Reviewerly/exHarmony huggingface dataset:
Keyboard Snooping from Mobile Phone Arrays with Mixed Convolutional and Recurrent Neural Networks

The ubiquity of modern smartphones, because they are equipped with a wide range of sensors, poses a potential security risk---malicious actors could utilize these sensors to detect private information such as the keystrokes a user enters on a nearby keyboard. Existing studies have examined the ability of phones to predict typing on a nearby keyboard but are limited by the realism of collected typing data, the expressiveness of employed prediction models, and are typically conducted in a relatively noise-free environment. We investigate the capability of mobile phone sensor arrays (using audio and motion sensor data) for classifying keystrokes that occur on a keyboard in proximity to phones around a table, as would be common in a meeting. We develop a system of mixed convolutional and recurrent neural networks and deploy the system in a human subjects experiment with 20 users typing naturally while talking. Using leave-one-user-out cross validation, we find that mobile phone arrays have the ability to detect 41.8% of keystrokes and 27% of typed words correctly in such a noisy environment---even without user specific training. To investigate the potential threat of this attack, we further developed the machine learning models into a realtime system capable of discerning keystrokes from an array of mobile phones and evaluated the system's ability with a single user typing in varying conditions. We conclude that, in order to launch a successful attack, the attacker would need advanced knowledge of the table from which a user types, and the style of keyboard on which a user types. These constraints greatly limit the feasibility of such an attack to highly capable attackers and we therefore conclude threat level of this attack to be low, but non-zero.
Minority Report

Introduction. Cyberbullying, as a form of abusive online behavior, although not well-defined, is a repetitive process, i.e., a sequence of harassing messages sent from a bully to a victim over a period of time with the intent to harm the victim. Numerous automated, data-driven approaches have been developed for the automatic classification of cyberbullying instances, with emphasis on classification accuracy. While the importance of highly accurate classifiers is undoubted, a key pitfall of existing cyberbullying detection methods is that (i) they disregard the repetitive nature of the harassing process, and (ii) they work retrospectively (i.e., after a cyberbullying incident has occurred), making it difficult to intervene before an interaction escalates. Motivated by the scarcity of methods to anticipate cyberbullying, we focus on cyberbullying prediction with the goal of reducing the time from detection to intervention. Methods. We formulate the prediction of the number of harassing comments a media session will receive over a period of time as a regularized multi-task regression problem. In our formulation, we consider two settings where (i) the progression of cyberbullying behavior from some time point in the near future to subsequent time points further into the future is modeled given limited knowledge of the recent past, and (ii) increasingly more historical data is accumulated to improve prediction accuracy. To validate our approach, we conduct an extensive experimental evaluation on a real-world dataset from Instagram, the online social media platform with the highest percentage of users reporting experiencing cyberbullying. Results. Intuitively, the larger the number of observed comments in the recent past of a media session, the better the predictive power of our approach. The downside to using more historical data is that decisions must be postponed until more comments are collected. Therefore, the trade-off between accuracy and decision speed is examined. In general, our approach outperforms competing approaches by up to 31.4% and 46.2% in Recall and Mathew correlation coefficient respectively. Discussion. Our approach can be used to effectively prioritize media sessions for increased monitoring as time goes by or for immediate intervention before a conversation escalates. In future work, we plan to incorporate additional features and investigate the generalizability of our approach on other key social networking venues where users frequently become victims of cyberbullying. Beyond cyberbullying prediction, our work is, to the best of our knowledge, the first to provide insights on the forecasting performance of multi-task regression as a function of the prediction horizon and the length of available historical data. We thus believe that our work can serve as a reference point on the forecasting performance of multi-task regression both for researchers and practitioners.
A CNN Model for Head Pose Recognition using Wholes and Regions

Head pose recognition and monitoring is key to many real-world applications, since it is a vital indicator for human attention and behavior. Currently, head pose is often computed by localizing landmarks on a targeted face and solving 2D to 3D correspondence problem with a mean head model. Recent research has shown that this is a brittle approach since it relies entirely on the accuracy of landmark detection, the extraneous head model and an ad-hoc alignment step. Recent work has also shown that the best-performing methods often combine multiple low-level image features with high-level contextual cues. In this paper, we present a novel end-to-end deep network, which is inspired by these ideas and explores regions within an image to capture topological changes due to changes in viewpoint. We adapt the existing state-of-the-art deep CNNs to use more than one region for accurate head pose recognition. Our regions consist of one or more consecutive cells and is adapted from the strategies used in computing HOG descriptor. Extensive experimental results on head pose recognition using four different large-scale datasets, demonstrate that the proposed approach outperforms many state-of-the-art deep CNN models. We also compare our pose recognition performance with the latest OpenFace 2.0 facial behavior analysis toolkit. In addition, we contribute head pose annotation to a large-scale dataset (VGGFace2).
ScanAT: Identification of Bytecode-Only Smart Contracts With Multiple Attribute Tags

Smart contracts on blockchain systems implement business logic and directly handle important assets. Although smart contracts play these critical roles, it is hard for users interacting with the system to understand the real behavior of the deployed bytecodes of smart contracts. The quirks of smart contracts, such as code reuse and limited unique datasets, make it challenging to recognize the functional details of smart contracts. In this paper, we propose a new method for characterizing bytecode-only smart contracts by automatically assigning multiple attribute tags. Using a deep learning approach, our system, the ScanAT, extracts attribute tags from the source code and metadata of known smart contracts and trains their bytecode with the attribute tags. The ScanAT then infers attribute tags from the bytecode of smart contracts alone. Our experiments show that ScanAT can achieve 81% accuracy in predicting attribute tags, using convolutional neural networks and a customized autoencoder.
Scramble Suit: A Profile Differentiation Countermeasure to Prevent Template Attacks

Ensuring protection against side channel attacks is a crucial requirement in the design of modern secure embedded systems.Profiled side channel attacks, the class to which template attacks and machine learning attacks belong, derive a model of the side channel behavior of a device identical to the target one, and exploit the said model to extract the key from the target, under the hypothesis that the side channel behaviors of the two devices match.We propose an architectural countermeasure against cross-device profiled attacks which differentiates the side-channel behavior of different instances of the same hardware design, preventing the reuse of a model derived on a device other than the target one.In particular, we describe an instance of our solution providing a protected hardware implementation of the AES block cipher and experimentally validate its resistance against both Bayesian templates and machine learning approaches based on support vector machines also considering different state of the art feature reduction techniques to increase the effectiveness of the profiled attacks.Results show that our countermeasure foils the key retrieval attempts via profiled attacks ensuring a key derivation accuracy equivalent to a random guess.
A Knowledge Sharing Framework for Green Supply Chain Management Based on Blockchain and Edge Computing

Aiming at the problem of knowledge asymmetry in supply chain enterprises in green supply chain management, this paper proposes a decentralized knowledge sharing framework based on blockchain and edge computing technologies. The security and reliability of the knowledge sharing framework are guaranteed by the security encryption, non-tamperability of the blockchain. Edge computing that provides edge intelligence services is integrated into the framework to meet the needs of the blockchain for distributed networks. Firstly, the concept of open green supply chain is introduced, and the model of knowledge creation processes of green supply chain enterprises. Then, the application prospects of blockchain and edge computing technologies in green supply chain management are illustrated, and a green supply chain management knowledge sharing framework based on these two technologies is proposed. Finally, an automotive enterprise is taken as an example to verify the effectiveness and feasibility of the proposed green supply management knowledge sharing framework.
Supervised Principal Component Analysis Via Manifold Optimization

High dimensional prediction problems are pervasive in the scientific community. In practice, dimensionality reduction (DR) is often performed as an initial step to improve prediction accuracy and interpretability. Principal component analysis (PCA) has been utilized extensively for DR, but does not take advantage of outcome variables inherent in the prediction task. Existing approaches for supervised PCA (SPCA) either take a multi-stage approach or incorporate supervision indirectly. We present a manifold optimization approach to SPCA that simultaneously solves the prediction and dimensionality reduction problems. The proposed framework is general enough for both regression and classification settings. Our empirical results show that the proposed approach explains nearly as much variation as PCA while outperforming existing methods in prediction accuracy.
Multi-Level Feature Network With Multi-Loss for Person Re-Identification

Person re-identification has become a challenging task due to various factors.One key to effective person re-identification is the extraction of the discriminative features of a person's appearance.Most previous works based on deep learning extract pedestrian characteristics from neural networks but only from the top feature layer.However, the low-layer feature could be more discriminative in certain circumstances.Hence, we propose a method, named the multi-level feature network with multiple losses (MFML), which has a multi-branch network architecture that consists of multiple middle layers and one top layer for feature representations.To extract the discriminative middle-layer features and have a good effect on deeper layers, we utilize the triplet loss function to train the middle-layer features.For the top layer, we focus on learning more discriminative feature representations, so we utilize the hybrid loss (HL) function to train the toplayer feature.Instead of concatenating multilayer features directly, we concatenate the weighted middlelayer features and the weighted top-layer feature as the discriminative features in the testing phase.The extensive evaluations conducted on three datasets show that our method achieves a competitive accuracy level compared with the state-of-the-art methods.
Data on the concentration of fractions and the total content of chemical elements in catenae within a small catchment area in the Trans Urals, Russia

Research on migration of chemical elements (ChEs) in soils is important for the understanding of geochemical processes in polluted and undisturbed landscapes. In this article, we report original data on Anthric Chernozems and Anthric Planosols within a small gully's catchment area in the Trans Urals (Russia). Mean total concentrations of 24 ChEs and content of mobile fractions (F1 - exchangeable, F2 - bound within organo-mineral complexes and F3 - bound with Fe and Mn hydroxides) of 61 ChEs including macro elements (Al, Ca, Fe, K, Mg, Mn, Na, P, Ti, S, Si), heavy metals (Ba, Co, Cr, Cu, Ni, Pb, Rb, Sr, Th, U, V, Zn), trace elements (Ag, As, B, Be, Bi, Br, Cd, Cs, Ge, Hf, Li, Mo, Nb, Pd, Sb, Sc, Se, Sn, Ta, Te, Tl, W, Zr) and rare earth elements (Ce, Er, Eu, Gd, La, Lu, Nd, Pr, Sm, Tb, Tm, Dy, Ho, Y, Yb) are determined from in a total of 60 samples from topsoil and subsoil of Anthric Chernozems and Anthric Planosols. The data obtained also include pH-value, total organic carbon content (TOC), seven particle-size classes (<2, 2-6.3, 6.3-20, 20-63, 63-200, 200-630 and 630-2000 μm), electrical conductivity and chemical composition (cations and anions) of water extracts as well as soil mineralogical composition.
SafeCheck: Safety Enhancement of Java Unsafe API

Java is a safe programming language by providing bytecode verification and enforcing memory protection. For instance, programmers cannot directly access the memory but have to use object references. Yet, the Java runtime provides an Unsafe API as a backdoor for the developers to access the low- level system code. Whereas the Unsafe API is designed to be used by the Java core library, a growing community of third-party libraries use it to achieve high performance. The Unsafe API is powerful, but dangerous, which leads to data corruption, resource leaks and difficult-to-diagnose JVM crash if used improperly. In this work, we study the Unsafe crash patterns and propose a memory checker to enforce memory safety, thus avoiding the JVM crash caused by the misuse of the Unsafe API at the bytecode level. We evaluate our technique on real crash cases from the openJDK bug system and real-world applications from AJDK. Our tool reduces the efforts from several days to a few minutes for the developers to diagnose the Unsafe related crashes. We also evaluate the runtime overhead of our tool on projects using intensive Unsafe operations, and the result shows that our tool causes a negligible perturbation to the execution of the applications.
Towards Systematic Design of Collective Remote Attestation Protocols

Networks of and embedded (IoT) devices are becoming increasingly popular, particularly, in settings such as smart homes, factories and vehicles. These networks can include numerous (potentially diverse) devices that collectively perform certain tasks. In order to guarantee overall safety and privacy, especially in the face of remote exploits, software integrity of each device must be continuously assured. This can be achieved by Remote Attestation (RA) - a security service for reporting current software state of a remote and untrusted device. While RA of a single device is well understood, collective RA of large numbers of networked embedded devices poses new research challenges. In particular, unlike single-device RA, collective RA has not benefited from any systematic treatment. Thus, unsurprisingly, prior collective RA schemes are designed in an ad hoc fashion. Our work takes the first step toward systematic design of collective RA, in order to help place collective RA onto a solid ground and serve as a set of design guidelines for both researchers and practitioners. We explore the design space for collective RA and show how the notions of security and effectiveness can be formally defined according to a given application domain. We then present and evaluate a concrete collective RA scheme systematically designed to satisfy these goals.
A Framework of Composite Functional Gradient Methods for Generative Adversarial Models

Generative adversarial networks (GAN) are trained through a minimax game between a generator and a discriminator to generate data that mimics observations. While being widely used, GAN training is known to be empirically unstable. This paper presents a new theory for generative adversarial methods that does not rely on the traditional minimax formulation. Our theory shows that with a strong discriminator, a good generator can be obtained by composite functional gradient learning, so that several distance measures (including the KL divergence and the JS divergence) between the probability distributions of real data and generated data are simultaneously improved after each functional gradient step until converging to zero. This new point of view leads to stable procedures for training generative models. It also gives a new theoretical insight into the original GAN. Empirical results on image generation show the effectiveness of our new method.
Toward Gender-Equitable Privacy and Security in South Asia

South Asia has one of the world's largest populations—India, Pakistan, and Bangladesh alone are home to more than 20% of all people. The region is also one of the fastest-growing technology markets as a result of its increased infrastructure and growing affordability. Despite this progress, South Asia faces one of the largest online gender disparities in the world, i.e., women are 58% less likely to connect to the mobile Internet than men.1 We believe that, as the Internet becomes more globally accessible, it is imperative for technologists to intentionally examine the biases and inequities perpetuated in technology to truly enable gender equity online.
Optimal Clustering and Cluster Identity in Understanding High-Dimensional Data Spaces with Tightly Distributed Points

The sensitivity of the elbow rule in determining an optimal number of clusters in high-dimensional spaces that are characterized by tightly distributed data points is demonstrated. The high-dimensional data samples are not artificially generated, but they are taken from a real world evolutionary many-objective optimization. They comprise of Pareto fronts from the last 10 generations of an evolutionary optimization computation with 14 objective functions. The choice for analyzing Pareto fronts is strategic, as it is squarely intended to benefit the user who only needs one solution to implement from the Pareto set, and therefore a systematic means of reducing the cardinality of solutions is imperative. As such, clustering the data and identifying the cluster from which to pick the desired solution is covered in this manuscript, highlighting the implementation of the elbow rule and the use of hyper-radial distances for cluster identity. The Calinski-Harabasz statistic was favored for determining the criteria used in the elbow rule because of its robustness. The statistic takes into account the variance within clusters and also the variance between the clusters. This exercise also opened an opportunity to revisit the justification of using the highest Calinski-Harabasz criterion for determining the optimal number of clusters for multivariate data. The elbow rule predicted the maximum end of the optimal number of clusters, and the highest Calinski-Harabasz criterion method favored the number of clusters at the lower end. Both results are used in a unique way for understanding high-dimensional data, despite being inconclusive regarding which of the two methods determine the true optimal number of clusters.
Soft Biometrics for Social Adaptive Robots

Soft biometric analysis aims at recognizing personal traits that provide some information about an individual. In this paper, we present a real-time system able to automatically recognize soft biometric traits to enhance the capability of a social robot, Pepper, in this case, to understand characteristics of people present in the environment and to properly interact with them. In particular, the proposed system is able to estimate several traits simultaneously, such as gender, age, the presence of eyeglasses and beard, of people in the field of view of the robot. Our hypothesis is that adding these capabilities to a social robot improves and makes more believable its social behavior. Results of the preliminary evaluation seems to support this hypothesis.
On construction of a big data warehouse accessing platform for campus power usages

With the emerging of the Internet of Things (IoT) technology, we can analyze and real-time monitoring the power consumption data of buildings or equipment. However, over time, many power data will accumulate to even the terabyte level. Traditional data processing techniques will not handle. It is a challenge to monitor and process the data in a reasonable time. In this paper, we construct an efficient and real-time power monitoring platform, based on open source techniques. The power-data of buildings or equipment is provided through smart meters equipped inside campus buildings. The technologies of data collected and stored on the server side were handled by the Hadoop ecosystem and the data process to Hive data warehouse is executed by the Spark. On the particulars of system evaluation, we make some experiments to compare with the performance of record query, use three different technologies separately: Apache Hive, Apache Spark, and Impala had been evaluated regarding query-response performance. Moreover, for the performance estimation on the data ETL processing and SQL functions. The relevant experiments have been conducted on the same three software modules as well.
Scenario-based Group Usability Testing as a Mixed Methods Approach to the Evaluation of Three-Dimensional Virtual Learning Environments

Although virtual reality became popular technology whose application is recognized in various domains, the field generally still lacks a widespread culture of usability. This is also evident when considering environments intended for learning, specifically virtual learning environments (VLEs). According to our findings, it is clear that there is a growing need for systematic evaluation approach to help with the design and development of usable learner-centered VLE solutions. After comprehensive introductory background and state of the art in the field, this article provides an insight into Scenario-based Group Usability Testing (ScerGUT), a mixed methods approach to the evaluation of three-dimensional VLEs which integrates several different methods of usability testing with measurements of educational value. While the majority of the existing work has made use of usually one single usability assessment technique, ScerGUT employs a number of methods putting in focus users and user testing. To examine efficiency and applicability of the approach, empirical validation is conducted as a case study of particular VLE. The contribution of the article is twofold: (a) ScerGUT as a mixed methods approach to the evaluation of VLEs, which brings new scientific value and could help other researchers and (b) ScerGUT’s application to a particular VLE, which brings quantitative and qualitative results, thus providing an insight into ease of use and educational value of specific VLE.