Reviewerly/exHarmony huggingface dataset:
Language Modeling, Lexical Translation, Reordering: The Training Process of NMT through the Lens of Classical SMT

Differently from the traditional statistical MT that decomposes the translation task into distinct separately learned components, neural machine translation uses a single neural network to model the entire translation process.Despite neural machine translation being defacto standard, it is still not clear how NMT models acquire different competences over the course of training, and how this mirrors the different models in traditional SMT.In this work, we look at the competences related to three core SMT components and find that during training, NMT first focuses on learning targetside language modeling, then improves translation quality approaching word-by-word translation, and finally learns more complicated reordering patterns.We show that this behavior holds for several models and language pairs.Additionally, we explain how such an understanding of the training process can be useful in practice and, as an example, show how it can be used to improve vanilla nonautoregressive neural machine translation by guiding teacher model selection.
Multi-Goal Multi-Agent Path Finding via Decoupled and Integrated Goal Vertex Ordering

We introduce multi-goal multi agent path finding (MG-MAPF) which generalizes the standard discrete multi-agent path finding (MAPF) problem. While the task in MAPF is to navigate agents in an undirected graph from their starting vertices to one individual goal vertex per agent, MG-MAPF assigns each agent multiple goal vertices and the task is to visit each of them at least once. Solving MG-MAPF not only requires finding collision free paths for individual agents but also determining the order of visiting agent's goal vertices so that common objectives like the sum-of-costs are optimized. We suggest two novel algorithms using different paradigms to address MG-MAPF: a heuristic search-based algorithm called Hamiltonian-CBS (HCBS) and a compilation-based algorithm built using the satisfiability modulo theories (SMT), called SMT-Hamiltonian-CBS (SMT-HCBS).
Impact of PV System Tracking on Energy Production and Climate Change

Green energy by PV systems reduces the dependence on fossil fuel-based power plants. Maximizing green energy to meet the demand reduces the burden on conventional power plants, hence lesser burning and greenhouse gases (GHG) emissions. For this purpose, this study draws a relationship between tracking schemes of the PV systems to GHG mitigation potential. The best fit location for detailed analyses is selected among the 15 most populous cities of Australia. The solar radiation potential is increased to 7.78 kWh/m2/d through dual axes tracking compared to 7.54, 6.82, 5.94, 5.73 kWh/m2/d through the one axis, azimuth based, fixed-tilted, and fixed-horizontal surface schemes, respectively. Through the dual axes tracking scheme, a 1 MW PV system per annum energy output avoids the burning of 796,065.3 L of gasoline, 4308.7 barrels of crude oil which is equal to the mitigation of 1852.7 tCO2 equivalent GHGs. Concisely, the PV system, through its green energy output, can avoid the release of greenhouse gases from fossil-fuel plants to tackle climate change more effectively.
REVIEW OF THE SPIRAL MODEL AND ITS APPLICATIONS

Software Development Life cycle is the process consisting of many phases used to develop a software and for eliciting the user’s requirements. There are a number of models available that are used according to the suitability of the situation. In this paper we discuss the spiral model and its application in various scenarios such as website development, mobile application development and traffic management system.
Sign language dactyl recognition based on machine learning algorithms

In the course of our research work, the American, Russian and Turkish sign languages were analyzed. The program of recognition of the Kazakh dactylic sign language with the use of machine learning methods is implemented. A dataset of 5000 images was formed for each gesture, gesture recognition algorithms were applied, such as Random Forest, Support Vector Machine, Extreme Gradient Boosting, while two data types were combined into one database, which caused a change in the architecture of the system as a whole. The quality of the algorithms was also evaluated. The research work was carried out due to the fact that scientific work in the field of developing a system for recognizing the Kazakh language of sign dactyls is currently insufficient for a complete representation of the language. There are specific letters in the Kazakh language, because of the peculiarities of the spelling of the language, problems arise when developing recognition systems for the Kazakh sign language. The results of the work showed that the Support Vector Machine and Extreme Gradient Boosting algorithms are superior in real-time performance, but the Random Forest algorithm has high recognition accuracy. As a result, the accuracy of the classification algorithms was 98.86 % for Random Forest, 98.68 % for Support Vector Machine and 98.54 % for Extreme Gradient Boosting. Also, the evaluation of the quality of the work of classical algorithms has high indicators. The practical significance of this work lies in the fact that scientific research in the field of gesture recognition with the updated alphabet of the Kazakh language has not yet been conducted and the results of this work can be used by other researchers to conduct further research related to the recognition of the Kazakh dactyl sign language, as well as by researchers, engaged in the development of the international sign language
Survey: (Blockchain-Based Solution for COVID-19 and Smart Contract Healthcare Certification)

The year 2020 has shown the quick spread and devastating effect of Coronavirus (COVID-19) on the world economy, health, and human life. Combating the COVID-19 pandemic is crucial. Blockchain technology can assist in the fight against the COVID-19 pandemic by assuring safe and reliable medical supplies, accurate identification of virus hot spots, and establishing data provenance to verify genuine personal protective equipment that is decentralized, reliable, traceable, and transparent. A major proportion of current healthcare systems are centralized and are short on privacy and information necessary for the detection of fraud connected to the certification by the COVID-19 vaccine. The quick vaccination rollout and the deployment of a global vaccination campaign are crucial and dependent on the availability of transparent and operational distribution chains, which may be audited by all the necessary parties. Blockchain technology might also be used to assure transparent monitoring, storage, and distribution of COVID19 vaccinations. Smart contracts are built for monitoring and tracking the proper circumstances of vaccine distribution in comparison to safe handling of vaccine producers, informing all network peers. This study aims to investigate the effectiveness of block chain and smart contracts in the health care system. The article presents a model of the decentralized infrastructure of the health care system. The blockchain and intelligent contract technology provided has shown promising results. The proposed solution is economically feasible and ensures data integrity, security, transparency, and traceability. Future work aims to expand the functionalities of smart contracts and the blockchain technology.
Knowing False Negatives: An Adversarial Training Method for Distantly Supervised Relation Extraction

Distantly supervised relation extraction (RE) automatically aligns unstructured text with relation instances in a knowledge base (KB).Due to the incompleteness of current KBs, sentences implying certain relations may be annotated as N/A instances, which causes the socalled false negative (FN) problem.Current RE methods usually overlook this problem, inducing improper biases in both training and testing procedures.To address this issue, we propose a two-stage approach.First, it finds out possible FN samples by heuristically leveraging the memory mechanism of deep neural networks.Then, it aligns those unlabeled data with the training data into a unified feature space by adversarial training to assign pseudo labels and further utilize the information contained in them.Experiments on two wildlyused benchmark datasets demonstrate the effectiveness of our approach.
De-identification is Insufficient to Protect Student Privacy, or – What Can a Field Trip Reveal?

Learning analytics have the potential to improve teaching and learning in K–12 education, but as student data is increasingly being collected and transferred for the purpose of analysis, it is important to take measures that will protect student privacy. A common approach to achieve this goal is the de-identification of the data, meaning the removal of personal details that can reveal student identity. However, as we demonstrate, de-identification alone is not a complete solution. We show how we can discover sensitive information about students by linking de-identified datasets with publicly available school data, using unsupervised machine learning techniques. This underlines that de-identification alone is insufficient if we wish to further learning analytics in K–12 without compromising student privacy.
Object Detection Approaches in Images: A Weighted Scoring Model based Comparative Study

Computer vision is a branch of artificial intelligence that trains computers to acquire high-level understanding of images and videos.Some of the most wellknown areas in Computer Vision are object detection, object tracking and motion estimation among others.Our focus in this paper concerns object detection subarea of computer vision which aims at recognizing instances of predefined sets of objects classes using bounding boxes or object segmentation.Object detection relies on various algorithms belonging to various families that differs in term of speed and quality of results.Hence, we propose in this paper to provide a comparative study of these algorithms based on a set of criteria.In this comparative study we will start by presenting each of these algorithms, selecting a set of criteria for comparison and applying a comparative methodology to get results.The methodology we chose to this purpose is called WSM (Weighted Scoring Model) which fits exactly our needs.Indeed, WSM method allows us to assign a weight to each of our criterion to calculate a final score of each of our compared algorithms.The obtained results reveal the weaknesses and the strengths of each one of them and opened breaches for their future enhancement.
Implicit consensus clustering from multiple graphs

Abstract Dealing with relational learning generally relies on tools modeling relational data. An undirected graph can represent these data with vertices depicting entities and edges describing the relationships between the entities. These relationships can be well represented by multiple undirected graphs over the same set of vertices with edges arising from different graphs catching heterogeneous relations. The vertices of those networks are often structured in unknown clusters with varying properties of connectivity. These multiple graphs can be structured as a three-way tensor, where each slice of tensor depicts a graph which is represented by a count data matrix. To extract relevant clusters, we propose an appropriate model-based co-clustering capable of dealing with multiple graphs. The proposed model can be seen as a suitable tensor extension of mixture models of graphs, while the obtained co-clustering can be treated as a consensus clustering of nodes from multiple graphs. Applications on real datasets and comparisons with multi-view clustering and tensor decomposition methods show the interest of our contribution.
Research on the Impact of Information Security Certification and Concealment on Financial Performance

With the rapid development of information technology, information security has been gaining attention. The International Organization for Standardization (ISO) has issued international standards and technical reports related to information security, which are gradually being adopted by enterprises. This study analyzes the relationship between information security certification (ISO 27001) and corporate financial performance using data from Chinese publicly listed companies. The study focusses on the impact of corporate decisions such as whether to obtain certification, how long to hold certification, and whether to publicize information regarding certification. The results show that there is a positive correlation between ISO 27001 and financial performance. Moreover, the positive impact of ISO 27001 on financial performance gradually increases with time. In addition, choosing not to publicize ISO 27001 certification can negatively affect enterprise performance.
Neural HMMS Are All You Need (For High-Quality Attention-Free TTS)

Neural sequence-to-sequence TTS has achieved significantly better output quality than statistical speech synthesis using HMMs. However, neural TTS is generally not probabilistic and uses non-monotonic attention. Attention failures increase training time and can make synthesis babble incoherently. This paper describes how the old and new paradigms can be combined to obtain the advantages of both worlds, by replacing attention in neural TTS with an autoregressive left-right no-skip hidden Markov model defined by a neural network. Based on this proposal, we modify Tacotron 2 to obtain an HMM-based neural TTS model with monotonic alignment, trained to maximise the full sequence likelihood without approximation. We also describe how to combine ideas from classical and contemporary TTS for best results. The resulting example system is smaller and simpler than Tacotron 2, and learns to speak with fewer iterations and less data, whilst achieving comparable naturalness prior to the post-net. Our approach also allows easy control over speaking rate.
Designing arbitrary single-axis rotations robust against perpendicular time-dependent noise

Abstract Low-frequency time-dependent noise is one of the main obstacles on the road toward a fully scalable quantum computer. The majority of solid-state qubit platforms, from superconducting circuits to spins in semiconductors, are greatly affected by 1/ f noise. Among the different control techniques used to counteract noise effects on the system, dynamical decoupling sequences are one of the most effective. However, most dynamical decoupling sequences require unbounded and instantaneous pulses, which are unphysical and can only implement identity operations. Among methods that do restrict to bounded control fields, there remains a need for protocols that implement arbitrary gates with lab-ready control fields. In this work, we introduce a protocol to design bounded and continuous control fields that implement arbitrary single-axis rotations while shielding the system from low-frequency time-dependent noise perpendicular to the control axis. We show the versatility of our method by presenting a set of non-negative-only control pulses that are immediately applicable to quantum systems with constrained control, such as singlet-triplet spin qubits. Finally, we demonstrate the robustness of our control pulses against classical 1/ f noise and noise modeled with a random quantum bath, showing that our pulses can even outperform ideal dynamical decoupling sequences.
Towards Indian Sign Language Sentence Recognition using INSIGNVID: Indian Sign Language Video Dataset

Sign language, a language used by Deaf community, is a fully visual language with its own grammar.The Deaf people find it very difficult to express their feelings to the other people, since the other people lack the knowledge of the sign language used by the Deaf community.Due to the differences in vocabulary and grammar of the sign languages, complete adoption of methods used for other international sign languages is not possible for Indian Sign Language (ISL) recognition.It is difficult to handle continuous sign language sentence recognition and translation into text as no large video dataset for ISL sentences is available.INSIGNVID -the first Indian Sign Language video dataset has been proposed and with this dataset as input, a novel approach is presented that converts video of ISL sentence in appropriate English sentence using transfer learning.The proposed approach gives promising results on our dataset with MobilNetV2 as pretrained model.
Cats vs. Spectre: An Axiomatic Approach to Modeling Speculative Execution Attacks

The SPECTRE family of speculative execution attacks has required a rethinking of formal methods for security. Approaches based on operational speculative semantics have made initial inroads towards finding vulnerable code and validating defenses. However, with each new attack grows the amount of microarchitectural detail that has to be integrated into the underlying semantics. We propose an alternative, lightweight and axiomatic approach to specifying speculative semantics that relies on insights from memory models for concurrency. We use the CAT modeling language for memory consistency to specify execution models that capture speculative control flow, store-to-load forwarding, predictive store forwarding, and memory ordering machine clears. We present a bounded model checking framework parameterized by our speculative CAT models and evaluate its implementation against the state of the art. Due to the axiomatic approach, our models can be rapidly extended to allow our framework to detect new types of attacks and validate defenses against them.
Dissipative dynamics of quantum correlation quantifiers under decoherence channels

In this work, we investigate the dynamics of quantum correlations captured by entropic and geometric measures of discord under the influence of dissipative channels for widely used two qubit X state with maximally mixed marginals. Identifying the phenomena of the preservation, sudden change and revival of quantum correlation quantifiers, we determine their hierarchy of robustness under memory-less decoherence channels. We find the analytical expressions of decoherence probabilities at which sudden change occur when the first qubit is subjected to the decoherence channels for multiple times. We deduce the exact analytical expression for the preservation time, the duration for which quantum correlations remain unaffected by noise. We also find the time duration between the two sudden change phenomena of trace distance discord, and show its inverse proportionality to the number of times a channel operates on the state. The constraint relations of decoherence probabilities corresponding to the sudden change region and preservation phenomena are obtained when both the qubits are subjected to locally independent quantum channels for multiple times. Our investigation provides physical insights and possible practical implementation of the discord measures in noisy environments.
Diagnostics-Guided Explanation Generation

Explanations shed light on a machine learning model's rationales and can aid in identifying deficiencies in its reasoning process. Explanation generation models are typically trained in a supervised way given human explanations. When such annotations are not available, explanations are often selected as those portions of the input that maximise a downstream task's performance, which corresponds to optimising an explanation's Faithfulness to a given model. Faithfulness is one of several so-called diagnostic properties, which prior work has identified as useful for gauging the quality of an explanation without requiring annotations. Other diagnostic properties are Data Consistency, which measures how similar explanations are for similar input instances, and Confidence Indication, which shows whether the explanation reflects the confidence of the model. In this work, we show how to directly optimise for these diagnostic properties when training a model to generate sentence-level explanations, which markedly improves explanation quality, agreement with human rationales, and downstream task performance on three complex reasoning tasks.
A Blockchain based Framework for Property Registration System in E-Governance

In recent years, the most cutting edges and promising technology emerged is Blockchain.It has huge potential to impact various industries.The append-only distributed ledger technology (DLT) and the consensus mechanism of Blockchain can also change the dimension of E-Governance.The Electronic Property Record (EPR) systems of government have challenges like data security, integrity, secure storage of data and automated service delivery.In this paper, we discuss how Smart Contract based Blockchain technology can effectively be used to address the challenges of EPR System over the existing available systems.We propose a Smart Contract based permissioned blockchain framework which is an innovative approach, especially in the Electronic Property Registration domain of E-Governance in India.The objective of our proposed framework is firstly to implement Smart Contract solving the security problems like confidentiality, integrity, authentication, and secondly to ensure secure storage of electronic records by defining access rules for the stakeholders of the proposed framework.Moreover, we address the issues of single-point-of-failure, data inter-operability between the organizations involved for sharing and verification of property information among various stakeholders.
The Ring-LWE Problem in Lattice-Based Cryptography: The Case of Twisted Embeddings

Several works have characterized weak instances of the Ring-LWE problem by exploring vulnerabilities arising from the use of algebraic structures. Although these weak instances are not addressed by worst-case hardness theorems, enabling other ring instantiations enlarges the scope of possible applications and favors the diversification of security assumptions. In this work, we extend the Ring-LWE problem in lattice-based cryptography to include algebraic lattices, realized through twisted embeddings. We define the class of problems Twisted Ring-LWE, which replaces the canonical embedding by an extended form. By doing so, we allow the Ring-LWE problem to be used over maximal real subfields of cyclotomic number fields. We prove that Twisted Ring-LWE is secure by providing a security reduction from Ring-LWE to Twisted Ring-LWE in both search and decision forms. It is also shown that the twist factor does not affect the asymptotic approximation factors in the worst-case to average-case reductions. Thus, Twisted Ring-LWE maintains the consolidated hardness guarantee of Ring-LWE and increases the existing scope of algebraic lattices that can be considered for cryptographic applications. Additionally, we expand on the results of Ducas and Durmus (Public-Key Cryptography, 2012) on spherical Gaussian distributions to the proposed class of lattices under certain restrictions. As a result, sampling from a spherical Gaussian distribution can be done directly in the respective number field while maintaining its format and standard deviation when seen in Zn via twisted embeddings.