Reviewerly/exHarmony huggingface dataset:
Efficient privacy-preserving variable-length substring match for genome sequence

Finding a similar substring that commonly appears in query and database sequences is an essential task for genome data analysis. This study proposes a secure two-party variable-length string search protocol based on secret sharing. The unique feature of our protocol is that time, communication, and round complexities are not dependent on the database length N, after the query input. This property brings dramatic performance improvements in search time, since N is usually quite large in an actual genome database, and the same database is repeatedly used for many queries. Our concept hinges on a technique that efficiently applies the compressed full-text index (FOCS 2000) for a secret-sharing scheme. We conducted an experiment using a human genomic sequence with the length of 10 million as the database and a query with the length of 100 and found that the query response time of our protocol was at least three orders of magnitude faster than a well-designed baseline protocol under the realistic computation/network environment.
Phenomenological Properties as Conceptual Tools for Research: The Case of Video Calls

Once we identify the properties of perceptual presence, we have a conceptual toolbox for systematically inspecting perceptual presence in various domains of human activity. To demonstrate one such …
Sensorimotor similarity: A fully grounded and efficient measure of semantic similarity

Author(s): Wingfield, Cai; Connell, Louise | Abstract: Experimental design and computational modelling across the cognitive sciences often rely on measures of semantic similarity between words/concepts. Traditional measures of semantic similarity typically rely on corpus-derived linguistic distributional similarity (e.g. LSA) or distance in taxonomic databases (e.g. WordNet), which are theoretically problematic in their lack of grounding in sensorimotor experience. We present a new measure of sensorimotor similarity between concepts, based on multidimensional comparisons of their experiential strength across 11 perceptual and action-effector dimensions in the Lancaster Sensorimotor norms. We demonstrate that, in modelling human similarity and relatedness judgements, sensorimotor similarity has comparable explanatory power to LSA and WordNet distance, explains variance in human judgements which is missed by other measures, and does so with the advantages of remaining both fully grounded and computationally efficient. We further introduce a web-based tool for easily calculating and visualising sensorimotor similarity between words, featuring coverage of nearly 800 million word pairs.
Learning Approximate and Exact Numeral Systems via Reinforcement Learning.

Author(s): Carlsson, Emil; Dubhashi, Devdatt; Johansson, Fredrik D | Abstract: Recent work (Xu et al., 2020) has suggested that numeral systems in different languages are shaped by a functional need for efficient communication in an information-theoretic sense. Here we take a learning-theoretic approach and show how efficient communication emerges via reinforcement learning. In our framework, two artificial agents play a Lewis signaling game where the goal is to convey a numeral concept. The agents gradually learn to communicate using reinforcement learning and the resulting numeral systems are shown to be efficient in the information-theoretic framework of Regier et al.(2015); Gibson et al. (2017). They are also shown to be similar to human numeral systems of same type. Our results thus provide a mechanistic explanation via reinforcement learning of the recent results in Xu et al. (2020) and can potentially be generalized to other semantic domains.
Assessment of Some Heavy Metals as Contaminants in Petroleum Contaminated Soils of Eleme, Port Harcourt Nigeria

Samples from petroleum contaminated soil Eleme, Port Harcourt and from non petroleum contaminated soil, Awka, Nigeria serving as control were analyzed for heavy metals. The results of the analysis showed that the metals  concentrations in mg/kg were Ni<0.05- 2.05, Cr 0.005 -0.37, Cd<0.005 - 0.008, Zn 0.14 -2.87 Pb<0.02- 0.19 Fe 12.32-29.11   ,Mn< 0.008- 0.036, Se<0.001, As<0.001, and V<0.001. Majority of the metal concentrations were above those of the control. Though the values obtained did not exceed soil quality standards stipulated in the Environmental Management Regulations, 2007, the toxic metals could accumulate to a threatening level over a period of time. Food poisoning, occupational hazards and various disorders emanate from heavy load of toxic metals in our environment.
Infrared Image Super Resolution with Deep Neural Networks

Recent studies have shown that Deep Learning (DL) algorithms can significantly improve Super Resolution (SR) performance. Single image SR is useful in producing High Resolution (HR) images from their Low Resolution (LR) counterparts. The motivation for SR is the potential to assist algorithms such as object detection, localization, and classification. Insufficient work has been conducted using Generative Adversarial Networks (GANs) for SR on infrared (IR) images despite its promising ability to increase object detection accuracy by extracting more precise features from a given image. This work adopts the idea of a relativistic GAN that utilizes Residual in Residual Dense blocks (RRDBs) for feature ex- traction, a novel residual image addition, and a Pixel Transposed Convolutional Layer (PixelTCL) for up-sampling. Recent work has validated the use of GANs for Visible Light (VL) images, making them a strong candidate. The inclusion of these components produce more realistic and natural features while also receiving superior metric values.
TEACHING THE DISCIPLINE “INFORMATICS AND PROGRAMMING” TO STUDENTS OF THE PROFILE “DIGITAL ART”

The article discusses the features of teaching the discipline “Computer Science and Programming” to students of the main professional educational program of higher education “Digital Art”. Various approaches to the implementation of an educational program in digital art are possible, in this regard, it seems relevant to search for approaches, methods and teaching aids on this topic. In particular, this applies to information technology training of students, within the framework of which professionally significant competencies of graduates are formed. Analysis of scientific literature has shown that the range of issues related to the teaching of specialized disciplines for future specialists in the field of digital art is quite new for modern pedagogical science and has not yet been reflected in publications. The purpose of this study is to identify the features of teaching the discipline “Computer Science and Programming” for students of the “Digital Art” profile. Based on the analysis of the Federal State Educational Standard “Applied Informatics” of 2015 and 2017, professional standards, approaches to teaching informatics in higher educational institutions, materials of work programs of academic disciplines, universal, general professional and professional competencies, it was revealed that the discipline “Computer Science and Programming” for educational program “Digital art” includes the content of fundamental and applied topics of informatics, has features in the system of lectures and practical tasks, in accordance with the future professional competencies of digital art specialists, the ability to take part in the development of IT projects for the sphere of culture and digital art objects. The discipline includes some profile activities of the profile, in particular, the Hackathon for the development of digital art objects and design layouts, is implemented with the support of an electronic course.
Progress in the analysis of debris cloud evolution: Fully based on probabilistic methods

In view of the intrinsic uncertainty of the debris cloud, a fully probabilistic framework of debris evolution is constructed. The breakup, evolution and collision of the debris cloud could be analyzed analytically, avoiding the low computational efficiency and poor robustness of numerical methods.
Content of pedagogical experience in the structure of physics teaching and methodological basis of its organization

The paper discusses methods for abstraction, analysis and synthesis, in theory study and the method ofpreparation of the empiricist teaching test and practical work. As well as, the article outlines the priorities of organizing physics education on the basis of a differentiated approach to the development of the intelligence of trainees. As a result, the same learning content and the same complexity of knowledge and skills provided to students do not ensure their dynamic development. Eventually, there will be a number of students who do not master science and lose interest in it. The short-term experiment used a differentiated learning process for students. In comparison with the control group, the level of aspiration, interest and knowledge of the students of the experimental group changed significantly, and positive results were achieved.
Secured Student Portal Using Cloud

Nowadays, a student portal is necessary for every educational institution as insecurity is rapidly increasing these days. The central goal of this proposed work is to suggest a secured student data portal, i.e., to provide security to the students, faculty data, and the question papers of concerned examinations. It is the process of collecting the details of the student mainly the concerned mac address of the laptops and Wi-Fi routers and the image of them with time stamp while marking attendance. The attendance is marked from these devices, and the faculty can cross-check their photograph to know if there is any proxy attendance among the students. There are two modules: One is the attendance, and another is the examination module. The attendance has sub-modules like registration, marking attendance, faculty granting permission to the student in case of emergency, and review attendance. While the faculty upload the question papers for the respective examinations, students can access their relevant examinations for the day, and faculty can view the performance that is marked for the evaluations.
Depth Estimation from Monocular Images and Sparse radar using Deep Ordinal Regression Network

We integrate sparse radar data into a monocular depth estimation model and introduce a novel preprocessing method for reducing the sparseness and limited field of view provided by radar. We explore the intrinsic error of different radar modalities and show our proposed method results in more data points with reduced error. We further propose a novel method for estimating dense depth maps from monocular 2D images and sparse radar measurements using deep learning based on the deep ordinal regression network by Fu et al. Radar data are integrated by first converting the sparse 2D points to a height-extended 3D measurement and then including it into the network using a late fusion approach. Experiments are conducted on the nuScenes dataset. Our experiments demonstrate state-of-the-art performance in both day and night scenes.
Open source hardware automated guitar player

We present the first open source hardware (OSH) design and build of a physical robotic automated guitar player. Users’ own instruments being different shapes and sizes, the system is designed to be used and/or modified to physically attach to a wide range of instruments. Design objectives include ease and low cost of build. Automation is split into three modules: the left-hand fretting, right-hand string picking, and right hand palm muting. Automation is performed using cheap electric linear solenoids. Software APIs are designed and implemented for both low level actuator control and high level music performance.
Constrained Labeling for Weakly Supervised Learning

Curation of large fully supervised datasets has become one of the major roadblocks for machine learning. Weak supervision provides an alternative to supervised learning by training with cheap, noisy, and possibly correlated labeling functions from varying sources. The key challenge in weakly supervised learning is combining the different weak supervision signals while navigating misleading correlations in their errors. In this paper, we propose a simple data-free approach for combining weak supervision signals by defining a constrained space for the possible labels of the weak signals and training with a random labeling within this constrained space. Our method is efficient and stable, converging after a few iterations of gradient descent. We prove theoretical conditions under which the worst-case error of the randomized label decreases with the rank of the linear constraints. We show experimentally that our method outperforms other weak supervision methods on various text- and image-classification tasks.
Optimization methodologies study for the development of prognostic artificial neural network

In this work, I discuss the implementation and optimization of an Artificial Neural Network based on the analysis of the back-EMF coefficient capable of making ElectroMechanical Actuator (EMA) prognostics. Aircraft manufacturers are increasingly focusing on the use of electromechanical actuators as they have numerous advantages in terms of weight and compactness respect to the technologies adopted at this time. However, they are early technology and for this reason, engineers are still studying the failure modes that characterize this component. The objective of this thesis is to study a methodology for the recognition of faults within the system. To solve the problem my supervisors have thought of implementing a logic, based on artificial intelligence, particularly on artificial neural networks, which allows to estimate the remaining useful life of the system components starting from a training dataset. The neural network learns autonomously the relationships that link the quantities given as input with those in output. However, during learning, the creators need to set the value of the hyperparameters. My job is to show how these values influence learning and how it is possible to optimize the network to make it performing in terms of computational cost and complexity, so that the variation of hyperparameters improves supervised learning. The future of aviation is certainly based on the more philosophy. Electricity is the only indispensable energy source for an aircraft. Nowadays, the remote hypothesis of full electric aircraft is still under study and yet there are several queries to be clarified. The results are very satisfactory considering the small number of examples present in the available dataset. In the future, I think that we can build a neural network having datasets with a greater number of examples and deeper even though this, as you can read in this/my thesis, does not always turn out to be an advantage. For this reason, optimizing the work is important.
Topological Strategies for the Investigation of Tall Dimensional Information Sets and 3D Protest Acknowledgment

This examination introduces a technique for distinguishing an item by utilizing a few highlights. Highlights are removed dependent on the measurable dissemination of focuses on the bitmap picture of the shape. It identify the state of a picture by utilizing geometrical highlights and afterward select the grouped of picture that coordinated that shape from an enormous database. In this methodology the picture of item are separated into zones and discover some element of each zone. The extricated highlights are utilized for characterization dependent on zones. To settle on a choice for arrangement the component of zone are separated according to the limit structure fragment the entirety of ON or frontal area pixels in every limit portion, an edge between the beginning and completion focuses, a separation between the beginning and consummation focuses, centroid of each zone, an edge between the beginning and centroid focuses, a separation between the beginning and centroid focuses, an edge between the centroid and closure focuses, a separation between the centroid and closure focuses and so forth.
Causation Entropy Method for Covariate Selection in Dynamic Models

When constructing models from data, it is often desirable to employ regression techniques that identify the important predictors of model behavior. This process of covariate selection enables construction of models with reduced size that may also be more accurate. LASSO and elastic net regularization are common techniques that are employed to shrink the size of a model by eliminating covariates that do not have a significant effect on predictive performance. However, these methods are subject to well-known limitations in that shrinkage performance must be controlled through the tuning of hyperparameters. This paper introduces a new technique for covariate selection for discrete-time dynamic models using an information theoretic quantity called causation entropy. The algorithm selects important state transition functions in the dynamic model from a set of candidates through the specially-formulated Causation Entropy Matrix (CEM). Unlike other covariate selection methods, the CEM technique does not require tuning but is seen to produce analogous results to LASSO and elastic net in many cases. While the basic structure of the CEM was introduced in prior work, this paper evaluates model shrinkage performance in the presence of varying levels of noise and training data. Performance comparisons are shown between the CEM method and LASSO and elastic net, highlighting the tradeoffs in terms of the effect of data length and robustness to noise. Overall, the CEM method is shown to be a useful technique for covariate selection in cases where limited training data is available and/or noise in the data is relatively low.
Few Shot Learning Based on the Street View House Numbers (SVHN) Dataset

In recent years, deep learning model has made remarkable achievements in image, voice, text recognition and other fields. However, deep learning model relies heavily on large number of labeled data, which limits its application in the special field of data shortage.
Experimental Analysis of Document Verification Scheme Using Blockchain Logics Over Secured Cloud Environment

In this modern era, each and every individual requires a Smartphone and related gadget to operate all day to day activities. Digital Document Management is the present world scenario and no one want to submit the documents in hard core manner; instead all are required to submit the documents to officials, authorities and any others in a digital form only. This situation causes an interest over document verification over digital environments. The documents while submitting over digital platform requires many clarifications such as how secure it is as well as need to analyze the novelty and trustability of such documents and so on. In other side, the user needs to get some higher end privacy check for such things while submit the documents online to further processing. The proposed system considers the following challenges to produce a resolution to the problems and queries quoted above such as: Security, Novelty, Integrity, Access Control and Durability. Blockchain a term, which covers all industry now-a-days even it may be a Government industry or it may be a Non-Government industry, this particular process place its leg around all such fields to prove its significance to all fields. Even the banking sectors use this Blockchain as a medium to provide security to the account holders. The proposed application use thidy Blockchain concept along with document verification process and provides the ultimate security to users to submit the documents digitally to authorities as well as the other end of authorities can verify the document in an intelligent manner with proper security norms. The Blockchain mechanism creates all required records as a Block, in which the initial block is considered as a Genisis Block, which leads all further blocks one by one without any intervention. The Blockchain mechanism follow Miner Verification process to check the records available in Block with further coming records, once the present record is unique, the Miner Verification process allows the records to place on a further block. If the records are already presented into the existing block that will be immediately blocked by the Miner Verification process, but in this proposed system, the Miner Verification process is assumed as a Volunteer Process, in which the Volunteers can take care of physical verifications with regards of Blockchain's Miner verification process, which will be explained in detail over further summary. Each block in the Blockchain process has its own identity, which will be in the cipher format that is handled by using powerful unidirectional encryption algorithm called SHA, in which it is operated based on 256-bit encryption procedure. The entire process of the proposed system assures the secured and intelligent Blockchain based document verification scheme with efficient novelty.
Cyber Security Management of Businesses and Their Formulation of Culture via a Theoretical Approach

Businesses in any country are vulnerable to cyber threats. The rise of Internet of Things, cloud computing, and communication platforms contributed to a drastic increase in those threats. Therefore, it is of importance to address the threats and risks that businesses face. Considering the significance of this situation, this paper aims to provide a theoretical understanding of cyber security management for businesses via literature review. Research framework in this work consists of three steps, (i) Cyber security risk management frameworks, (ii) Cyber security management measures, and (iii) Cyber security culture. Cyber security risk management frameworks can be adopted on the strategic level, and cyber security risk management measures can be used on the practical level. If these are taken successfully, cyber security culture is to be cultivated for the perpetuation of cyber security management within a business. In this paper, first, various models and elements of the frameworks and measures are explained, and second, a balanced approach is discussed in order to make a success of cyber security management. It is expected to lay the foundation for conceptualisation of cyber security management.
Active Learning in Incomplete Label Multiple Instance Multiple Label Learning

In multiple instance multiple label learning, each sample, a bag, consists of multiple instances. To alleviate labeling complexity, each sample is associated with a set of bag-level labels leaving instances within the bag unlabeled. This setting is more convenient and natural for representing complicated objects, which have multiple semantic meanings. Compared to single instance labeling, this approach allows for labeling larger datasets at an equivalent labeling cost. However, for sufficiently large datasets, labeling all bags may become prohibitively costly. Active learning uses an iterative labeling and retraining approach aiming to provide reasonable classification performance using a small number of labeled samples. To our knowledge, only a few works in the area of active learning in the MIML setting are available. These approaches can provide practical solutions to reduce labeling cost but their efficacy remains unclear. In this paper, we propose a novel bag-class pair based approach for active learning in the MIML setting. Due to the partial availability of bag-level labels, we focus on the incomplete-label MIML setting for the proposed active learning approach. Our approach is based on a discriminative graphical model with efficient and exact inference. For the query process, we adapt active learning criteria to the novel bag-class pair selection strategy. Additionally, we introduce an online stochastic gradient descent algorithm to provide an efficient model update after each query. Numerical experiments on benchmark datasets illustrate the robustness of the proposed approach.