Reviewerly/exHarmony huggingface dataset:
On the Decoding Failure Rate of QC-MDPC Bit-Flipping Decoders

Quasi-cyclic moderate density parity check codes [1] allow the design of McEliece-like public-key encryption schemes with compact keys and a security that provably reduces to hard decoding problems for quasi-cyclic codes. In particular, QC-MDPC are among the most promising code-based key encapsulation mechanisms (KEM) that are proposed to the NIST call for standardization of quantum safe cryptography (two proposals, BIKE and QC-MDPC KEM). The first generation of decoding algorithms suffers from a small, but not negligible, decoding failure rate (DFR in the order of $$10^{-7}$$ to $$10^{-10}$$ ). This allows a key recovery attack that exploits a small correlation between the faulty message patterns and the secret key of the scheme [2], and limits the usage of the scheme to KEMs using ephemeral public keys. It does not impact the interactive establishment of secure communications (e.g. TLS), but the use of static public keys for asynchronous applications (e.g. email) is rendered dangerous. Understanding and improving the decoding of QCMDPC is thus of interest for cryptographic applications. In particular, finding parameters for which the failure rate is provably negligible (typically as low as $$2^{-64}$$ or $$2^{-128}$$ ) would allow static keys and increase the applicability of the mentioned cryptosystems. We study here a simple variant of bit-flipping decoding, which we call step-by-step decoding. It has a higher DFR but its evolution can be modelled by a Markov chain, within the theoretical framework of [3]. We study two other, more efficient, decoders. One is the textbook algorithm implemented as in [3]. The other is (close to) the BIKE decoder. For all those algorithms we provide simulation results, and, assuming an evolution similar to the step-by-step decoder, we extrapolate the value of the DFR as a function of the block length. This will give an indication of how much the code parameters must be increased to ensure resistance to the GJS attack.
Online Incremental Learning of the Terrain Traversal Cost in Autonomous Exploration

In this paper, we address motion efficiency in autonomous robot exploration with multi-legged walking robots that can traverse rough terrains at the cost of lower efficiency and greater body vibration.We propose a robotic system for online and incremental learning of the terrain traversal cost that is immediately utilized to reason about next navigational goals in building spatial model of the robot surrounding.The traversal cost experienced by the robot is characterized by incrementally constructed Gaussian Processes using Bayesian Committee Machine.During the exploration, the robot builds the spatial terrain model, marks untraversable areas, and leverages the Gaussian Process predictive variance to decide whether to improve the spatial model or decrease the uncertainty of the terrain traversal cost.The feasibility of the proposed approach has been experimentally verified in a fully autonomous deployment with a hexapod walking robot.
Improved Knowledge Graph Embedding Using Background Taxonomic Information

Knowledge graphs are used to represent relational information in terms of triples. To enable learning about domains, embedding models, such as tensor factorization models, can be used to make predictions of new triples. Often there is background taxonomic information (in terms of subclasses and subproperties) that should also be taken into account. We show that existing fully expressive (a.k.a. universal) models cannot provably respect subclass and subproperty information. We show that minimal modifications to an existing knowledge graph completion method enables injection of taxonomic information. Moreover, we prove that our model is fully expressive, assuming a lower-bound on the size of the embeddings. Experimental results on public knowledge graphs show that despite its simplicity our approach is surprisingly effective.
Adaptive Ensemble Active Learning for Drifting Data Stream Mining

Learning from data streams is among the most vital contemporary fields in machine learning and data mining. Streams pose new challenges to learning systems, due to their volume and velocity, as well as ever-changing nature caused by concept drift. Vast majority of works for data streams assume a fully supervised learning scenario, having an unrestricted access to class labels. This assumption does not hold in real-world applications, where obtaining ground truth is costly and time-consuming. Therefore, we need to carefully select which instances should be labeled, as usually we are working under a strict label budget. In this paper, we propose a novel active learning approach based on ensemble algorithms that is capable of using multiple base classifiers during the label query process. It is a plug-in solution, capable of working with most of existing streaming ensemble classifiers. We realize this process as a Multi-Armed Bandit problem, obtaining an efficient and adaptive ensemble active learning procedure by selecting the most competent classifier from the pool for each query. In order to better adapt to concept drifts, we guide our instance selection by measuring the generalization capabilities of our classifiers. This adaptive solution leads not only to better instance selection under sparse access to class labels, but also to improved adaptation to various types of concept drift and increasing the diversity of the underlying ensemble classifier.
Driver Face Verification with Depth Maps

Face verification is the task of checking if two provided images contain the face of the same person or not. In this work, we propose a fully-convolutional Siamese architecture to tackle this task, achieving state-of-the-art results on three publicly-released datasets, namely Pandora, High-Resolution Range-based Face Database (HRRFaceD), and CurtinFaces. The proposed method takes depth maps as the input, since depth cameras have been proven to be more reliable in different illumination conditions. Thus, the system is able to work even in the case of the total or partial absence of external light sources, which is a key feature for automotive applications. From the algorithmic point of view, we propose a fully-convolutional architecture with a limited number of parameters, capable of dealing with the small amount of depth data available for training and able to run in real time even on a CPU and embedded boards. The experimental results show acceptable accuracy to allow exploitation in real-world applications with in-board cameras. Finally, exploiting the presence of faces occluded by various head garments and extreme head poses available in the Pandora dataset, we successfully test the proposed system also during strong visual occlusions. The excellent results obtained confirm the efficacy of the proposed method.
Knowledge-enhanced Hierarchical Attention for Community Question Answering with Multi-task and Adaptive Learning

In this paper, we propose a Knowledge-enhanced Hierarchical Attention for community question answering with Multi-task learning and Adaptive learning (KHAMA). First, we propose a hierarchical attention network to fully fuse knowledge from input documents and knowledge base (KB) by exploiting the semantic compositionality of the input sequences. The external factual knowledge helps recognize background knowledge (entity mentions and their relationships) and eliminate noise information from long documents that have sophisticated syntactic and semantic structures. In addition, we build multiple CQA models with adaptive boosting and then combine these models to learn a more effective and robust CQA system. Further- more, KHAMA is a multi-task learning model. It regards CQA as the primary task and question categorization as the auxiliary task, aiming at learning a category-aware document encoder and enhance the quality of identifying essential information from long questions. Extensive experiments on two benchmarks demonstrate that KHAMA achieves substantial improvements over the compared methods.
PAC it up: Towards Pointer Integrity using ARM Pointer Authentication

Run-time attacks against programs written in memory-unsafe programming languages (e.g., C and C++) remain a prominent threat against computer systems. The prevalence of techniques like return-oriented programming (ROP) in attacking real-world systems has prompted major processor manufacturers to design hardware-based countermeasures against specific classes of run-time attacks. An example is the recently added support for pointer authentication (PA) in the ARMv8-A processor architecture, commonly used in devices like smartphones. PA is a low-cost technique to authenticate pointers so as to resist memory vulnerabilities. It has been shown to enable practical protection against memory vulnerabilities that corrupt return addresses or function pointers. However, so far, PA has received very little attention as a general purpose protection mechanism to harden software against various classes of memory attacks. In this paper, we use PA to build novel defenses against various classes of run-time attacks, including the first PA-based mechanism for data pointer integrity. We present PARTS, an instrumentation framework that integrates our PA-based defenses into the LLVM compiler and the GNU/Linux operating system and show, via systematic evaluation, that PARTS provides better protection than current solutions at a reasonable performance overhead
A Computing-in-Memory Engine for Searching on Homomorphically Encrypted Data

The high volumes of data stored in the cloud, coupled with growing concerns about security and privacy, have motivated research on homomorphic encryption (HE), i.e., a technique that enables computation directly on encrypted data, obviating the need for prior decryption.Recent algorithmic advances have enabled a diverse set of homomorphic operations (e.g., addition, multiplication, and division).Looking at the applications, recent work also suggests extensibility secure, homomorphically encrypted content-addressable memories [or secure content-addressable memories (SCAMs)].Still, the large datawords that result from homomorphic data encodings (i.e., that must be stored/transferred for computation), compounded with the implicit computational complexity of HE, still impede the deployment of homomorphic computer hardware.As an alternative, computing-in-memory (CiM) architectures could significantly reduce the volume of data transfers for SCAM (and other) applications, leading to considerable energy savings and latency reduction.In this regard, we propose a CiM-compatible engine for SCAM (CiM-SCAM) and analyze the pros and cons of three different memory cells: a 6T CMOS SRAM and two memory cells based on ferroelectric field-effect transistors (FeFETs) (specifically 2T + 1FeFET and 1-FeFET designs).CiM-SCAM leverages in-place copy buffers (IPCBs), along with customized sense amplifiers that include two types of in-memory adders.Our results suggest that energy (and search time) improvements of up to 16× (3.2×) for 1-FeFET memory cells are possible, compared with an application-specific integrated circuit (ASIC) approach.Similar improvements are also possible with SRAM and 2T + 1-FeFET memory cells.For the latter, we achieve up to 13× (3.1×) of energy (speedup).
Variable Chromosome Genetic Algorithm for Structure Learning in Neural Networks to Imitate Human Brain

This paper proposes the variable chromosome genetic algorithm (VCGA) for structure learning in neural networks. Currently, the structural parameters of neural networks, i.e., number of neurons, coupling relations, number of layers, etc., have mostly been designed on the basis of heuristic knowledge of an artificial intelligence (AI) expert. To overcome this limitation, in this study evolutionary approach (EA) has been utilized to automatically generate the proper artificial neural network (ANN) structures. VCGA has a new genetic operation called a chromosome attachment. By applying the VCGA, the initial ANN structures can be flexibly evolved toward the proper structure. The case study applied to the typical exclusive or (XOR) problem shows the feasibility of our methodology. Our approach is differentiated with others in that it uses a variable chromosome in the genetic algorithm. It makes a neural network structure vary naturally, both constructively and destructively. It has been shown that the XOR problem is successfully optimized using a VCGA with a chromosome attachment to learn the structure of neural networks. Research on the structure learning of more complex problems is the topic of our future research.
DepthText: Leveraging Head Movements towards the Depth Dimension for Hands-free Text Entry in Mobile Virtual Reality Systems

Text entry is a common activity in virtual reality (VR) systems. However, there is a limited number of approaches available for mobile VR systems, where it might be inconvenient for users to carry an input device. We propose a novel hands-free text entry technique we call DepthText which leverages the acceleration sensing abilities of built-in IMU sensors of mobile VR systems. Users are able to enter text by moving their head forward. The results of a 5-day study indicate that users can achieve an average of 10.76 words per minute (wpm) on the last day with low errors. This performance is comparable to the dwell-based technique which is the most common way of entering text that is hands-free. One advantage of DepthText over the dwell-based technique is that users can have more control of the pace of selecting characters, rather than being pushed by a pre-set dwell time.
Using Temporal Features to Provide Data-Driven Clinical Early Warnings for Chronic Obstructive Pulmonary Disease and Asthma Care Management: Protocol for a Secondary Analysis

Background: Both chronic obstructive pulmonary disease (COPD) and asthma incur heavy health care burdens.To support tailored preventive care for these 2 diseases, predictive modeling is widely used to give warnings and to identify patients for care management.However, 3 gaps exist in current modeling methods owing to rarely factoring in temporal aspects showing trends and early health change: (1) existing models seldom use temporal features and often give late warnings, making care reactive.A health risk is often found at a relatively late stage of declining health, when the risk of a poor outcome is high and resolving the issue is difficult and costly.A typical model predicts patient outcomes in the next 12 months.This often does not warn early enough.If a patient will actually be hospitalized for COPD next week, intervening now could be too late to avoid the hospitalization.If temporal features were used, this patient could potentially be identified a few weeks earlier to institute preventive therapy; (2) existing models often miss many temporal features with high predictive power and have low accuracy.This makes care management enroll many patients not needing it and overlook over half of the patients needing it the most; (3) existing models often give no information on why a patient is at high risk nor about possible interventions to mitigate risk, causing busy care managers to spend more time reviewing charts and to miss suited interventions.Typical automatic explanation methods cannot handle longitudinal attributes and fully address these issues.Objective: To fill these gaps so that more COPD and asthma patients will receive more appropriate and timely care, we will develop comprehensible data-driven methods to provide accurate early warnings of poor outcomes and to suggest tailored interventions, making care more proactive, efficient, and effective.Methods: By conducting a secondary data analysis and surveys, the study will: (1) use temporal features to provide accurate early warnings of poor outcomes and assess the potential impact on prediction accuracy, risk warning timeliness, and outcomes;
Intelligent Framework for Learning Physics with Aikido (Martial Art) and Registered Sensors

Physics is considered a tough academic subject by learners. To leverage engagement in the learning of this STEM area, teachers try to come up with creative ideas about the design of their classroom lessons. Sports-related activities can foster intuitive knowledge about physics (gravity, speed, acceleration, etc.). In this context, martial arts also provide a novel way of visualizing these ideas when performing the predefined motions needed to master the associated techniques. The recent availability of cheap monitoring hardware (accelerometers, cameras, etc.) allows an easy tracking of the aforementioned movements, which in the case of aikido, usually involve genuine circular motions. In this paper, we begin by reporting a user study among high-school students showing that the physics concept of moment of inertia can be understood by watching live exhibitions of specific aikido techniques. Based on these findings, we later present Phy + Aik, a tool for educators that enables the production of innovative visual educational material consisting of high-quality videos (and live demonstrations) synchronized/tagged with the inertial data collected by sensors and visual tracking devices. We think that a similar approach, where sensors are automatically registered within an intelligent framework, can be explored to teach other difficult-to-learn STEM concepts.
A Pseudorandom Number Generator Based on a 4D Piecewise Logistic Map with Coupled Parameters

The piecewise logistic map (PLM) is an improved version of logistic map, which is specifically designed for cryptographic application. However, the probability density distribution of PLM is not uniform. To overcome this shortage, a parameter-coupled piecewise logistic map (PCPLM) is presented. Using the PCPLM as a basic unit, we construct a four-dimensional chaotic model, which has even probability density distribution. The four-dimensional chaotic model based on PCPLMs also has a parallel structure, which is beneficial to enhancing the running efficiency. Finally, a novel pseudorandom number generator (PRNG) is presented based on this four-dimensional chaotic model. The security analysis and simulation tests on the proposed PRNG are performed. The results both confirm that the proposed PRNG is secure and efficient. Therefore, it can be used as a candidate for data security.
Neural Machine Translation: Hindi-Nepali

With the extensive use of Machine Translation (MT) technology, there is progressively interest in directly translating between pairs of similar languages.Because the main challenge is to overcome the limitation of available parallel data to produce a precise MT output.Current work relies on the Neural Machine Translation (NMT) with attention mechanism for the similar language translation of WMT19 shared task in the context of Hindi-Nepali pair.The NMT systems trained the Hindi-Nepali parallel corpus and tested, analyzed in Hindi ⇔ Nepali translation.The official result declared at WMT19 shared task, which shows that our NMT system obtained Bilingual Evaluation Understudy (BLEU) score 24.6 for primary configuration in Nepali to Hindi translation.Also, we have achieved BLEU score 53.7 (Hindi to Nepali) and 49.1 (Nepali to Hindi) in contrastive system type.
Experimental certification of the steering criterion based on a general entropic uncertainty relation

Quantum steering describes a phenomenon whereby one system can immediately influence another via local measurements. It can be detected by violating the powerful and useful steering criterion based on a general entropic uncertainty relation. This criterion can be straightforwardly evaluated by using probability distributions from a finite set of measurements. Herein, we experimentally verify the steering criterion by means of the two-photon Werner-like states and three Pauli measurements. The results indicate that quantum steering can be captured by the criterion in a convenient way. In particular, there is no need to perform the usual quantum state tomography in the experiment, which greatly reduces experimental resources. Moreover, we demonstrate that the criterion is stronger than the linear one for detecting quantum steering of the Werner-like states.
Adaptive video streaming with current codecs and formats: Extensions to parametric video quality model ITU-T P.1203

Adaptive streaming is fast becoming the most widely used method for video delivery to the end users over the internet. The ITU-T P.1203 standard is the first standardized quality of experience model for audiovisual HTTP-based adaptive streaming. This recommendation has been trained and validated for H.264 and resolutions up to and including full-HD. The paper provides an extension for the existing standardized short-term video quality model mode 0 for new codecs i.e., H.265, VP9 and AV1 and resolutions larger than full-HD (e.g. UHD-1). The extension is based on two subjective video quality tests. In the tests, in total 13 different source contents of 10 seconds each were used. These sources were encoded with resolutions ranging from 360p to 2160p and various quality levels using the H.265, VP9 and AV1 codecs. The subjective results from the two tests were then used to derive a mapping/correction function for P.1203.1 to handle new codecs and resolutions. It should be noted that the standardized model was not re-trained with the new subjective data, instead only a mapping/correction function was derived from the two subjective test results so as to extend the existing standard to the new codecs and resolutions.
Interaction Order and Historical Body Shaping Children’s Making Projects—A Literature Review

The importance of familiarizing children with the Maker Movement, Makerspaces and Maker mindset has been acknowledged. In this literature review, we examine the complex social action of children, aged from 7 to 17 (K-12), engaging in technology Making activities as it is seen in the extant literature. The included papers contain empirical data from actual digital Making workshops and diverse research projects with children, conducted in both formal and non-formal/informal settings, such as schools or museums, libraries, Fab Labs and other makerspaces. We utilized the theoretical lens of nexus analysis and its concepts of interaction order and historical body, and as a result of our analysis, we report best practices and helping and hindering factors. Two gaps in the current knowledge were identified: (1) the current research focuses on success stories instead of challenges in the working, and, (2) histories of the participants and interaction between them are very rarely in the focus of the existing studies or reported in detail, even though they significantly affect what happens and what is possible to happen in Making sessions.
Hybrid Image Enhancement With Progressive Laplacian Enhancing Unit

In this paper, we propose a novel hybrid network with Laplacian enhancing unit for image enhancement. We combine the merits of two representative enhancement methods, i.e., the scaling scheme and the generative scheme, by forming a hybrid enhancing module. Meanwhile, we model image enhancement in a progressive manner with a deep cascading CNN architecture, in which the previous feature maps are used to enhance subsequent features to get an improved performance. Specifically, we propose a Laplacian enhancing unit, which can adjustably enhance the detail information by adding the residual of previous feature maps. This unit is embedded across layers for progressively enhancing the features. We build our network on the U-Net architecture and name it Hybrid Progressive Enhancing U-Net. Experiments show that our method achieves superior image enhancement results compared with the state-of-the-arts, while retaining competitive implementation efficiency.