Reviewerly/exHarmony huggingface dataset:
Robust Models Are More Interpretable Because Attributions Look Normal

Recent work has found that adversarially-robust deep networks used for image classification are more interpretable: their feature attributions tend to be sharper, and are more concentrated on the objects associated with the image's ground-truth class. We show that smooth decision boundaries play an important role in this enhanced interpretability, as the model's input gradients around data points will more closely align with boundaries' normal vectors when they are smooth. Thus, because robust models have smoother boundaries, the results of gradient-based attribution methods, like Integrated Gradients and DeepLift, will capture more accurate information about nearby decision boundaries. This understanding of robust interpretability leads to our second contribution: \emph{boundary attributions}, which aggregate information about the normal vectors of local decision boundaries to explain a classification outcome. We show that by leveraging the key factors underpinning robust interpretability, boundary attributions produce sharper, more concentrated visual explanations -- even on non-robust models. Any example implementation can be found at \url{https://github.com/zifanw/boundary}.
MARI STATE PEDAGOGICAL INSTITUTE NAMED AFTER N.K. KRUPSKAYA IN 1992–2008: THE END OF HISTORY

The relevance of the research topic is due to the fact that in 2021 the Republic of Mari El celebrates the 90th anniversary of higher education. The Mari State Pedagogical Institute named after N.K. Krupskaya in the 1990s–2000s was represented by the following faculties: historical and philological, physical and mathematical, industrial and pedagogical, foreign languages, primary classes, pedagogy and psychology, physical culture, supplementary pedagogical professions, pre-university training. In 1992, the Law of the Russian Federation «On Education» was adopted, which determined restructuring of the higher education system as a whole and the Mari State Institute named after N.K. Krupskaya in particular. Until 2000, the state funding of the institute was used only for sharply reduced salaries and scholarships of students. Despite financial difficulties, the staff of the Mari State Institute named after N.K. Krupskaya achieved good results in the 1990s. The Institute was successfully attested. In 1997, he was attested in all specialties for the period of five years. In 1998, the university underwent an accreditation process. In 2003, the Institute was also successfully attested, passed licensing and accreditation. In general, the state attestation commissions noted a good level of specialists’ training. In 2008 The Mari State Pedagogical Institute named after N.K. Krupskaya merged with the Mari State University. The classical University became the legal successor of the pedagogical institute. The Mari State Pedagogical Institute named after N.K. Krupskaya during the period of its activity since 1931, has trained more than 40 000 specialists with higher professional education for the schools of the republic. It was the only university in the republic that trained specialists with the qualification of «teacher».
Bringing Generalization to Deep Multi-view Detection.

Multi-view Detection (MVD) is highly effective for occlusion reasoning and is a mainstream solution in various applications that require accurate top-view occupancy maps. While recent works using deep learning have made significant advances in the field, they have overlooked the generalization aspect, which makes them \emph{impractical for real-world deployment}. The key novelty of our work is to \emph{formalize} three critical forms of generalization and \emph{propose experiments to investigate them}: i) generalization across a varying number of cameras, ii) generalization with varying camera positions, and finally, iii) generalization to new scenes. We find that existing \sota models show poor generalization by overfitting to a single scene and camera configuration. We propose modifications in terms of pre-training, pooling strategy, regularization, and loss function to an existing state-of-the-art framework, leading to successful generalization across new camera configurations and new scenes. We perform a comprehensive set of experiments on the \wildtrack and \multiviewx datasets to (a) motivate the necessity to evaluate MVD methods on generalization abilities and (b) demonstrate the efficacy of the proposed approach. The code is publicly available at \url{this https URL}
LoOp: Looking for Optimal Hard Negative Embeddings for Deep Metric Learning.

Deep metric learning has been effectively used to learn distance metrics for different visual tasks like image retrieval, clustering, etc. In order to aid the training process, existing methods either use a hard mining strategy to extract the most informative samples or seek to generate hard synthetics using an additional network. Such approaches face different challenges and can lead to biased embeddings in the former case, and (i) harder optimization (ii) slower training speed (iii) higher model complexity in the latter case. In order to overcome these challenges, we propose a novel approach that looks for optimal hard negatives (LoOp) in the embedding space, taking full advantage of each tuple by calculating the minimum distance between a pair of positives and a pair of negatives. Unlike mining-based methods, our approach considers the entire space between pairs of embeddings to calculate the optimal hard negatives. Extensive experiments combining our approach and representative metric learning losses reveal a significant boost in performance on three benchmark datasets.
Consistent Counterfactuals for Deep Models

Counterfactual examples are one of the most commonly-cited methods for explaining the predictions of machine learning models in key areas such as finance and medical diagnosis. Counterfactuals are often discussed under the assumption that the model on which they will be used is static, but in deployment models may be periodically retrained or fine-tuned. This paper studies the consistency of model prediction on counterfactual examples in deep networks under small changes to initial training conditions, such as weight initialization and leave-one-out variations in data, as often occurs during model deployment. We demonstrate experimentally that counterfactual examples for deep models are often inconsistent across such small changes, and that increasing the cost of the counterfactual, a stability-enhancing mitigation suggested by prior work in the context of simpler models, is not a reliable heuristic in deep networks. Rather, our analysis shows that a model's local Lipschitz continuity around the counterfactual is key to its consistency across related models. To this end, we propose Stable Neighbor Search as a way to generate more consistent counterfactual explanations, and illustrate the effectiveness of this approach on several benchmark datasets.
Role of Raised Bed Technique on Water Productivity and Wheat and under the conditions of irrigated agriculture in central Iraq

A field experiment was conducted during the agricultural season of 2017-2018 in the site is located at 82" 10' 33ᵒ North, and longitude 51" 32' 44ᵒ , East at an altitude of 33m above sea level. In order to evaluate the productivity of irrigation water by method of cultivation on irrigated furrows, when growing crops of wheat. Two factors were experienced in the cultivation of wheat, The first factor is the method of cultivation of five treatments were used included: Treatment of the cultivation of wheat in basins (B), treatment of the cultivation of wheat on bed with 50 cm width (S1), 60cm (S2), 70cm (S3) and 80cm (S4), The second factor is irrigation levels depletion included: 40, 60 and 80% of available water coded as W1, W2 and W3, respectively,. The results were as follows: Actual water consumption values for basin and bed treatments reached 389, 384, 365, 369.20, 367and 341mm for treatment BW1, BW2, BW3,SW1, SW2 and SW3 respectively. Treatment of wheat crops on bed with 80 cm width gave the highest average field water use efficiency reached 6.84 kg m-3 while BW3 treatment gave lowest average field water use efficiency reached 1.47 kg m-3. The highest average crop water use efficiency were found in S4W1ٚ S4W2 reached 2.06 and 2.07 kg m-3, respectively, It was lowest value for crop water use efficiency at BW3 reached 1.06 kg m-3. The percentage increase in the average efficiency of crop water using for bed treatments 40.37, 57.80, 73.39 and 85.32% for treatments S1, S2, S3 and S4 respectively Compared with basin treatment (B).The highest average total grains yield for Treatment of wheat crops on bed with 80 cm width was 7253kg ha-1 , Irrigation levels also affected the total grains yield, irrigation treatment of depletion 40% gave highest average 6300 kg ha-1 , That did not differ significantly from the irrigation treatments of depletion 60% In which the total grain yield was reached 6228 kg ha-1 ,In the interference factors between the cultivation method and the irrigation levels, the interference factors excelled S4W1, S3W2 and S4W2 Without significant differences in the total yield average 7600,7310 and 7600 kg ha-1, Respectively
Nana-HDR: A Non-attentive Non-autoregressive Hybrid Model for TTS

This paper presents Nana-HDR, a new non-attentive non-autoregressive model with hybrid Transformer-based Dense-fuse encoder and RNN-based decoder for TTS. It mainly consists of three parts: Firstly, a novel Dense-fuse encoder with dense connections between basic Transformer blocks for coarse feature fusion and a multi-head attention layer for fine feature fusion. Secondly, a single-layer non-autoregressive RNN-based decoder. Thirdly, a duration predictor instead of an attention model that connects the above hybrid encoder and decoder. Experiments indicate that Nana-HDR gives full play to the advantages of each component, such as strong text encoding ability of Transformer-based encoder, stateful decoding without being bothered by exposure bias and local information preference, and stable alignment provided by duration predictor. Due to these advantages, Nana-HDR achieves competitive performance in naturalness and robustness on two Mandarin corpora.
Need of Face-Mask Detector Model to Tackle COVID-19 Situation in India

The COVID - 19 epidemic is affecting everyone whether rich or poor, strong or weak. The use of a mask can surely aid in the control of the spread of the virus. This becomes even more important for a country like India where the total population is above 135+ crores and due to lockdowns and various other reasons, a huge percentage of the population has dipped below the poverty line and health standards of the poor are declining day by day and all these factors are contributing to an uncontrolled outbreak of coronavirus in India. As the government has already mandated the use of masks as a stop-gap measure to control the pandemic but due to the carelessness of the people they can be categorized into 3 groups based on the use of a mask. Specifically, a person who is properly wearing a mask, or is wearing a mask but improperly, or is not wearing a mask at all. By deep learning, we can make a model which detects masks accurately without human-to-human interactions and can help in controlling this corona pandemic. The Face mask detection system algorithm can be used to detect faces in still images as well as live video streaming and can tell whether the mask is worn properly or not. The mask detector model used should be as flexible as possible and on the same hand simple in structure, so that it can be used by a camera monitoring 1 person at a time or by a public CCTV monitoring hundreds of people at a time. Considering mass screening is possible, it may be utilized in congested areas such as transport hubs, bus stops, supermarkets, sidewalks, mall entrances, schools, and campuses. We can ensure that a person wears the face mask correctly and in a proper way and contribute to minimizing the spread of this destructive virus.
Code Polymorphism Meets Code Encryption: Confidentiality and Side-channel Protection of Software Components

In this paper, we consider that, in practice, attack scenarios involving side-channel analysis combine two successive phases:an analysis phase, targeting the extraction of information about the target and the identification of possible vulnerabilities;and an exploitation phase, applying attack techniques on candidate vulnerabilities. We advocate that protections need to coverthese two phases in order to be effective against real-life attacks. We present PolEn, a toolchain and a processor architecturethat combine countermeasures in order to provide an effective mitigation of side-channel attacks: as a countermeasure againstthe analysis phase, our approach considers the use of code encryption; as a countermeasure against the exploitation phase,our approach considers the use of code polymorphism, because it relies on runtime code generation, and its combinationwith code encryption is particularly challenging. Code encryption is supported by a processor extension such that machineinstructions are only decrypted inside the CPU, which effectively prevents reverse engineering or any extraction of usefulinformation from memory dumps. Code polymorphism is implemented by software means. It regularly changes the observablebehaviour of the program, making it unpredictable for an attacker, hence reducing the possibility to exploit side-channelleakages. We present a prototype implementation, based on the RISC-V Spike simulator and a modified LLVM toolchain. Inour experimental evaluation, we illustrate that PolEn effectively reduces side-channel leakages. For the protected functionsevaluated, static memory use increases by a factor of 5 to 22, corresponding to the joint application of code encryption andcode polymorphism. The overhead, in terms of execution time, ranges between a factor of 1.8 and 4.6.
Optimal, truthful, and private securities lending

We consider a fundamental dynamic allocation problem motivated by the problem of securities lending in financial markets, the mechanism underlying the short selling of stocks. A lender would like to distribute a finite number of identical copies of some scarce resource to n clients, each of whom has a private demand that is unknown to the lender. The lender would like to maximize the usage of the resource --- avoiding allocating more to a client than her true demand --- but is constrained to sell the resource at a pre-specified price per unit, and thus cannot use prices to incentivize truthful reporting. We first show that the Bayesian optimal algorithm for the one-shot problem --- which maximizes the resource's expected usage according to the posterior expectation of demand, given reports --- actually incentivizes truthful reporting as a dominant strategy. Because true demands in the securities lending problem are often sensitive information that the client would like to hide from competitors, we then consider the problem under the additional desideratum of (joint) differential privacy. We give an algorithm, based on simple dynamics for computing market equilibria, that is simultaneously private, approximately optimal, and approximately dominant-strategy truthful. Finally, we leverage this private algorithm to construct an approximately truthful, optimal mechanism for the extensive form multi-round auction where the lender does not have access to the true joint distributions between clients' requests and demands.
Estimation of the Wigner distribution of single-mode Gaussian states: A comparative study

In this work, we consider the estimation of single mode Gaussian states using four different measurement schemes namely: i) homodyne measurement, ii) sequential measurement, iii) Arthurs-Kelly scheme, and iv) heterodyne measurement, with a view to compare their relative performance. To that end, we work in the phase space formalism, specifically at the covariance matrix level, which provides an elegant and intuitive way to explicitly carry out involved calculations. We show that the optimal performance of the Arthurs-Kelly scheme and the sequential measurement is equal to the heterodyne measurement. While the heterodyne measurement outperforms the homodyne measurement in the mean estimation of squeezed state ensemble, the homodyne measurement outperforms the heterodyne measurement for variance estimation of squeezed state ensemble up to a certain range of squeezing parameter. We then modify the Hamiltonian in the Arthurs-Kelly scheme, such that the two meters can have correlations and show that the optimal performance is achieved when the meters are uncorrelated. We expect that the results will be useful in analyzing various quantum information and quantum communication protocols.
Predicting COVID-19 Patient Shielding: A Comprehensive Study

There are many ways machine learning and big data analytics are used in the fight against the COVID-19 pandemic, including predictions, risk management, diagnostics, and prevention. This study focuses on predicting COVID-19 patient shielding -- identifying and protecting patients who are clinically extremely vulnerable from coronavirus. This study focuses on techniques used for the multi-label classification of medical text. Using the information published by the United Kingdom NHS and the World Health Organisation, we present a novel approach to predicting COVID-19 patient shielding as a multi-label classification problem. We use publicly available, de-identified ICU medical text data for our experiments. The labels are derived from the published COVID-19 patient shielding data. We present an extensive comparison across 12 multi-label classifiers from the simple binary relevance to neural networks and the most recent transformers. To the best of our knowledge this is the first comprehensive study, where such a range of multi-label classifiers for medical text are considered. We highlight the benefits of various approaches, and argue that, for the task at hand, both predictive accuracy and processing time are essential.
Plasmodium Detection using Machine Learning

Plasmodium is one of India's biggest public health problems. Early prediction of a malaria epidemic is that the secret to malaria morbidity management, mortality as well as reducing the risk of malaria transmission in the community will benefit politicians, health care providers, medical officers, health ministry and other health organizations to better target medical resources to areas of greatest need. In this project, we acquire data sets from hospital databases, which have the information about the causes of malaria, and the images of cells infected with malaria. We then analyze these data sets and feed them to our machine-learning model. Here we are using contour detection and random forest algorithms for training the model and predicting the output
A Novel and Dynamic Framework for People Counting

A real time person counting from video records is a primary structure coalition for some applications in dense urban areas. Practically speaking, this undertaking typically experiences numerous issues, such as the absence of real time processing of the recorded videos or the event of errors because of insignificant individuals being counted. To defeat the above issues, we propose a novel and dynamic framework for real time people counting named YOLO-PC (People Counting based on YOLO).
A generalised multipath delayed-choice experiment on a large-scale quantum nanophotonic chip

Bohr’s complementarity is one central tenet of quantum physics. The paradoxical wave-particle duality of quantum matters and photons has been tested in Young’s double-slit (double-path) interferometers. The object exclusively exhibits wave and particle nature, depending measurement apparatus that can be delayed chosen to rule out too-naive interpretations of quantum complementarity. All experiments to date have been implemented in the double-path framework, while it is of fundamental interests to study complementarity in multipath interferometric systems. Here we demonstrate generalised multipath wave-particle duality in a quantum delayed-choice experiment, implemented by large-scale silicon-integrated multipath interferometers. Single-photon displays sophisticated transitions between wave and particle characters, determined by the choice of quantum-controlled generalised Hadamard operations. We characterise particle-nature by multimode which-path information and wave-nature by multipath coherence of interference, and demonstrate the generalisation of Bohr’s multipath duality relation. Our work provides deep insights into multidimensional quantum physics and benchmarks controllability of integrated photonic quantum technology.
On sets of linear forms of maximal complexity

We present a uniform description of sets of $m$ linear forms in $n$ variables over the field of rational numbers whose computation requires $m(n - 1)$ additions.
A Mobile Anti-Phishing System Using Linkguard Algorithm

Phishing is a type of cyber-attack where the attacker deploys a combination of social engineering and technological skills to trick users into revealing private information like bank account details, usernames and passwords by creating an imitation of an existing web page. This research developed an Android-based anti-phishing system leveraging on the ubiquity of mobile devices and their increasing applications for business and personal purposes. The system was developed by implementing an end-host based algorithm called the Linkguard algorithm which is effective in detecting and preventing both known and unknown phishing attacks. A total of over 500 links which was a blend of both phishing and non-phishing links were collected from Phishtank and Alexa to validate the proposed system which achieved an accuracy of 96%. It is recommended that concerted efforts should be geared towards continuously sensitizing users to improve their phishing awareness as this cybercrime keeps evolving and users play a very crucial role in mitigating it. Keywords— android, cybercrime, Linkguard algorithm, mobile, phishing
An enumeration of 1-perfect ternary codes

We study codes with parameters of the ternary Hamming $(n=(3^m-1)/2,3^{n-m},3)$ code, i.e., ternary $1$-perfect codes. The rank of the code is defined to be the dimension of its affine span. We characterize ternary $1$-perfect codes of rank $n-m+1$, count their number, and prove that all such codes can be obtained from each other by a sequence of two-coordinate switchings. We enumerate ternary $1$-perfect codes of length $13$ obtained by concatenation from codes of lengths $9$ and $4$; we find that there are $93241327$ equivalence classes of such codes. Keywords: perfect codes, ternary codes, concatenation, switching.
Encryption and Decryption Timing Computation of Different Sizes of JPEG Image

With the increasing popularity of the cloud, web service technology has emerged as a popular way for building distributed applications involving distributed databases. It is the next generation technology in the long journey from functions to objects to components to services. In this paper, encryption and decryption technique is used for timing computation of different sizes of JPEG images which is based on enhanced AES algorithm. It is improve its performance by reducing the number of rounds and by increasing the block length with fixed length during encryption and decryption and also give the general introduction about cryptography.