Reviewerly/exHarmony huggingface dataset:
Blockchain-Based Digital Forensics Investigation Framework in the Internet of Things and Social Systems

The decentralized nature of blockchain technologies can well match the needs of integrity and provenances of evidences collecting in digital forensics (DF) across jurisdictional borders. In this paper, a novel blockchain-based DF investigation framework in the Internet of Things (IoT) and social systems environment is proposed, which can provide proof of existence and privacy preservation for evidence items examination. To implement such features, we present a block-enabled forensics framework for IoT, namely, IoT forensic chain (IoTFC), which can offer forensic investigation with good authenticity, immutability, traceability, resilience, and distributed trust between evidential entitles as well as examiners. The IoTFC can deliver a guarantee of traceability and track provenance of evidence items. Details of evidence identification, preservation, analysis, and presentation will be recorded in chains of block. The IoTFC can increase trust of both evidence items and examiners by providing transparency of the audit train. The use case demonstrated the effectiveness of the proposed method.
On-line collision avoidance for collaborative robot manipulators by adjusting off-line generated paths: An industrial use case

Human–robot collision avoidance is a key in collaborative robotics and in the framework of Industry 4.0. It plays an important role for achieving safety criteria while having humans and machines working side-by-side in unstructured and time-varying environment. This study introduces the subject of manipulator’s on-line collision avoidance into a real industrial application implementing typical sensors and a commonly used collaborative industrial manipulator, KUKA iiwa. In the proposed methodology, the human co-worker and the robot are represented by geometric primitives (capsules). The minimum distance and relative velocity between them is calculated, when human/obstacles are nearby the concept of hypothetical repulsion and attraction vectors is used. By coupling this concept with a mathematical representation of robot’s kinematics, a task level control with collision avoidance capability is achieved. Consequently, the off-line generated nominal path of the industrial task is modified on-the-fly so the robot is able to avoid collision with the co-worker safely while being able to fulfill the industrial operation. To guarantee motion continuity when switching between different tasks, the notion of repulsion-vector-reshaping is introduced. Tests on an assembly robotic cell in automotive industry show that the robot moves smoothly and avoids collisions successfully by adjusting the off-line generated nominal paths.
Context Attentive Document Ranking and Query Suggestion

We present a context-aware neural ranking model to exploit users' on-task search activities and enhance retrieval performance. In particular, a two-level hierarchical recurrent neural network is introduced to learn search context representation of individual queries, search tasks, and corresponding dependency structure by jointly optimizing two companion retrieval tasks: document ranking and query suggestion. To identify variable dependency structure between search context and users' ongoing search activities, attention at both levels of recurrent states are introduced. Extensive experiment comparisons against a rich set of baseline methods and an in-depth ablation analysis confirm the value of our proposed approach for modeling search context buried in search tasks.
Attention-Based Adaptive Selection of Operations for Image Restoration in the Presence of Unknown Combined Distortions

Many studies have been conducted so far on image restoration, the problem of restoring a clean image from its distorted version. There are many different types of distortion affecting image quality. Previous studies have focused on single types of distortion, proposing methods for removing them. However, image quality degrades due to multiple factors in the real world. Thus, depending on applications, e.g., vision for autonomous cars or surveillance cameras, we need to be able to deal with multiple combined distortions with unknown mixture ratios. For this purpose, we propose a simple yet effective layer architecture of neural networks. It performs multiple operations in parallel, which are weighted by an attention mechanism to enable selection of proper operations depending on the input. The layer can be stacked to form a deep network, which is differentiable and thus can be trained in an end-to-end fashion by gradient descent. The experimental results show that the proposed method works better than previous methods by a good margin on tasks of restoring images with multiple combined distortions.
Reasoning-RCNN: Unifying Adaptive Global Reasoning Into Large-Scale Object Detection

In this paper, we address the large-scale object detection problem with thousands of categories, which poses severe challenges due to long-tail data distributions, heavy occlusions, and class ambiguities. However, the dominant object detection paradigm is limited by treating each object region separately without considering crucial semantic dependencies among objects. In this work, we introduce a novel Reasoning-RCNN to endow any detection networks the capability of adaptive global reasoning over all object regions by exploiting diverse human commonsense knowledge. Instead of only propagating the visual features on the image directly, we evolve the high-level semantic representations of all categories globally to avoid distracted or poor visual features in the image. Specifically, built on feature representations of basic detection network, the proposed network first generates a global semantic pool by collecting the weights of previous classification layer for each category, and then adaptively enhances each object features via attending different semantic contexts in the global semantic pool. Rather than propagating information from all semantic information that may be noisy, our adaptive global reasoning automatically discovers most relative categories for feature evolving. Our Reasoning-RCNN is light-weight and flexible enough to enhance any detection backbone networks, and extensible for integrating any knowledge resources. Solid experiments on object detection benchmarks show the superiority of our Reasoning-RCNN, e.g. achieving around 16% improvement on VisualGenome, 37% on ADE in terms of mAP and 15% improvement on COCO.
An experimental study of a virtual reality counselling paradigm using embodied self-dialogue

Abstract When faced with a personal problem people typically give better advice to others than to themselves. A previous study showed how it is possible to enact internal dialogue in virtual reality (VR) through participants alternately occupying two different virtual bodies – one representing themselves and the other Sigmund Freud. They could maintain a self-conversation by explaining their problem to the virtual Freud and then from the embodied perspective of Freud see and hear the explanation by their virtual doppelganger, and then give some advice. Alternating between the two bodies they could maintain a self-dialogue, as if between two different people. Here we show that the process of alternating between their own and the Freud body is important for successful psychological outcomes. An experiment was carried out with 58 people, 29 in the body swapping Self-Conversation condition and 29 in a condition where they only spoke to a Scripted Freud character. The results showed that the Self-Conversation method results in a greater perception of change and help compared to the Scripted. We compare this method with the distancing paradigm where participants imagine resolving a problem from a first or third person perspective. We consider the method as a possible strategy for self-counselling.
The profitability of technical trading rules in the Bitcoin market

We apply seven trend-following indicators to assess the profitability of technical trading rules in the Bitcoin market. Using daily price data from July 2010 to January 2019, our main results show that specific technical analysis trading rules, mainly trading range breakout, contain significant forecasting power for Bitcoin prices, allowing the outperformance of the buy-and-hold strategy through the Sharpe ratio computed via the bootstrapping method. Results from various sub-periods, representing normal and boom markets, generally confirm our main finding and show that the added value of the trading range breakout rule delivers outperformance in strongly trending markets.
Deep Learning-Based Picture-Wise Just Noticeable Distortion Prediction Model for Image Compression

Picture Wise Just Noticeable Difference (PW-JND), which accounts for the minimum difference of a picture that human visual system can perceive, can be widely used in perception-oriented image and video processing. However, the conventional Just Noticeable Difference (JND) models calculate the JND threshold for each pixel or sub-band separately, which may not reflect the total masking effect of a picture accurately. In this paper, we propose a deep learning based PW-JND prediction model for image compression. Firstly, we formulate the task of predicting PW-JND as a multi-class classification problem, and propose a framework to transform the multi-class classification problem to a binary classification problem solved by just one binary classifier. Secondly, we construct a deep learning based binary classifier named perceptually lossy/lossless predictor which can predict whether an image is perceptually lossy to another or not. Finally, we propose a sliding window based search strategy to predict PW-JND based on the prediction results of the perceptually lossy/lossless predictor. Experimental results show that the mean accuracy of the perceptually lossy/lossless predictor reaches 92%, and the absolute prediction error of the proposed PW-JND model is 0.79 dB on average, which shows the superiority of the proposed PW-JND model to the conventional JND models.
Dextt: Deterministic Cross-Blockchain Token Transfers

Current blockchain technologies provide very limited interoperability. Restrictions with regard to asset transfers and data exchange between different blockchains reduce the usability and comfort of users, and hinder novel developments within the blockchain space. As a first step towards blockchain interoperability, we propose the DeXTT cross-blockchain transfer protocol, which can be used to record a token transfer on any number of blockchains simultaneously in a decentralized manner. We provide a reference implementation using Solidity, and evaluate its performance. We show logarithmic scalability of DeXTT with respect to the number of participating nodes, and analyze cost requirements of the transferred tokens.
Unsupervised Monocular Depth and Ego-Motion Learning With Structure and Semantics

We present an approach which takes advantage of both structure and semantics for unsupervised monocular learning of depth and ego-motion. More specifically we model the motions of individual objects and learn their 3D motion vector jointly with depth and ego-motion. We obtain more accurate results, especially for challenging dynamic scenes not addressed by previous approaches. This is an extended version of Casser et al. [1]. Code and models have been open sourced at: https://sites.google.com/corp/view/struct2depth.
Eye&amp;Head

Eye gaze involves the coordination of eye and head movement to acquire gaze targets, but existing approaches to gaze pointing are based on eye-tracking in abstraction from head motion. We propose to leverage the synergetic movement of eye and head, and identify design principles for Eye&Head gaze interaction. We introduce three novel techniques that build on the distinction of head-supported versus eyes-only gaze, to enable dynamic coupling of gaze and pointer, hover interaction, visual exploration around pre-selections, and iterative and fast confirmation of targets. We demonstrate Eye&Head interaction on applications in virtual reality, and evaluate our techniques against baselines in pointing and confirmation studies. Our results show that Eye&Head techniques enable novel gaze behaviours that provide users with more control and flexibility in fast gaze pointing and selection.
A New Model for Industry–Academic Partnerships

ABSTRACT The mission of the social sciences is to understand and ameliorate society’s greatest challenges. The data held by private companies, collected for different purposes, hold vast potential to further this mission. Yet, because of consumer privacy, trade secrets, proprietary content, and political sensitivities, these datasets are often inaccessible to scholars. We propose a novel organizational model to address these problems. We also report on the first partnership under this model, to study the incendiary issues surrounding the impact of social media on elections and democracy: Facebook provides (privacy-preserving) data access; eight ideologically and substantively diverse charitable foundations provide initial funding; an organization of academics we created, Social Science One, leads the project; and the Institute for Quantitative Social Science at Harvard and the Social Science Research Council provide logistical help.
Key management for blockchain technology

Public Key Infrastructure (PKI) is used in Blockchain Technology to authenticate the entities and to ensure the integrity of the blockchain. Proper Protection of Bitcoin wallet is required for private keys, seeds and keys stored in external hardware in Blockchain infrastructure. In this paper, overview of Blockchain, analysis of existing PKI for Blockchain and key management for Blockchain wallet are discussed. To achieve the confidentiality of sensitive records over the Blockchain network, a Group Key Management scheme for secure group communication is also proposed.
PrivateSQL

Differential privacy is considered a de facto standard for private data analysis. However, the definition and much of the supporting literature applies to flat tables. While there exist variants of the definition and specialized algorithms for specific types of relational data (e.g. graphs), there isn't a general privacy definition for multi-relational schemas with constraints, and no system that permits accurate differentially private answering of SQL queries while imposing a fixed privacy budget across all queries posed by the analyst. This work presents PrivateSQL, a first-of-its-kind end-to-end differentially private relational database system. PrivateSQL allows an analyst to query data stored in a standard database management system using a rich class of SQL counting queries. PrivateSQL adopts a novel generalization of differential privacy to multi-relational data that takes into account constraints in the schema like foreign keys, and allows the data owner to flexibly specify entities in the schema that need privacy. PrivateSQL ensures a fixed privacy loss across all the queries posed by the analyst by answering queries on private synopses generated from several views over the base relation that are tuned to have low error on a representative query workload. We experimentally evaluate PrivateSQL on a real-world dataset and a workload of more than 3, 600 queries. We show that for 50% of the queries PrivateSQL offers at least 1, 000x better error rates than solutions adapted from prior work.
A machine learning based framework for IoT device identification and abnormal traffic detection

Abstract Network security is a key challenge for the deployment of Internet of Things (IoT). New attacks have been developed to exploit the vulnerabilities of IoT devices. Moreover, IoT immense scale will amplify traditional network attacks. Machine learning has been extensively applied for traffic classification and intrusion detection. In this paper, we propose a framework, specifically for IoT devices identification and malicious traffic detection. Pushing the intelligence to the network edge, this framework extracts features per network flow to identify the source, the type of the generated traffic, and to detect network attacks. Different machine learning algorithms are compared with random forest, which gives the best results: Up to 94.5% accuracy for device‐type identification, up to 93.5% accuracy for traffic‐type classification, and up to 97% accuracy for abnormal traffic detection.
New Primitives for Actively-Secure MPC over Rings with Applications to Private Machine Learning

At CRYPTO 2018 Cramer et al. presented SPDZ2k , a new secret-sharing based protocol for actively secure multi-party computation against a dishonest majority, that works over rings instead of fields. Their protocol uses slightly more communication than competitive schemes working over fields. However, implementation-wise, their approach allows for arithmetic to be carried out using native 32 or 64-bit CPU operations rather than modulo a large prime. The authors thus conjectured that the increased communication would be more than made up for by the increased efficiency of implementations. In this work we answer their conjecture in the affirmative. We do so by implementing their scheme, and designing and implementing new efficient protocols for equality test, comparison, and truncation over rings. We further show that these operations find application in the machine learning domain, and indeed significantly outperform their field-based competitors. In particular, we implement and benchmark oblivious algorithms for decision tree and support vector machine (SVM) evaluation.
HGR‐Net: a fusion network for hand gesture segmentation and recognition

We propose a two‐stage convolutional neural network (CNN) architecture for robust recognition of hand gestures, called HGR‐Net, where the first stage performs accurate semantic segmentation to determine hand regions, and the second stage identifies the gesture. The segmentation stage architecture is based on the combination of fully convolutional residual network and atrous spatial pyramid pooling. Although the segmentation sub‐network is trained without depth information, it is particularly robust against challenges such as illumination variations and complex backgrounds. The recognition stage deploys a two‐stream CNN, which fuses the information from the red–green–blue and segmented images by combining their deep representations in a fully connected layer before classification. Extensive experiments on public datasets show that our architecture achieves almost as good as state‐of‐the‐art performance in segmentation and recognition of static hand gestures, at a fraction of training time, run time, and model size. Our method can operate at an average of 23 ms per frame.
Enjoyment and Intensity of Physical Activity in Immersive Virtual Reality Performed on Innovative Training Devices in Compliance with Recommendations for Health

The aim of the study is to assess the enjoyment and intensity of physical exercise while practicing physical activity (PA) in immersive virtual reality (IVR) using innovative training devices (omni-directional Omni treadmill and Icaros Pro flight simulator). The study also contains the results of subjective research on the usefulness of such a form of PA in the opinion of users. In total, 61 adults (10 women and 51 men) took part in the study. To assess the enjoyment level (EL) Interest/Enjoyment subscale of Intrinsic Motivation Inventory (IMI) was used. Exercise intensity was assessed during 10-min sessions of active video games (AVGs) in IVR based on heart rate (HR). The average enjoyment level during physical exercise in IVR on the tested training devices was high (Omni 5.74 points, Icaros 5.60 points on a 1-7 Likert scale) and differed significantly in favor of PA on Omni. In the opinion of the majority of participants, AVGs in IVR on the tested devices constitute a sufficiently useful form of PA to meet the needs of leisure time activities, and they can even replace some forms of physical effort performed in a classic way. The intensity of PA during games on training devices was at the level recommended for health benefits for 80.55% (Omni) and 50.77% (Icaros Pro) of its duration. Based on the conducted research, it can be assumed that AVGs in IVR using a multi-directional treadmill and a flight simulator can be an effective tool for increasing participation in health-oriented PA.
Smartphone Sensor-Based Human Activity Recognition Using Feature Fusion and Maximum Full <i>a Posteriori</i>

Human activity recognition (HAR) using smartphone sensors has attracted great attention due to its wide range of applications. A standard solution for HAR is to first generate some features defined based on domain knowledge ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">handcrafted</i> features) and then to train an activity classification model based on these features. Very recently, deep learning with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">automatic</i> feature learning from raw sensory data has also achieved great performance for HAR task. We believe that both the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">handcrafted</i> features and the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">learned</i> features may convey some unique information that can complement each other for HAR. In this article, we first propose a feature fusion framework to combine handcrafted features with automatically learned features by a deep algorithm for HAR. Then, taking the regular dynamics of human behavior into consideration, we develop a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">maximum full a posteriori</i> algorithm to further enhance the performance of HAR. Our extensive experimental results show the proposed approach can achieve superior performance comparing with the state-of-the-art methodologies across both a public data set and a self-collected data set.
The destabilising effects of cryptocurrency cybercriminality

This paper investigates the financial market effects of recent cybercriminality in cryptocurrency markets. Hacking events are found to increase both the price volatility of the targeted cryptocurrency and broad cross-cryptocurrency correlations. Further, cybercrime events significantly reduce price discovery sourced within the hacked currency relative to other cryptocurrencies. Finally, abnormal returns in the hours prior to the cybercrime event, revert to zero when news is publicly announced.