Reviewerly/exHarmony huggingface dataset:
Impeccable Circuits

By injecting faults, active physical attacks pose serious threats to cryptographic hardware where Concurrent Error Detection (CED) schemes are promising countermeasures. They are usually based on an Error-Detecting Code (EDC) which enables detecting certain injected faults depending on the specification of the underlying code. Here, we propose a methodology to enable correct, practical, and robust implementation of code-based CEDs. We show that straightforward hardware implementations of given code-based CEDs can suffer from severe vulnerabilities, not providing the desired protection level. In particular, propagation of faults into combinatorial logic is often ignored in security evaluation of these schemes. First, we formally define this detrimental effect and demonstrate its destructive impact. Second, we introduce an implementation strategy to limit the fault propagation effect. Third, in contrast to many other works where the fault coverage is the main focus, we present a detailed implementation strategy which can guarantee the detection of any fault covered by the underlying EDC. This holds for any time of the computation and any location in the circuit, both in data processing and control unit. In short, we provide practical guidelines how to construct efficient CED schemes with arbitrary EDCs to achieve the desired protection level. We practically evaluate the efficiency of our methodology by case studies covering different symmetric block ciphers and various linear EDCs.
Does Generative Face Completion Help Face Recognition?

Face occlusions, covering either the majority or discriminative parts of the face, can break facial perception and produce a drastic loss of information. Biometric systems such as recent deep face recognition models are not immune to obstructions or other objects covering parts of the face. While most of the current face recognition methods are not optimized to handle occlusions, there have been a few attempts to improve robustness directly in the training stage. Unlike those, we propose to study the effect of generative face completion on the recognition. We offer a face completion encoder-decoder, based on a convolutional operator with a gating mechanism, trained with an ample set of face occlusions. To systematically evaluate the impact of realistic occlusions on recognition, we propose to play the occlusion game: we render 3D objects onto different face parts, providing precious knowledge of what the impact is of effectively removing those occlusions. Extensive experiments on the Labeled Faces in the Wild (LFW), and its more difficult variant LFW-BLUFR, testify that face completion is able to partially restore face perception in machine vision systems for improved recognition.
Collaborative Ehealth Privacy and Security: An Access Control With Attribute Revocation Based on OBDD Access Structure

The digitization of health records due to technological developments has paved the way for patients to be collaboratively treated by different healthcare institutions. In collaborative ehealth systems, a patient's health data is stored remotely in the cloud for sharing with different healthcare service providers. However, the use of third parties for storage exposes the data to several privacy and security violation threats. Ciphertext policy attribute-based encryption (CP-ABE) which provides a fine-grained access control is a promising solution to privacy and security issues in the cloud environment and as a result, it has been widely studied for secure sharing of health data in cloud-based ehealth systems. Addressing the aspects of expressiveness, efficiency, user collusion resistance and attribute/user revocation in CP-ABE have been at the forefront of these studies. Thus, in this article, we proposed a novel expressive, efficient and collusion-resistant access control scheme with immediate attribute/user revocation for secure sharing of health data in collaborative ehealth systems. The proposed scheme additionally achieves forward and backward security. To realize these features, our access control is based on the ordered binary decision diagram (OBDD) access structure and it binds the user keys to the user identities. Security and performance analysis show that our proposed scheme is secure, expressive and efficient.
Predicting Infections Using Computational Intelligence – A Systematic Review

Infections encompass a set of medical conditions of very diverse kinds that can pose a significant risk to health, and even death. As with many other diseases, early diagnosis can help to provide patients with proper care to minimize the damage produced by the disease, or to isolate them to avoid the risk of spread. In this context, computational intelligence can be useful to predict the risk of infection in patients, raising early alarms that can aid medical teams to respond as quick as possible. In this paper, we survey the state of the art on infection prediction using computer science by means of a systematic literature review. The objective is to find papers where computational intelligence is used to predict infections in patients using physiological data as features. We have posed one major research question along with nine specific subquestions. The whole review process is thoroughly described, and eight databases are considered which index most of the literature published in different scholarly formats. A total of 101 relevant documents have been found in the period comprised between 2003 and 2019, and a detailed study of these documents is carried out to classify the works and answer the research questions posed, resulting to our best knowledge in the most comprehensive study of its kind. We conclude that the most widely addressed infection is by far sepsis, followed by Clostridium difficile infection and surgical site infections. Most works use machine learning techniques, from which logistic regression, support vector machines, random forest and naive Bayes are the most common. Some machine learning works provide some ideas on the problems of small data and class imbalance, which can be of interest. The current systematic literature review shows that automatic diagnosis of infectious diseases using computational intelligence is well documented in the medical literature.
Classifying emotions in Stack Overflow and JIRA using a multi-label approach

A forum or social media post can express multiple emotions, such as love, joy or anger. Emotion classification has been proven useful for measuring aspects such as user satisfaction. Despite its usefulness, research in emotion classification is limited, because the task is multi-label and publicly available data sets and lexica are very limited. A number of emotion classifiers for general-domain text have been proposed recently, but only a few for text in the domain of Open Source Software (OSS), such as EmoTxt. In this paper, we explore different lexica and two multi-label algorithms for classifying emotions in text related to OSS. We trained various multi-label classifiers using HOMER and RAkEL on a data set of Stack Overflow posts and a data set of JIRA Issue Tracker comments. The classifiers have been enriched with features derived from different state-of-the-art lexica. We achieved multi-label Micro F-scores up to 0.811 and Subset 0/1 Loss of 0.290. These results represent a statistically significant improvement over the state-of-the-art.
Voltage Stability Margin Index Estimation Using a Hybrid Kernel Extreme Learning Machine Approach

This paper presents a novel approach for Voltage Stability Margin (VSM) estimation that combines a Kernel Extreme Learning Machine (KELM) with a Mean-Variance Mapping Optimization (MVMO) algorithm. Since the performance of a KELM depends on a proper parameter selection, the MVMO is used to optimize such task. In the proposed MVMO-KELM model the inputs and output are the magnitudes of voltage phasors and the VSM index, respectively. A Monte Carlo simulation was implemented to build a data base for the training and validation of the model. The data base considers different operative scenarios for three type of customers (residential commercial and industrial) as well as N-1 contingencies. The proposed MVMO-KELM model was validated with the IEEE 39 bus power system comparing its performance with a support vector machine (SVM) and an Artificial Neural Network (ANN) approach. Results evidenced a better performance of the proposed MVMO-KELM model when compared to such techniques. Furthermore, the higher robustness of the MVMO-KELM was also evidenced when considering noise in the input data.
DeepTrax: Embedding Graphs of Financial Transactions

Financial transactions can be considered edges in a heterogeneous graph between entities sending money and entities receiving money. For financial institutions, such a graph is likely large (with millions or billions of edges) while also sparsely connected. It becomes challenging to apply machine learning to such large and sparse graphs. Graph representation learning seeks to embed the nodes of a graph into a euclidean vector space such that graph topological properties are preserved after the transformation. In this paper, we present a novel application of representation learning to bipartite graphs of credit card transactions in order to learn embeddings of account and merchant entities. Our framework is inspired by popular approaches in graph embeddings and is trained on two internal transaction datasets. This approach yields highly effective embeddings, as quantified by link prediction AUC and F1 score. Further, the resulting entity vectors retain intuitive semantic similarity that is explored through visualizations and other qualitative analyses. Finally, we show how these embeddings can be used as features in downstream machine learning business applications such as fraud detection.
A pragmatic elliptic curve cryptography-based extension for energy-efficient device-to-device communications in smart cities

The rise of Smart Cities with underlying adoptions of technologies like the IoT and Cloud Computing has made the integration between them a promising field with different challenges including security. Authentication is one of the foremost attempts to address these issues. Allowing direct device-to-device rather than only device-to-service communications can introduce several benefits like high data transmission rate and reliable communications even when the central clouds fail. However, the resource constraint nature of IoT devices makes it more difficult to develop secure protocols that can provide a sustainable deployment in practice. This article proposes an authentication scheme extension providing secure control from resourceful cloud servers to devices while also enabling the direct secure communications between them. The scheme is designed to use ECC and low-cost operations to provide efficient resource and energy consumption. The protocol correctness is proven by using a formal security analysis with BAN-logic. Detailed analysis is presented to show its resilience to common attacks. A performance analysis is also given to show the scheme's practical value as it only consumes at most 29 mJ on each device in addition to the amount required by the original protocol.
Availability-Aware and Energy-Efficient Virtual Cluster Allocation Based on Multi-Objective Optimization in Cloud Datacenters

With greater numbers of cloud applications being deployed in virtual clusters (VCs) and running in datacenters, the energy consumption of datacenters is experiencing rapidly cumulative growth. Thus, it is a critical issue that how to allocate virtual machines (VMs) of the VC in physical machines (PMs) for energy savings as much as possible. Considering each PM/switch has a certain failure rate, VMs may not be executed when they meet with any PM/switch fault. So, a compact allocation scheme may benefit in low energy consumption but increase the risk of violating the availability of the VC. In this paper, we consider four optimization objectives about the VC and the datacenter, i.e., availability, energy consumption, average resource utilization, and resource load balance. Then we propose a multi-objective optimization model and raise an evolution algorithm to trade-off among these four optimization objectives. Finally, experimental results show the effectiveness and efficiency of our algorithm.
Speech-to-Speech Translation Between Untranscribed Unknown Languages

In this paper, we explore a method for training speech-to-speech translation tasks without any transcription or linguistic supervision. Our proposed method consists of two steps: First, we train and generate discrete representation with unsupervised term discovery with a discrete quantized autoencoder. Second, we train a sequence-to-sequence model that directly maps the source language speech to the target languages discrete representation. Our proposed method can directly generate target speech without any auxiliary or pre-training steps with a source or target transcription. To the best of our knowledge, this is the first work that performed pure speech-to-speech translation between untranscribed unknown languages.
RF Transmitter Fingerprinting Exploiting Spatio-Temporal Properties in Raw Signal Data

The recent advances of wireless technologies in RF environments coupled with large scale usage of such technologies has warranted more autonomous deployments of wireless systems. Machine learning techniques, that include recurrent structures, have shown promise in creating such autonomous deployments using the idea of Radio Frequency Machine Learning (RFML). In large scale autonomous deployments of wireless communication networks, the signals received from one component play a crucial role in the decision making process of other components. In order to efficiently implement such systems each component of the network should be uniquely identifiable. In this paper we propose a transmitter fingerprinting technique for radio device identification using recurrent structures, by exploiting the temporal property of the received radio signal. We design and implement three recurrent neural networks (RNNs) using different types of cell models: (i) long short term memory (LSTM); (ii) gated recurrent unit (GRU) and (iii) convolutional long short term memory (ConvLSTM), for this task. We program 8 universal software radio peripheral (USRP) software defined radios (SDRs) as transmitters and collect over-the-air raw in-phase (I) and quadrature (Q) (I/Q) time series data from them using a DVB-T RTL-SDR receiver, in a laboratory setting. We exploit both the temporal variations as well as the inherent spatial dependencies in the collected I/Q time series data, to learn unique feature representations and use these as "fingerprints'" for identifying the transmitters. Experimental results reveal that the RNNs with LSTM, GRU, and ConvLSTM cells are able to correctly distinguish between the 8 transmitters with 92%, 95.3%, 97.2% accuracy respectively.
Implications of blockchain distributed ledger technology for records management and information governance programs

Purpose The purpose of this paper is to assist records managers and information governance professionals to understand the challenges presented by their organization’s use of blockchain distributed ledger technology (DLT). Design/methodology/approach An extensive literature review was conducted, which revealed a multitude of articles based on research into blockchain DLT, most written from the technology perspective. This paper differs in that it applies a records management lens to an analysis of the records created, registered or stored on a blockchain. A six-stage blockchain records consideration model is provided to illustrate examples of the records management challenges presented by the implementation of blockchain DLT. Questions are posed and recommendations are made to aid the reader in developing a blockchain DLT records management and information governance strategy. Findings Because there is no one universal configuration for a blockchain DLT solution, each implementation must be analyzed to understand the resultant records management and information governance challenges. A series of questions that should be asked and answered can not only help records management and information governance professionals adapt their policies and practices to the technology but also provide a basis for discussion with those designing the blockchain DLT solutions so they can include records management features in their designs. Originality/value This paper contributes an original analysis of the implications of the adoption of Blockchain DLT for records management and information governance programs through the lens of a six-stage Blockchain Records Consideration Model.
Scheduled Restart Momentum for Accelerated Stochastic Gradient Descent

Stochastic gradient descent (SGD) algorithms, with constant momentum and its variants such as Adam, are the optimization methods of choice for training deep neural networks (DNNs). There is great interest in speeding up the convergence of these methods due to their high computational expense. Nesterov accelerated gradient with a time-varying momentum (NAG) improves the convergence rate of gradient descent for convex optimization using a specially designed momentum; however, it accumulates error when the stochastic gradient is used, slowing convergence at best and diverging at worst. In this paper, we propose scheduled restart SGD (SRSGD), a new NAG-style scheme for training DNNs. SRSGD replaces the constant momentum in SGD by the increasing momentum in NAG but stabilizes the iterations by resetting the momentum to zero according to a schedule. Using a variety of models and benchmarks for image classification, we demonstrate that, in training DNNs, SRSGD significantly improves convergence and generalization; for instance, in training ResNet-200 for ImageNet classification, SRSGD achieves an error rate of 20.93% versus the benchmark of 22.13%. These improvements become more significant as the network grows deeper. Furthermore, on both CIFAR and ImageNet, SRSGD reaches similar or even better error rates with significantly fewer training epochs compared to the SGD baseline. Our implementation of SRSGD is available at https://github.com/minhtannguyen/SRSGD.
A Sidechain-Based Decentralized Authentication Scheme via Optimized Two-Way Peg Protocol for Smart Community

In this work, we propose a novel sidechain structure via an optimized two-way peg protocol for device authentication in the smart community in order to overcome the limitations of existing approaches. The proposed scheme uses private side blockchains to distribute and manage the local registration and authentication processes, in addition to a local mainchain block to circulate the information record with other smart systems. More importantly, we propose the optimized two-way peg protocol in the proposed sidechain system in order to prevent the worthless information injection attack during the authentication information sharing procedure between the main chain and side blockchains. The optimized two-way peg protocol supervises the availability of the required information by dynamically evaluating the trustworthiness of each smart device. The evaluation is based on numerous criteria, such as the authentication method, previous authentication information sharing history, and local authentication results. Consequently, the simulation results prove the superiority of the proposed scheme in terms of reducing authentication time, improving information management efficiency and decreasing storage consumption as compared to existing works, and the applicability and feasibility of the optimized two-way peg protocol have been approved. It is noteworthy that the proposed sidechain-based method shows its superiority in reducing the cost of authentication time compared with the blockchain-based method when using blockchain structure. The reflected savings are 33.33%, 34.29%, and 36.36% when in comparison to the conventional authentication process without applying any additional method, the authentication process using the proposed sidechain based method, and the authentication process using the blockchain based method, respectively.
KirigamiTable: Designing for Proxemic Transitions with a Shape-Changing Tabletop

A core challenge in tabletop research is to support transitions between individual activities and team work. Shape-changing tabletops open up new opportunities for addressing this challenge. However, interaction design for shape-changing furniture is in its early stages - so far, research has mainly focused on triggering shape-changes, and less on the actual interface transitions. We present KirigamiTable - a novel actuated shape-changing tabletop for supporting transitions in collaborative work. Our work builds on the concept of Proxemic Transitions, considering the dynamic interplay between social interactions, interactive technologies and furniture. With KirigamiTable, we demonstrate the potential of interactions for proxemic transitions that combine transformation of shape and digital contents. We highlight challenges for shape-changing tabletops: initiating shape and content transformations, cooperative control, and anticipating shape-change. To address these challenges, we propose a set of novel interaction techniques, including shape-first and content-first interaction, cooperative gestures, and physical and digital preview of shape-changes.
A basic end-member model algorithm for grain-size data of marine sediments

Abstract End-member (EM) unmixing algorithms are widely used in the earth sciences for unmixing compositional data such as grain-size data of sediments. However, many unmixing algorithms only use goodness-of-fit measures that justify the EMs in the absence of geological feasibility, and others tend to find EMs that enclose the sample points as tightly as possible, resulting in the calculated EMs are still mixed products, especially when unmixing highly mixed dataset. This paper proposes that EM unmixing algorithms should search for the Basic-EMs, i.e., the outermost points in the EM space. The EMs that can be unmixed by mathematics can also be purified by physical processes, i.e., the actual EMs are most likely the Basic-EMs. This paper also introduces a basic end-member model algorithm (BasEMMA) that uses genetic algorithms which mimic natural evolution processes, to seek the Basic-EMs. The evaluations by BasEMMA using both artificial and actual grain-size data show that BasEMMA can accurately find the Basic-EMs. This paper also introduces a procedure for determining the appropriate EM number, which has plagued previous researchers. In summary, this paper introduces a new way to determine geologically feasible EMs, a new EM unmixing algorithm and a new method to determine the appropriate EM number.