Reviewerly/exHarmony huggingface dataset:
Offline Handwritten Telugu Character Dataset and Recognition

Telugu is a Dravidian Language spoken mainly in Southern parts of India. It has close to 81 million native speakers, making it the fifteenth most widely-spoken language in the world. Here we present a comprehensive database of handwritten Telugu characters to drive progress in handwriting recognition for this script. We claim that this is significant since we have put together the largest set of vowel, consonant, vowel-consonant and consonant-consonant pairs of the Telugu orthography. This work produces such a database with real-world offline handwritten characters extracted from scanned documents, making it the largest and most varied database in this domain. The method of collecting data, preprocessing steps, as well as the extraction approach to obtain individual Telugu characters is explained in detail. The dataset is also made open to use as a test set to evaluate handwriting recognition approaches and other related tasks. This work also presents a method of handwritten Telugu character recognition using Convolutional Neural Networks as a baseline classifier, as well as Visual Attention Networks as a more advanced and effective solution. Finally, the proposed architecture is compared with previous solutions and the results are discussed.
On the Application of the Complexity Zeta Function to Modelling Complexity and Emergence in Neuro-Engineering

Emergence is a phenomenal that describes behaviors and properties of systems that are not attributable as simply the sum of the constituent parts. In engineering, emergence can lead to unforeseen and perhaps unexplained failures. Complex systems are more likely to lead to emergent behaviors. Thus, one approach to mitigating emergent properties leading to engineering failures is to quantify complexity and thus provide the ability to predict likely points of emergence. Bioengineering is particularly susceptible to emergence related failures due to the inherent complexity of integrating biological systems with engineered systems. This paper explores a methodology for predicting probable points of emergence and mitigating likely engineering failures due to said emergence.
Using Mention Segmentation to Improve Event Detection with Multi-head Attention

Sentence-level event detection (ED) is a task of detecting words that describe specific types of events, including the subtasks of trigger word identification and event type classification. Previous work straightforwardly inputs a sentence into neural classification models and analyzes deep semantics of words in the sentence one by one. Relying on the semantics, probabilities of event classes can be predicted for each word, including the carefully defined ACE event classes and a “N/A” class(i.e., non-trigger word). The models achieve remarkable successes nowadays. However, our findings show that a natural sentence may posses more than one trigger word and thus entail different types of events. In particular, the closely related information of each event only lies in a unique sentence segment but has nothing to do with other segments. In order to reduce negative influences from noises in other segments, we propose to perform semantics learning for event detection only in the scope of segment instead of the whole sentence. Accordingly, we develop a novel ED method which integrates sentence segmentation into the neural event classification architecture. Bidirectional Long Short-Term Memory (Bi-LSTM) with multi-head attention is used as the classification model. Sentence segmentation is boiled down to a sequence labeling problem, where BERT is used. We combine embeddings, and use them as the input of the neural classification model. The experimental results show that the performance of our method reaches 76.8% and 74.2% F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -scores for trigger identification and event type classification, which outperforms the state-of-the-art.
Dissecting Cyberadversarial Intrusion Stages via Interdisciplinary Observations

Advanced Persistent Threats (APTs) are professional, sophisticated threats that pose a serious concern to our technologically-dependent society. As these threats become more common, conventional response-driven cyberattack management needs to be substituted with anticipatory defense measures. Understanding adversarial behavior and movement is critical to improve our ability to proactively defend. This paper focuses on understanding adversarial movement and adaptation using a case study from a real-time cybersecurity exercise. Through multidisciplinary methodologies from social and hard sciences, this paper presents a mechanism to dissect cyberadversarial intrusion chains to unpack movement, and adaptations.
The Ancient Syncretism and the Modern Syncretism

The genesis of cinema (in 1890) is immediately accompanied by the almost global resonance around the "moving picture", a rather unusual communicative phenomenon. Unfortunately, all the root causes of the resonance have not yet been sufficiently studied. Today, however, given the achievements of a whole range of humanities and (not only) scientific disciplines, it becomes obvious that the "moving picture" invented by engineers Edison and the Lumière brothers arises, above all, as an upgrade to completely new aesthetic, technological and other settings of human culture of the ancient phenomenon of socalled "original syncretism". It is as if the breakdawn of the artistic and generally semiotic-communicative culture of the humanity. That syncretism combines all the receptive means of a man to create a holistic space-and-time picture of the world. The "moving picture", which characteristically arose almost synchronously with the advent of the efforts of the science of the time (the school of Academician Alexander Veselovsky and others) around the phenomenon of original syncretism with a surprising sequence restores decisively the whole semiotic sum of it — but clearly, on a completely new so-called technological basis. Accordingly, this circumstance immediately casts a new cognitive light on the whole history of the "moving picture" — from its debut to our present.
Application of natural magnesium-containing solution in correction heart failure in experiment

In the pathogenesis of heart failure, one of the leading places is occupied by disturbances in the redox processes in the main energy cycles in cardiomyocytes.According to modern concepts, the magnesium ion is one of the major macronutrients that activate the enzymes of these reactions.The use of mineral water with a high content of magnesium in white rats with heart failure had a significant corrective effect on cardiovascular parameters.
Recent Progress of Mandrain Spontaneous Speech Recognition on Mandrain Conversation Dialogue Corpus

This paper presents a progress report on a relatively difficult ASR task on a spontaneous speech corpus - Mandarin Conversational Dialogue Corpus (MCDC). A DNN-based acoustic model is constructed based on the CLDNN structure with a large dataset that comprises two spontaneous-speech corpora and one read-speech corpus. The study uses a large text dataset formed by seven corpora to train an efficient general language model (LM). Two adapted LMs specially for spontaneous speech recognition are also constructed. Experimental results showed that the best performances of 26.3% in character error rate (CER) and 32.5% in word error rate (WER) were reached on MCDC. They represented 27.9% and 22.2% of relative CER and WER reductions as compared with the performances by the previous best HMM-based method. This confirms that the proposed method is promising in tackling on Mandarin spontaneous speech recognition.
Architectonics of Pedagogical Practice Programs for Master’s Degree Students of Pedagogical Specialities under Introduction of New Federal State Educational Standards

The article reveals peculiarities of architectonics of future teachers’ pedagogical practice programs implemented under the conditions of school-university partnership. The authors summarize positive experience of realizing pedagogical practice programs for training direction 44.04.01 “Pedagogical Education”. For the first time the paper justifies peculiar architectonics of the mentioned programs, which ensures efficiency of professional pedagogical education due to continuous renovation of pedagogical practice system, special logic when choosing a program content, orthogonality of content.
Deep Metric Learning for Sensor-based Human Activity Recognition

Although human activity recognition using wearable sensors has become a useful technology, activity recognition using acceleration sensor data is still under development. We obtained ideas from the image field and verified the method of introducing deep metric learning into sensor-based activity recognition. As verification of this method, we confirmed the effect on the estimation accuracy and the effect of the visualization of the feature representation. In addition, we proposed three methods for verification and searched for suitable methods by comparing them. There was a difference in the estimation accuracy and the visualization result with the proposed method. We also confirmed that significant features can be obtained for activity recognition when a suitable method is used.
Overparameterized Nonlinear Optimization with Applications to Neural Nets

Occam's razor is a fundamental problem-solving principal and states that one should seek the simplest possible explanation. Indeed, classical machine learning models such as (sparse) linear regression aims to find simple explanations to data by using with as few parameters as possible. On the other hand, modern techniques such as deep networks are often trained in the overparameterized regime where the model size exceeds the size of the training dataset. While this increases the risk of overfitting and the complexity of the explanation, deep networks are known to have good generalization properties. In this talk, we take a step towards resolving this paradox: We show that solution found by first order methods, such as gradient descent, has the property that it has near shortest distance to the initialization of the algorithm among all other solutions. We also advocate that shortest distance property can be a good proxy for the simplest explanation. We discuss the implications of these results on neural net training and also highlight some outstanding challenges.
Technology Transfer System. Overcoming a Cliché to Communicate Technology

Clichés are useful for communication, in their intrinsic meaning of visual shortcut. A shortcut that immediately allows to associate areas and concepts to conventional elements. In this sense, a designer is able to recognize a cliché starting from the repetition of certain elements and visual cues, now emptied of their original meaning. In the eyes of a designer, clichés look like roads taken all too often, sets of symbols that now no longer cause interest, much less surprise. Static situations that can be overcome. The Technology Transfer System (TTS) project, aims to overcome clichés about technology communication in the university environment for the realization of the so-called Third Mission, defining a method that involves the analysis of each element of the communication system and its ad hoc design. Carefully considering every formal, conceptual and material element of the system, a personalized structure is constructed. A structure that can take original and surprising shapes, exceeding any given convention.
A Robust State Estimator for Multi-Agent Systems Under Impulsive Noise and Missing Measurements

We address the problem of estimating the state of a Multi-Agent System (MAS) with dynamics subjected to impulsive disturbances, based on measurements that are corrupted with impulsive noise and are sometimes missing. To facilitate the online implementation of the proposed state estimator for MAS, a graph formulation is proposed first. Then, making use of the Huber Loss, the estimator adopts a general cost function that addresses missing measurements and is robust to impulsive noise and disturbances. The solution is validated under a synthetic scenario, where a team of UAVs equipped with onboard video cameras, inertial sensors, transceivers, and GPS, cooperatively geolocate and track a ground moving target agent. Comparison results with respect to three different state-of-the-art estimators are provided to show the superior performance and benefits of the proposed robust estimator.
On Weak Feller Continuity Properties of Non-linear Filters

Weak Feller property of controlled and control-free Markov chains leads to many desirable properties. In control-free setups this leads to the existence of invariant probability measures for compact spaces and applicability of numerical approximation methods. For controlled setups, this leads to existence and approximation results for optimal control policies. We know from stochastic control theory that partially observed systems can be converted to fully observed systems by replacing the original state space with a probability measure-valued state space, with the corresponding kernel acting on probability measures known as the non-linear filter process. In this paper, we prove the weak Feller property of the non-linear filter process (i) first under weak continuity of the transition probability of controlled Markov chain and total variation continuity of its observation channel, and then, (ii) under total variation continuity of the transition probability of controlled Markov chain. The former result (i) has first appeared in Feinberg et. al. (2016). Here, we present a concise and easy to follow alternative proof for this existing result. The latter result (ii) establishes weak Feller property of non-linear filter process under conditions which have not been previously reported in the literature.
Productify News Article Classification Model with Sagemaker

Natural language processing NLP Paragraph vectorsNews companies have a need to automate and make the process of writing about popular and new events more effective.Current technologies involve robotic programs that fill in values in templates and website listeners that notify editors when changes are made so that the editor can read up on the source change on the actual website.Editors can provide news faster and better if directly provided with abstracts of the external sources and categorical meta-data that supports what the text is about.To make categorical meta-data a reality an auto-categorization model was created and optimized for Swedish articles written by local news journalists.The problem was that it was not scale-able enough to use out of the box.Instead of having this local model that could make good predictions of the text documents, the model is to be deployed in the cloud and an API interface is created.The API can be accessed from the tools where the articles is being written and therefore these services can automatically assign categories to the articles once the journalist is done writing it.To allow scale-ability to several thousands of simultaneously categorized articles and at the same time improving the workflow of deploying new models easier the API is uploaded to Sagemaker where several models are trained and once an improved model is found that model will be used in production in such a way that the system organically adapts to new written articles.An evaluation of Sagemaker API was done and it was concluded that the complexity of this solution was polynomial.
Vehicle Queue Length Estimation Using Uncalibrated Cameras

Vehicle queue lengths at city road intersections are important parameters in designing intelligent transport systems, and are particularly useful in traffic modeling and in designing smart signals. In this paper, we propose a simple and scalable method for estimation of vehicle queue lengths that works in real time. The proposed solution is based on cameras (with unknown intrinsics) that are commonly found on most city roads, and does not require the installation of any sensors. The approach consists of three stages. In the first stage, we automatically identify the road region in the camera view. This is achieved through unsupervised segmentation that uses a convolutional neural network. In the second stage, we identify the presence of the queue by using low level features such as corners. In the third stage, we estimate the physical length (in metres) of the queue. Our experiments show that the proposed method is effective, with less than 10% error in the length estimates.
Research and Application of Integrated SQLite Based on Ground Meteorological Observation

With the continuous development of science and technology, embedded devices are more and more widely used in our daily life. In the meteorological industry, a large number of embedded devices are also used, which greatly promotes the rapid development of the meteorological industry, but also improves the management and service methods of meteorological industry staff. At present, the embedded devices used in the meteorological industry are mainly used to monitor, store and send meteorological data, which are often stored in the storage devices of embedded devices, which causes great pressure on its system resources. Therefore, combining the advantages of embedded system development and SQLite database, this paper proposes a research and application of integrating small and lightweight database SQLite in embedded devices. It uses SQLite database to store meteorological data information, and reasonably establishes database tables. The database we choose is SQLite, software environment VMware Workstation 10, ubautu, Linux 3.14, and hardware environment is NXP. Imax6ul core board and meteorological serial server of Feiling Embedded Company. The sqlite-amalgamation3.6.10.tar.bz2 installation package is downloaded from the official website of SQLite, decompressed and cross-compiled in the virtual machine, then the compiled library files and connection files are copied to the hardware development board. Finally, the database program and other applications are compiled. After cross-compiled by the virtual machine, the generated executable files are copied to the embedded hardware to run. Experiments show that the construction of SQLite database on embedded hardware improves the complexity of meteorological business processes, greatly facilitates the storage and transmission of meteorological data, and promotes the intelligent development of meteorological industry.
Long-term 3D Registration Method Based on LCT Tracking and Improved ORB Detection

Aiming at the complex environment such as fast moving of registered area, occlusion, illumination change and the high requirement of real-time and precision of feature detection in the 3D registration of augmented reality system, a long-term 3D registration method based on LCT tracking and improved ORB detection is proposed in this paper. Firstly, the reliability of LCT algorithm in long-term tracking is used to track the area to be registered in augmented reality; secondly, ORB algorithm with excellent real-time performance is improved by setting adaptive thresholds, number of feature points and distance thresholds to optimize the dense area of image feature points. Parallel algorithm is used to retain feature points with larger eigenvalues, and discrete difference feature is used to enhance illumination unevenness. The stability of ORB operator under uniform change can solve the problem of low precision of feature detection and poor anti-jamming ability. Finally, the 3D registration matrix is calculated by using the detected feature points to enhance the real world. The simulation results show that the LCT algorithm has high reliability in long-term tracking and registration. Compared with ORB algorithm, the improved ORB algorithm improves the precision of feature detection by about 22%. It effectively improves the real-time and precision of feature matching in augmented reality system. The performance of the long-term 3D registration method based on LCT tracking and improved ORB detection is excellent, which improves the robustness, stability and practicability of the augmented reality system.
A Network Management Protocol for Sonification of Software-Defined Infrastructures

Network management traffic is usually carried in-band with the data plane traffic on a logically separate plane. For example, separate service queues and/or VLANs may be reserved for reliable and timely delivery of management messages. This approach has roots in history but carries a fundamental problem: fate sharing between the data plane traffic and management traffic. Failure in data plane networks often cuts off management traffic to the exact network regions at fault, making it impossible to achieve important and relevant management tasks, such as diagnostics and recovery. In this paper, we propose a network management protocol that enables programmability of out-of-channel management applications to combat the fate-sharing problem in Software-Defined Infrastructures. To test our approach and our implementation we used acoustics, a fairly unexplored physical layer for the control and management plane. We then use the notion of sonification to implement a few representative applications such as k-superseeder detection and TraceSound, a sonified version of traceroute, for out-of-channel network verification and debugging. While scaling acoustics could be an insurmountable challenge, our tests show how our protocol brings about a sound software-defined approach that can be expanded to other (combinations of) frequencies and even multiple physical channels.
Using a Hierarchical Softmax Based on the Huffman Coding Tree for Authenticating Arabic Tweets

Attributing a piece of text to its true author is called Authorship Authentication (AA). This work addresses the AA problem of Arabic tweets. Arabic language is both challenging and understudied. Existing approaches on authenticating Arabic tweets used bag of words features or Stylometric Features coupled with classifiers like SVM. However, the reported accuracy for these approaches is rather low and did not even reach 69%. In this work, we address this problem using two approaches. (a) A baseline approach that uses SVM along with BoW features, and (b) a character-level linear classifier (char-LC) with a rank constraint and a fast loss approximation along with word embeddings based on fasttext. Both approaches give significantly higher accuracies than the results reported in literature with 78.28% for the SVM along with BoW approach and 79.4% for the char-LC.
Data‐driven nonrigid object feature analysis: Subspace application of incidence structure

Abstract In object recognition, feature extraction algorithms are designed to capture the discriminate statistics of objects. Due to pose, deformation and background clutter, the recognition of objects becomes nontrivial, particularly nonrigid samples. Through incidence and geometric structure, this article reports on the data‐driven identification of critical features located on object exemplar profiles. The investigation is demonstrated using the features of a cat's head and the application of the Hough transform to extract planar geometric features. A data‐driven recognition routine is described that accumulates prior knowledge for evaluating the error contribution of critical features impacting recognition confidence. In measuring affine facial features, feature parallelism is tracked to determine rotations and elevations of a cat's head. A preliminary recognition error of 8.2% and 17.8% is determined for a front training profile. An analysis proceeds to determine contributions to this error due the identified critical features.