Reviewerly/exHarmony huggingface dataset:
Handling Non-ignorably Missing Features in Electronic Health Records Data Using Importance-Weighted Autoencoders.

Electronic Health Records (EHRs) are commonly used to investigate relationships between patient health information and outcomes. Deep learning methods are emerging as powerful tools to learn such relationships, given the characteristic high dimension and large sample size of EHR datasets. The Physionet 2012 Challenge involves an EHR dataset pertaining to 12,000 ICU patients, where researchers investigated the relationships between clinical measurements, and in-hospital mortality. However, the prevalence and complexity of missing data in the Physionet data present significant challenges for the application of deep learning methods, such as Variational Autoencoders (VAEs). Although a rich literature exists regarding the treatment of missing data in traditional statistical models, it is unclear how this extends to deep learning architectures. To address these issues, we propose a novel extension of VAEs called Importance-Weighted Autoencoders (IWAEs) to flexibly handle Missing Not At Random (MNAR) patterns in the Physionet data. Our proposed method models the missingness mechanism using an embedded neural network, eliminating the need to specify the exact form of the missingness mechanism a priori. We show that the use of our method leads to more realistic imputed values relative to the state-of-the-art, as well as significant differences in fitted downstream models for mortality.
Model for Translation of English Language Noun Phrases to Luganda

In Uganda Luganda is the most dominant local language spoken all over the country with over 6 million speakers thus learning Luganda would enable English speaking foreign traders tourists and Non- Governmental Organisations NGOs to get the best out of their dealings with the Luganda speakers. Leading Neural Machine Translation NMT Systems such as Google Translate GT Microsoft Translator and those based on Statistical Machine Translation SMT cannot satisfactorily support Noun Phrase translations between major and minor languages such as the English language and Luganda pair because of inadequate digital resources for minority languages. Machine Translation MT tools which are potentially affordable and   nvenient  anguage learning options cannot help majority language speakers to learn minority languages due to diversities in the syntax and semantic structures of the languages but cheaper and more effective MT approach between major and minor languages is Rule- based Machine Translation RBMT which involves harvesting a language pair’s linguistic knowledge. Design Science Research Methodology DSRM allows for continuous refinement of a translation model and its implementation through  rototyping techniques Document Analysis and Focus Group Discussions to incorporate new translation rules into the model and use Holdout validation with Human Evaluation to test the model output. Deterministic Finite Automaton DFA automates the Noun Phrase Translation model Java Formal Languages and Automata Package JFLAP designs the DFA while Python is the programming language. The bilingual dictionaries are implemented as Coma Separated Values CSV files the Natural Language Tool Kit NLTK supports Natural Language Processing NLP tasks such as Part-Of-Speech POS tagging parsing and tkinker develops a Graphical User Interface GUI for the MT application. Py2Exe creates an executable file from the python codes and Nullsoft Scriptable Install System NSIS builds the window installer for the application. The translation model does not cover complex noun phrases consisting of other phrases such as prepositional phrases and focuses on the commonest noun phrase pattern of the Pre-modifier Head structure. Luganda as a noun centric language with 10 noun classes does not have standalone articles but exist as prefixes and noun modifiers are determined by noun’s grammatical number and its class. Evaluation results offers clear and unambiguous translation of English to Luganda noun phrase which invariably facilitate tutoring and teaching of Luganda. This research work aids the protection of both the language and the culture from a possible extinction. As a matter of fact the dominancy of English in African societies has its advantages but it has damaged African local languages values and virtues that is language is culture therefore if a language dies culture dies and when a culture dies its people die.
Video Inpainting: A Complete Framework

Reconstructing and repairing of corrupted or missing parts after object removal in digital video is an important trend in artwork restoration. Video inpainting is an active subject in video processing, which deals with the recovery of the corrupted or missing data. Most previous video inpainting approaches consume more time in extensive search to find the best patch to restore the damaged frames. In addition to that, most of them cannot handle the gradual and sudden illumination changes, dynamic background, full object occlusion, and object changes in scale. In this paper, we present a complete video inpainting framework without the extensive search process. The proposed framework consists of a segmentation stage based on low-resolution version and background subtraction. A background inpainting stage is applied to restore the damaged background regions after static or moving object removal based on the gray-level co-occurrence matrix (GLCM). A foreground inpainting stage is based on objects repository. GLCM is used to complete the moving occluded objects during the occlusion. The proposed method reduces the inpainting time from hours to a few seconds and maintains the spatial and temporal consistency. It works well when the background has clutter or fake motion, and it can handle the changes in object size and in posture. Moreover, it is able to handle full occlusion and illumination changes.
Using Artificial Neural Networks for Fractal Interpolation Approach in 3D

In this paper, we introduce a new method for fractal interpolation, herein called Neural Network Algorithm (NNA), which is based on Iterated Functions Systems (IFS); proposed to self-affine signals interpolation with error of expected interpolation. Experiments on theoretical data show that the proposed interpolation schemes can obtain the expected point value and work with great precision in rebuilding the specified data profile, which leads to a significant advantage over other interpolation methods.
Don't stack layers in graph neural networks, wire them randomly

Graph neural networks have become a staple in problems addressing learning and analysis of data defined over graphs. However, several results suggest an inherent difficulty in extracting better performance by increasing the number of layers. Besides the classic vanishing gradient issues, recent works attribute this to a phenomenon peculiar to the extraction of node features in graph-based tasks, i.e., the need to consider multiple neighborhood sizes at the same time and adaptively tune them. In this paper, we investigate the recently proposed randomly wired architectures in the context of graph neural networks. Instead of building deeper networks by stacking many layers, we prove that employing a randomly-wired architecture can be a more effective way to increase the capacity of the network and obtain richer representations. We show that such architectures behave like an ensemble of paths, which are able to merge contributions from receptive fields of varied size. Moreover, these receptive fields can also be modulated to be wider or narrower through the trainable weights over the paths. We also provide extensive experimental evidence of the superior performance of randomly wired architectures over three tasks and five graph convolution definitions, using a recent benchmarking framework that addresses the reliability of previous testing methodologies.
An Accurate Persian Part-of-Speech Tagger

The processing of any natural language requires that the grammatical properties of every word in that language are tagged by a part of speech (POS) tagger. To present a more accurate POS tagger for the Persian language, we propose an improved and accurate tagger called IAoM that supports properties of text to speech systems such as Lexical Stress Search, Homograph words Disambiguation, Break Phrase Detection, and main aspects of Persian morphology. IAoM uses Maximum Likelihood Estimation (MLE) to determine the tags of unknown words. In addition, it uses a few defined rules for the sake of achieving high accuracy. For tagging the input corpus, IAoM uses a Hidden Markov Model (HMM) alongside the Viterbi algorithm. To present a fair evaluation, we have performed various experiments on both homogeneous and heterogeneous Persian corpora and studied the effect of the size of training set on the accuracy of IAoM. Experimental results demonstrate the merit of the proposed tagger in achieving an overall accuracy of 97.6%.
Disaster Capitalism, Rampant EdTech Opportunism, and the Advancement of Online Learning in the Era of COVID19

The authors consider the ways in which educational responses to COVID19 exemplify  opportunistic disaster capitalism. Prior to the pandemic, neoliberal influence increasingly impacted education systems all over the world, pushing for increased privatization in/of schools.  COVID19 has created conditions for private technology companies to push for increased participation in public schools. That is, corporations are using this health crisis to further mobilize the neoliberal agenda, and encourage policies, practices, and technological infrastructure that will be used to rationalize ongoing online learning. In turn, we ask: What are the motivations and implications of inviting private EdTech into public education? How does EdTech encourage a move to online learning; c) what are the overall impacts of online learning? Under the veil of the panic of a global health crisis, our public education systems in Canada are being put at risk.
The Proof Checkers Pacheck and Pastèque for the Practical Algebraic Calculus

Generating and checking proof certificates is important to increase the trust in automated reasoning tools. In recent years formal verification using computer algebra became more important and is heavily used in automated circuit verification. An existing proof format which covers algebraic reasoning and allows efficient proof checking is the practical algebraic calculus. In this paper we present two independent proof checkers Pacheckand PastEque.The checker Pacheckchecks algebraic proofs more efficiently than PastEque,but the latter is formally verified using the proof assistant Isabelle/HOL. Furthermore, we introduce extension rules to simulate essential rewriting techniques required in practice. For efficiency we also make use of indices for existing polynomials and include deletion rules too.
Towards Robustness to Label Noise in Text Classification via Noise Modeling

Large datasets in NLP suffer from noisy labels, due to erroneous automatic and human annotation procedures. We study the problem of text classification with label noise, and aim to capture this noise through an auxiliary noise model over the classifier. We first assign a probability score to each training sample of having a noisy label, through a beta mixture model fitted on the losses at an early epoch of training. Then, we use this score to selectively guide the learning of the noise model and classifier. Our empirical evaluation on two text classification tasks shows that our approach can improve over the baseline accuracy, and prevent over-fitting to the noise.
Multiclass Severity Classification for Software Bugs Using Support Vector Machine, K-Nearest Neighbor, Decision Tree and Naïve Bayes

During the software development, all most 30–35 present cost is due to the testing. This means that if a bug travels from one phase to succeeding phases without detection, it will definitely increase the cost of the software development and due to this software quality may be compromised. So use of the data mining algorithm for the software bug classification is highly appreciable. Bug severity may be categorised into S1, S2, S3, S4 and S5 categories, depending on the impact of the severity. In this paper, multiclass of bug severity is done using SVM, KNN, Decision Tree and Naïve Bayes. Comparative analysis of these algorithms is done with respect to accuracy, precision, recall and execution time.
A Study on the Detection Method of Malicious URLs based on the Internet Search Engines using the Machine Learning

As internet technology develops and there are increases in cyber activities such as e-mail, SNS and e-commerce, cyber security is being emphasized as one of the most important social issues. In particular, spam mail, SMS phishing and other malicious URLs to breach personal data are inflicting financial and physical damage. This paper proposes a malicious URL detection method based on search volume from internet search engines to resolve such problem. The search volume-based malicious URL detection method uses portal search engines to automatically search URLs linked to emails, text messages and SNS to determine whether or not they can be categorized as malicious URLs according to the volume of the search result. This method allows normal URLs, but will protect to abnormal behaviors.
From constitutional freedoms to the power of the platforms : protecting fundamental rights online in the algorithmic society

The rise of the algorithmic society has led to a paradigmatic shift where constitutional liberties granted to online platforms have turned into newfound powers. This situation is not only the result of new technological developments but also of the recognition of the online platforms' exclusive role in implementing an online public regulatory framework, as the cases of content management and the right to be forgotten online illustrate. Behind such delegated competences, online intermediaries can exercise sovereign powers over their online spaces through instruments based on private law and technology. In this scenario, the liberal constitutional approach adopted in relation to online platforms has played a crucial role in increasing the possibilities for these actors to affect individuals' fundamental rights. This work will focus on two potential solutions to limit the extent of such private powers from a (digital) constitutional law perspective. The first will focus on the introduction of new user rights whose aim is to regulate online platforms' decision-making processes and provide new legal remedies against such decisions. The insertion of new procedural rights in the online environment, including, for example, the obligation to explain the reasons behind platforms' decisions, would be appropriate in order to reduce the opacity of automated decision-making processes and foster human awareness in the algorithmic society. The second solution will question the doctrine of horizontal effect in order to establish a mechanism to enforce constitutional rights vis-a-vis online platforms that operate in a global framework.
Optimization of a Similarity Performance on Bounded Content of Motion Histogram by Using Distributed Model

In this paper, a content-based video retrieval (CBVR) system called Bounded Coordinate of Motion Histogram version 2 (BCMH v2) was processed on a distributed computing platform by using Apache Hadoop framework and a real-time distributed storage system using HBase. In fact, the amount of multimedia data is growing exponentially. Most of this data is available in image and video models. Analyzing huge data involves complex algorithms, which leads to challenges in optimizing processing time and data storage capacity. Many content-based video retrieval systems are suitable for processing large video dataset, but they are limited by the computational time and/or storage on a single machine. Thus, this paper presents the effectiveness of the proposed method with the distributed computing platform and its evaluation on the HOLLYWOOD2 video dataset. The experimental results demonstrate the good performances of the proposed approach.
An Interpretable Intensive Care Unit Mortality Risk Calculator

Mortality risk is a major concern to patients who have just been discharged from the intensive care unit (ICU). Many studies have been directed to construct machine learning models to predict such risk. Although these models are highly accurate, they are less amenable to interpretation and clinicians are typically unable to gain further insights into the patients’ health conditions and the underlying factors that influence their mortality risk. In this paper, we use patients’ profiles extracted from the MIMIC-III clinical database to construct risk calculators based on different machine learning techniques such as logistic regression, decision trees, random forests, k-nearest neighbors and multilayer perceptrons. We perform an extensive benchmarking study that compares the most salient features as predicted by various methods. We observe a high degree of agreement across the considered machine learning methods; in particular, age, blood urea nitrogen level and the indicator variable - whether the patient is discharged from the cardiac surgery recovery unit are commonly predicted to be the most salient features for determining patients’ mortality risks. Our work has the potential to help clinicians interpret risk predictions.
Vulnerabilities in Biometric Authentication of Smartphones

Smartphones are now ubiquitous in every aspect of our lives. Users are more inclined towards saving and accessing vital and personal information on smartphones. However, there is always a privacy and security issue, which are covered by PIN numbers and fingerprints authentication. The choice of choosing between a PIN number and fingerprints has always been challenging by the fear of the PIN to be read and fingerprint image to be compromised. This research is done to test the security of fingerprints authentication by using different household items to industrial-grade materials. It will give a practical understanding of how a person's fingerprints can be compromised and used against them on their personal smartphones.
Sundials in the Shade: An Internet-wide Perspective on ICMP Timestamps

ICMP timestamp request and response packets have been standardized for nearly 40 years, but have no modern practical application, having been superseded by NTP. However, ICMP timestamps are not deprecated, suggesting that while hosts must support them, little attention is paid to their implementation and use. In this work, we perform active measurements and find 2.2 million hosts on the Internet responding to ICMP timestamp requests from over 42,500 unique autonomous systems. We develop a methodology to classify timestamp responses, and find 13 distinct classes of behavior. Not only do these behaviors enable a new fingerprinting vector, some behaviors leak important information about the host e.g., OS, kernel version, and local timezone.
Classification of Marathi Text Using Hierarchical Attention (HAN)-Based Encoder-Decoder Model

With social media explosively growing, a vast amount of data is generated, which has to be automatically analyzed. Classification assigns one of the predefined categories to given documents. Various machine learning techniques have been implemented for the classification of Marathi text documents, which is one of the popular languages spoken in India. This paper presents an attention-based hierarchical encoder-decoder model for the classification of Marathi text documents. First, important words from a sentence are computed by providing attention at the word level, using bidirectional LSTM. Then, the attention mechanism is applied at the sentence level to find important sentences. The document is classified based on important sentences from it. The experimentation is done for Marathi polarity labeled tourism dataset (MPLC) with 75 positive and 75 negative reviews and news dataset (94 positive reviews and 102 negative reviews). The accuracy of news data is 77.5%, and for MPLC dataset, accuracy is almost 100%.
Entanglement performance of light through the composite free space channel

Abstract Entanglement transmission is utilized widely in quantum communication. In this paper we establish a model, which characterizes the performance of entanglement state passing through the composite free space channel. This free space channel is compounded with atmosphere, sea surface and underwater channel. Based on the model, the entanglement photon pairs transmitted through composite channel are simulated. Simulation results show that the beam wandering, the incident angle of the beam on the sea surface, the concentration of chlorophyll in the seawater and other factors will lead to the degradation of the entanglement and these factors have a nonlinear relationship with transmittance. Moreover, the increase of the chlorophyll concentration is found to be a relatively heavy impact on the entanglement. In addition, expanding the aperture size of the receiving telescope will improve entanglement. The research of this paper has momentous meaning to the transmission of quantum entanglement in free space. What’s more, the results have an extremely vital reference value for quantum communications in diverse natural environments.
Introduction of school students to the use of digital technologies in agricultural production

The article proves the relevance of the introduction of school students to digital technologies. The roles of the educational environment and subjects are determined, as well as the importance of the subject “Technology”. The key problems of the introduction of students to the use of digital technologies in agricultural production (motivation, material base, social partnership, teacher training) are identified. The authors summarize the experience in training future teachers to introduce students to the use of digital technologies in agricultural production.
Cryptocurrency Market Reactions to Regulatory News

Cryptocurrencies are often thought to operate out of the reach of national regulation, but in fact their valuations, transaction volumes and user bases react substantially to news about regulatory actions. The impact depends on the specific regulatory category to which the news relates: events related to general bans on cryptocurrencies or to their treatment under securities law have the greatest adverse effect, followed by news on combating money laundering and the financing of terrorism, and on restricting the interoperability of cryptocurrencies with regulated markets. News pointing to the establishment of specific legal frameworks tailored to cryptocurrencies and initial coin offerings coincides with strong market gains. These results suggest that cryptocurrency markets rely on regulated financial institutions to operate and that these markets are segmented across jurisdictions.
Learning Synthetic Environments for Reinforcement Learning with Evolution Strategies.

This work explores learning agent-agnostic synthetic environments (SEs) for Reinforcement Learning. SEs act as a proxy for target environments and allow agents to be trained more efficiently than when directly trained on the target environment. We formulate this as a bi-level optimization problem and represent an SE as a neural network. By using Natural Evolution Strategies and a population of SE parameter vectors, we train agents in the inner loop on evolving SEs while in the outer loop we use the performance on the target task as a score for meta-updating the SE population. We show empirically that our method is capable of learning SEs for two discrete-action-space tasks (CartPole-v0 and Acrobot-v1) that allow us to train agents more robustly and with up to 60% fewer steps. Not only do we show in experiments with 4000 evaluations that the SEs are robust against hyperparameter changes such as the learning rate, batch sizes and network sizes, we also show that SEs trained with DDQN agents transfer in limited ways to a discrete-action-space version of TD3 and very well to Dueling DDQN.