Reviewerly/exHarmony huggingface dataset:
Theoretical Insights into the Use of Structural Similarity Index in Generative Models and Inferential Autoencoders

Generative models and inferential autoencoders mostly make use of $$\ell _2$$ norm in their optimization objectives. In order to generate perceptually better images, this short paper theoretically discusses how to use Structural Similarity Index (SSIM) in generative models and inferential autoencoders. We first review SSIM, SSIM distance metrics, and SSIM kernel. We show that the SSIM kernel is a universal kernel and thus can be used in unconditional and conditional generated moment matching networks. Then, we explain how to use SSIM distance in variational and adversarial autoencoders and unconditional and conditional Generative Adversarial Networks (GANs). Finally, we propose to use SSIM distance rather than $$\ell _2$$ norm in least squares GAN.
An intuitionistic formula hierarchy based on high‐school identities

We revisit the notion of intuitionistic equivalence and formal proof representations by adopting the view of formulas as exponential polynomials. After observing that most of the invertible proof rules of intuitionistic (minimal) propositional sequent calculi are formula (i.e. sequent) isomorphisms corresponding to the high-school identities, we show that one can obtain a more compact variant of a proof system, consisting of non-invertible proof rules only, and where the invertible proof rules have been replaced by a formula normalisation procedure. Moreover, for certain proof systems such as the G4ip sequent calculus of Vorob'ev, Hudelmaier, and Dyckhoff, it is even possible to see all of the non-invertible proof rules as strict inequalities between exponential polynomials; a careful combinatorial treatment is given in order to establish this fact. Finally, we extend the exponential polynomial analogy to the first-order quantifiers, showing that it gives rise to an intuitionistic hierarchy of formulas, resembling the classical arithmetical hierarchy, and the first one that classifies formulas while preserving isomorphism.
Error-run-time trade-off in the adiabatic approximation beyond scaling relations

The use of the adiabatic approximation in practical applications, as in adiabatic quantum computation, demands an assessment of the errors made in finite-time evolutions. Aiming at such scenarios, we derive bounds relating error and evolution time in the adiabatic approximation that go beyond typical scaling relations. Using the Adiabatic Perturbation Theory, we obtain leading-order expressions valid for long evolution time T, while explicitly determining the shortest time T and the largest error ε for which they are valid. In this validity regime, we can make clear and precise statements about the evolution time needed to reach a given error and vice-versa. As an example of practical importance, we apply these results to the adiabatic search, and obtain for the first time an error-run-time trade-off relation that fully reproduces the discrete-Grover-search scaling. We also pioneer the obtention of tight numerical values for ε and T under the error-reducing strategy “boundary cancelation”.
Enabling Machine Learning Across Heterogeneous Sensor Networks with Graph Autoencoders

Machine Learning (ML) has been applied to enable many life-assisting appli-cations, such as abnormality detection and emdergency request for the soli-tary elderly. However, in most cases machine learning algorithms depend on the layout of the target Internet of Things (IoT) sensor network. Hence, to deploy an application across Heterogeneous Sensor Networks (HSNs), i.e. sensor networks with different sensors type or layouts, it is required to repeat the process of data collection and ML algorithm training. In this paper, we introduce a novel framework leveraging deep learning for graphs to enable using the same activity recognition system across HSNs deployed in differ-ent smart homes. Using our framework, we were able to transfer activity classifiers trained with activity labels on a source HSN to a target HSN, reaching about 75% of the baseline accuracy on the target HSN without us-ing target activity labels. Moreover, our model can quickly adapt to unseen sensor layouts, which makes it highly suitable for the gradual deployment of real-world ML-based applications. In addition, we show that our framework is resilient to suboptimal graph representations of HSNs.
Iterative method for tuning complex simulation code

Tuning a complex simulation code refers to the process of improving the agreement of a code calculation with respect to a set of experimental data by adjusting parameters implemented in the code. This process belongs to the class of inverse problems or model calibration. For this problem, the approximated nonlinear least squares (ANLS) method based on a Gaussian process (GP) metamodel has been employed by some researchers. A potential drawback of the ANLS method is that the metamodel is built only once and not updated thereafter. To address this difficulty, we propose an iterative algorithm in this study. In the proposed algorithm, the parameters of the simulation code and GP metamodel are alternatively re-estimated and updated by maximum likelihood estimation and the ANLS method. This algorithm uses both computer and experimental data repeatedly until convergence. A study using toy-models including inexact computer code with bias terms reveals that the proposed algorithm performs better than the ANLS method and the conditional-likelihood-based approach. Finally, an application to a nuclear fusion simulation code is illustrated.
Relaxed 2-D Principal Component Analysis by Lp Norm for Face Recognition

A relaxed two dimensional principal component analysis (R2DPCA) approach is proposed for face recognition. Different to the 2DPCA, 2DPCA-$L_1$ and G2DPCA, the R2DPCA utilizes the label information (if known) of training samples to calculate a relaxation vector and presents a weight to each subset of training data. A new relaxed scatter matrix is defined and the computed projection axes are able to increase the accuracy of face recognition. The optimal $L_p$-norms are selected in a reasonable range. Numerical experiments on practical face databased indicate that the R2DPCA has high generalization ability and can achieve a higher recognition rate than state-of-the-art methods.
On Non-elitist Evolutionary Algorithms Optimizing Fitness Functions with a Plateau

We consider the expected runtime of non-elitist evolutionary algorithms (EAs), when they are applied to a family of fitness functions $$\text {Plateau} _r$$ with a plateau of second-best fitness in a Hamming ball of radius r around a unique global optimum. On one hand, using the level-based theorems, we obtain polynomial upper bounds on the expected runtime for some modes of non-elitist EA based on unbiased mutation and the bitwise mutation in particular. On the other hand, we show that the EA with fitness proportionate selection is inefficient if the bitwise mutation is used with the standard settings of mutation probability.
Bounding the Classical Capacity of Multilevel Damping Quantum Channels

Abstract A recent method to detect lower bonds to the classical capacity of quantum communication channels is applied for general damping channels in finite dimension . The method compares the mutual information obtained by coding on the computational basis and on a Fourier basis, which can be obtained by just two local measurement settings and classical optimization. The results for large representative classes of different damping structures for high dimensional quantum systems are presented.
Open Access: all you wanted to know and never dared to ask.

This editorial presents the various forms of open access, discusses their pros and cons from the perspective of the Journal of Object Technology and its editors in chiefs, and illustrates how JOT implements a platinum open access model.The regular reader will also notice that this editorial features a new template for the journal that will be used from now on.
Semi-supervised Learning with Bidirectional GANs

In this work we introduce a novel approach to train Bidirectional Generative Adversarial Model (BiGAN) in a semi-supervised manner. The presented method utilizes triplet loss function as an additional component of the objective function used to train discriminative data representation in the latent space of the BiGAN model. This representation can be further used as a seed for generating artificial images, but also as a good feature embedding for classification and image retrieval tasks. We evaluate the quality of the proposed method in the two mentioned challenging tasks using two benchmark datasets: CIFAR10 and SVHN.
Message randomization and strong security in quantum stabilizer-based secret sharing for classical secrets

Abstract We improve the flexibility in designing access structures of quantum stabilizer-based secret sharing schemes for classical secrets, by introducing message randomization in their encoding procedures. We generalize the Gilbert–Varshamov bound for deterministic encoding to randomized encoding of classical secrets. We also provide an explicit example of a ramp secret sharing scheme with which multiple symbols in its classical secret are revealed to an intermediate set, and justify the necessity of incorporating strong security criterion of conventional secret sharing. Finally, we propose an explicit construction of strongly secure ramp secret sharing scheme by quantum stabilizers, which can support twice as large classical secrets as the McEliece–Sarwate strongly secure ramp secret sharing scheme of the same share size and the access structure.
Introduction to Unreal Engine 4

Hello there, and welcome to this beginner’s guide to Unreal Engine 4. Throughout this book, you learn about different aspects of Unreal Engine 4, and you learn to create a sample game with the knowledge you gain. In this chapter, you learn how to download Unreal Engine through Epic Games Launcher and GitHub. After that, you learn how a project is structured and become familiar with the Unreal Editor interface.
Definite Descriptions in Intuitionist Positive Free Logic

This paper presents rules of inference for a binary quantifier $I$ for the formalisation of sentences containing definite descriptions within intuitionist positive free logic. $I$ binds one variable and forms a formula from two formulas. $Ix[F, G]$ means `The $F$ is $G$'. The system is shown to have desirable proof-theoretic properties: it is proved that deductions in it can be brought into normal form. The discussion is rounded up by comparisons between the approach to the formalisation of definite descriptions recommended here and the more usual approach that uses a term-forming operator $\iota$, where $\iota xF$ means `the F'.
Human-Centered A.I. and Security Primitives

The paper reviews how human-centered artificial intelligence and security primitive have influenced life in the modern world and how it’s useful in the future. Human-centered A.I. has enhanced our capabilities by the way of intelligence, human informed technology. It has created a technology that has made machines and computer intelligently carry their function. The security primitive has enhanced the safety of the data and increased accessibility of data from anywhere regardless of the password is known. This has improved personalized customer activities and filled the gap between the human-machine. This has been successful due to the usage of heuristics which solve belowems by experimental, support vector machine which evaluates and group the data, natural language processing systems which change speech to language. The results of this will lead to image recognition, games, speech recognition, translation, and answering questions. In conclusion, human-centered A.I. and security primitives is an advanced mode of technology that uses statistical mathematical models that provides tools to perform certain work. The results keep on advancing and spreading with years and it will be common in our lives.
An emergency response system created to combat injuries during physical education training in a university using deep learning

Purpose The main goal of the physical education (PE) environment is that each individual trained should achieve self-fulfillment with the large group of students involved with their own efforts. Deep learning is applying transferrable knowledge in new situations to help the students master in tough circumstances. In PE training, injuries occur when working together as a team. Safety measures are taken immediately as an emergency response to reduce the potential risk in students by providing first aid. To provide safety measures for the injured student immediately, the environment is monitored in real-time using a GPS. Design/methodology/approach Theory of Humanities Education (ToHE) infers that it has less collection of theories and a wide range of applications than the state-of-the-art systems. ToHE allows students to think creatively and play a vital role in one’s health which is a critical aspect in PE. The ToHE theory focuses on two main concepts, i.e. by using a methodological approach to analyse and deep learning to solve the problem. PE motivates college students to follow a healthy and active lifestyle. Findings The proposed system is deployed in real time for monitoring the student’s performance and provides an emergency response with an accuracy rate of 90%. Originality/value The deep learning offers solutions to the injuries by using the deep convolutional neural network to provide interpretability of the consequence by training it with various injuries that occur in the playground and inappropriate use of sports equipment. A case study provided in this paper outlines an emergency response scenario to an injured student in sports training.
Improving Handwritten Word Synthesis for Annotation-free Word Spotting

Annotation-free word spotting aims at retrieving relevant word images from a document collection without the need of a manually labeled training dataset. As annotated data is usually scarce in the application scenarios of a word spotting system, transfer learning and annotation-free methods became increasingly popular. One possibility to alleviate the annotation problem is to train on synthetically generated word images. Therefore, a common approach is to render word images from electronic fonts and to vary the synthesis parameters randomly. In this work, we show that an annotation-free word spotting method benefits from an adapted synthesis procedure. We investigate the influence of the choice of the underlying vocabulary and the combination of synthesis and data augmentation. Furthermore, we present a method to adapt the style of the synthesized word images to the target dataset. We evaluate the proposed changes to the synthesis procedure on three benchmark datasets and improve performances considerably.
On the Memory Fault Resilience of TLS 1.3

Recently, Aranha et al. (Eurocrypt 2020) as well as Fischlin and Günther (CT-RSA 2020) investigated the possibility to model memory fault attacks like Rowhammer in security games, and to deduce statements about the (in)security of schemes against such attacks. They looked into the fault-resistance of signature and AEAD schemes. Here, we extend the approach to the TLS 1.3 key exchange protocol. Our results give a mixed picture about the fault resistance of TLS 1.3. Full fault attacks on the handshake protocol, where the adversary can modify the content of variables arbitrarily, render the protocol completely insecure. On the positive side we argue that differential faults, where the adversary can flip selected memory cells, do not seem to be harmful to key derivation in the pre-shared-key mode for the handshake. The weaker random fault attacks, where some bits in memory are flipped randomly, still enable successful attacks against the record layer. We therefore present a slight modification for the nonce generation in TLS 1.3 which withstands such attacks.
Model-Free Neural Counterfactual Regret Minimization With Bootstrap Learning

Counterfactual Regret Minimization (CFR) has achieved many fascinating results in solving large-scale Imperfect Information Games (IIGs). Neural network approximation CFR (neural CFR) is one of the promising techniques that can reduce computation and memory consumption by generalizing decision information between similar states. Current neural CFR algorithms have to approximate cumulative regrets. However, efficient and accurate approximation in a large-scale IIG is still a tough challenge. In this paper, a new CFR variant, Recursive CFR (ReCFR), is proposed. In ReCFR, Recursive Substitute Values (RSVs) are learned and used to replace cumulative regrets. It is proven that ReCFR can converge to a Nash equilibrium at a rate of $O({1}/{\sqrt{T}})$. Based on ReCFR, a new model-free neural CFR with bootstrap learning, Neural ReCFR-B, is proposed. Due to the recursive and non-cumulative nature of RSVs, Neural ReCFR-B has lower-variance training targets than other neural CFRs. Experimental results show that Neural ReCFR-B is competitive with the state-of-the-art neural CFR algorithms at a much lower training cost.
Distributed machine learning for collaborative mobile robots

The Internet-of-things (IoT) devices and technologies led to a revolutionary breakthrough over the data collection procedure and Machine Learning (ML) approaches of a distributed network. It is preferable to store sensitive data on-device without sharing with a centralized computation agent and carry-out computation at the edge devices to ensure security and privacy. A recently invented distributed ML technique, Federated Learning (FL) holds the same theme that allows the edge devices to perform training on their edges and obtains a final model by learning from the model information of all the distributed edge clients. As the clients' raw data remain at local and models are generated on clients' edge, so it enhances security, privacy, and reduces computation cost in large-scale ML problems. The FL technique deals with various distributed clients that may have statistical heterogeneity and systems heterogeneity. This paper aims at dealing with such heterogeneity within an FL environment by monitoring each client's activities and leveraging resources based on the required computation during the model training phase. For each training round, we consider the proficient and trustworthy client by inspecting their resource-availability and previous history. We assign a trust score to each client based on their performance and update that score after each training period. To bring systems heterogeneity within our FL environment, we consider distributed mobile robots as FL clients with heterogeneous system configurations in terms of memory, processor, bandwidth, or battery life to understand their performance and resource-constraint behavior in a real-world setting. We filter-out the weak clients who cannot perform computation based on their available resources and exclude the untrustworthy clients who has previous record of repeatedly infusing incorrect or diverge model information, or, become stragglers during FL training. After eliminating the stragglers and untrustworthy FL clients, we conduct local training on each selected FL clients. To further mitigate the straggler issue, we enable asynchronous FL technique that can handle the clients' variant response time and continue FL training without waiting for a particular client for a long period.
Requirements analysis for reminder system in daily activity recognition dementia

Technology assistance refers to all tools or systems that allow a people with dementia to carry out their daily activities in order to increase convenience and security in these activities. Dementia is a condition of a collection of symptoms of irreversible brain damage that occurs over a relatively long time period, resulting in problems concentrating, planning or organizing daily activities. This paper is focused on estimating daily activity recognition time by considering the feasibility of activity types, feature extraction techniques for speech recognition, and the design effectiveness of a system that is easy to use by people with dementia. The results from activity recognition need to be further analyzed to find the best pattern for people with dementia. After that, monitoring applications can develop into prevention of abnormal activity in dementia.
EdgeBERT: Optimizing On-Chip Inference for Multi-Task NLP.

Transformer-based language models such as BERT provide significant accuracy improvement to a multitude of natural language processing (NLP) tasks. However, their hefty computational and memory demands make them challenging to deploy to resource-constrained edge platforms with strict latency requirements. We present EdgeBERT an in-depth and principled algorithm and hardware design methodology to achieve minimal latency and energy consumption on multi-task NLP inference. Compared to the ALBERT baseline, we achieve up to 2.4x and 13.4x inference latency and memory savings, respectively, with less than 1%-pt drop in accuracy on several GLUE benchmarks by employing a calibrated combination of 1) entropy-based early stopping, 2) adaptive attention span, 3) movement and magnitude pruning, and 4) floating-point quantization. Furthermore, in order to maximize the benefits of these algorithms in always-on and intermediate edge computing settings, we specialize a scalable hardware architecture wherein floating-point bit encodings of the shareable multi-task embedding parameters are stored in high-density non-volatile memory. Altogether, EdgeBERT enables fully on-chip inference acceleration of NLP workloads with 5.2x, and 157x lower energy than that of an un-optimized accelerator and CUDA adaptations on an Nvidia Jetson Tegra X2 mobile GPU, respectively.
Virtual exchange strengthens international youth work

Pathways to Youth Leadership was an Erasmus+: Youth in Action long term training involving 24 youngsters from 12 countries. Grant cuts required squeezing the residential programme, originally planned to last three weeks, into two. That is why, after taking part in the Erasmus+ Virtual Exchange (E+VE) training, we successfully added five VE sessions to the project. Youth work relies on personal interaction, experiential learning, and non-formal education. Transferring that into online interaction has been our greatest innovation and challenge. In our field, there is resistance to online activities, but the Covid-19 pandemic is changing this. We need to practise online facilitation, develop the required skills, and define the role E+VE can play in youth work.
Institutional library reforms are needed to improve ayurveda education, practice and research: Inferences from a case study

Libraries have always been the integral components of the academic institutions world over. Earlier known for housing the spectrum of books permitting their ready access to knowledge seekers, libraries of current age are no more confined spaces with limited functions. With the advancing information technology, the conventional definition of a library is changing to accommodate newer roles and challenges. Twenty-first century academic libraries are extending beyond the walls of their individual institutions and are evolving as open access information spaces, dealing with intelligent sharing through social communication and networking technologies. Despite such massive changes in the information storage and retrieval patterns across the globe, libraries at Ayurveda institutions in India seem by and large unbothered and unaffected. Poor educational and research standards in Ayurveda seem to have direct links with libraries contributing minimally to academic exchange and progress. An SCONUL survey conducted for the first time in any ayurvedic library setting in the country came as an eye opener stating clearly that Ayurveda institutional libraries require a major reform first to bring them at par with other academic libraries and subsequently to evolve them as the nucleus of knowledge fostering. These setups direly need a hand of help to become eligible for contributing what they are really meant to.