Reviewerly/exHarmony huggingface dataset:
Neural Image Representations for Multi-Image Fusion and Layer Separation

We propose a framework for aligning and fusing multiple images into a single view using neural image representations (NIRs), also known as implicit or coordinate-based neural representations. Our framework targets burst images that exhibit camera ego motion and potential changes in the scene. We describe different strategies for alignment depending on the nature of the scene motion -- namely, perspective planar (i.e., homography), optical flow with minimal scene change, and optical flow with notable occlusion and disocclusion. With the neural image representation, our framework effectively combines multiple inputs into a single canonical view without the need for selecting one of the images as a reference frame. We demonstrate how to use this multi-frame fusion framework for various layer separation tasks. The code and results are available at https://shnnam.github.io/research/nir.
Explaining large-for-gestational-age births

We proposed a novel local interpretation method for a random forest classifier based on feature occurrence frequency in trees that give the same prediction as the random forest classifier. The method shows promising results when applied to our random forest classifier for large-for-gestational-age births. Further validation of the method is required.
Connecting formal and informal learning in Smart Learning Environments

This PhD research explores how Smart Learning Environments can support the connection between formal and informal learning. Thanks to the information offered by learning systems and tools such as Virtual Learning Environments, mobile and Internet of Things devices, SLEs can characterize the individual learning needs and context of students to provide them with personalized support across the boundaries of the classroom. In a similar fashion to approaches related with mobile learning, the connection offered by SLEs can help students to reflect on learning concepts in real scenarios, but also adapting the offered resources to their progression and performance throughout the learning situation. However, existing attempts in SLEs face difficulties regarding the preparation of possible interventions by teachers or the understanding of the formal learning situation. This work attempts to overcome this limitations with the usage of the learning design.
Secure Wire Shuffling in the Probing Model

In this paper we describe the first improvement of the wire shuffling countermeasure against side-channel attacks described by Ishai, Sahai and Wagner at Crypto 2003. More precisely, we show how to get worst case statistical security against t probes with running time $${\mathcal O}(t)$$ instead of $${\mathcal O}(t \log t)$$ ; our construction is also much simpler. Recall that the classical masking countermeasure achieves perfect security but with running time $${\mathcal O}(t^2)$$ . We also describe a practical implementation for AES that outperforms the masking countermeasure for $$t \ge 6\,000$$ .
Temporal Transaction Scraping Assisted Point of Compromise Detection With Autoencoder Based Feature Engineering

Credit card fraudsters exploit various methods to capture card information. One of the common methods is to duplicate the credit cards by skimming. In this study, we introduce a new point of compromise detection method in order to trace and identify merchants where the skimming operation took place and card information has been captured by criminals. The proposed method first extracts discriminative features by using principle component analysis(PCA) and Autoencoder extractors and then it clusters similar fraudulent transactions with K-Means algorithm, afterwards it highlights possible merchants that are involved in this scheme by finding matching merchants in the produced clusters with a retrospective analysis of all transactions. Our experiments showed that the proposed method could achieve promising results with zero-knowledge on the existing skimming points. The application of our proposed method on real-life card transactions enabled us to pinpoint 7 out of 9 point of compromise previously identified by the reporting bank.
A variant S2 measure for algebraic integers all of whose conjugates lie in a sector

Let α be a nonzero algebraic integer of degree d with conjugates α1=α,…,αd. It is well known that Sk(α)= ∑i=1dαik. Here, we define Sk′(α)= ∑i=1d|αi|k and sk′(α)=Sk′(α)∕d. Then, we focus our attention on the case k=2 when α is an algebraic integer all of whose conjugates lie in a sector |argz|≤𝜃, 0<𝜃<π2. We compute the greatest lower bound c(𝜃) of s2′(α) for 𝜃 belonging to eleven subintervals of [0,π2). Moreover, among these subintervals, there are twice two consecutive and complete subintervals. We use the method of explicit auxiliary functions for which the involved polynomials are found by our recursive algorithm.
Provable Super-Convergence With a Large Cyclical Learning Rate

Conventional wisdom dictates that learning rate should be in the stable regime so that gradient-based algorithms don't blow up. This letter introduces a simple scenario where an unstably large learning rate scheme leads to a super fast convergence, with the convergence rate depending only logarithmically on the condition number of the problem. Our scheme uses a Cyclical Learning Rate (CLR) where we periodically take one large unstable step and several small stable steps to compensate for the instability. These findings also help explain the empirical observations of [Smith and Topin, 2019] where they show that CLR with a large maximum learning rate can dramatically accelerate learning and lead to so-called "super-convergence". We prove that our scheme excels in the problems where Hessian exhibits a bimodal spectrum and the eigenvalues can be grouped into two clusters (small and large). The unstably large step is the key to enabling fast convergence over the small eigen-spectrum.
A Security Gate for Mask Detection and Temperature Control

Influenza viruses have caused a substantial spread in the community, especially in the school environment. Besides that, the management of individuals in close contact with people at risk of infection plays an important role in tracing people with F0 and neighboring infections. In this paper, we propose a potential system for detecting risk cases of influenza to assist with the control of flu. We used a thermal camera to measure body temperature for individuals who come to school, warning if the temperature reading is above the allowable threshold. In addition, our system detects cases of not wearing a mask when going to school and alerts. In particular, we implemented a complete detection system that improved processing speed and optimized resources by using a sensor to detect a coming object instead of using the video stream model. With the large data set we tested and deployed, it was highly accurate in even the case of partial mask recognition. The product is deployed on the Raspberry Pi device at a reasonable cost, suitable for the limited conditions of low-income areas.
Stylizing Volumes with Neural Networks

In ”Raya and The Last Dragon”, a blast of energy rings across the desiccated lands of the ancient world of Kumandra. This climactic story point represents a powerful force of magic and transformation, and as such, is art directed to be composed of stylized wave patterns and harmonic textures, as if created by sound vibrations. To achieve this, we artistically stylize a simulated volume using Neural Style Transfer. In this talk, we describe the integration of deep learning-based tools into our effects pipeline to accomplish this.
Rapping-Singing Voice Synthesis based on Phoneme-level Prosody Control

In this paper, a text-to-rapping/singing system is introduced, which can be adapted to any speaker's voice. It utilizes a Tacotron-based multispeaker acoustic model trained on read-only speech data and which provides prosody control at the phoneme level. Dataset augmentation and additional prosody manipulation based on traditional DSP algorithms are also investigated. The neural TTS model is fine-tuned to an unseen speaker's limited recordings, allowing rapping/singing synthesis with the target's speaker voice. The detailed pipeline of the system is described, which includes the extraction of the target pitch and duration values from an a capella song and their conversion into target speaker's valid range of notes before synthesis. An additional stage of prosodic manipulation of the output via WSOLA is also investigated for better matching the target duration values. The synthesized utterances can be mixed with an instrumental accompaniment track to produce a complete song. The proposed system is evaluated via subjective listening tests as well as in comparison to an available alternate system which also aims to produce synthetic singing voice from read-only training data. Results show that the proposed approach can produce high quality rapping/singing voice with increased naturalness.
On the weak intuitionistic fuzzy implication based on △ operation

In this paper, a new weak intuitionistic fuzzy implication is introduced. Fulfillment of some axioms and properties, together with the Modus Ponens and Modus Tollens inference rules are investigated. Negation induced by the newly proposed implication is presented.
Crypto Politics: Notes on Sociotechnical Imaginaries of Governance in Blockchain Based Technologies

Farocki.Together with his sister, Anna Groos, Jan directed and produced multiple film projects (including www.endzeit.at).The joy of 1 The Etherum project is a public, global, open-source, blockchain-based distributed computing platform for 'decentralised' applications.First proposed in December 2013 by Vitalik Buterin, it went 'live' in July 2015.Like other blockchains, it has its own cryptocurrency (in this case, 'Ether' or in short ETH) as inherent part of its protocol design.Cf ethereum.org/what-is-ethereum/ 2 These points will be taken up directly, but see: CleanApp (2018) 'The Wood-Zamfir Governance Debates,' blogger, Crypto Law Review,
Professional Training of Masters of Technological Education in Pedagogical Higher Education Institute

The transition of higher pedagogical school to a continuous education system involves updating the content, forms and methods of professional training of masters as specialists of the highest qualification level, who in the future will join scientists from various fields of science and teaching staff of universities, colleges, gymnasiums, lyceums and more. The purpose - on the basis of the integral scientific analysis to develop theoretical and methodical bases of research, to substantiate and experimentally check pedagogical system of professional training of masters of technological education. In the course of the research a complex of pedagogical conditions of professional training of future teachers of technological education in the process of master's degree is singled out. 531 undergraduates of experimental and control groups took part in the experiment, 266 of them were involved in the formative stage. The results of experimental data were processed according to the methods of S. Arkhangelsky (1980) (determination of a sufficient representative sample of students), A. Kiverlyag (1980) (determination of arithmetic mean, variance), D. Novikov (2005) (verification of the probability of the obtained results), which made it possible compare the indicators of the formation of levels of professional and pedagogical competence of masters of technological education before and after the experiment. As a result of experimental work, undergraduates of the experimental group showed higher levels of professional and pedagogical competence than in the control. The results of the experiment showed that the effectiveness of the author's pedagogical system of vocational training for masters of technological education is sufficient, and the general strategy of the proposed pedagogical system of vocational training is innovative, expedient and deserving widespread implementation in higher pedagogical educational institutions that train future teachers in the specialty "Technological education".
O QUE A PANDEMIA DA COVID-19 FEZ COM A PRESSÃO ARTERIAL SISTÊMICA?

The aim was to identify whether there was any influence of the pandemic in the lives of patients with chronic non-communicable diseases accompanied by a family health strategy. This work was developed through a qualitative descriptive field research, carried out in a family health strategy in a neighborhood located in the North Zone of the State of Rio de Janeiro, from September to December 2020. proven effectiveness in preventive measures in combating complications caused by diseases caused by systemic arterial hypertension, treatments, whether pharmacological or not, impact on the population and the government. The results of this study showed that the pandemic caused by the new coronavirus influenced the blood pressure levels of patients with systemic arterial hypertension.
PROBLEMS OF VOCATIONAL EDUCATION STUDENTS’ READINESS FOR SUCCESSFUL SOCIAL INTERACTION

The article deals with the problem of vocational education students’ readiness for successful social interaction. The structural units of the concept under consideration are also analyzed in detail. Special attention is paid to the influence of different social groups that determine the formation of students’ readiness for successful social interaction in the secondary vocational education system.
iDARTS: Improving DARTS by Node Normalization and Decorrelation Discretization

Differentiable ARchiTecture Search (DARTS) uses a continuous relaxation of network representation and dramatically accelerates Neural Architecture Search (NAS) by almost thousands of times in GPU-day. However, the searching process of DARTS is unstable, which suffers severe degradation when training epochs become large, thus limiting its application. In this paper, we claim that this degradation issue is caused by the imbalanced norms between different nodes and the highly correlated outputs from various operations. We then propose an improved version of DARTS, namely iDARTS, to deal with the two problems. In the training phase, it introduces node normalization to maintain the norm balance. In the discretization phase, the continuous architecture is approximated based on the similarity between the outputs of the node and the decorrelated operations rather than the values of the architecture parameters. Extensive evaluation is conducted on CIFAR-10 and ImageNet, and the error rates of 2.25\% and 24.7\% are reported within 0.2 and 1.9 GPU-day for architecture search respectively, which shows its effectiveness. Additional analysis also reveals that iDARTS has the advantage in robustness and generalization over other DARTS-based counterparts.
Blockchain, Smart Contracts and Copyright Management Disruption

Blockchain, smart contracts and copyright management disruption. The article examines the differences between new, blockchain and smart contract-based private ordering regime and the fundamental components of copyright law, such as exceptions and limitations, the doctrine of exhaustion, restrictions on formalities, the public domain and fair remuneration.
Image Steganography Using Ridgelet Transform and SVD

Image steganography involves embedding the secret information into a cover media without creating noticeable changes in it and keeping its presence hidden. In this paper, transform domain image steganography is proposed that benefits from the ridgelet transform and SVD matrix decomposition technique. The principal component obtained by singular value decomposition of ridgelet coefficients acts as an embedding location. Stego image is obtained by embedding the secret image in it. Experimental results in terms of imperceptibility and robustness are verified using standard image quality metrics. Simulation results validate superior visual quality with a large payload. Proposed approach is also robust to several image processing attacks like noise addition, geometric transformations, histogram equalisation, JPEG compression, etc.
EF4, EF4-M and EF4-Ł: A companion to BN4 and two modal four-valued systems without strong Łukasiewicz-type modal paradoxes

The logic BN4 was defined by R.T. Brady as a four-valued extension of Routley and Meyer’s basic logic B. The system EF4 is defined as a companion to BN4 to represent the four-valued system of (relevant) implication. The system Ł was defined by J. Łukasiewicz and it is a four-valued modal logic that validates what is known as strong Łukasiewicz-type modal paradoxes. The systems EF4-M and EF4-Ł are defined as alternatives to Ł without modal paradoxes. This paper aims to define a Belnap-Dunn semantics for EF4, EF4-M and EF4-Ł. It is shown that EF4, EF4-M and EF4-Ł are strongly sound and complete w.r.t. their respective semantics and that EF4-M and EF4-Ł are free from strong Łukasiewicz-type modal paradoxes.
Targeted Poverty Alleviation System Based on Industrialized Blockchain

Toward the existing problems in targeted poverty alleviation system, such as unclear poverty alleviation records, inaccurate poverty alleviation targets, easy tampering of poverty alleviation data and single data storage structure, a targeted poverty alleviation system based on blockchain + IPFS + distributed database is designed.For different links in the industrial field, different consensus schemes are used to balance reliability and performance. Using the characteristics of blockchain technology to ensure that data are traceable and tampering-proof, and then combining this technology with the interstellar file system (IPFS) as data storage to solve the problem of high cost of large files; what’s more, the distributed database can play a role in achieving data partition management to solve the problem of single data storage structure. The data nodes are independent of each other, thus, they can realize the efficient control of resources and improve the concurrency and scalability of the system. Through the performance test and comparative analysis of the system, it is proved that the system has a great improvement in throughput, data read-write delay and other indicators.
Application of smart phone agro-advisory services of m4agriNEI in climate smart natural resource management in agriculture by tribal farmers of Meghalaya: an empirical study with structural equation modeling

Smart phone applications are increasingly being used by farmers of North East India to help them make informed decision on Climate-Smart Natural Resource Management (CSNRM).The research project m4agriNEI is an innovative mix of Smart Phone and web applications along with Toll Free IVRS based farmer specific agro-advisory system being implemented at College of P.G.Studies in Agricultural Sciences of CAU, Imphal at Umiam, Meghalaya in collaboration with Digital India Corporation, New
NEIGHBOURHOOD CANONICITY FOR EK, ECK, AND RELATIVES: A CONSTRUCTIVE PROOF

Abstract We prove neighbourhood canonicity and strong completeness for the logics $\mathbf {EK}$ and $\mathbf {ECK}$ , obtained by adding axiom (K), resp. adding both (K) and (C), to the minimal modal logic $\textbf {E}$ . In contrast to an earlier proof in [10], ours is constructive. More precisely, we construct minimal characteristic models for both logics and do not rely on compactness of first order logic. The proof involves a specific circumscription technique and quite some set-theoretic maneuvers to establish that the models satisfy the appropriate frame conditions. After giving both proofs, we briefly spell out how they generalize to four stronger logics and to the extensions of the resulting six logics with a global modality.
Low-Resource Adaptation of Open-Domain Generative Chatbots

Recent work building open-domain chatbots has demonstrated that increasing model size improves performance (Adiwardana et al., 2020;Roller et al., 2020).On the other hand, latency and connectivity considerations dictate the move of digital assistants on the device (Verge, 2021).Giving a digital assistant like Siri, Alexa, or Google Assistant the ability to discuss just about anything leads to the need for reducing the chatbot model size such that it fits on the user's device.We demonstrate that low parameter models can simultaneously retain their general knowledge conversational abilities while improving in a specific domain.Additionally, we propose a generic framework that accounts for variety in question types, tracks reference throughout multiturn conversations, and removes inconsistent and potentially toxic responses.Our framework seamlessly transitions between chatting and performing transactional tasks, which will ultimately make interactions with digital assistants more human-like.We evaluate our framework on 1 internal and 4 public benchmark datasets using both automatic (Perplexity) and human (SSA -Sensibleness and Specificity Average) evaluation metrics and establish comparable performance while reducing model parameters by 90%.
SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation

In autonomous driving, a LiDAR-based object detector should perform reliably at different geographic locations and under various weather conditions. While recent 3D detection research focuses on improving performance within a single domain, our study reveals that the performance of modern detectors can drop drastically cross-domain. In this paper, we investigate unsupervised domain adaptation (UDA) for LiDAR-based 3D object detection. On the Waymo Domain Adaptation dataset, we identify the deteriorating point cloud quality as the root cause of the performance drop. To address this issue, we present Semantic Point Generation (SPG), a general approach to enhance the reliability of LiDAR detectors against domain shifts. Specifically, SPG generates semantic points at the predicted foreground regions and faithfully recovers missing parts of the foreground objects, which are caused by phenomena such as occlusions, low reflectance or weather interference. By merging the semantic points with the original points, we obtain an augmented point cloud, which can be directly consumed by modern LiDAR-based detectors. To validate the wide applicability of SPG, we experiment with two representative detectors, PointPillars and PV-RCNN. On the UDA task, SPG significantly improves both detectors across all object categories of interest and at all difficulty levels. SPG can also benefit object detection in the original domain. On the Waymo Open Dataset and KITTI, SPG improves 3D detection results of these two methods across all categories. Combined with PV-RCNN, SPG achieves state-of-the-art 3D detection results on KITTI.
Lessons from the Clustering Analysis of a Search Space: A Centroid-based Approach to Initializing NAS.

Lots of effort in neural architecture search (NAS) research has been dedicated to algorithmic development, aiming at designing more efficient and less costly methods. Nonetheless, the investigation of the initialization of these techniques remain scare, and currently most NAS methodologies rely on stochastic initialization procedures, because acquiring information prior to search is costly. However, the recent availability of NAS benchmarks have enabled low computational resources prototyping. In this study, we propose to accelerate a NAS algorithm using a data-driven initialization technique, leveraging the availability of NAS benchmarks. Particularly, we proposed a two-step methodology. First, a calibrated clustering analysis of the search space is performed. Second, the centroids are extracted and used to initialize a NAS algorithm. We tested our proposal using Aging Evolution, an evolutionary algorithm, on NAS-bench-101. The results show that, compared to a random initialization, a faster convergence and a better performance of the final solution is achieved.