Reviewerly/exHarmony huggingface dataset:
VoltKey

The explosive proliferation of Internet-of-Things (IoT) ecosystem fuels the needs for a mechanism for the user to easily and securely interconnect multiple heterogeneous devices with minimal involvement. However, the current paradigm of context-unaware pairing and authentication methods (e.g., using a preset or user-defined password) poses severe challenges in the usability and security aspects due to the limited and siloed user interface that requires substantial effort on establishing or maintaining a secure network. In this paper, we present VoltKey, a method that transparently and continuously generates secret keys for colocated devices, leveraging spatiotemporally unique noise contexts observed in commercial power line infrastructure. We introduce a novel scheme to extract randomness from power line noise and securely convert it into the same key by a pair of devices. The unique noise pattern observed only by trusted devices connected to a local power line prevents malicious devices without physical access from obtaining unauthorized access to the network. VoltKey can be implemented on top of standard USB power supplies as a platform-agnostic bolt-on addition to any IoT devices or wireless access points that are constantly connected to the power outlet. Through extensive experiments under various realistic deployment environments, we demonstrate that VoltKey can successfully establish a secret key among colocated devices with over 90% success rate, while effectively rejecting malicious devices that do not have access to the local power line (but may have access to a spatially nearby line).
Distributed Inference over Decision Tree Ensembles on Clusters of FPGAs

Given the growth in data inputs and application complexity, it is often the case that a single hardware accelerator is not enough to solve a given problem. In particular, the computational demands and I/O of many tasks in machine learning often require a cluster of accelerators to make a relevant difference in performance. In this article, we explore the efficient construction of FPGA clusters using inference over Decision Tree Ensembles as the target application. The article explores several levels of the problem: (1) a lightweight inter-FPGA communication protocol and routing layer to facilitate the communication between the different FPGAs, (2) the data partitioning and distribution strategies maximizing performance, (3) and an in depth analysis on how applications can be efficiently distributed over such a cluster. The experimental analysis shows that the resulting system can support inference over decision tree ensembles at a significantly higher throughput than that achieved by existing systems.
X-Ray Image Compression Using Convolutional Recurrent Neural Networks

In the advent of a digital health revolution, vast amounts of clinical data are being generated, stored and processed on a daily basis. This has made the storage and retrieval of large volumes of health-care data, especially, high-resolution medical images, particularly challenging. Effective image compression for medical images thus plays a vital role in todays healthcare information system, particularly in teleradiology. In this work, an X-ray image compression method based on a Convolutional Recurrent Neural Networks (RNN-Conv) is presented. The proposed architecture can provide variable compression rates during deployment while it requires each network to be trained only once for a specific dimension of X-ray images. The model uses a multi-level pooling scheme that learns contextualized features for effective compression. We perform our image compression experiments on the National Institute of Health (NIH) ChestX-ray8 dataset and compare the performance of the proposed architecture with a state-of-the-art RNN based technique and JPEG 2000. The experimental results depict improved compression performance achieved by the proposed method in terms of Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR) metrics. To the best of our knowledge, this is the first reported evaluation on using a deep convolutional RNN for medical image compression.
Supporting Anatomy Education with a 3D Puzzle in a VR Environment - Results from a Pilot Study

We designed an immersive VR system to support the imagination of complex anatomical structures. By solving a 3D puzzle of anatomical models, medical students learn names and spatial relations. The approach is proposed as a supplement to the work with traditional human body donors. To meet the needs of medical students and satisfy the different stages of learning, the approach contains different modes that help the student to gather and review medical knowledge. Additionally, different feedback mechanisms that support the student with the puzzle task are proposed. The concept is realized as a prototype application using the HTC Vive and the Unity game engine. This prototype is evaluated in a pilot study, where we investigate the general usefulness as well as the feedback mechanisms. The results suggest that a 3D puzzle in a fully immersive virtual reality environment can be a valuable extension to traditional teaching methods in the medical curriculum. Regarding the feedback, it is clear that a combination of vibrotactile feedback and a ghost copy approach are most promising.
The Analytic Truth and Falsity of Disjunctions

Abstract Disjunctive inferences are difficult. According to the theory of mental models, it is because of the alternative possibilities to which disjunctions refer. Three experiments corroborated further predictions of the mental model theory. Participants judged that disjunctions, such as Either this year is a leap year or it is a common year are true. Given a disjunction such as Either A or B , they tended to evaluate the four cases in its ‘partition’: A and B , A and not‐B , not‐A and B , not‐A and not‐B , as ‘possible’ or ‘impossible’ in ways that bore out the difference between inclusive disjunctions (‘or both’) and exclusive disjunctions (‘but not both’). Knowledge usually concerns what is true, and so when participants judge that a disjunction is false, or contingent, and evaluate the cases in its partition, they depend on inferences that yield predictable errors. They tended to judge that disjunctions, such as follows: Either the food is cold or else it is tepid, but not both , are true, though in fact they could be false. They tended to infer ‘mirror‐image’ evaluations that yield the same possibilities for false disjunctions as those for true disjunctions. The article considers the implications of these results for alternative theories based on classical logic or on the probability calculus.
Hazy Image Decolorization With Color Contrast Restoration

It is challenging to convert a hazy color image into a gray-scale image because the color contrast field of a hazy image is distorted. In this paper, a novel decolorization algorithm is proposed to transfer a hazy image into a distortion-recovered gray-scale image. To recover the color contrast field, the relationship between the restored color contrast and its distorted input is presented in CIELab color space. Based on this restoration, a nonlinear optimization problem is formulated to construct the resultant gray-scale image. A new differentiable approximation solution is introduced to solve this problem with an extension of the Huber loss function. Experimental results show that the proposed algorithm effectively preserves the global luminance consistency while represents the original color contrast in gray-scales, which is very close to the corresponding ground truth gray-scale one.
SCANCPECLENS: A Framework for Automatic Lexicon Generation and Sentiment Analysis of Micro Blogging Data on China Pakistan Economic Corridor

With the growing availability of internet and opinion rich resources such as social networks and personal blogs, the task of mining public opinion and exploring facts has become more popular than ever before during the last decade. The latest trend has deeply transformed the way the governments interact with their citizens and offer them various services through continuous public engagement. The proposed framework SCANCPECLENS is an initiative to support performance assessment framework for e-government in Pakistan. The research takes into account the opinion of masses on one of the most crucial and widely discussed development projects, China Pakistan Economic Corridor (CPEC), considered as a game changer due to its promise of bringing economic prosperity to the region. The proposed framework suggests to use machine learning algorithms to automatically discover the public sentiment from microblogs on the matter nationally as well as internationally. We also present an automated way to create sentiment lexicon of positive, negative and neutral words on the subject. To the best of our knowledge, this theme has not been explored for opinion mining before and helps one in effectively assessing public satisfaction over government's policies in the CPEC region. The research is an initiative to discover new avenues of future research and direction for the government, policy making institutions and other stake holders and demonstrates the power of text mining as an effective tool to extract business value from vast amount of social media data.
Compact and Simple RLWE Based Key Encapsulation Mechanism

In this paper, we propose a key encapsulation scheme based on NewHope and Kyber, two NIST post-quantum standardization project candidates. Our scheme is based on NewHope, thus it is simple and has fast implementation while it is making use of smaller key sizes and easily changeable security level advantages of Kyber . The scheme heavily use recent advances on Number Theoretic Transform (NTT) in a way that transformation from one degree polynomial to another is easy. To make it possible, we changed the definition of component in component-wise multiplication during polynomial multiplication and show that changing security level only requires to change the size of polynomial and the definition of component. Our scheme has $$11.5\%$$ smaller communication cost for the same security level comparing with NewHope. In addition, it is at least $$17\%$$ faster C implementation comparing with non-optimized Kyber implementation from the first round of the NIST standardization process.
Explainable Artificial Intelligence Based Heat Recycler Fault Detection in Air Handling Unit

We are entering a new age of AI applications where machine learning is the core technology but machine learning models are generally non-intuitive, opaque and usually complicated for people to understand. The current AI applications inability to explain is decisions and actions to end users have limited its effectiveness. The explainable AI will enable the users to understand, accordingly trust and effectively manage the decisions made by machine learning models. The heat recycler’s fault detection in Air Handling Unit (AHU) has been explained with explainable artificial intelligence since the fault detection is particularly burdensome because the reason for its failure is mostly unknown and unique. The key requirement of such systems is the early diagnosis of such faults for its economic and functional efficiency. The machine learning models, Support Vector Machine and Neural Networks have been used for the diagnosis of the fault and explainable artificial intelligence has been used to explain the models’ behaviour.
Personalized diffusions for top-n recommendation

This paper introduces PerDif; a novel framework for learning personalized diffusions over item-to-item graphs for top-n recommendation. PerDif learns the teleportation probabilities of a time-inhomogeneous random walk with restarts capturing a user-specific underlying item exploration process. Such an approach can lead to significant improvements in recommendation accuracy, while also providing useful information about the users in the system. Per-user fitting can be performed in parallel and very efficiently even in large-scale settings. A comprehensive set of experiments on real-world datasets demonstrate the scalability as well as the qualitative merits of the proposed framework. PerDif achieves high recommendation accuracy, outperforming state-of-the-art competing approaches---including several recently proposed methods relying on deep neural networks.
Adaptive illumination-invariant face recognition via local nonlinear multi-layer contrast feature

Traditional face recognition method usually faces the challenge of varying lighting condition. In this paper, we propose an illumination-invariant local binary descriptor learning method for face recognition. Unlike local binary descriptor (LBP) and its variants, which usually utilize the rigid sign function for binarization despite of data distributions. We first determine a dynamic thresholds strategy including the information of illumination variation to extract nonlinear multi-layer contrast features. Specially, Exponential Discriminant Analysis (EDA) is designed to act as preprocessing which can contribute to improve the discriminative ability of the face image by enlarging the margin between different classes relative to the same class. To further improve the recognition performance, we combined our preliminary work, the adaptive fuzzy fusion framework, to integrate the recognition results for multi-scale features spaces. Extensive experiments conducted on four face databases validate the effectiveness of the proposed method for illumination face recognition.
Spatio-temporal PV forecasting sensitivity to modules’ tilt and orientation

Using deployed PV generation as inputs for spatio-temporal forecasting approaches has the potential for fast and scalable very short-term PV forecasting in the urban environment but one has to consider the effect of their tilt and orientation on the forecasting accuracy. To address this issue, tilted irradiance data sets were simulated using state of the art solutions on a horizontal irradiance data set from a pyranometer network deployed in Oahu, Hawaii, and used as inputs to train a 10-s ahead linear ARX model. Results showed that the mismatch in tilt/orientation degrades the forecast skill, justified by the difference in the diffuse fraction of each surface and, thus, how each reacts to changes in cloud cover. From 4000 simulated sets, it was shown that using information from more sites led to better forecasts and made the model performance less sensitive to the PV modules’ tilt and orientation. Forecast skill showed to be quite sensitive to the tilt and orientation ensemble when the inputs consisted of only rooftop or façade systems (between 18.1–29.6% and 8.2–19.4%, respectively). Forecasting a rooftop system with vertically tilted neighbors lead to considerably lower skill values (9.8–16.2%) and benefitted when all shared the same orientation. On the other hand, forecasting a vertically tilted system with rooftop neighbors had a lower impact (9.2–14.7%) and benefitted from diversely oriented neighbors.
Enabling Encrypted Boolean Queries in Geographically Distributed Databases

The persistent growth of big data applications has being raising new challenges in managing large volumes of datasets with high scalability, confidentiality protection, and flexible types of search queries. In this paper, we propose a secure design to disassemble the private dataset with the aim to store them across geographically distributed servers while supporting secure multi-client Boolean queries. In this design, the data owner encrypts the private database with the searchable index attributes. The encrypted dataset will be disassembled and distributed evenly across multiple servers by leveraging the property of a distributed index framework. By constructing an encryption structure, generating search tokens, and enabling parallel query, we show how the proposed design performs the secure while efficient Boolean search. These queries are not only limited to those initiated by the data owner but also can be extended to support multiple authorized clients, where each client is allowed to access a necessary part of the private database. In this stage, we advocate a non-interactive authorization scheme where data owner is not required to stay online to process the query request. Moreover, the query operation can be executed in parallel, which significantly improves the search efficiency. We formally characterize the leakage profile, which allow us to follow the existing security analysis method to demonstrate that our system can guarantee data confidentiality and query privacy. To validate our protocol, we implement a system prototype and evaluate the efficiency of our construction. Through experimental results, we demonstrate the effectiveness of our protocol in terms of data outsourcing time and Boolean query time.
Game of Sloanes: best known packings in complex projective space

It is often of interest to identify a given number of points in projective space such that the minimum distance between any two points is as large as possible. Such configurations yield representations of data that are optimally robust to noise and erasures. The minimum distance of an optimal configuration not only depends on the number of points and the dimension of the projective space, but also on whether the space is real or complex. For decades, Neil Sloane's online Table of Grassmannian Packings has been the go-to resource for putatively or provably optimal packings of points in real projective spaces. Using a variety of numerical algorithms, we have created a similar table for complex projective spaces. This paper surveys the relevant literature, explains some of the methods used to generate the table, presents some new putatively optimal packings, and invites the reader to competitively contribute improvements to this table.
An inverse scattering approach for geometric body generation: a machine learning perspective

In this paper, we are concerned with the 2D and 3D geometric shape generation by prescribing a set of characteristic values of a specific geometric body. One of the major motivations of our study is the 3D human body generation in various applications. We develop a novel method that can generate the desired body with customized characteristic values. The proposed method follows a machine-learning flavour that generates the inferred geometric body with the input characteristic parameters from a training dataset. The training dataset consists of some preprocessed body shapes associated with appropriately sampled characteristic parameters. One of the critical ingredients and novelties of our method is the borrowing of inverse scattering techniques in the theory of wave propagation to the body generation. This is done by establishing a delicate one-to-one correspondence between a geometric body and the far-field pattern of a source scattering problem. It enables us to establish the one-to-one correspondence between the geometric body space and the function space defined by the far-field patterns. Hence, the far-field patterns can act as the shape generators. The shape generation with prescribed characteristic parameters is achieved by first manipulating the shape generators and then reconstructing the corresponding geometric body from the obtained shape generator by a stable multiple-frequency Fourier method. The proposed method is in sharp difference from the existing methodologies in the literature, which usually treat the human body as a suitable Riemannian manifold and the generation is based on non-Euclidean approximation and interpolation. Our method is easy to implement and produces more efficient and stable body generations. We provide both theoretical analysis and extensive numerical experiments for the proposed method. The main goal of the study is to introduce inverse scattering approaches in combination with machine learning to the geometric body generation and it opens up many opportunities for further developments.
User Modeling on Mobile Device Based on Facial Clustering and Object Detection in Photos and Videos

The article describes an approach for extraction of user preferences based on the analysis of a gallery of photos and videos on mobile device. It is proposed to firstly use fast SSD-based methods in order to detect objects of interests in offline mode directly on mobile device. Next we perform facial analysis of all visual data: extract feature vectors from detected facial regions, cluster them and select public photos and videos which do not contain faces from the large clusters of an owner of mobile device and his or her friends and relatives. At the second stage, these public images are processed on the remote server using very accurate but rather slow object detectors. Experimental study of several contemporary detectors is presented with the specially designed subset of MS COCO, ImageNet and Open Images datasets.
Impact of Distributed Processing on Power Consumption for IoT Based Surveillance Applications

With the rapid proliferation of connected devices in the Internet of Things (IoT), the centralized cloud solution faces several challenges, out of which, power consumption is one of the top priorities among the research community. In this paper, we evaluate the impact of demand splitting over heterogeneous processing resources in an IoT platform, supported by Fog and Cloud infrastructure. We develop a Mixed Integer Linear Programming (MILP) model to study the gains of splitting resource intensive demands among IoT nodes, Fog devices and Cloud servers. A surveillance application is considered, which consists of multiple smart cameras capable of capturing and analyzing real-time video streams. The PON access network aggregates IoT layer demands for processing in the Fog or the Cloud, which is accessed through the IP/WDM network. For typical video analysis workloads, the results show that, splitting medium demand sizes among IoT and Fog resources yields a total power consumption saving of up to 32%, even if these layers can host only 10% of the total workload and this can reach up to 93% for lower number of demands, compared to the centralized cloud solution. However, the gains in power savings from splitting decreases as the number of splits increases.
Seismogeodetic P‐wave Amplitude: No Evidence for Strong Determinism

Whether the final properties of large earthquakes can be inferred from initial observations of rupture (deterministic rupture) is valuable for understanding earthquake source processes and is critical for operational earthquake and tsunami early warning. Initial (P-wave) characteristics of small to moderate earthquakes scale with magnitude, yet observations of large to great earthquakes saturate, resulting in magnitude underestimation. Whether saturation is inherent to earthquake dynamics or rather is due to unreliable observation of long-period signals with inertial seismic instrumentation is unclear. Seismogeodetic methods are better suited for broadband observation of large events in the near-field. In this study, we investigate the deterministic potential of seismogeodetically derived P-wave amplitude using a dataset of 14 medium-to-great earthquakes around Japan. Our results indicate that seismogeodetic P-wave amplitude is not a reliable predictor of magnitude, opposing the notion of strong determinism in the first few seconds of rupture.