exclusive right to move data privately in a public or private space, the paper describes a blockchain as a digital actor-network platform that makes it possible to define and distribute these data transfer rights.
Attention-Aware Polarity Sensitive Embedding for Affective Image Retrieval

Images play a crucial role for people to express their opinions online due to the increasing popularity of social networks. While an affective image retrieval system is useful for obtaining visual contents with desired emotions from a massive repository, the abstract and subjective characteristics make the task challenging. To address the problem, this paper introduces an Attention-aware Polarity Sensitive Embedding (APSE) network to learn affective representations in an end-to-end manner. First, to automatically discover and model the informative regions of interest, we develop a hierarchical attention mechanism, in which both polarity- and emotion-specific attended representations are aggregated for discriminative feature embedding. Second, we present a weighted emotion-pair loss to take the inter- and intra-polarity relationships of the emotional labels into consideration. Guided by attention module, we weight the sample pairs adaptively which further improves the performance of feature embedding. Extensive experiments on four popular benchmark datasets show that the proposed method performs favorably against the state-of-the-art approaches.
Deep Adversarial Metric Learning

Learning an effective distance measurement between sample pairs plays an important role in visual analysis, where the training procedure largely relies on hard negative samples. However, hard negative samples usually account for the tiny minority in the training set, which may fail to fully describe the data distribution close to the decision boundary. In this paper, we present a deep adversarial metric learning (DAML) framework to generate synthetic hard negatives from the original negative samples, which is widely applicable to existing supervised deep metric learning algorithms. Different from existing sampling strategies which simply ignore numerous easy negatives, our DAML aim to exploit them by generating synthetic hard negatives adversarial to the learned metric as complements. We simultaneously train the feature embedding and hard negative generator in an adversarial manner, so that adequate and targeted synthetic hard negatives are created to learn more precise distance metrics. As a single transformation may not be powerful enough to describe the global input space under the attack of the hard negative generator, we further propose a deep adversarial multi-metric learning (DAMML) method by learning multiple local transformations for more complete description. We simultaneously exploit the collaborative and competitive relationships among multiple metrics, where the metrics display unity against the generator for effective distance measurement as well as compete for more training data through a metric discriminator to avoid overlapping. Extensive experimental results on five benchmark datasets show that our DAML and DAMML effectively boost the performance of existing deep metric learning approaches through adversarial learning.
Spams meet Cryptocurrencies

In the past year, a new spamming scheme has emerged: sexual extortion messages requiring payments in the cryptocurrency Bitcoin, also known as sextortion. This scheme represents a first integration of the use of cryptocurrencies by members of the spamming industry. Using a dataset of 4,340,736 sextortion spams, this research aims at understanding such new amalgamation by uncovering spammers' operations. To do so, a simple, yet effective method for projecting Bitcoin addresses mentioned in sextortion spams onto transaction graph abstractions is computed over the entire Bitcoin blockchain. This allows us to track and investigate monetary flows between involved actors and gain insights into the financial structure of sextortion campaigns. We find that sextortion spammers are somewhat sophisticated, following pricing strategies and benefiting from cost reductions as their operations cut the upper-tail of the spamming supply chain. We discover that one single entity is likely controlling the financial backbone of the majority of the sextortion campaigns and that the 11-month operation studied yielded a lower-bound revenue between $1,300,620 and $1,352,266. We conclude that sextortion spamming is a lucrative business and spammers will likely continue to send bulk emails that try to extort money through cryptocurrencies.
A Two-Step Cross-Modal Hashing by Exploiting Label Correlations and Preserving Similarity in Both Steps

In this paper, we present a novel Two-stEp Cross-modal Hashing method, TECH for short, for cross-modal retrieval tasks. As a two-step method, it first learns hash codes based on semantic labels, while preserving the similarity in the original space and exploiting the label correlations in the label space. In the light of this, it is able to make better use of label information and generate better binary codes. In addition, different from other two-step methods that mainly focus on the hash codes learning, TECH adopts a new hash function learning strategy in the second step, which also preserves the similarity in the original space. Moreover, with the help of well designed objective function and optimization scheme, it is able to generate hash codes discretely and scalable for large scale data. To the best of our knowledge, it is the first cross-modal hashing method exploiting label correlations, and also the first two-step hashing model preserving the similarity while leaning hash function. Extensive experiments demonstrate that the proposed approach outperforms some state-of-the-art cross-modal hashing methods.
Privacy-preserving clustering for big data in cyber-physical-social systems: Survey and perspectives

Clustering technique plays a critical role in data mining, and has received great success to solve application problems like community analysis, image retrieval, personalized recommendation, activity prediction, etc. This paper first reviews the traditional clustering and the emerging multiple clustering methods, respectively. Although the existing methods have superior performance on some small or certain datasets, they fall short when clustering is performed on CPSS big data because of the high cost of computation and storage. With the powerful cloud computing, this challenge can be effectively addressed, but it brings enormous threat to individual or company’s privacy. Currently, privacy preserving data mining has attracted widespread attention in academia. Compared to other reviews, this paper focuses on privacy preserving clustering technique, guiding a detailed overview and discussion. Specifically, we introduce a novel privacy-preserving tensor-based multiple clustering, propose a privacy-preserving tensor-based multiple clustering analytic and service framework, and give an illustrated case study on the public transportation dataset. Furthermore, we indicate the remaining challenges of privacy preserving clustering and discuss the future significant research in this area.
The Missing-Link between Financial Development and Economic Growth: Financial Innovation

The paper investigates the relationship between financial development, financial innovation, and economic growth using a panel data analysis. The sample covers fifteen countries for the period of 2003-2016. Financial development has been considered as a composite variable consisting of four components: financial access, financial depth, financial efficiency, and financial stability. The distinguishing feature of the paper is that it includes financial innovation as another component of financial development in addition to the cited four. We find statistically significant and positive relationship between financial innovation and economic growth (The higher the number of financial innovation, the higher the rate of economic growth). The paper concludes that both financial development and financial innovation have significant impact on economic growth.
Learning Non-Volumetric Depth Fusion Using Successive Reprojections

Given a set of input views, multi-view stereopsis techniques estimate depth maps to represent the 3D reconstruction of the scene; these are fused into a single, consistent, reconstruction -- most often a point cloud. In this work we propose to learn an auto-regressive depth refinement directly from data. While deep learning has improved the accuracy and speed of depth estimation significantly, learned MVS techniques remain limited to the planesweeping paradigm. We refine a set of input depth maps by successively reprojecting information from neighbouring views to leverage multi-view constraints. Compared to learning-based volumetric fusion techniques, an image-based representation allows significantly more detailed reconstructions; compared to traditional point-based techniques, our method learns noise suppression and surface completion in a data-driven fashion. Due to the limited availability of high-quality reconstruction datasets with ground truth, we introduce two novel synthetic datasets to (pre-)train our network. Our approach is able to improve both the output depth maps and the reconstructed point cloud, for both learned and traditional depth estimation front-ends, on both synthetic and real data.
Tailoring Onion Routing to the Internet of Things: Security and Privacy in Untrusted Environments

An increasing number of IoT scenarios involve mobile, resource-constrained IoT devices that rely on untrusted networks for Internet connectivity. In such environments, attackers can derive sensitive private information of IoT device owners, e.g., daily routines or secret supply chain procedures, when sniffing on IoT communication and linking IoT devices and owner. Furthermore, untrusted networks do not provide IoT devices with any protection against attacks from the Internet. Anonymous communication using onion routing provides a well-proven mechanism to keep the relationship between communication partners secret and (optionally) protect against network attacks. However, the application of onion routing is challenged by protocol incompatibilities and demanding cryptographic processing on constrained IoT devices, rendering its use infeasible. To close this gap, we tailor onion routing to the IoT by bridging protocol incompatibilities and offloading expensive cryptographic processing to a router or web server of the IoT device owner. Thus, we realize resource-conserving access control and end-to-end security for IoT devices. To prove applicability, we deploy onion routing for the IoT within the well-established Tor network enabling IoT devices to leverage its resources to achieve the same grade of anonymity as readily available to traditional devices.
Blockchain-Based Private Provable Data Possession

Remote data secure storage is of crucial importance in cloud computing. In order to check remote data integrity, an important paradigm PDP (i.e., provable data possession) is proposed. All the existing PDP schemes make use of RSA or bilinear pairings. One large file has to be divided into a great many of blocks. For example, 1T (Terabit) file has to be divided into <inline-formula><tex-math notation="LaTeX">$1.0737 \times 10^9$</tex-math></inline-formula> blocks (RSA where the length of the index is 1024 bits) or <inline-formula><tex-math notation="LaTeX">$6.8719 \times 10^9$</tex-math></inline-formula> blocks (bilinear pairings where the order of the elliptic curve is 160 bits). The huge computation cost and communication cost incurs the inefficient PDP implementation. In other words, they are not practical. In order to solve the problem, we propose a new PDP model: blockchain-based private PDP. The new concept makes use of blockchain which is the core of cryptocurrency. For the new concept, the paper formalizes its system model and security model. Then, a concrete blockchain-based private PDP scheme is designed by making use of blockchain and RSA. The proposed blockchain-based private PDP scheme is provably secure. At the same time, we also analyze its performance from two parts: theory analysis and implementation prototype. Our analysis shows that the proposed PDP scheme is secure, efficient and practical.