Reviewerly/exHarmony huggingface dataset:
Hybrid sabotage modal logic

Abstract We introduce a new hybrid modal logic HSML for reasoning about sabotage-style graph games with edge deletions and provide a complete Hilbert-style axiomatization. We extend the completeness analysis to protocol models with restrictions on available edge deletions and clarify the connections between HSML-style logics of edge deletions and recent modal logics for stepwise point deletion from graphs.
Analysis of the Influence Degree of Network Pruning on Fine-grained Image Processing Tasks

The network pruning algorithm has achieved significant effects in the compression and acceleration of neural network models, and has been used to reduce the inference cost of neural network models. However, the impact of the compressed model on fine-grained image analysis tasks is rarely considered in the evaluation of this algorithm. In response to this problem, this article carried out an analysis of the influence of the model after network pruning on fine-grained image processing tasks. For the selection of fine-grained image processing tasks, this article mainly takes the style transfer task based on the fine-grained features of the Gram matrix as the background. Considers the performance of the model before and after the network pruning on the task, the paper carries out relevant analysis on effect of generating image from the style transfer, different color channels, cosine similarity and etc. Experimental results show that the model after network pruning has a more streamlined model structure, which will not affect the overall effect of the fine-grained image processing, but there may be a certain loss in some details of the fine-grained image processing task. The research results of this paper show that the model after network pruning can still be well qualified for fine-grained image processing tasks, which has important reference value for the research and application of network pruning methods in fine-grained image processing tasks.
Vibrato Learning in Multi-Singer Singing Voice Synthesis

Decent vibratos are a trait of good vocal training, often associated with perceived level of singing skill. In this paper we present a system for multi-singer singing voice synthesis, which is capable of producing high quality singing with convincing, controllable vibratos, and can also synthesize natural singing voices for target speakers with only speech data. This is enabled by using a unified speech-and-singing acoustic model that not only bridges the modality gap but also helps make best use of both types of data. The acoustic model exposes the full F0 contour therefore allowing explicitly modelling of F0 characteristics specific to singing voice. We observe that short-time Fourier transform of the F0 contour sparsely encode vibrato characteristics, and derive a learning objective therefrom for improved vibrato production. Control of the synthesized vibrato extent is possible by wiring in a supervised “extent” neuron and expose it to outer system. Experimental results confirm the effectiveness of proposed objective in producing good vibratos and improving overall perceived singing voice quality.
Learning to Translate Low-Resourced Swiss German Dialectal Speech into Standard German Text

For a low-resourced language like Swiss German with no standard orthography and a significant variation in its written form, spoken language resources are more likely to come with translations than transcriptions. Moreover, the desired output of an automatic transcription system for Swiss German multi-dialectal speech is Standard German. This, in turn, is due to many applications that include our TV Box voice assistant and broadcast media. It follows that a translation is usually required as Swiss German and Standard German have mismatches on all linguistic levels. Unfortunately, there are not enough parallel text corpora available for training a proper translation system, nor enough in-domain speech translation (ST) data for training an ST system. We aim at investigating an end-to-end approach for multi-dialect Swiss German ST using transfer learning. Our ST model is based on an encoder-decoder architecture where we initialize the encoder with a cross-lingual speech representation model which is adapted to in-domain Swiss German speech data. We demonstrate that training the decoder on an out-of-domain ST corpus by preserving the encoder unit and then fine-tuning on in-domain ST data can be more effective than a cascade or vanilla direct ST.
Ising-Based Combinatorial Clustering Using the Kernel Method

Combinatorial clustering based on the Ising model is getting attention as a method to obtain high-quality clustering results. Furthermore, combinatorial clustering using the kernel method can handle any irregular data type by using a kernel trick. The kernel trick is an approach to the extension of the data to an arbitrary high-dimensional feature space by switching the kernel function. However, the conventional kernel clustering based on the Ising model can only be used in the limited case where the number of clusters is two. This is because the Ising model is composed of decision variables that represent binary values. This paper proposes Ising-based combinatorial clustering using a kernel method that can handle two or more clusters. The key idea of the proposed method is to represent clustering results using one-hot encoding. One-hot encoding represents a cluster to which a single data belongs by using bits whose number is the same as that of clusters. However, the one-hot constraint caused by the use of one-hot encoding decreases the quality of clustering. To this problem, in this paper, combinatorial clustering based on an externally defined one-hot constraint is used. The proposed kernel-based combinatorial clustering works with more than two clusters. Therefore, the proposed method is compared with Euclidean distance-based combinatorial clustering that divides the data into two or more clusters as the conventional method. Through experiments, it is clarified that the quality of the clustering results of the proposed method for irregular data is significantly better than that of the conventional method.
Designing Mobile App “Digital Professional Navigation” (DPN) for Self-determination of Schoolchildren and University Students on the Basis of a Multidisciplinary University

The article dwells upon the experience of developing and implementing software tools for professional navigation and self-determination of schoolchildren and university students. There is an annual growing interest in various types of services, which allows expanding, systematizing and choosing the most relevant professions of the future, evaluating yourself in terms of both professional and soft skills. The effectiveness of the development of digital services and digital tutors in vocational guidance of young people is noted. The article substantiates the relevance of using mobile apps at the stage of the professional choice both at undergraduate and post-graduate levels. The article presents the designing model of the authors' mobile app for vocational guidance "Digital professional navigation" (DPN) on the basis of a multidisciplinary university. The requirements for the functionality and structure of the mobile app used for the professional navigation of schoolchildren and university students are defined. A detailed description of the main structural blocks of the app is presented, which allow testing, evaluating, and choosing the trajectory of the professional self- determination.
Design and Implementation of Trustzone-Based Blockchain Chip Wallet

With the rapid development of the digital currency market, the security of transactions is constantly challenged, and a blockchain chip wallet security trading system based on TrustZone is designed. The system uses OP-TEE (Open-source Portable Trusted Execution Environment) to build a trusted execution environment, encrypt the private key with secure storage functions and store it in secure files, isolating user applications and wallet. Through the guidance of dirf.db file, the private key location of the security file is protected, and multi-level data protection and transaction security are realized. Compared with the traditional hardware wallet scheme, the proposed system retains the characteristics of hardware wallet security isolation and realizes chip-level transaction security with simplicity and portability.
Temporal Detection of Anomalies via Actor-Critic Based Controlled Sensing

We address the problem of monitoring a set of binary stochastic processes and generating an alert when the number of anomalies among them exceeds a threshold. For this, the decision-maker selects and probes a subset of the processes to obtain noisy estimates of their states (normal or anomalous). Based on the received observations, the decision-maker first determines whether to declare that the number of anomalies has exceeded the threshold or to continue taking observations. When the decision is to continue, it then decides whether to collect observations at the next time instant or defer it to a later time. If it chooses to collect observations, it further determines the subset of processes to be probed. To devise this three-step sequential decision-making process, we use a Bayesian formulation wherein we learn the posterior probability on the states of the processes. Using the posterior probability, we construct a Markov decision process and solve it using deep actor-critic reinforcement learning. Via numerical experiments, we demonstrate the superior performance of our algorithm compared to the traditional model-based algorithms.
Blockchain Technology Model to Protect Higher Education E-Certificates with Open Source system

The phenomenon of fake diplomas used for certain purposes by irresponsible people will harm many parties, including the original certificate issuing institution and institutions that make diplomas a requirement. Certificate forgery occurs because the manufacturing process uses a manual process and is not integrated with a computer system that can protect against certificate forgery. one of the solutions is with block chain technology, but one of the obstacles in implementing block chain is the high cost and limited human resource skills. The purpose of this study is to build a blockchain technology model to protect diplomas or other important e-certificates in universities from certificate counterfeiting by using open source-based blockchain technology. The research method uses interviews and observations to obtain direct data from the relevant agencies and conducts a literature study to strengthen the findings on the object of research. The result of this study is a block chain model to protect college e-certificates from being counterfeited by using an open source system
Remarks about the numerical inversion of injective nonlinear maps

The problem of designing an algorithm which computes the left inverse of an injective immersion is studied. It is first shown how standard Newton and gradient descent algorithms fail to work when no assumption is made on the image set of the map to be inverted. In particular, solutions might converge to the wrong point or diverge in finite/infinite time even when the map is a (global) diffeomorphism. Inspired by recent results concerning the implementation of observers in given coordinates, we investigate possible means to modify the given map in order to guarantee completeness and convergence of those algorithms.
Optimization-based Block Coordinate Gradient Coding

Existing gradient coding schemes introduce identical redundancy across the coordinates of gradients and hence cannot fully utilize the computation results from partial stragglers. This motivates the introduction of diverse redundancies across the coordinates of gradients. This paper considers a distributed computation system consisting of one master and <tex>$N$</tex> workers characterized by a general partial straggler model and focuses on solving a general large-scale machine learning problem with <tex>$L$</tex> model parameters. We show that it is sufficient to provide at most <tex>$N$</tex> levels of redundancies for tolerating <tex>$0,1, \cdots, N-1$</tex> stragglers, respectively. Consequently, we propose an optimal block coordinate gradient coding scheme based on a stochastic optimization problem that optimizes the partition of the <tex>$L$</tex> coordinates into <tex>$N$</tex> blocks, each with identical redundancy, to minimize the expected overall runtime for collaboratively computing the gradient. We obtain an optimal solution using a stochastic projected subgradient method and propose two low-complexity approximate solutions with closed-from expressions, for the stochastic optimization problem. We also show that under a shifted-exponential distribution, for any <tex>$L$</tex>, the expected overall runtimes of the two approximate solutions and the minimum overall runtime have sub-linear multiplicative gaps in <tex>$N$</tex>. To the best of our knowledge, this is the first work that optimizes the redundancies of gradient coding introduced across the coordinates of gradients.
Generating Multiview Hand Gestures with Conditional Adversarial Network

Gesture recognition is one interesting and important topic of research. However, many a times the recognition fails due to self-occlusion in the gesture images. To overcome this problem generally, multiviews are used for hand gesture recognition. Unfortunately, not many multiview gesture datasets are publicly available for conducting research on this topic. A solution to this may be to create multiple views of one particular gesture from one given single view of the same gesture using some gesture synthesis technique. In view of this, we propose a Generative Adversarial Network (GAN) based gesture synthesis model that uses conditional translation. Conditioning the translation model provides appearance information for generating the target view. Various combinations of losses are used as objective functions for training the GAN. The proposed method is experimentally evaluated by comparing the artificially generated gesture images to the original multiview gesture images available in the HGM-4 dataset. The generated images are proved to be highly photorealistic, allowing them to be used reliably in multiview gesture recognition research.
How to Find My Task? Chatbot to Assist Newcomers in Choosing Tasks in OSS Projects

Open Source Software (OSS) is making a meteoric rise in the software industry since several big companies have entered this market. Unfortunately, newcomers enter these projects and usually lose interest in contributing because of several factors. This paper aims to reduce the problems users face when they walk their first steps into OSS projects: finding the appropriate task. This paper presents a chatbot that filters tasks to help newcomers choose a task that fits their skills. We performed a quantitative and a qualitative study comparing the chatbot with the current GitHub issue tracker interface, which uses labels to categorize and identify tasks. The results show that users perceived the chatbot as easier to use than the GitHub issue tracker. Additionally, users tend to interpret the use of chatbots as situational, helping mainly newcomers and inexperienced contributors.
EPS: An earthquake prediction system using Federated Learning

Earthquake prediction is a challenging job and researchers are struggling to predict earthquakes since long time. The involvement of huge multidimensional data, data privacy considerations and transmission latency make it difficult to predict earthquakes in real time. In this research, an earthquake prediction system &#x2018;EPS&#x2019; has been developed using python. It is unique among early earthquake warning systems as federated learning technique (FL) has been applied for incorporating the intelligence from cloud to the mobile edge devices. Features like onsite data collection, training of local data models and aggregation of these models for the generation of global data model makes FL capable enough to handle data privacy, data availability and resolve latency issues involved in earthquake prediction. The proposed system integrates multiple data sources using FL for generation of global data model, applies time series regression for earthquake prediction and transmits earthquake alerts to all connected users and agencies. Dataset from Western Himalayan zone has been collected from 1985 to 2020 considering five different aspects including seismicity, atmospheric temperature, atmospheric pressure, regional rainfall, and reservoir parameters using federated learning technique for experimentation. EPS has successfully predicted 97.96&#x0025; of the earthquakes that have encountered in the region during past thirty-five years.
Using Interactive Technology to Develop Preservice Teachers’ STEAM Competencies in Early Childhood Education Program

Using interactive technology leads to an interactive learning environment where learners develop their STEAM competencies, including critical thinking, collaboration, communication, creativity and innovation, self-direction, connection, and the use of interactive technology tools effectively. This research aims to investigate the use of interactive technology in developing preservice teachers’ STEAM competencies. The participants were preservice teachers (n=80) in an early childhood education program at a Federal University in the United Arab Emirates. An explanatory sequential mixed-method approach used quantitative analysis (quasi-experiment) followed by a qualitative approach (a focus group discussion was conducted). An online survey was used to collect the quantitative data from the participants before the semester started. It was sent again to participants at the end of the semester. The focus group discussions were used to collect the data from selected participants from the experimental group (n=18). The study results reveal a significant positive impact on the development of preservice teachers’ STEAM competencies after using the interactive technology.
Decomposition of Clifford Gates

In fault-tolerant quantum computation and quantum error-correction one is interested on Pauli matrices that commute with a circuit/unitary. We provide a fast algorithm that decomposes any Clifford gate as a $\textit{minimal}$ product of Clifford transvections. The algorithm can be directly used for finding all Pauli matrices that commute with any given Clifford gate. To achieve this goal, we exploit the structure of the symplectic group with a novel graphical approach.
Transparent subsidized agri‐product distribution during pandemics with reputation based PoA blockchain

Summary Technological advancements play a crucial role in agriculture sector aiming at eradicating numerous problems. These problems are intensified due to pandemics like delay overhead in the subsidized agri‐products supply chains. The existing technology‐based solutions are not readily adopted by stakeholders due to absence of trust. Blockchain can impart more transparency and trust in agri‐supply chains leading to fair pricing. The idea of using blockchain for financing processes in agriculture has resulted from the managerial pressure and fiscal scams which eventually aggravate the plight of farmers. Thus, a blockchain‐based subsidized agri‐products distribution (SAPD) is proposed. Blockchain has been optimized by a reputation‐based Proof‐of‐Authority (PoA) consensus algorithm. It has been implemented using Ethereum blockchain on AWS EC2 cloud. The smart contract is designed in solidity. Detailed performance evaluation has been conducted to compute cost and processing time that shows a substantial reduction in cost of managerial operations with respect to previous work by 41.65%. Finally, to assess the utility of SAPD system a qualitative user survey has been conducted. Managerial significance of system is reflected in memory space reduction by 85% in light clients and increase in transaction execution per second by 31% using reputation‐based PoA.
Occlusion face detection is based on Yolov5s

Computer vision is a field of artificial intelligence that trains computers to interpret the visual world and humans in a way similar to vision. Due to the rapid development of technology and the relatively small cost of data storage, face detection has developed rapidly in the field of computer vision in the past decade. Among them, face detection and recognition are the most widely used, ranging from mobile applications and augmented reality for entertainment purposes to social networks and security cameras. Most importantly, with the development of face detection technology, the yolov5s model has made great achievements in recent years. Based on the latest and fastest Yolov5s model, this paper uses transfer learning to fine-tune the model parameters to extract facial features. Finally, the MAFA data set is tested. The test results show that the model designed in this paper can effectively detect faces in various scenarios.
Editorial for Special Issue: “Mineralogy of Noble Metals and “Invisible” Speciations of These Elements in Natural Systems”

This Special Issue of Minerals covers a broad range of topics related to the mineralogy of noble metals (Au, Ag, Pt, Pd, Rh, Ru) and the forms of occurrence, formation and distribution of these elements in natural ore-forming systems [...]
Generative Adversarial Analysis using U-LSB Based Audio Steganography

Audio steganography is the technique of hiding data within a carrier audio file, where concealed data is imperceptible to humans. The existing deep-learning-based approaches depend on human handcraft for the generation and steganalysis of the steganographic audio. Generative Adversarial Network (GAN) based models are used nowadays for the generation of audio data from random latent space have proven to be efficient. This can be further utilized to strengthen the existing audio steganography methods. The proposed framework is a GAN model consisting of the U-Net-based generator, LSB-based embedder, and the discriminator. The method relies on an unsupervised adversarial training algorithm for embedding secret audio within the carrier audio in the temporal domain, making it imperceptible to humans. The experimental results on the 1-second Speech Command dataset show that the model can effectively produce steganographic audio using embedding probabilities. The steganographic method has proven to produce high-fidelity audio files capable of resisting steganalysis as compared to the audios generated using conventional methods. This technique can be effectively used for secured communication of the audio files transmission in midst of cyberspace hacking tricks.