Reviewerly/exHarmony huggingface dataset:
NLP Meets Vision for Visual Interpretation - A Retrospective Insight and Future directions

Recent advances in the field of NLP (Natural Language Processing) and CV (Computer Vision) have sparked a lot of curiosity among researchers to test the limitations of latest Deep learning techniques by employing them in more complex AI tasks. One such kind of task is VQA (Visual Question Answering) which is inherently divided into many layers of complexities. Some questions are simple having obvious answers while some are more complex which need logical reasoning, common sense and factual knowledge. Starting simple and gradually incorporating complexity, is always a good idea in scientific research and development. At first, datasets were simpler consisting of simple question-answer pairs with images depicting simpler concepts and relatively naive VQA models were trained on them. Slowly, with time, the VQA datasets got more complicated and tangled demanding more cognitive capabilities from VQA models. This evolution pushed the VQA models to be more efficient in matching human cognitive abilities, using reasoning based on common sense and factual knowledge. In this survey, we will first discuss some of the famous datasets in the domain of VQA and then we will discuss some of the crucial advancements in the VQA architectures and what is currently being done for integrating common sense and knowledge into these models. Moreover, reasoning is very crucial for truly intelligent systems but representations in deep learning models are inherently very fuzzy and vague. We need models that can transparently generate reasoning about their predictions like old school expert systems which used to work on symbolic knowledge, so the architectures based on the amalgam of deep learning techniques and Symbolic representations would also be a part of our discussion. We will also shed some light on the impact of transformers in the field of deep learning and how these transformer based models are quickly becoming state-of-the-art in almost every deep learning task.
Single-Server Private Linear Transformation: The Individual Privacy Case

This paper considers the single-server Private Linear Transformation (PLT) problem with individual privacy guarantees. In this problem, there is a user that wishes to obtain $L$ independent linear combinations of a $D$-subset of messages belonging to a dataset of $K$ messages stored on a single server. The goal is to minimize the download cost while keeping the identity of each message required for the computation individually private. The individual privacy requirement ensures that the identity of each individual message required for the computation is kept private. This is in contrast to the stricter notion of joint privacy that protects the entire set of identities of all messages used for the computation, including the correlations between these identities. The notion of individual privacy captures a broad set of practical applications. For example, such notion is relevant when the dataset contains information about individuals, each of them requires privacy guarantees for their data access patterns. We focus on the setting in which the required linear transformation is associated with a maximum distance separable (MDS) matrix. In particular, we require that the matrix of coefficients pertaining to the required linear combinations is the generator matrix of an MDS code. We establish lower and upper bounds on the capacity of PLT with individual privacy, where the capacity is defined as the supremum of all achievable download rates. We show that our bounds are tight under certain conditions.
Lower Body Rehabilitation Dataset and Model Optimization

Human pose estimation has enabled numerous applications by classifying and tracking body movements. Although a few open datasets have emerged to facilitate the evaluation of pose detection methods, they are too generic to benefit do-main specific applications such as physical therapy which has quantitative clinical metrics and requires precise differentiation and measurement. To address this issue, we construct the first human keypoints detection dataset for physical therapy, in particular lower body rehabilitation. The dataset consists of 1,885,637 distinctive human poses for 31 lower body rehab exercises, which are performed by 20 actors under the guidance of a licensed physical therapist. Their motion are captured with state of art of motion tracking system in both 3D and 2D to establish the ground truth. Furthermore, to optimize a number of deep learning models applied on this unique dataset, we utilize Extremely Efficient Spatial Pyramid (EESP) and attention mechanism to reduce the models’ computational complexity. Our experiment results show that the optimized models achieve comparable performance with nearly 4x reduction in complexity.
Gim edukasi berbasis android sebagai optimasi pembelajaran daring di masa pandemi Covid 19

The innovation in Information and Communication Technology (ICT)-based learning media is a critical learning alternative during the Covid-19 pandemic. One of the ICT-based learning media is android-based educational games. This research aims to develop android-based educational games on object-oriented programming (OOP) courses for Information Technology Education (ITE) students and to find out the feasibility of this product in online learning optimization. This research employed Research and Development (R&amp;D) with Analysis, Design, Develop, Implement, Evaluate (ADDIE) development model. Participants of this research were the fifth-semester ITE students. Questionnaires validated by learning material and media experts were tested on limited groups before being distributed to the participants. Results show that the feasibility level on the material content aspect is 93.2%, and the language aspect is 93.3%, referring to very feasible criteria. The feasibility level on the content quality is 90%, the design and audio aspects are 95%, and interaction and feedback are 93.75%, referring to very feasible criteria. The trial results of the limited group are 96% with very feasible criteria. These findings indicate that android-based educational games are feasible and can be implemented in the learning process.
Oort: Efficient Federated Learning via Guided Participant Selection

Federated Learning (FL) is an emerging direction in distributed machine learning (ML) that enables in-situ model training and testing on edge data. Despite having the same end goals as traditional ML, FL executions differ significantly in scale, spanning thousands to millions of participating devices. As a result, data characteristics and device capabilities vary widely across clients. Yet, existing efforts randomly select FL participants, which leads to poor model and system efficiency. In this paper, we propose Oort to improve the performance of federated training and testing with guided participant selection. With an aim to improve time-to-accuracy performance in model training, Oort prioritizes the use of those clients who have both data that offers the greatest utility in improving model accuracy and the capability to run training quickly. To enable FL developers to interpret their results in model testing, Oort enforces their requirements on the distribution of participant data while improving the duration of federated testing by cherry-picking clients. Our evaluation shows that, compared to existing participant selection mechanisms, Oort improves time-to-accuracy performance by 1.2x-14.1x and final model accuracy by 1.3%-9.8%, while efficiently enforcing developer-specified model testing criteria at the scale of millions of clients.
Formulaicity in constrained communication An intermodal approach

In this exploratory study bordering on corpus linguistics, formulaic language and studies on constrained communication (focusing on translation, interpreting, and L2) we aim to verify whether constrained texts found in the Polish-English component of an intermodal EPTIC corpus differ from native texts in terms of use of adjacent word combinations commonly known as bigrams and whether similar patterns can be found across spoken and written registers. To that end, we fit a Poisson regression model with fixed and random effects. The results show that the translated language variety contributes to the higher number of the most frequent bigram types in both spoken and written registers, and that the number of frequent bigrams in texts generally increases when the speech/source speech is delivered impromptu, but the effect is significant only for the written register. The findings reveal the considerable impact of individual variation on formulaicity as most of the bigram variation within both models is explained by text-specific random variables rather than fixed variables.
The Long and Winding Road towards FAIR Data as an Integral Component of the Computational Modelling and Dissemination of Chemistry

Abstract Fifty years of the author‘s activities as a “computational chemist” are described, linked by the theme of scientific data. For the first half of this period, data was often handled in a very impoverished way, appearing as in printed form as supporting information or as appendices in e. g. PhD theses and these methods resulted not only in considerable loss of information but caused severe and error‐prone difficulties in the re‐use of the data. The second period charts the explosive growth of the digital information era and describes the author‘s own involvement in developing some of the required infrastructures that are nowadays often taken for granted. This culminates in the present‐day increasing use of data repositories holding FAIR data, which is now firmly regarded as a first‐class citizen of the scientific processes. Many examples of these experiences are included in the essay for the readers to experiment with themselves.
Blockchain Technology in Payment Systems

The paper is devoted to the analysis of the practice and prospects of application of blockchain technology to provide the exercise of payment services. In particular, it is noted that this technology can be used in the architecture of payment systems, as well as as the technological basis of payment instruments. In addition, blockchain can be used in areas directly related to payment services (for example, for customer identification, currency exchange operations, etc.). The author defines a number of concepts, in particular “peering payment system”, “mining”, “cryptocurrency wallet”. The paper highlights the necessity to differentiate between the actual peering payment systems (networks) and payment services operating with the use of blockchain technology. The latter can be decentralized in terms of how information is transmitted, but not managed. The presence of an entity controlling, administering or otherwise managing individual processes within such a system does not allow it to be regarded as truly decentralized — peering networks are based on the equality of all participants. According to the results of the study, it is concluded that the blockchain technology indeed has a high potential of practical application in the payment field. Meanwhile, currently a cryptocurrency wallet has significant limitations of practical application for the organization of mass payments. Therefore, this technology should not be expected to replace traditional payment institutions in the nearest future. It can be predicted that blockchain will be just implemented into the existing payment infrastructure, rather than replace it completely.
Projection Robust Wasserstein Barycenters

Collecting and aggregating information from several probability measures or histograms is a fundamental task in machine learning. One of the popular solution methods for this task is to compute the barycenter of the probability measures under the Wasserstein metric. However, approximating the Wasserstein barycenter is numerically challenging because of the curse of dimensionality. This paper proposes the projection robust Wasserstein barycenter (PRWB) that has the potential to mitigate the curse of dimensionality. Since PRWB is numerically very challenging to solve, we further propose a relaxed PRWB (RPRWB) model, which is more tractable. The RPRWB projects the probability measures onto a lower-dimensional subspace that maximizes the Wasserstein barycenter objective. The resulting problem is a max-min problem over the Stiefel manifold. By combining the iterative Bregman projection algorithm and Riemannian optimization, we propose two new algorithms for computing the RPRWB. The complexity of arithmetic operations of the proposed algorithms for obtaining an $\epsilon$-stationary solution is analyzed. We incorporate the RPRWB into a discrete distribution clustering algorithm, and the numerical results on real text datasets confirm that our RPRWB model helps improve the clustering performance significantly.
Post COVID-19 implementation of a bidirectional counter with reduced complexity for people counting application

Abstract This paper presents a system for counting people through infrared detection. The system tallies the count and update based on people moving in/out through the supervised area/premise. The counter requires two steps: detection coming in and leaving the area. Obstacle detection from infrared sensor updates the counters and the current value will be displayed in a thin-film-transistor module. With minimal hardware configuration, the developed system is robust and easier to install.
Research on High Precision Algorithm of Scale-Spatial in Computer Vision

Abstract The intra class differences of object categories will bring great challenges to the image matching algorithm in machine vision. In order to guarantee the accuracy and quality of image matching algorithm, it is necessary to accurately define and understand the scene and semantics of the image, so as to find out the typical similar features of the image. On account of this, this paper first analyses the role and traits of scale-spatial theory, then studies the high-precision algorithm of scale-spatial in computer vision, and finally gives the extremum detection of scale-spatial, the determination of extremum position and the generation of feature point descriptor.
Towards Certifying L-infinity Robustness using Neural Networks with L-inf-dist Neurons

It is well-known that standard neural networks, even with a high classification accuracy, are vulnerable to small $\ell_\infty$-norm bounded adversarial perturbations. Although many attempts have been made, most previous works either can only provide empirical verification of the defense to a particular attack method, or can only develop a certified guarantee of the model robustness in limited scenarios. In this paper, we seek for a new approach to develop a theoretically principled neural network that inherently resists $\ell_\infty$ perturbations. In particular, we design a novel neuron that uses $\ell_\infty$-distance as its basic operation (which we call $\ell_\infty$-dist neuron), and show that any neural network constructed with $\ell_\infty$-dist neurons (called $\ell_{\infty}$-dist net) is naturally a 1-Lipschitz function with respect to $\ell_\infty$-norm. This directly provides a rigorous guarantee of the certified robustness based on the margin of prediction outputs. We then prove that such networks have enough expressive power to approximate any 1-Lipschitz function with robust generalization guarantee. We further provide a holistic training strategy that can greatly alleviate optimization difficulties. Experimental results show that using $\ell_{\infty}$-dist nets as basic building blocks, we consistently achieve state-of-the-art performance on commonly used datasets: 93.09% certified accuracy on MNIST ($\epsilon=0.3$), 35.42% on CIFAR-10 ($\epsilon=8/255$) and 16.31% on TinyImageNet ($\epsilon=1/255$).
Understanding Authentic Leadership Style: The Satya Nadella Microsoft Approach

The paper endeavours to analyze authentic dimensions of Leadership with Satya Nadella's leadership and his aggressive strategy leading to an increase in Microsoft's profitability.A total of 7 interviews of Nadella from 2014 to 2020 were coded in NVivo to prepare Word query, TreeMap and cluster analysis.Also, besides tweets associated with #Satya Nadella were captured using NCapture and exported to NVivo to obtain the sentiments by performing sentiment analysis.The Twitter analysis of Nadella's personality is majorly positive and neutral which reflects his success and influence across the globe.The traits of Nadella are linked with authentic leadership style.The study discusses the latent dimensions for becoming a successful authentic leader that is highly recommended for leaders to boost their leadership skills and increase organization's profitability in this competitive world.
Edukasi Tips Memilih, Mengolah, dan Menyajikan Makanan yang Aman pada Mahasiswa STIKES Graha Medika Secara Daring Menggunakan Aplikasi Zoom Cloud Meetings

Efforts to achieve Indonesian citizen with quality are closely related to food and nutrition factors. Fulfillment of food and nutrition adequacy can be seen from the level of food provided and the quality and diversity of food and nutrition available. Approximately 70% of cases of food poisoning in the world are caused by ready-to-eat food, namely processed food, especially by catering businesses, restaurants, canteens, restaurants, or street food. Food poisoning is usually caused by the food being contaminated with bacteria or microbes. The purpose of this community service is to increase students' knowledge and understanding regarding selecting, processing, and serving safe food. Methods used consisted of counseling and health education on tips for selecting, processing, and serving food using the zoom cloud meeting application, with the target of Graha Medika Kotamobagu Health Institute students. The activity was held on Monday, October 12, 2020. The results of the community service activities showed that of the 34 students who participated in the activity, 80% of students knew and could mention tips on choosing, processing, and serving safe food. The implementation of social service is very important to increase knowledge in selecting, processing and serving food that is safe for consumption.
PAC-Bayes, MAC-Bayes and Conditional Mutual Information: Fast rate bounds that handle general VC classes

We give a novel, unified derivation of conditional PAC-Bayesian and mutual information (MI) generalization bounds. We derive conditional MI bounds as an instance, with special choice of prior, of conditional MAC-Bayesian (Mean Approximately Correct) bounds, itself derived from conditional PAC-Bayesian bounds, where `conditional' means that one can use priors conditioned on a joint training and ghost sample. This allows us to get nontrivial PAC-Bayes and MI-style bounds for general VC classes, something recently shown to be impossible with standard PAC-Bayesian/MI bounds. Second, it allows us to get faster rates of order $O \left(({\text{KL}}/n)^γ\right)$ for $γ&gt; 1/2$ if a Bernstein condition holds and for exp-concave losses (with $γ=1$), which is impossible with both standard PAC-Bayes generalization and MI bounds. Our work extends the recent work by Steinke and Zakynthinou [2020] who handle MI with VC but neither PAC-Bayes nor fast rates, the recent work of Hellström and Durisi [2020] who extend the latter to the PAC-Bayes setting via a unifying exponential inequality, and Mhammedi et al. [2019] who initiated fast rate PAC-Bayes generalization error bounds but handle neither MI nor general VC classes.
A new model for automatic text classification

AbstractIn this paper, a new method for automatic classification of texts is presented. This system includes two phases; text processing and text categorization. In the first phase, various indexing criteria such as bigram, trigram and quad-gram are presented to extract the properties. Then, in the second phase, the W-SMO machine learning algorithm is used to train the system. In order to evaluate and compare the results of the two criteria of accuracy and readability, Macro-F1 and Micro-F1 have been calculated for different indexing methods. The results of experiments performed on 7676 standard text documents of Reuters showed that our proposed method has the best performance compared to the W-j48, Naïve Bayes, K-NN and Decision Tree algorithms.
Toward Fully‐Fledged Quantum and Classical Communication Over Deployed Fiber with Up‐Conversion Module

Quantum key distribution (QKD), the distribution of quantum secured keys useful for data encryption, is expected to have a crucial impact in the next decades. However, although the notable achievements accomplished in the last twenty years, many practical and serious challenges are limiting the full deployment of this novel quantum technology in the current telecommunication infrastructures. In particular, the co-propagation of quantum signals and high-speed data traffic within the same optical fiber, is not completely resolved, due to the intrinsic noise caused by the high intensity of the classical signals. As a consequence, current co-propagation schemes limit the amount of classical optical power in order to reduce the overall link noise. However, this ad-hoc solution restrains the overall range of possibilities for a large-scale QKD deployment. Here, we propose and demonstrate a new method, based on up-conversion assisted receiver, for co-propagating classical light and QKD signals. In addition, we compare the performances of this scheme with an off-the-shelf quantum receiver, equipped with a standard InGaAs detector, over different lengths of an installed fiber link. Our proposal exhibits higher tolerance for noise in comparison to the standard receiver, thus enabling the distribution of secret keys in the condition of 4 dB-higher classical power.
Nutri-bullets Hybrid: Consensual Multi-document Summarization

We present a method for generating comparative summaries that highlights similarities and contradictions in input documents.The key challenge in creating such summaries is the lack of large parallel training data required for training typical summarization systems.To this end, we introduce a hybrid generation approach inspired by traditional concept-to-text systems.To enable accurate comparison between different sources, the model first learns to extract pertinent relations from input documents.The content planning component uses deterministic operators to aggregate these relations after identifying a subset for inclusion into a summary.The surface realization component lexicalizes this information using a text-infilling language model.By separately modeling content selection and realization, we can effectively train them with limited annotations.We implemented and tested the model in the domain of nutrition and health -rife with inconsistencies.Compared to conventional methods, our framework leads to more faithful, relevant and aggregation-sensitive summarization -while being equally fluent. 1 Transformer (baseline) * Whole -grain cereals may protect against obesity , diabetes and certain cancers.However , more research is needed .* Whole grains , such as mozambican grass , are safe to eat with no serious side effects .* Whole -grain cereals may protect against obesity , diabetes and certain cancers.However , more research is needed .* Whole grains , such as blueberries , are likely safe to eat with no serious side effects .* Whole grains are safe to eat.However , people with type 2 diabetes should avoid whole grains .* Whole grains are lower in carbs than whole grains , making them a good choice for people with type 2 diabetes.Our Method * Whole grains has been shown to lower weight gain and improve various type 2 diabetes risk factors .* Whole grains has been shown to lower insulin resistance and improve various cancer risk factors .* Whole grains has been linked to several other potential health benefits , such as improved CVD risk , eyesight , and memory.However , more studies are needed to draw stronger conclusions.* There is some evidence , in both animals and humans , that whole grains can reduce mortality by regulating the hormone ghrelin.